From 1edffe9f98cbf0f691dc65b4cf3fe8d981773d23 Mon Sep 17 00:00:00 2001
From: Peeeaje <74146834+Peeeaje@users.noreply.github.com>
Date: Sat, 3 Jul 2021 00:26:27 +0900
Subject: [PATCH 001/368] Completed 1.1.

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+# Introduction to machine learning
+
+[![ML, AI, deep learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?")
+
+> 🎥 上�画�をクリック�る��機械学習�AI�深層学習�������説明��動画�表示�れ��。
+
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+
+### イントロダクション
+
+�心者�����典的�機械学習�コース�よ���! ��テーマ�全�触れ������方も���分野をブラッシュアップ���経験豊富�方も�����加����。���������ML�学習�����親��や��スタート地点を作り���考�����。����[フィード�ック](https://github.com/microsoft/ML-For-Beginners/discussions)を評価��対応���り入れる�����れ�幸���。 
+[![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
+
+> 🎥 上�画�をクリック�る��MIT�John Guttag�機械学習を紹介�る動画�表示�れ��。
+### 機械学習を始�る�����
+
+��カリキュラムを始�る���コンピュータを設定��ノートブックをローカル�実行��るよ���る必���り��。
+
+- **��ら�ビデオ�マシン�設定を行������。** マシン�設定方法������[�れら�ビデオ](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)を�覧����。
+- **Pythonを学習�る。** 本講座�使用�る�データサイエンティスト�有用�プログラミング言語��る[Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)�基本的��解��る���望�����。
+- **Node.js�JavaScriptを学習�る。** ��コース��ウェブアプリを構築�る際�JavaScriptも何度�使用������[node](https://nodejs.org)�[npm](https://www.npmjs.com/)�インストール�れ��る���Python�JavaScript�両方�開発�必��[Visual Studio Code](https://code.visualstudio.com/)�利用�能��る���必���。
+- **GitHub�アカウントを作��る。** [GitHub](https://github.com)����を見�������ら����アカウントを����も�れ��ん��も�������れ��アカウントを作������カリキュラムをフォーク���自分��使�����。(スターを��る��も�忘れ��😊)
+- **Scikit-learnを探索�る。** ��レッスン��照�るMLライブラリ�セット��る[Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)�慣れ親�ん�����。
+
+### 機械学習��何�？
+
+"機械学習(Machine Learning)"���言葉���在最も人気��り�頻��使用�れ��る言葉�一���。�ん�分野�技術者����も�多少�り�も技術�精通���れ��一度���言葉を耳�������る�能性�少���り��ん。����機械学習�仕組�����ん��人����謎�包�れ��り�機械学習��心者�������テーマ�時�圧倒�れるよ��感�られ��。�����機械学習��何�を実際��解��実践的�例を通��段階的�学ん����������。
+
+![ml hype curve](images/hype.png)
+
+> Google Trends�よる�「機械学習����言葉�最近�盛り上�りを示�グラフ。
+
+�����魅力的�謎�満��宇宙��ん����。ホーキング�士やアインシュタイン�士を�����る�大�科学者�������を�り巻�世界�謎を解�明���味��る情報を探����人生を�������。人間��供��大人��る���間��年々新����を学��自分�世界�構造を明ら��������。
+
+�供�脳�感覚��周囲�事実を�識���々�人生�隠れ�パターンを学��学習��パターンを識別�る���論�的�ルールを作る��役立���。�����学習プロセス��人間を��世�最も洗練�れ�生物������。隠れ�パターンを発見�る���継続的�学習����パターン�基����新を行���������生涯を通��自分自身をより良������������。��学習能力�進化能力��[「脳��塑性�](https://www.simplypsychology.org/brain-plasticity.html)�呼�れる概念�関連�����。表�的���人間�脳�学習プロセス�機械学習�コンセプト���モ�ベーション��������共通点��り��。
+
+[人間�脳](https://www.livescience.com/29365-human-brain.html)���実世界�物事を知覚��知覚��情報を処�����的�判断を下��状��応���る行動を���。�れ�知的行動�呼�れ��。��知的行動�プロセスを機械�プログラム�る��を人工知能（AI）�����。
+
+��言葉�混��れる����り����機械学習（ML）�人工知能����サブセット��。**ML��特殊�アルゴリズムを使用����味��る情報を発見��知覚�れ�データ�ら隠れ�パターンを見������的���決定プロセスを�付�る���関係�����。**
+
+![AI, ML, deep learning, data science](images/ai-ml-ds.png)
+
+
+>[��グラフ](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)�触発�れ�[Jen Looper](https://twitter.com/jenlooper)��よるインフォグラフィック
+
+## ��コース�学���
+
+��カリキュラム����心者�知������れ��ら��機械学習�コア�概念��を�り上���。����「�典的�機械学習��呼�も�を�多��学生�基礎を学����使用�る優れ�ライブラリ��るScikit-learnを主�使��カ�ー���。人工知能や深層学習���より広�概念を�解�る�����機械学習�強力�基礎知識���欠���������供���。
+
+- 機械学習�核��るコンセプト
+- ML�歴�
+- ML�公平性
+- ML�よる回帰�手法
+- ML�よる分類技術
+- ML�よるクラスタリング
+- ML�よる自然言語処��技術
+- ML�よる時系列予測�技術
+- 強化学習
+- ML��実世界��応用
+## ��コース�扱�����
+
+- ディープラーニング
+- ニューラル�ットワーク
+- AI
+  
+ニューラル�ットワークやディープラーニング（ニューラル�ットワークを用��多層的�モデル構築）�AI���複雑�分野��より良�学習環境を�供�る������������れら�別�カリキュラム��り上���。����れら�大��分野�中�も特�データサイエンス�焦点を当��カリキュラムを�供�る予定��。
+## ��機械学習を学���
+
+機械学習���システム�観点�ら�データ�ら隠れ�パターンを学習��知的���決定を支��る自動化�れ�システムを構築�る���定義�れ��。
+
+��動機��人間�脳�外界�ら�識��データ�基���特定�事柄を学習�る仕組���ゆるや��インスパイア�れ����。
+
+✅ ��ビジ�ス����ードコード�れ�ルールベース�エンジンを作る������機械学習戦略を使���よ��������ょ��考��������。
+
+
+### 機械学習�応用
+
+機械学習�アプリケーション��今や��ん�����も�り�スマートフォンやコ�クテッドデ�イス���他�システム�ら生��れ�����社会��れ��るデータ��様��り�れ�も��������。最先端�機械学習アルゴリズム�計り知れ���能性を考慮���研究者����多次元的・多分野的��実��題を解決�る�����能力を探求���常�良��果を得����。
+
+**機械学習�様々�形�利用����**:
+
+- 患者�病歴や報告書�ら病気��能性を予測�る。
+- 気象データを活用��気象�象を予測�る。
+- 文章�感情を�解�る。
+- プロパガンダ�拡散を防����フェイクニュースを検出�る。
+
+金��経済�地�科学�宇宙開発�生物医学工学��知科学��ら��文科系�分野�も��れ�れ�分野�データ処��伴�困難��題を解決�る����機械学習�採用�れ����。
+
+機械学習��実世界�データや生��れ�データ�ら�味��る洞察を見出��パターンを発見�るプロセスを自動化���。機械学習��ビジ�ス��康�金����分野��常�有用��る���証明�れ����。
+
+近�将��機械学習�基礎を�解�る����機械学習�普��伴���らゆる分野�人々����必須�も���る��ょ�。
+
+---
+## 🚀 Challenge
+AI�ML�深層学習�データサイエンス��������解���る��を�紙や[Excalidraw](https://excalidraw.com/)���オンラインアプリを使��スケッ�������。����れ�れ�技術�得���る�題�アイデアを加��������。
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+
+## 振り返り�自習
+
+クラウド上�MLアルゴリズムを��よ��扱������る���������[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)�従������。.
+
+## 課題
+
+[起動��実行������。](assignment.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/images/ai-ml-ds.png b/1-Introduction/1-intro-to-ML/translations/images/ai-ml-ds.png
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From c00f6552413c7f9f38c69fc5fa82225519450971 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 3 Jul 2021 10:48:41 +0800
Subject: [PATCH 002/368] translate 1.2-history-of-ML to Simplified Chinese

---
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diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
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+# History of machine learning
+
+![Summary of History of machine learning in a sketchnote](../../sketchnotes/ml-history.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+
+In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence.
+
+The history of artificial intelligence, AI, as a field is intertwined with the history of machine learning, as the algorithms and computational advances that underpin ML fed into the development of AI. It is useful to remember that, while these fields as distinct areas of inquiry began to crystallize in the 1950s, important [algorithmical, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era. In fact, people have been thinking about these questions for [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence): this article discusses the historical intellectual underpinnings of the idea of a 'thinking machine.'
+
+## Notable discoveries
+
+- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) and its predecessors. This theorem and its applications underlie inference, describing the probability of an event occurring based on prior knowledge.
+- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) by French mathematician Adrien-Marie Legendre. This theory, which you will learn about  in our Regression unit, helps in data fitting.
+- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) named after Russian mathematician Andrey Markov is used to describe a sequence of possible events based on a previous state.
+- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) is an algorithm originally designed to map routes. In an ML context it is used to  detect patterns.
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) is used to train [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network).
+- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) are artificial neural networks derived from feedforward neural networks that create temporal graphs.
+
+✅ Do a little research. What other dates stand out as pivotal in the history of ML and AI?
+## 1950: Machines that think
+
+Alan Turing, a truly remarkable person who was voted [by the public in 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) as the greatest scientist of the 20th century, is credited as helping to lay the foundation for the concept of a 'machine that can think.' He grappled with naysayers and his own need for empirical evidence of this concept in part by creating the [Turing Test](https://www.bbc.com/news/technology-18475646), which you will explore in our NLP lessons.
+
+## 1956: Dartmouth Summer Research Project
+
+"The Dartmouth Summer Research Project on artificial intelligence was a seminal event for artificial intelligence as a field," and it was here that the term 'artificial intelligence' was coined ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
+
+The lead researcher, mathematics professor John McCarthy, hoped "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." The participants included another luminary in the field, Marvin Minsky.
+
+The workshop is credited with having initiated and encouraged several discussions including "the rise of symbolic methods, systems focussed on limited domains (early expert systems), and deductive systems versus inductive systems." ([source](https://wikipedia.org/wiki/Dartmouth_workshop)).
+
+## 1956 - 1974: "The golden years"
+
+From the 1950s through the mid '70s, optimism ran high in the hope that AI could solve many problems. In 1967, Marvin Minsky stated confidently that "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+natural language processing research flourished, search was refined and made more powerful, and the concept of 'micro-worlds' was created, where simple tasks were completed using plain language instructions.
+
+Research was well funded by government agencies, advances were made in computation and algorithms, and prototypes of intelligent machines were built. Some of these machines include:
+
+* [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), who could maneuver and decide how to perform tasks 'intelligently'.
+
+    ![Shakey, an intelligent robot](images/shakey.jpg)
+    > Shakey in 1972
+
+* Eliza, an early 'chatterbot', could converse with people and act as a primitive 'therapist'. You'll learn more about Eliza in the NLP lessons.
+
+    ![Eliza, a bot](images/eliza.png)
+    > A version of Eliza, a chatbot
+
+* "Blocks world" was an example of a micro-world where blocks could be stacked and sorted, and experiments in teaching machines to make decisions could be tested. Advances built with libraries such as [SHRDLU](https://wikipedia.org/wiki/SHRDLU) helped propel language processing forward.
+
+    [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
+    
+    > 🎥 Click the image above for a video: Blocks world with SHRDLU
+
+## 1974 - 1980: "AI Winter"
+
+By the mid 1970s, it had become apparent that the complexity of making 'intelligent machines' had been understated and that its promise, given the available compute power, had been overblown. Funding dried up and confidence in the field slowed. Some issues that impacted confidence included:
+
+- **Limitations**. Compute power was too limited.
+- **Combinatorial explosion**. The amount of parameters needed to be trained grew exponentially as more was asked of computers, without a parallel evolution of compute power and capability.
+- **Paucity of data**. There was a paucity of data that hindered the process of testing, developing, and refining algorithms.
+- **Are we asking the right questions?**. The very questions that were being asked began to be questioned. Researchers began to field criticism about their approaches:
+  - Turing tests came into question by means, among other ideas, of the 'chinese room theory' which posited that, "programming a digital computer may make it appear to understand language but could not produce real understanding." ([source](https://plato.stanford.edu/entries/chinese-room/))
+  - The ethics of introducing artificial intelligences such as the "therapist" ELIZA into society was challenged.
+
+At the same time, various AI schools of thought began to form. A dichotomy was established between ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) practices. _Scruffy_ labs tweaked programs for hours until they had the desired results. _Neat_ labs "focused on logic and formal problem solving". ELIZA and SHRDLU were well-known _scruffy_ systems. In the 1980s, as demand emerged to make ML systems reproducible, the _neat_ approach gradually took the forefront as its results are more explainable.
+
+## 1980s Expert systems
+
+As the field grew, its benefit to business became clearer, and in the 1980s so did the proliferation of 'expert systems'. "Expert systems were among the first truly successful forms of artificial intelligence (AI) software." ([source](https://wikipedia.org/wiki/Expert_system)).
+
+This type of system is actually _hybrid_, consisting partially of a rules engine defining business requirements, and an inference engine that leveraged the rules system to deduce new facts.
+
+This era also saw increasing attention paid to neural networks.
+
+## 1987 - 1993: AI 'Chill'
+
+The proliferation of specialized expert systems hardware had the unfortunate effect of becoming too specialized. The rise of personal computers also competed with these large, specialized, centralized systems. The democratization of computing had begun, and it eventually paved the way for the modern explosion of big data.
+
+## 1993 - 2011
+
+This epoch saw a new era for ML and AI to be able to solve some of the problems that had been caused earlier by the lack of data and compute power. The amount of data began to rapidly increase and become more widely available, for better and for worse, especially with the advent of the smartphone around 2007. Compute power expanded exponentially, and algorithms evolved alongside. The field began to gain maturity as the freewheeling days of the past began to crystallize into a true discipline.
+
+## Now
+
+Today, machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+It remains to be seen what the future holds, but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
+
+[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
+> 🎥 Click the image above for a video: Yann LeCun discusses the history of deep learning in this lecture
+
+---
+## 🚀Challenge
+
+Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+
+## Review & Self Study
+
+Here are items to watch and listen to:
+
+[This podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739)
+
+[![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
+
+## Assignment
+
+[Create a timeline](assignment.md)

From 136658e970c40ff8bc16dffa018186b6e3293104 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 3 Jul 2021 10:53:06 +0800
Subject: [PATCH 003/368] translate 1.3-fairness to Simplified Chinese

---
 .../3-fairness/translations/README.zh-cn.md   | 212 ++++++++++++++++++
 1 file changed, 212 insertions(+)
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diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
new file mode 100644
index 0000000000..063c189813
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+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -0,0 +1,212 @@
+# Fairness in Machine Learning 
+ 
+![Summary of Fairness in Machine Learning in a sketchnote](../../sketchnotes/ml-fairness.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+ 
+## Introduction
+
+In this curriculum, you will start to discover how machine learning can and is impacting our everyday lives. Even now, systems and models are involved in daily decision-making tasks, such as health care diagnoses or detecting fraud. So it is important that these models work well in order to provide fair outcomes for everyone.
+
+Imagine what can happen when the data you are using to build these models lacks certain demographics, such as race, gender, political view, religion, or disproportionally represents such demographics. What about when the model's output is interpreted to favor some demographic? What is the consequence for the application? 
+
+In this lesson, you will:
+
+- Raise your awareness of the importance of fairness in machine learning.
+- Learn about fairness-related harms.
+- Learn about unfairness assessment and mitigation.
+
+## Prerequisite
+
+As a prerequisite, please take the "Responsible AI Principles" Learn Path and watch the video below on the topic:
+
+Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
+
+[![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
+
+> 🎥 Click the image above for a video: Microsoft's Approach to Responsible AI
+
+## Unfairness in data and algorithms
+
+> "If you torture the data long enough, it will confess to anything - Ronald Coase
+
+This statement sounds extreme, but it is true that data can be manipulated to support any conclusion. Such manipulation can sometimes happen unintentionally. As humans, we all have bias, and it's often difficult to consciously know when you are introducing bias in data.
+
+Guaranteeing fairness in AI and machine learning remains a complex sociotechnical challenge. Meaning that it cannot be addressed from either purely social or technical perspectives.
+
+### Fairness-related harms
+
+What do you mean by unfairness? "Unfairness" encompasses negative impacts, or "harms", for a group of people, such as those defined in terms of race, gender, age, or disability status.  
+
+The main fairness-related harms can be classified as:
+
+- **Allocation**, if a gender or ethnicity for example is favored over another.
+- **Quality of service**. If you train the data for one specific scenario but reality is much more complex, it leads to a poor performing service.
+- **Stereotyping**. Associating a given group with pre-assigned attributes.
+- **Denigration**. To unfairly criticize and label something or someone.
+- **Over- or under- representation**. The idea is that a certain group is not seen in a certain profession, and any service or function that keeps promoting that is contributing to harm.
+
+Let’s take a look at the examples.
+
+### Allocation
+
+Consider a hypothetical system for screening loan applications. The system tends to pick white men as better candidates over other groups. As a result, loans are withheld from certain applicants.
+
+Another example would be an experimental hiring tool developed by a large corporation to screen candidates. The tool systemically discriminated against one gender by using the models were trained to prefer words associated with another. It resulted in penalizing candidates whose resumes contain words such as "women’s rugby team". 
+
+✅ Do a little research to find a real-world example of something like this
+
+### Quality of Service
+
+Researchers found that several commercial gender classifiers had higher error rates around images of women with darker skin tones as opposed to images of men with lighter skin tones. [Reference](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/) 
+
+Another infamous example is a hand soap dispenser that could not seem to be able to sense people with dark skin. [Reference](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+
+### Stereotyping
+
+Stereotypical gender view was found in machine translation. When translating “he is a nurse and she is a doctor� into Turkish, problems were encountered. Turkish is a genderless language which has one pronoun, “o� to convey a singular third person, but translating the sentence back from Turkish to English yields the stereotypical and incorrect as “she is a nurse and he is a doctor�.
+
+![translation to Turkish](images/gender-bias-translate-en-tr.png)
+
+![translation back to English](images/gender-bias-translate-tr-en.png)
+
+### Denigration
+
+An image labeling technology infamously mislabeled images of dark-skinned people as gorillas. Mislabeling is harmful not just because the system made a mistake because it specifically applied a label that has a long history of being purposefully used to denigrate Black people.
+
+[![AI: Ain't I a Woman?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, Ain't I a Woman?")
+> 🎥 Click the image above for a video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
+
+### Over- or under- representation
+ 
+Skewed image search results can be a good example of this harm. When searching images of professions with an equal or higher percentage of men than women, such as engineering, or CEO, watch for results that are more heavily skewed towards a given gender.
+
+![Bing CEO search](images/ceos.png)
+> This search on Bing for 'CEO' produces pretty inclusive results
+
+These five main types of harms are not mutually exclusive, and a single system can exhibit more than one type of harm. In addition, each case varies in its severity. For instance, unfairly labeling someone as a criminal is a much more severe harm than mislabeling an image. It's important, however, to remember that even relatively non-severe harms can make people feel alienated or singled out and the cumulative impact can be extremely oppressive. 
+ 
+✅ **Discussion**: Revisit some of the examples and see if they show different harms.  
+
+|                         | Allocation | Quality of service | Stereotyping | Denigration | Over- or under- representation |
+| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
+| Automated hiring system |     x      |         x          |      x       |             |               x                |
+| Machine translation     |            |                    |              |             |                                |
+| Photo labeling          |            |                    |              |             |                                |
+
+
+## Detecting unfairness 
+
+There are many reasons why a given system behaves unfairly. Social biases, for example, might be reflected in the datasets used to train them. For example, hiring unfairness might have been exacerbated by over reliance on historical data. By using the patterns in resumes submitted to the company over a 10-year period, the model determined that men were more qualified because the majority of resumes came from men, a reflection of past male dominance across the tech industry.
+
+Inadequate data about a certain group of people can be the reason for unfairness. For example, image classifiers a have higher rate of error for images of dark-skinned people because darker skin tones were underrepresented in the data.  
+
+Wrong assumptions made during development cause unfairness too. For example, a facial analysis system intended to predict who is going to commit a crime based on images of people’s faces can lead to damaging assumptions. This could lead to substantial harms for people who are misclassified.
+
+## Understand your models and build in fairness
+ 
+Although many aspects of fairness are not captured in quantitative fairness metrics, and it is not possible to fully remove bias from a system to guarantee fairness, you are still responsible to detect and to mitigate fairness issues as much as possible. 
+
+When you are working with machine learning models, it is important to understand your models by means of assuring their interpretability and by assessing and mitigating unfairness.
+
+Let’s use the loan selection example to isolate the case to figure out each factor's level of impact on the prediction.
+
+## Assessment methods
+
+1. **Identify harms (and benefits)**. The first step is to identify harms and benefits. Think about how actions and decisions can affect both potential customers and a business itself.
+  
+1. **Identify the affected groups**. Once you understand what kind of harms or benefits that can occur, identify the groups that may be affected. Are these groups defined by gender, ethnicity, or social group?
+
+1. **Define fairness metrics**. Finally, define a metric so you have something to measure against in your work to improve the situation.
+
+### Identify harms (and benefits)
+
+What are the harms and benefits associated with lending? Think about false negatives and false positive scenarios: 
+
+**False negatives** (reject, but Y=1) - in this case, an applicant who will be capable of repaying a loan is rejected. This is an adverse event because the resources of the loans are withheld from qualified applicants.
+
+**False positives** (accept, but Y=0) - in this case, the applicant does get a loan but eventually defaults. As a result, the applicant's case will be sent to a debt collection agency which can affect their future loan applications.
+
+### Identify affected groups
+
+The next step is to determine which groups are likely to be affected. For example, in case of a credit card application, a model might determine that women should receive much lower credit limits compared with their spouses who share household assets. An entire demographic, defined by gender, is thereby affected.
+
+### Define fairness metrics
+ 
+You have identified harms and an affected group, in this case, delineated by gender. Now, use the quantified factors to disaggregate their metrics. For example, using the data below, you can see that women have the largest false positive rate and men have the smallest, and that the opposite is true for false negatives.
+
+✅ In a future lesson on Clustering, you will see how to build this 'confusion matrix' in code
+
+|            | False positive rate | False negative rate | count |
+| ---------- | ------------------- | ------------------- | ----- |
+| Women      | 0.37                | 0.27                | 54032 |
+| Men        | 0.31                | 0.35                | 28620 |
+| Non-binary | 0.33                | 0.31                | 1266  |
+
+ 
+This table tells us several things. First, we note that there are comparatively few non-binary people in the data. The data is skewed, so you need to be careful how you interpret these numbers.
+
+In this case, we have 3 groups and 2 metrics. When we are thinking about how our system affects the group of customers with their loan applicants, this may be sufficient, but when you want to define larger number of groups, you may want to distill this to smaller sets of summaries. To do that, you can add more metrics, such as the largest difference or smallest ratio of each false negative and false positive. 
+ 
+✅ Stop and Think: What other groups are likely to be affected for loan application? 
+ 
+## Mitigating unfairness 
+ 
+To mitigate unfairness, explore the model to generate various mitigated models and compare the tradeoffs it makes between accuracy and fairness to select the most fair model. 
+
+This introductory lesson does not dive deeply into the details of algorithmic unfairness mitigation, such as post-processing and reductions approach, but here is a tool that you may want to try. 
+
+### Fairlearn 
+ 
+[Fairlearn](https://fairlearn.github.io/) is an open-source Python package that allows you to assess your systems' fairness and mitigate unfairness.  
+
+The tool helps you to assesses how a model's predictions affect different groups, enabling you to compare multiple models by using fairness and performance metrics, and supplying a set of algorithms to mitigate unfairness in binary classification and regression. 
+
+- Learn how to use the different components by checking out the Fairlearn's [GitHub](https://github.com/fairlearn/fairlearn/)
+
+- Explore the [user guide](https://fairlearn.github.io/main/user_guide/index.html), [examples](https://fairlearn.github.io/main/auto_examples/index.html)
+
+- Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
+  
+- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning. 
+  
+- Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning. 
+
+---
+## 🚀 Challenge 
+ 
+To prevent biases from being introduced in the first place, we should: 
+
+- have a diversity of backgrounds and perspectives among the people working on systems 
+- invest in datasets that reflect the diversity of our society 
+- develop better methods for detecting and correcting bias when it occurs 
+
+Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider? 
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## Review & Self Study 
+ 
+In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.  
+ 
+Watch this workshop to dive deeper into the topics: 
+
+- YouTube: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik [Fairness-related harms in AI systems: Examples, assessment, and mitigation - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
+
+Also, read: 
+
+- Microsoft’s RAI resource center: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
+
+- Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
+
+Explore the Fairlearn toolkit
+
+[Fairlearn](https://fairlearn.org/)
+
+Read about Azure Machine Learning's tools to ensure fairness
+
+- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
+
+## Assignment
+
+[Explore Fairlearn](assignment.md) 

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+# Techniques of Machine Learning
+
+The process of building, using, and maintaining machine learning models and the data they use is a very different process from many other development workflows. In this lesson, we will demystify the process, and outline the main techniques you need to know. You will:
+
+- Understand the processes underpinning machine learning at a high level.
+- Explore base concepts such as 'models', 'predictions', and 'training data'.
+  
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## Introduction
+
+On a high level, the craft of creating machine learning (ML) processes is comprised of a number of steps:
+
+1. **Decide on the question**. Most ML processes start by asking a question that cannot be answered by a simple conditional program or rules-based engine. These questions often revolve around predictions based on a collection of data.
+2. **Collect and prepare data**. To be able to answer your question, you need data. The quality and, sometimes, quantity of your data will determine how well you can answer your initial question. Visualizing data is an important aspect of this phase. This phase also includes splitting the data into a training and testing group to build a model.
+3. **Choose a training method**. Depending on your question and the nature of your data, you need to choose how you want to train a model to best reflect your data and make accurate predictions against it. This is the part of your ML process that requires specific expertise and, often, a considerable amount of experimentation.
+4. **Train the model**. Using your training data, you'll use various algorithms to train a model to recognize patterns in the data. The model might leverage internal weights that can be adjusted to privilege certain parts of the data over others to build a better model.
+5. **Evaluate the model**. You use never before seen data (your testing data) from your collected set to see how the model is performing.
+6. **Parameter tuning**. Based on the performance of your model, you can redo the process using different parameters, or variables, that control the behavior of the algorithms used to train the model.
+7. **Predict**. Use new inputs to test the accuracy of your model.
+
+## What question to ask
+
+Computers are particularly skilled at discovering hidden patterns in data. This utility is very helpful for researchers who have questions about a given domain that cannot be easily answered by creating a conditionally-based rules engine. Given an actuarial task, for example, a data scientist might be able to construct handcrafted rules around the mortality of smokers vs non-smokers.
+
+When many other variables are brought into the equation, however, a ML model might prove more efficient to predict future mortality rates based on past health history. A more cheerful example might be making weather predictions for the month of April in a given location based on data that includes latitude, longitude, climate change, proximity to the ocean, patterns of the jet stream, and more.
+
+✅ This [slide deck](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) on weather models offers a historical perspective for using ML in weather analysis.  
+
+## Pre-building tasks
+
+Before starting to build your model, there are several tasks you need to complete. To test your question and form a hypothesis based on a model's predictions, you need to identify and configure several elements.
+
+### Data
+
+To be able to answer your question with any kind of certainty, you need a good amount of data of the right type. There are two things you need to do at this point:
+
+- **Collect data**. Keeping in mind the previous lesson on fairness in data analysis, collect your data with care. Be aware of the sources of this data, any inherent biases it might have, and document its origin.
+- **Prepare data**. There are several steps in the data preparation process. You might need to collate data and normalize it if it comes from diverse sources. You can improve the data's quality and quantity through various methods such as converting strings to numbers (as we do in [Clustering](../../5-Clustering/1-Visualize/README.md)). You might also generate new data, based on the original (as we do in [Classification](../../4-Classification/1-Introduction/README.md)). You can clean and edit the data (as we did prior to the [Web App](../3-Web-App/README.md) lesson). Finally, you might also need to randomize it and shuffle it, depending on your training techniques.
+
+✅ After collecting and processing your data, take a moment to see if its shape will allow you to address your intended question. It may be that the data will not perform well in your given task, as we discover in our [Clustering](../../5-Clustering/1-Visualize/README.md) lessons!
+
+### Selecting your feature variable
+
+A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**?
+
+🎓 **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." ([source](https://wikipedia.org/wiki/Feature_selection))
+### Visualize your data
+
+An important aspect of the data scientist's toolkit is the power to  visualize data using several excellent libraries such as Seaborn or MatPlotLib. Representing your data visually might allow you to uncover hidden correlations that you can leverage. Your visualizations might also help you to uncover bias or unbalanced data (as we discover in [Classification](../../4-Classification/2-Classifiers-1/README.md)).
+### Split your dataset
+
+Prior to training, you need to split your dataset into two or more parts of unequal size that still represent the data well.
+
+- **Training**. This part of the dataset is fit to your model to train it. This set constitutes the majority of the original dataset.
+- **Testing**. A test dataset is an independent group of data, often gathered from the original data, that you use to confirm the performance of the built model.
+- **Validating**. A validation set is a smaller independent group of examples that you use to tune the model's hyperparameters, or architecture, to improve the model. Depending on your data's size and the question you are asking, you might not need to build this third set (as we note in [Time Series Forecasting](../7-TimeSeries/1-Introduction/README.md)).
+
+## Building a model
+
+Using your training data, your goal is to build a model, or a statistical representation of your data, using various algorithms to **train** it. Training a model exposes it to data and allows it to make assumptions about perceived patterns it discovers, validates, and accepts or rejects.
+
+### Decide on a training method
+
+Depending on your question and the nature of your data, your will choose a method to train it. Stepping through [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html) - which we use in this course - you can explore many ways to train a model. Depending on your experience, you might have to try several different methods to build the best model. You are likely to go through a process whereby data scientists evaluate the performance of a model by feeding it unseen data, checking for accuracy, bias, and other quality-degrading issues, and selecting the most appropriate training method for the task at hand.
+### Train a model
+
+Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your data as an array of values (usually 'X') and a feature variable (usually 'y').
+### Evaluate the model
+
+Once the training process is complete (it can take many iterations, or 'epochs', to train a large model), you will be able to evaluate the model's quality by using test data to gauge its performance. This data is a subset of the original data that the model has not previously analyzed. You can print out a table of metrics about your model's quality.
+
+🎓 **Model fitting**
+
+In the context of machine learning, model fitting refers to the accuracy of the model's underlying function as it attempts to analyze data with which it is not familiar.
+
+🎓 **Underfitting** and **overfitting** are common problems that degrade the quality of the model, as the model fits either not well enough or too well. This causes the model to make predictions either too closely aligned or too loosely aligned with its training data. An overfit model predicts training data too well because it has learned the data's details and noise too well. An underfit model is not accurate as it can neither accurately analyze its training data nor data it has not yet 'seen'.
+
+![overfitting model](images/overfitting.png)
+> Infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+## Parameter tuning
+
+Once your initial training is complete, observe the quality of the model and consider improving it by tweaking its 'hyperparameters'. Read more about the process [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+
+## Prediction
+
+This is the moment where you can use completely new data to test your model's accuracy. In an 'applied' ML setting, where you are building web assets to use the model in production, this process might involve gathering user input (a button press, for example) to set a variable and send it to the model for inference, or evaluation.
+
+In these lessons, you will discover how to use these steps to prepare, build, test, evaluate, and predict - all the gestures of a data scientist and more, as you progress in your journey to become a 'full stack' ML engineer.
+
+---
+
+## 🚀Challenge
+
+Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you?
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+
+## Review & Self Study
+
+Search online for interviews with data scientists who discuss their daily work. Here is [one](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+
+## Assignment
+
+[Interview a data scientist](assignment.md)

From 8f7dd4181b2ae896daafecc29edf8679c6818da6 Mon Sep 17 00:00:00 2001
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From dc2874ad537e71280a4a21828126c148c7b588a7 Mon Sep 17 00:00:00 2001
From: Peeeaje <74146834+Peeeaje@users.noreply.github.com>
Date: Sat, 3 Jul 2021 15:26:19 +0900
Subject: [PATCH 006/368] translated 1-introduction README.md

---
 1-Introduction/README.ja.md | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 1-Introduction/README.ja.md

diff --git a/1-Introduction/README.ja.md b/1-Introduction/README.ja.md
new file mode 100644
index 0000000000..1113d19f2a
--- /dev/null
+++ b/1-Introduction/README.ja.md
@@ -0,0 +1,22 @@
+# 機械学習��導入
+
+��セクション���機械学習�分野�基礎��る概念�機械学習��何�を紹介����歴�や研究者�機械学習を扱�際�使用�る技術����学���。 新��ML�世界を一緒�探求������ょ��
+
+![globe](images/globe.jpg)
+> Photo by <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> on <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+### Lessons
+
+1. [Introduction to machine learning](1-intro-to-ML/README.md)
+1. [The History of machine learning and AI](2-history-of-ML/README.md)
+1. [Fairness and machine learning](3-fairness/README.md)
+1. [Techniques of machine learning](4-techniques-of-ML/README.md)
+### Credits
+
+"Introduction to Machine Learning "��[Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan)�[Ornella Altunyan](https://twitter.com/ornelladotcom)�[Jen Looper](https://twitter.com/jenlooper)����ーム�よ��制作�れ���。
+
+"The History of Machine Learning" �[Jen Looper](https://twitter.com/jenlooper)�[Amy Boyd](https://twitter.com/AmyKateNicho)�よ��制作�れ���。
+
+"Fairness and Machine Learning"�[Tomomi Imura](https://twitter.com/girliemac) �よ��制作�れ���。
+
+"Techniques of Machine Learning"�[Jen Looper](https://twitter.com/jenlooper)�[Chris Noring](https://twitter.com/softchris) �よ��制作�れ���。
\ No newline at end of file

From 99fda249e52fa0ae5e8a1ca07f3baad9f4c8936d Mon Sep 17 00:00:00 2001
From: Peeeaje <74146834+Peeeaje@users.noreply.github.com>
Date: Sat, 3 Jul 2021 18:07:11 +0900
Subject: [PATCH 007/368] translated 1.2 history of ML README.md

---
 .../2-history-of-ML/translations/README.ja.md | 114 ++++++++++++++++++
 1 file changed, 114 insertions(+)
 create mode 100644 1-Introduction/2-history-of-ML/translations/README.ja.md

diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md
new file mode 100644
index 0000000000..437f156fa1
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/README.ja.md
@@ -0,0 +1,114 @@
+# History of machine learning
+
+![Summary of History of machine learning in a sketchnote](../../../sketchnotes/ml-history.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+
+��授業���機械学習�人工知能�歴����る主��出�事を紹介���。
+
+人工知能（AI）�歴���機械学習�歴��密接�関係�����。���ら��機械学習を支�るアルゴリズム�計算�進歩��AI�発展�������ら��。�れら�分野��1950年代�明確��り始���������[アルゴリズム�統計�数学�計算�技術的�発見](https://wikipedia.org/wiki/Timeline_of_machine_learning)����時代よりも�������時�行�れ�����を覚�����よ���ょ�。実際�人々�[何百年も](https://wikipedia.org/wiki/History_of_artificial_intelligence)���題����考������。（��記事���「考�る機械����アイデア�歴�的�知的基盤����説明�れ����。）
+
+
+## 注目���発見
+- 1763年�1812年 [ベイズ�定�](https://wikipedia.org/wiki/Bayes%27_theorem)����身�発見。�る事象�起�る確率を�事��知識�基���記述�る推論�基礎��る定����応用。
+- 1805年 フランス�数学者アドリアン�マリー・レジェンドル�よる[最�二乗�論](https://wikipedia.org/wiki/Least_squares)。���論��データ�フィッティング�役立�。
+- 1913年 ロシア�数学者アンドレイ・マルコフ���ん��付�られ�[マルコフ連鎖](https://wikipedia.org/wiki/Markov_chain)��以��状態�基���起�り�る一連�事象を記述�る���使用�れる。
+- 1957年 [パーセプトロン](https://wikipedia.org/wiki/Perceptron)��アメリカ�心�学者フランク・ローゼンブラット�発明��線形分類器�一種��り�深層学習�基盤�����る。
+- 1967 [最�近�法](https://wikipedia.org/wiki/Nearest_neighbor)��元々�経路探索����考案�れ�アルゴリズム。ML��パターン�検出�用�られる。
+- 1970年 [�ックプロパゲーション](https://wikipedia.org/wiki/Backpropagation)を用��[フィードフォワード・ニューラル�ットワーク（順�播型ニューラル�ットワーク）](https://wikipedia.org/wiki/Feedforward_neural_network)を学習�る。
+- 1982年 [回帰型ニューラル�ットワーク](https://wikipedia.org/wiki/Recurrent_neural_network) ��フィードフォワード・ニューラル�ットワーク�ら派生��人工的�ニューラル�ットワーク��時間的�グラフを作����。
+
+✅ 少�調��������。ML�AI�歴��中����日付�他��り���？
+
+## 1950: �考�る機械
+アラン・�ューリング��[2019年�世間�ら](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century)20世紀最大�科学者���投票�れ��真�優れ�人物��「考�る�����る機械����概念�基礎を築���貢献����れ����。彼���定的��見や���概念�実証的�証拠を必���る自分自身����先自然言語処��授業�触れる����る[�ューリング・テスト](https://www.bbc.com/news/technology-18475646)を作��る���戦����。
+
+## 1956: ダートマス・サマー・リサー�・プロジェクト
+ダートマス・サマー・リサー�・プロジェクト��分野����人工知能�������出�事��り����「人工知能����言葉�作られ���（[出典](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)）
+
+> 学習や��他�知能��らゆる����原�的��常�正確�記述�る�����る����れをシミュレート�る機械を作る�����る。
+
+主任研究者��る数学�ジョン・マッカーシー教授��「学習��らゆる��や知能���他�特徴��原�的��常�正確�記述�る�����る����れをシミュレート�る機械を作る�����る���推測�基���進��������考������。�加者�中�����分野�著�人��るマービン・ミンスキーも����。
+
+��ワークショップ���「記�的手法��頭��定�れ�領域�焦点を当��システム（�期�エキスパートシステム）�演繹的システム�帰�的システム�比較����議論�開始�れ�促進�れ��評価�れ����。（[出典](https://wikipedia.org/wiki/Dartmouth_workshop)）
+
+## 1956 - 1974: 黄金期
+
+1950年代�ら70年代������AI�������題を解決���れる���������楽観的�見方�広�������。1967年�マービン・ミンスキー�「一世代����...『人工知能�を作る����題�実質的�解決�れる�����自信を���述���る。(Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+自然言語処��研究�盛ん��り�検索�洗練�れ�より強力��り�平易�言語�よる指示�簡��作業を���「マイクロワールド����概念�生�れ�。
+
+研究�政府機関�ら潤沢�資金��供�れ�計算�アルゴリズム�進歩��知的機械�プロトタイプ�作られ�。��中��次�よ��も���る。
+
+* 移動��り�タスクを実行�る方法を「知的��決定�る�����るロボット[「Shakey�](https://wikipedia.org/wiki/Shakey_the_robot) 
+
+    ![Shakey, an intelligent robot](../images/shakey.jpg)
+    > Shakey in 1972
+
+* �期�「��ゃ�りロボット���るEliza��人�会話�る������原始的�「セラピスト��役割を果���。エリザ������NLP�レッスン�詳��説明���。
+
+    ![Eliza, a bot](../images/eliza.png)
+    > A version of Eliza, a chatbot
+
+* 「Blocks world���ブロックを��上��り並�替��り�るマイクロワールド�一例��機械�判断力を身�����る実験を行��。[SHRDLU](https://wikipedia.org/wiki/SHRDLU)を�����るライブラリ�進歩��言語処��発展�大��貢献��。
+
+    [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
+    
+    > 🎥 上�画�をクリック�る�動画�見られ��："Blocks world with SHRDLU"
+
+## 1974 - 1980: AI�冬
+
+1970年代����る��「知的�機械�を作る���複雑����評価�れ�����や�利用�能�計算能力を考慮�る����将�性��大評価�れ������明ら���り���。資金�枯渇����分野��信頼�低下��。信頼性�影響を与���題��以下�よ��も���る。:
+
+- **�界**. 計算能力��界
+- **組�����爆発**. 学習�必��パラメータ����コンピュータ��求�れる���多��る��れ�指数関数的�増加������コンピュータ�性能や能力�並行��進化���ん���。
+- **データ�少��**. データ��足�������アルゴリズム�テスト�開発�改良�プロセス�妨�られ�。
+- **正��質�を���る�����**. �������質���も��疑�視�れ始��。研究者����自分���アプロー��批判的��見を��よ�����。
+  - �ューリングテスト��「コンピュータをプログラミング�る����言語を�解���るよ��見���る�����る��本当��味���解��������る「�ャイニーズルーム�論����よ���疑�視�れるよ�����。([出典](https://plato.stanford.edu/entries/chinese-room/))
+  - セラピスト���ELIZA�よ��人工知能を社会�導入�る���倫�性���れ�。
+�れ��時�������AI��派�形��れ始����。一���["Scruffy"� "Neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies)���二分法��る。Scruffy�研究室���目的��果�得られる��何時間もプログラムを������一方�neat�研究室���論��形�的��題解決を�視�る。ELIZAやSHRDLU���有��Scruffy��るシステム��る。1980年代�入���MLシステム���性�求�られるよ���る���果�説明�能��る���ら�次第�neat�アプロー��主����������。
+
+## 1980s エキスパートシステム
+
+分野�発展�る��れ�ビジ�ス��貢献�明確��り�1980年代��「エキスパートシステム��普�����。「エキスパートシステム��人工知能（AI）ソフトウェア�中�最��真��功��形態�一���る。��言�れ����。（[出典](https://wikipedia.org/wiki/Expert_system)）
+
+��タイプ�システム��ビジ�ス�件を定義�るルールエンジン��ルールシステムを活用��新��事実を推論�る推論エンジン�構��れる�イブリッド型��。
+
+�����時代�ニューラル�ットワーク�も注目�集���。
+
+## 1987 - 1993: AI�冷�込�
+
+専門分野�特化��エキスパートシステム��ードウェア�普�������専門性�高��り���������残念��果��り���。���パーソナルコンピュータ��頭���れら�大�模�専門的�中央集権的システム�競���。コンピューティング�民主化�始�り�最終的���代�爆発的�ビッグデータ����開�れ���。
+
+## 1993 - 2011
+
+��期間����れ以��データ�計算能力��足�よ��引�起��れ����題を�MLやAI�解決��るよ�������。特�2007年頃�スマートフォン�登場������良�も悪�もデータ��急速�増加��広�利用�れるよ���り���。計算機�性能も飛�的��上��アルゴリズムも�れ�����進化�������。�去�自由奔放�時代�ら�真�学������晶化�始�り���分野��熟�������。
+
+## �在
+
+�在�機械学習やAI������生活�������部分�関������。��よ��時代����れら�アルゴリズム�人間�生活����リスクや潜在的�影響を注�深��解�る���求�られ��。マイクロソフト�ブラッド・スミス��「情報技術��プライ�シーや表��自由����基本的�人権�護�核心�迫る�題を�起���。情報技術��プライ�シーや表��自由����基本的�人権�護�根幹�関�る�題を�起���。我々�見解����れら��題��政府�よる�慮深��制��許容�れる使用方法�関�る�範�策定を必�������。��述�����。（[出典](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)）
+
+未�����る������り��ん���れら�コンピュータシステム���れを動��ソフトウェアやアルゴリズムを�解�る�������。��カリキュラム�自身�判断�る���り�より良��解を助�るも���る�幸���。
+
+[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
+> 🎥  上�画�をクリック�る�動画�見られ��：Yann LeCun discusses the history of deep learning in this lecture
+
+---
+## 🚀Challenge
+
+�れら�歴�的瞬間�1�を掘り下�����背後��る人々����学���ょ�。魅力的�人々������文化的�空白�状態�科学的発見���れ�����り��ん。��������見��る��ょ��？
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+
+## 振り返り�自習
+
+視��る��教��以下��り��:
+
+[Amy Boyd�AI�進化����述���る�ッドキャスト](http://runasradio.com/Shows/Show/739)
+
+[![Amy Boyd�よるAI�歴�](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
+
+## 課題
+
+[時系列を制作������](../assignment.md)

From b0e06d6927cc49865a2dff68aba147bb5a194417 Mon Sep 17 00:00:00 2001
From: Peeeaje <74146834+Peeeaje@users.noreply.github.com>
Date: Sat, 3 Jul 2021 23:20:24 +0900
Subject: [PATCH 008/368] translated titles, alt tags

also fixed some paths of images
---
 1-Introduction/1-intro-to-ML/translations/README.ja.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md
index 568a11e34c..aded0f7e65 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ja.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md
@@ -1,6 +1,6 @@
-# Introduction to machine learning
+# 機械学習��導入
 
-[![ML, AI, deep learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?")
+[![ML, AI, deep learning - ���何�？](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - ���何�？")
 
 > 🎥 上�画�をクリック�る��機械学習�AI�深層学習�������説明��動画�表示�れ��。
 
@@ -9,7 +9,7 @@
 ### イントロダクション
 
 �心者�����典的�機械学習�コース�よ���! ��テーマ�全�触れ������方も���分野をブラッシュアップ���経験豊富�方も�����加����。���������ML�学習�����親��や��スタート地点を作り���考�����。����[フィード�ック](https://github.com/microsoft/ML-For-Beginners/discussions)を評価��対応���り入れる�����れ�幸���。 
-[![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
+[![機械学習��導入](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "機械学習��導入")
 
 > 🎥 上�画�をクリック�る��MIT�John Guttag�機械学習を紹介�る動画�表示�れ��。
 ### 機械学習を始�る�����
@@ -26,7 +26,7 @@
 
 "機械学習(Machine Learning)"���言葉���在最も人気��り�頻��使用�れ��る言葉�一���。�ん�分野�技術者����も�多少�り�も技術�精通���れ��一度���言葉を耳�������る�能性�少���り��ん。����機械学習�仕組�����ん��人����謎�包�れ��り�機械学習��心者�������テーマ�時�圧倒�れるよ��感�られ��。�����機械学習��何�を実際��解��実践的�例を通��段階的�学ん����������。
 
-![ml hype curve](images/hype.png)
+![機械学習�人気を示�グラフ](../images/hype.png)
 
 > Google Trends�よる�「機械学習����言葉�最近�盛り上�りを示�グラフ。
 
@@ -38,7 +38,7 @@
 
 ��言葉�混��れる����り����機械学習（ML）�人工知能����サブセット��。**ML��特殊�アルゴリズムを使用����味��る情報を発見��知覚�れ�データ�ら隠れ�パターンを見������的���決定プロセスを�付�る���関係�����。**
 
-![AI, ML, deep learning, data science](images/ai-ml-ds.png)
+![AI, ML, ディープラーニング�データサイエンス](../images/ai-ml-ds.png)
 
 
 >[��グラフ](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)�触発�れ�[Jen Looper](https://twitter.com/jenlooper)��よるインフォグラフィック

From cb1470b007cbc415c5bbe735f86f122bc9939d93 Mon Sep 17 00:00:00 2001
From: Peeeaje <74146834+Peeeaje@users.noreply.github.com>
Date: Sat, 3 Jul 2021 23:29:40 +0900
Subject: [PATCH 009/368] translated title and alt tags for 1-introduction
 readme.md

---
 1-Introduction/README.ja.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/1-Introduction/README.ja.md b/1-Introduction/README.ja.md
index 1113d19f2a..7752ae312a 100644
--- a/1-Introduction/README.ja.md
+++ b/1-Introduction/README.ja.md
@@ -2,21 +2,21 @@
 
 ��セクション���機械学習�分野�基礎��る概念�機械学習��何�を紹介����歴�や研究者�機械学習を扱�際�使用�る技術����学���。 新��ML�世界を一緒�探求������ょ��
 
-![globe](images/globe.jpg)
-> Photo by <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> on <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+![地�](images/globe.jpg)
+> <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>�<a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a>�よる写真
   
 ### Lessons
 
-1. [Introduction to machine learning](1-intro-to-ML/README.md)
-1. [The History of machine learning and AI](2-history-of-ML/README.md)
-1. [Fairness and machine learning](3-fairness/README.md)
-1. [Techniques of machine learning](4-techniques-of-ML/README.md)
+1. [機械学習��導入](1-intro-to-ML/README.md)
+1. [機械学習�AI�歴�](2-history-of-ML/README.md)
+1. [公平性�機械学習](3-fairness/README.md)
+1. [機械学習�技術](4-techniques-of-ML/README.md)
 ### Credits
 
-"Introduction to Machine Learning "��[Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan)�[Ornella Altunyan](https://twitter.com/ornelladotcom)�[Jen Looper](https://twitter.com/jenlooper)����ーム�よ��制作�れ���。
+"機械学習��導入 "��[Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan)�[Ornella Altunyan](https://twitter.com/ornelladotcom)�[Jen Looper](https://twitter.com/jenlooper)����ーム�よ��制作�れ���。
 
-"The History of Machine Learning" �[Jen Looper](https://twitter.com/jenlooper)�[Amy Boyd](https://twitter.com/AmyKateNicho)�よ��制作�れ���。
+"機械学習�AI�歴�" �[Jen Looper](https://twitter.com/jenlooper)�[Amy Boyd](https://twitter.com/AmyKateNicho)�よ��制作�れ���。
 
-"Fairness and Machine Learning"�[Tomomi Imura](https://twitter.com/girliemac) �よ��制作�れ���。
+"公平性�機械学習"�[Tomomi Imura](https://twitter.com/girliemac) �よ��制作�れ���。
 
-"Techniques of Machine Learning"�[Jen Looper](https://twitter.com/jenlooper)�[Chris Noring](https://twitter.com/softchris) �よ��制作�れ���。
\ No newline at end of file
+"機械学習�技術"�[Jen Looper](https://twitter.com/jenlooper)�[Chris Noring](https://twitter.com/softchris) �よ��制作�れ���。
\ No newline at end of file

From 5619c3079922a2adb22f0cfec6226c030698b3cb Mon Sep 17 00:00:00 2001
From: Peeeaje <74146834+Peeeaje@users.noreply.github.com>
Date: Sat, 3 Jul 2021 23:40:50 +0900
Subject: [PATCH 010/368] translated titles and alt tags

---
 .../2-history-of-ML/translations/README.ja.md | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md
index 437f156fa1..f9b4c0457f 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ja.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ja.md
@@ -1,7 +1,7 @@
-# History of machine learning
+# 機械学習�歴�
 
-![Summary of History of machine learning in a sketchnote](../../../sketchnotes/ml-history.png)
-> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+![機械学習�歴�を����スケッ�](../../../sketchnotes/ml-history.png)
+> [Tomomi Imura](https://www.twitter.com/girlie_mac)�よるスケッ�
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
 
@@ -43,19 +43,19 @@
 
 * 移動��り�タスクを実行�る方法を「知的��決定�る�����るロボット[「Shakey�](https://wikipedia.org/wiki/Shakey_the_robot) 
 
-    ![Shakey, an intelligent robot](../images/shakey.jpg)
-    > Shakey in 1972
+    ![知的�ロボット��るShakey](../images/shakey.jpg)
+    > 1972�Shakey
 
 * �期�「��ゃ�りロボット���るEliza��人�会話�る������原始的�「セラピスト��役割を果���。エリザ������NLP�レッスン�詳��説明���。
 
-    ![Eliza, a bot](../images/eliza.png)
-    > A version of Eliza, a chatbot
+    ![Bot��るEliza](../images/eliza.png)
+    > �ャットボットEliza
 
 * 「Blocks world���ブロックを��上��り並�替��り�るマイクロワールド�一例��機械�判断力を身�����る実験を行��。[SHRDLU](https://wikipedia.org/wiki/SHRDLU)を�����るライブラリ�進歩��言語処��発展�大��貢献��。
 
-    [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
+    [![SHRDLUを用��blocks world](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "SHRDLUを用��blocks world")
     
-    > 🎥 上�画�をクリック�る�動画�見られ��："Blocks world with SHRDLU"
+    > 🎥 上�画�をクリック�る�動画�見られ��："SHRDLUを用��blocks world"
 
 ## 1974 - 1980: AI�冬
 
@@ -91,8 +91,8 @@
 
 未�����る������り��ん���れら�コンピュータシステム���れを動��ソフトウェアやアルゴリズムを�解�る�������。��カリキュラム�自身�判断�る���り�より良��解を助�るも���る�幸���。
 
-[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
-> 🎥  上�画�をクリック�る�動画�見られ��：Yann LeCun discusses the history of deep learning in this lecture
+[![ディープラーニング�歴�](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "ディープラーニング�歴�")
+> 🎥  上�画�をクリック�る�動画�見られ��：��レク�ャー��Yann LeCun�ディープラーニング�歴�����議論�����。 
 
 ---
 ## 🚀Challenge
@@ -107,7 +107,7 @@
 
 [Amy Boyd�AI�進化����述���る�ッドキャスト](http://runasradio.com/Shows/Show/739)
 
-[![Amy Boyd�よるAI�歴�](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
+[![Amy Boyd�よるAI�歴�](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Amy Boyd�よるAI�歴�")
 
 ## 課題
 

From 2020f144afc20780ccded726d7dbc231657d999c Mon Sep 17 00:00:00 2001
From: Seryozni <tofignazarbayov@gmail.com>
Date: Sun, 4 Jul 2021 16:52:27 +0400
Subject: [PATCH 011/368] Translated README files to all the topics to Russian

---
 1-Introduction/translations/README.ru.md   | 22 +++++++++
 2-Regression/translations/README.ru.md     | 33 ++++++++++++++
 3-Web-App/translations/README.ru.md        | 22 +++++++++
 4-Classification/translations/README.ru.md | 25 +++++++++++
 5-Clustering/translations/README.ru.md     | 26 +++++++++++
 6-NLP/translations/README.ru.md            | 24 ++++++++++
 7-TimeSeries/translations/README.ru.md     | 22 +++++++++
 8-Reinforcement/translations/README.ru.md  | 52 ++++++++++++++++++++++
 9-Real-World/translations/README.ru.md     | 14 ++++++
 9 files changed, 240 insertions(+)
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diff --git a/1-Introduction/translations/README.ru.md b/1-Introduction/translations/README.ru.md
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+# Введение в машинное обучение
+
+В �том разделе учебной программы вы познакомите�ь � базовыми концепци�ми, лежащими в о�нове обла�ти машинного обучени�, что �то такое, и узнаете о его и�тории и методах, которые и��ледователи и�пользуют дл� работы � ним. Давайте вме�те и��ледуем �тот новый мир машинного обучени�!
+
+! [глобу�](images/global.jpg)
+> Фото <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Билла Ок�форда </a> на <a href="https://unsplash.com/s/ photos / global? utm_source = unsplash & utm_medium = referral & utm_content = creditCopyText "> Unsplash </a>
+  
+### Уроки
+
+1. [Введение в машинное обучение](1-intro-to-ML/README.md)
+1. [И�тори� машинного обучени� и и�ку��твенного интеллекта](2-history-of-ML/README.md)
+1. [Справедливо�ть и машинное обучение](3-fairness/README.md)
+1. [Приемы машинного обучени�](4-techniques-of-ML/README.md)
+### Благодарно�ти
+
+«Введение в машинное обучение» было напи�ано � ♥ �группой людей, включа� [Мухаммад Сакиб Хан Инан](https://twitter.com/Sakibinan), [Орнелла �лтун�н](https://twitter.com/ornelladotcom) и [Джен Лупер](https://twitter.com/jenlooper)
+
+«И�тори� машинного обучени�» была напи�ана � ♥ �[Джен Лупер](https://twitter.com/jenlooper) и [Эми Бойд](https://twitter.com/AmyKateNicho)
+
+«Справедливо�ть и машинное обучение» напи�ано � ♥ �[Томоми Имура](https://twitter.com/girliemac)
+
+«Методы машинного обучени�» были напи�аны � ♥ �[Джен Лупер](https://twitter.com/jenlooper) и [Кри� �оринг](https://twitter.com/softchris)
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diff --git a/2-Regression/translations/README.ru.md b/2-Regression/translations/README.ru.md
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+# Модели регре��ии дл� машинного обучени�
+## Региональна� тема: модели регре��ии дл� цен на тыкву в Северной �мерике 🎃
+
+В Северной �мерике на Х�ллоуин из тыкв ча�то вырезают �трашные лица. Давайте узнаем больше об �тих во�хитительных овощах!
+
+! [jack-o-lanterns](./images/jack-o-lanterns.jpg)
+> Фото <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Бет Тойчманн </a> на <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a>
+  
+## Что вы узнаете
+
+Уроки в �том разделе охватывают типы регре��ии в контек�те машинного обучени�. Модели регре��ии могут помочь определить отношени� между переменными. Этот тип модели может пред�казывать такие значени�, как длина, температура или возра�т, тем �амым вы�вл�� взаимо�в�зи между переменными при анализе точек данных.
+
+В �той �ерии уроков вы обнаружите разницу между линейной регре��ией и логи�тиче�кой регре��ией, а также когда вам �ледует и�пользовать ту или иную регре��ию.
+
+В �той группе уроков вы будете на�троены, чтобы при�тупить к задачам машинного обучени�, включа� на�тройку кода Visual Studio дл� управлени� запи�ными книжками, общей �редой дл� �пециали�тов по данным. Вы откроете дл� �еб� Scikit-learn, библиотеку дл� машинного обучени�, и �оздадите �вои первые модели, удел�� о�обое внимание модел�м регре��ии в �той главе.
+
+> Суще�твуют полезные ин�трументы � небольшим количе�твом кода, которые могут помочь вам узнать о работе � модел�ми регре��ии. Попробуйте [Azure ML дл� �той задачи](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+### Уроки
+
+1. [Ин�трументы торговли](1-Tools/README.md)
+2. [Управление данными](2-Data/README.md)
+3. [Линейна� и полиномиальна� регре��и�](3-Linear/README.md)
+4. [Логи�тиче�ка� регре��и�](4-Logistic/README.md)
+
+---
+### Благодарно�ти
+
+«ML � регре��ией» был напи�ан � помощью ♥ �[Джен Лупер](https://twitter.com/jenlooper)
+
+♥ � Среди уча�тников викторины: [Мухаммад Сакиб Хан Инан](https://twitter.com/Sakibinan) и [Орнелла �лтун�н](https://twitter.com/ornelladotcom)
+
+�абор данных по тыкве предлагает�� [�тот проект на Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices), а его данные вз�ты из [Стандартных отчетов по рынкам �пециальных культур на терминалах](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) ра�про�тран�ет�� Мини�тер�твом �ель�кого хоз�й�тва СШ�. Мы добавили не�колько точек вокруг цвета на о�нове разнообрази�, чтобы нормализовать ра�пределение. Эти данные наход�т�� в открытом до�тупе.
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diff --git a/3-Web-App/translations/README.ru.md b/3-Web-App/translations/README.ru.md
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+# Создайте веб-приложение дл� и�пользовани� вашей модели машинного обучени�
+
+В �том разделе учебной программы вы познакомите�ь � прикладной темой машинного обучени�: как �охранить модель Scikit-learn в виде файла, который можно и�пользовать дл� прогнозировани� в веб-приложении. По�ле �охранени� модели вы узнаете, как и�пользовать ее в веб-приложении, �озданном во Flask. Сначала вы �оздадите модель, и�пользу� некоторые данные о наблюдени�х �ЛО! Затем вы �оздадите веб-приложение, которое позволит вам вве�ти количе�тво �екунд � широтой и долготой, чтобы пред�казать, кака� �трана �ообщила о видении �ЛО.
+
+! [Парковка �ЛО](images/ufo.jpg)
+
+Фото <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Майкла Херрена </a> на <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a>
+  
+
+## Уроки
+
+1. [Создайте веб-приложение](1-Web-App/README.md)
+
+## Благодарно�ти
+
+«Создайте веб-приложение» было напи�ано � помощью ♥ �[Джен Лупер](https://twitter.com/jenlooper).
+
+♥ � Те�ты были напи�аны Роханом Раджем.
+
+�абор данных вз�т из [Kaggle](https://www.kaggle.com/NUFORC/ufo-sightings).
+
+�рхитектура веб-приложени� была ча�тично предложена в [�той �татье](https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4) и [�той репозитории](https://github.com/abhinavsagar/machine-learning-deployment) �бхинава Сагара.
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diff --git a/4-Classification/translations/README.ru.md b/4-Classification/translations/README.ru.md
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+# �ачало работы � кла��ификацией
+## Региональна� тема: Вку�ные блюда азиат�кой и индий�кой кухни �
+
+В �зии и Индии традиции кухни чрезвычайно разнообразны и очень вку�ны! Давайте по�мотрим на данные о региональных кухн�х, чтобы попытать�� пон�ть их �о�тав.
+
+! [Продавец тай�кой еды](./images/thai-food.jpg)
+> Фото <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Лишенг Чанг </a> на <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText "> Unsplash </a>
+  
+## Что вы узнаете
+
+В �том разделе вы во�пользуете�ь навыками, полученными в первой ча�ти учебной программы, по�в�щенными регре��ии, и узнаете о других кла��ификаторах, которые вы можете и�пользовать и которые помогут вам изучить �вои данные.
+
+> Суще�твуют полезные ин�трументы � небольший количе�твом кода, которые могут помочь вам узнать о работе � модел�ми кла��ификации. Попробуйте [Azure ML дл� �той задачи](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+## Уроки
+
+1. [Введение в кла��ификацию](1-Introduction/README.md)
+2. [Другие кла��ификаторы](2-Classifiers-1/README.md)
+3. [Еще кла��ификаторы](3-Classifiers-2/README.md)
+4. [Прикладное машинное обучение: �оздание веб-приложени�](4-Applied/README.md)
+## Благодарно�ти
+
+«�ачало работы � кла��ификацией» было напи�ано � ♥ �[К���и Бревиу](https://www.twitter.com/cassieview) и [Джен Лупер](https://www.twitter.com/jenlooper)
+
+�абор данных о вку�ных блюдах вз�т из [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)
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diff --git a/5-Clustering/translations/README.ru.md b/5-Clustering/translations/README.ru.md
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+# Модели кла�теризации дл� машинного обучени�
+
+Кла�теризаци� - �то задача машинного обучени�, при которой она ищет объекты, которые похожи друг на друга, и группирует их в группы, называемые кла�терами. Что отличает кла�теризацию от других подходов в машинном обучении, так �то то, что в�е прои�ходит автоматиче�ки, и �праведливо �казать, что �то противоположно�ть supervised learning.
+
+## Региональна� тема: модели кла�теризации дл� музыкальных вку�ов нигерий�кой публики 🎧
+
+У разнообразной публики �игерии �амые разные музыкальные вку�ы. И�пользование данных, извлеченных из Spotify (на о�нове [�той �татьи](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421), давайте по�мотрим на музыку, попул�рную в �игерии. Этот набор данных включает данные о различных пе�н�х "танцевально�ть", "аку�тично�ть", "громко�ть", "речево�ть", "попул�рно�ть" и "�нерги�". Будет интере�но обнаружить закономерно�ти в �тих данных!
+
+![Поворотный �тол](./images/turntable.jpg)
+
+Фото <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Мар�ела Ла�ко�ки </a> на <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a>
+  
+В �той �ерии уроков вы откроете дл� �еб� новые �по�обы анализа данных � помощью методов кла�теризации. Кла�теризаци� о�обенно полезна, когда в наборе данных от�ут�твуют метки. Е�ли на нем е�ть �рлыки, тогда могут быть более полезными методы кла��ификации, подобные тем, которые вы изучили на предыдущих уроках. �о в �луча�х, когда вы хотите �группировать немаркированные данные, кла�теризаци� - отличный �по�об обнаружить закономерно�ти.
+
+> Суще�твуют полезные ин�трументы � небольшим количе�твом кода, которые могут помочь вам узнать о работе � модел�ми кла�теризации. Попробуйте [Azure ML дл� �той задачи](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+## Уроки
+
+1. [Введение в кла�теризацию](1-Visualize/README.md)
+2. [Кла�теризаци� K-Means](2-K-Means/README.md)
+## Благодарно�ти
+
+Эти уроки были напи�аны � помощью 🎶 [Джен Лупер](https://www.twitter.com/jenlooper) � полезными отзывами [Ришит Дагли](https://rishit_dagli) и [Мухаммад Сакиб Хан Инан](https://twitter.com/Sakibinan).
+
+�абор данных [�игерий�кие пе�ни](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) был получен из Kaggle, как и из Spotify.
+
+Полезные примеры K-Means, которые помогли в �оздании �того урока, включают [и��ледование радужной оболочки глаза](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), [вводный блокнот](https://www.kaggle.com/prashant111/k-means-clustering-with-python) и [пример гипотетиче�кой �ПО](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).
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diff --git a/6-NLP/translations/README.ru.md b/6-NLP/translations/README.ru.md
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+# �ачало работы � обработкой е�те�твенного �зыка
+
+Обработка е�те�твенного �зыка, NLP, - �то обла�ть и�ку��твенного интеллекта. В�� �та обла�ть направлена ​​на то, чтобы помочь машинам понимать и обрабатывать человече�кий �зык. Затем �то можно и�пользовать дл� выполнени� таких задач, как проверка орфографии или машинный перевод.
+
+## Региональна� тема: европей�кие �зыки и литература и романтиче�кие отели Европы ��
+
+В �том разделе учебной программы вы познакомите�ь � одним из наиболее ра�про�траненных �по�обов и�пользовани� машинного обучени�: обработкой е�те�твенного �зыка (NLP). Эта категори� и�ку��твенного интеллекта, выведенна� из компьютерной лингви�тики, �вл�ет�� мо�том между людьми и машинами по�ред�твом голо�овой или тек�товой коммуникации.
+
+�а �тих уроках мы изучим о�новы NLP, �оздав небольших диалоговых ботов, чтобы узнать, как машинное обучение помогает �делать �ти разговоры в�е более и более «умными». Вы отправите�ь в прошлое, болта� � Элизабет Беннетт и ми�тером Дар�и из кла��иче�кого романа Джейн О�тин **Гордо�ть и предубеждение**, опубликованного в 1813 году. Затем вы ра�ширите �вои знани�, узнав об анализе на�троений из отзывов об отел�х в Европе.
+
+![Книга о гордо�ти и предубеждени�х и чай](images/p&p.jpg)
+> Фото <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Элейн Хоулин </a> на <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a>
+  
+## Уроки
+
+1. [Введение в обработку е�те�твенного �зыка](1-Introduction-to-NLP/README.md)
+2. [Общие задачи и техники NLP](2-Tasks/README.md)
+3. [Перевод и анализ тонально�ти � помощью машинного обучени�](3-Translation-Sentiment/README.md)
+4. [Подготовка данных](4-Hotel-Reviews-1/README.md)
+5. [NLTK дл� анализа на�троений](5-Hotel-Reviews-2/README.md)
+
+## Благодарно�ти
+
+Эти уроки обработки е�те�твенного �зыка были напи�аны � помощью ☕ [Стивен Хау�лл](https://twitter.com/Howell_MSFT)
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diff --git a/7-TimeSeries/translations/README.ru.md b/7-TimeSeries/translations/README.ru.md
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+# Введение в прогнозирование временных �ерий
+
+Что такое прогнозирование временных �ерий? Речь идет о пред�казании будущих �обытий, анализиру� тенденции прошлого.
+
+## Региональна� тема: потребление �лектро�нергии во в�ем мире ✨
+
+В �тих двух уроках вы познакомите�ь � прогнозированием временных �ерий, не�колько менее изве�тной обла�тью машинного обучени�, котора�, тем не менее, чрезвычайно ценна дл� промышленно�ти и бизне�-приложений, �реди других обла�тей. Хот� нейронные �ети можно и�пользовать дл� повышени� полезно�ти �тих моделей, мы будем изучать их в контек�те кла��иче�кого машинного обучени�, по�кольку модели помогают прогнозировать будущую производительно�ть на о�нове прошлого.
+
+�аш региональный фоку� - и�пользование �лектро�нергии в мире, интере�ный набор данных, позвол�ющий узнать о прогнозировании будущего и�пользовани� �нергии на о�нове моделей прошлой нагрузки. Вы можете увидеть, на�колько такое прогнозирование может быть чрезвычайно полезным в деловой �реде.
+
+![�лектриче�ка� �еть](images / electric-grid.jpg)
+
+�втор фотографии <a href="https://unsplash.com/@shutter_log?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Педди Саи Хритика </a> �лектриче�ких башен на дороге в Раджа�тане на <a href="https://unsplash.com/s/photos/electric-india?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash </a>
+
+## Уроки
+
+1. [Введение в прогнозирование временных р�дов](1-Introduction/README.md)
+2. [По�троение моделей временных р�дов ARIMA] (2-ARIMA/README.md)
+
+## Благодарно�ти
+
+«Введение в прогнозирование временных р�дов» было напи�ано � ⚡� [Франче�ка Лазерри](https://twitter.com/frlazzeri) и [Джен Лупер](https://twitter.com/jenlooper)
\ No newline at end of file
diff --git a/8-Reinforcement/translations/README.ru.md b/8-Reinforcement/translations/README.ru.md
new file mode 100644
index 0000000000..6d6217500b
--- /dev/null
+++ b/8-Reinforcement/translations/README.ru.md
@@ -0,0 +1,52 @@
+# Введение в reinforcement learning
+Reinforcement learning (обучение � подкреплением), RL, ра��матривает�� как одна из о�новных парадигм машинного обучени�, нар�ду � supervised learning и unsupervised learning. RL - �то в�е о решени�х: прин�тие правильных решений или, по крайней мере, извлечение уроков из них.
+
+Пред�тавьте, что у ва� е�ть �моделированна� �реда, така� как фондовый рынок. Что произойдет, е�ли вы введете определенное правило. Имеет ли �то положительный или отрицательный �ффект? Е�ли прои�ходит что-то негативное, вам нужно прин�ть �то _негативное подкрепление_, извлечь из него урок и изменить кур�. Е�ли �то положительный результат, вам нужно и�пользовать �то _положительное подкрепление_.
+
+![peter and the wolf](images/peter.png)
+
+> Петьке и его друзь�м нужно �па�ти�ь от голодного волка! �втор изображени� [Jen Looper](https://twitter.com/jenlooper)
+
+## Региональна� тема: Пет� и Волк (Ро��и�)
+
+[Пет� и Волк](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) - музыкальна� �казка ру��кого композитора [Серге� Прокофьева] (https://en.wikipedia.org/wiki/Sergei_Prokofiev). Это и�тори� о юном пионере Пете, который �мело выходит из �воего дома на ле�ную пол�ну, чтобы пре�ледовать волка. В �том разделе мы обучим алгоритмы машинного обучени�, которые помогут Пете:
+
+- **И��ледуйте** окре�тно�ти и �оздайте оптимальную навигационную карту.
+- **Учите�ь** пользовать�� �кейтбордом и балан�ировать на нем, чтобы двигать�� бы�трее.
+
+[![Пет� и Волк](https://img.youtube.com/vi/Fmi5zHg4QSM/0.jpg)] (https://www.youtube.com/watch?v=Fmi5zHg4QSM)
+
+> 🎥 �ажмите на изображение выше, чтобы по�лушать Петю и Волка Прокофьева
+
+## Обучение � подкреплением
+
+В предыдущих разделах вы видели два примера проблем машинного обучени�:
+
+- **Supervised**, где у на� е�ть наборы данных, которые предлагают примеры решений проблемы, которую мы хотим решить. [Кла��ификаци�](../4-Classification/README.md) и [регре��и�] (../ 2-Регре��и� / README.md) �вл�ют�� контролируемыми учебными задачами.
+- **Unsupervised**, в котором у на� нет помеченных данных обучени�. О�новным примером unsupervised learning �вл�ет�� [Кла�теризаци�](../5-Clustering/README.md).
+
+В �том разделе мы познакомим ва� � новым типом задач обучени�, которые не требуют маркированных данных обучени�. Е�ть не�колько типов таких проблем:
+
+- **[Semi-supervised learning](https://wikipedia.org/wiki/Semi-supervised_learning)**, где у на� е�ть много немаркированных данных, которые можно и�пользовать дл� предварительного обучени� модели.
+- **[Reinforcement learning](https://wikipedia.org/wiki/Reinforcement_learning)**, в котором агент учит�� ве�ти �еб�, провод� �к�перименты в некоторой моделируемой �реде.
+
+### Пример - компьютерна� игра
+
+Предположим, вы хотите научить компьютер играть в игру, например, в шахматы или [Супер Марио](https://wikipedia.org/wiki/Super_Mario). Чтобы компьютер мог играть в игру, нам нужно, чтобы он пред�казывал, какой ход �делать в каждом из игровых �о�то�ний. Хот� �то может показать�� проблемой кла��ификации, �то не так - потому что у на� нет набора данных � �о�то�ни�ми и �оответ�твующими дей�тви�ми. Хот� у на� могут быть некоторые данные, такие как �уще�твующие шахматные матчи или запи�и игроков, играющих в Super Mario, вполне веро�тно, что �ти данные не будут в до�таточной �тепени охватывать до�таточно большое количе�тво возможных �о�то�ний.
+
+Вме�то пои�ка �уще�твующих игровых данных **Обучение � подкреплением** (RL) о�новано на идее *за�тавить компьютер играть* много раз и наблюдать за результатом. Таким образом, чтобы применить обучение � подкреплением, нам нужны две вещи:
+
+- **Среда** и **�имул�тор**, которые позвол�ют нам играть в игру много раз. Этот �имул�тор будет определ�ть в�е правила игры, а также возможные �о�то�ни� и дей�тви�.
+
+- **Функци� вознаграждени�**, котора� �ообщит нам, на�колько хорошо мы �делали каждый ход или игру.
+
+О�новное различие между другими типами машинного обучени� и RL заключает�� в том, что в RL мы обычно не знаем, выиграем мы или проиграем, пока не закончим игру. Таким образом, мы не можем �казать, �вл�ет�� ли конкретный ход хорошим или нет - мы получаем награду только в конце игры. И наша цель - разработать алгоритмы, которые позвол�т нам обучать модель в неопределенных у�лови�х. Мы узнаем об одном алгоритме RL под названием **Q-Learning**.
+
+## Уроки
+
+1. [Введение в обучение � подкреплением и Q-Learning](1-QLearning/README.md)
+2. [И�пользование тренажерного зала](2-Gym/README.md)
+
+## Благодарно�ти
+
+«Введение в обучение � подкреплением» напи�ано � ♥ �[Дмитрием Сошниковым](http://soshnikov.com)
\ No newline at end of file
diff --git a/9-Real-World/translations/README.ru.md b/9-Real-World/translations/README.ru.md
new file mode 100644
index 0000000000..73691de97c
--- /dev/null
+++ b/9-Real-World/translations/README.ru.md
@@ -0,0 +1,14 @@
+# По�т�криптум: реальные приложени� кла��иче�кого машинного обучени�
+
+В �том разделе учебной программы вы познакомите�ь � некоторыми реальными приложени�ми кла��иче�кого машинного обучени�. Мы обы�кали Интернет в пои�ках техниче�ких документов и �татей о приложени�х, которые и�пользовали �ти �тратегии, избега�, на�колько �то возможно, нейронных �етей, глубокого обучени� и и�ку��твенного интеллекта. Узнайте о том, как машинное обучение и�пользует�� в бизне�-�и�темах, �кологиче�ких приложени�х, финан�ах, и�ку��тве и культуре и многом другом.
+
+![chess](images/chess.jpg)
+
+> Фото �делано <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> на <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Урок
+
+1. [Реальные приложени� дл� машинного обучени�](1-Applications/README.md)
+## Благодарно�ти
+
+"Реальные приложени� дл� машинного обучени�" была напи�ана группой людей, включа�  [Джен Лупер](https://twitter.com/jenlooper) и [Орнелла �лтун�н](https://twitter.com/ornelladotcom).
\ No newline at end of file

From 74adefb0ff1a4eee7585d9cc269c4ab5c51a08ed Mon Sep 17 00:00:00 2001
From: Seryozni <tofignazarbayov@gmail.com>
Date: Sun, 4 Jul 2021 16:54:08 +0400
Subject: [PATCH 012/368] Link fix

---
 7-TimeSeries/translations/README.ru.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/7-TimeSeries/translations/README.ru.md b/7-TimeSeries/translations/README.ru.md
index 9cf9be25fa..6f69ed58dd 100644
--- a/7-TimeSeries/translations/README.ru.md
+++ b/7-TimeSeries/translations/README.ru.md
@@ -15,7 +15,7 @@
 ## Уроки
 
 1. [Введение в прогнозирование временных р�дов](1-Introduction/README.md)
-2. [По�троение моделей временных р�дов ARIMA] (2-ARIMA/README.md)
+2. [По�троение моделей временных р�дов ARIMA](2-ARIMA/README.md)
 
 ## Благодарно�ти
 

From 129f1b05e06401561755b90e434be56c6c2cb1f6 Mon Sep 17 00:00:00 2001
From: seryozni <tofignazarbayov@gmail.com>
Date: Sun, 4 Jul 2021 18:33:19 +0400
Subject: [PATCH 013/368] Moved all the translations to their proper folders

---
 1-Introduction/{ => translations}/README.es.md | 0
 1-Introduction/{ => translations}/README.ja.md | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename 1-Introduction/{ => translations}/README.es.md (100%)
 rename 1-Introduction/{ => translations}/README.ja.md (100%)

diff --git a/1-Introduction/README.es.md b/1-Introduction/translations/README.es.md
similarity index 100%
rename from 1-Introduction/README.es.md
rename to 1-Introduction/translations/README.es.md
diff --git a/1-Introduction/README.ja.md b/1-Introduction/translations/README.ja.md
similarity index 100%
rename from 1-Introduction/README.ja.md
rename to 1-Introduction/translations/README.ja.md

From ee1590f3020b8ba6e53b01ba78dc48030300bc43 Mon Sep 17 00:00:00 2001
From: Peeeaje <74146834+Peeeaje@users.noreply.github.com>
Date: Mon, 5 Jul 2021 03:39:57 +0900
Subject: [PATCH 014/368] draft of 1.3 fairness readme.ja

I also fixed a typo and an expression
---
 1-Introduction/3-fairness/README.md           |   2 +-
 .../3-fairness/translations/README.ja.md      | 211 ++++++++++++++++++
 1-Introduction/README.ja.md                   |   2 +-
 3 files changed, 213 insertions(+), 2 deletions(-)
 create mode 100644 1-Introduction/3-fairness/translations/README.ja.md

diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md
index 063c189813..79bf481929 100644
--- a/1-Introduction/3-fairness/README.md
+++ b/1-Introduction/3-fairness/README.md
@@ -29,7 +29,7 @@ Learn more about Responsible AI by following this [Learning Path](https://docs.m
 
 ## Unfairness in data and algorithms
 
-> "If you torture the data long enough, it will confess to anything - Ronald Coase
+> "If you torture the data long enough, it will confess to anything" - Ronald Coase
 
 This statement sounds extreme, but it is true that data can be manipulated to support any conclusion. Such manipulation can sometimes happen unintentionally. As humans, we all have bias, and it's often difficult to consciously know when you are introducing bias in data.
 
diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
new file mode 100644
index 0000000000..5794b23fca
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -0,0 +1,211 @@
+# 機械学習���る公平�
+ 
+![機械学習���る公平性を����スケッ�](../../../sketchnotes/ml-fairness.png)
+> [Tomomi Imura](https://www.twitter.com/girlie_mac)�よるスケッ�
+
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+ 
+## イントロダクション
+
+��カリキュラム���機械学習�����日常生活���よ��影響を与���る�を知る�������。���今�医療�診断や�正�検出���日常���決定�システムやモデル�関������。�����誰も�公平��果を得られるよ���る������れら�モデル����機能�る�������。
+
+�����れら�モデルを構築�る���使用���るデータ��人種�性別�政治的見解�宗教���特定�属性�欠����り���よ��属性������り�る��何�起�る�想���������。���モデル�出力�特定�層�有利��るよ��解釈�れ�場������ょ��。���果�アプリケーション���よ��影響を��る���ょ��？
+
+��レッスン���以下���を行���:
+
+- 機械学習���る公平性���性�対�る�識を高�る。
+- 公平性�関連�る�題����学�。
+- 公平性�評価�緩和����学�。
+
+## ���件
+���件����"Responsible AI Principles"�Learn Pathを�講����トピック�関�る以下�ビデオを視�������。
+
+��ら�[Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)より�責任��るAI����学�。
+
+[![Microsoft�責任�るAI�対�る�り組�](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft�責任�るAI�対�る�り組�")
+
+> 🎥 上�画�をクリック�る�動画�表示�れ��：Microsoft�責任�るAI�対�る�り組�
+
+## データやアルゴリズム��公平�
+
+> 「データを長�拷��れ��何�も自白�るよ���る� - Ronald Coase
+
+��言葉�極端��������データ��ん��論をも�付�るよ���作��る���事実��。������よ���作��時��図���行�れる����り��。人間�誰�も�イアスを����り�自分���データ��イアスを導入���る�を�識的�知る���難�����多����。
+
+AIや機械学習���る公平性��証���然���複雑�社会技術的課題��。��り�純粋�社会的�視点や技術的�視点���ら�らも対処�����������。
+
+### 公平性�関連���題
+
+�公平�������味���？�公平���人種�性別�年齢�障害�有無���定義�れ�人々�グループ�悪影響を与�る����る���被害を与�る����。
+
+主��公平�関連�る�題�以下�よ��分類�れ��。:
+
+- **アロケーション**。�る性別や民��他�性別や民�よりも優��れ��る場�。
+- **サービス�質**。�る特定�シナリオ����データを訓練��も��実�より複雑�場���サービス�質�低下����り��。
+- **固定観念**。特定�グループ��ら���割り当�られ�属性を関連��る��。
+- **誹謗中傷**。何�や誰�を�当�批判��り�レッテルを貼る��。
+- **�剰表������表�**。特定�グループ�特定��業�就���る姿�見られ���れを宣��続�るサービスや機能�被害を助長���る���考�。
+
+�れ�������例を見�����ょ�。
+
+### アロケーション
+
+ローン申請を審査�る仮想的�システムを考�����ょ�。��システム���他�グループよりも白人男性を優秀�候補者�����傾���り��。���果�特定�申請者��ローン��供�れ��ん���。
+
+も�一��例��大�業�候補者を審査�る���開発��実験的�採用ツール��。��ツール���る性別�関連�る言葉を好むよ��訓練�れ�モデルを使����る性別をシステム的�差別������。���果�履歴書�「女�ラグビー�ーム�����語���れ��る候補者�ペナルティを課�も���������。
+
+✅ ����上記�よ��実例を少�調��������。
+
+### サービス�質
+
+研究者�������市販�ジェンダー分類法��明る�肌色�男性�画��比較���暗�肌色�女性�画���高��正解率を示����を発見��。[�照](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/) 
+
+���肌�色�暗�人を感知�������ンドソープディスペンサー�例も悪��味�有���。[�照](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+
+### 固定観念
+
+機械翻訳���ステレオタイプ�性別観�見られ��。「彼�ナース��彼女�医者��。(“he is a nurse and she is a doctor�)����文をトルコ語�翻訳�る際��題�発生����。トルコ語��数�三人称を表�代�詞「o��1��る����性別�区別���言語����文章をトルコ語�ら英語�翻訳�直���「彼女�ナース��彼�医者��。(“she is a nurse and he is a doctor�)����ステレオタイプ�よる正����文章���������。
+
+![トルコ語�対�る翻訳](../images/gender-bias-translate-en-tr.png)
+
+![英語�復元�る翻訳](../images/gender-bias-translate-tr-en.png)
+
+### 誹謗中傷
+
+画�ラベリング技術�より�肌�色�黒�人�画�をゴリラ�誤表示�����有���。誤表示��システム���間��を�����������黒人を誹謗中傷�る�����表���図的�使�れ���長�歴�を��������有害��る。
+
+[![AI: 自分�女性�����？](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI: 自分�女性�����？")
+> 🎥 上�画�をクリック�る�動画�表示�れ��: AI: 自分�女性�����？ - AI�よる人種差別的�誹謗中傷�よる被害を示�パフォーマンス
+
+### �剰表������表�
+ 
+異常�画�検索��果����題�良�例��。エンジニアやCEO���男性�女性�割������れ以上��業�画�を検索�る����ら��性別�大������果�表示�れる��注��必���。
+
+![Bing�CEO�検索](../images/ceos.png)
+> This search on Bing for 'CEO' produces pretty inclusive results
+
+�れら�5��主��タイプ��題��相互�排他的�も������1��システム�複数�タイプ�害を示���も�り��。�ら���れ�れ�ケース������大性�異�り��。例����る人��当�犯罪者�レッテルを貼る����画�を誤��表示�る��よりも�る��深刻��題��。����比較的深刻����被害����も�人々�疎外感を感��り�特別視�れ��る�感��り�る����り���累�的�影響��常�抑圧的�も���り�る��を覚�����������ょ�。
+ 
+✅ **ディスカッション**: �����例を�検討��異�る害を示���る����を確�������。 
+
+|                         | アロケーション | サービス�質 | 固定観念 | 誹謗中傷 | �剰表�/��表� |
+| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
+| 採用システム�自動化 |     x      |         x          |      x       |             |               x                |
+| 機械翻訳     |            |                    |              |             |                                |
+| 写真�ラベリング          |            |                    |              |             |                                |
+
+
+## �公平�検出
+
+�るシステム��公平�動作を�る�由�������。例���社会的��イアス��学習�使�れ�データセット��映�れ��る�も�れ�������去�データ�頼り�������採用��公平�悪化���も�れ��ん。�るモデル��10年間�会社��出�れ�履歴書�パターンを利用���男性�ら�履歴書�大�を��������ら�男性�方��格��る�判断����。
+
+特定�グループ�関�るデータ���分��る��も��公平�原因��り��。例���肌�色�濃�人�データ�少������画�分類����肌�色�濃�人�画��エラー率�高��り��。
+
+���開発時�誤��仮定も�公平�原因��り��。例���人�顔�画��ら犯罪を犯�人を予測�る��を目的���顔分�システム���有害�推測を���������り��。���果�誤��分類を�れ�人�大��被害を��る����り����ん。
+
+## モデルを�解��公平性を構築�る
+ 
+Although many aspects of fairness are not captured in quantitative fairness metrics, and it is not possible to fully remove bias from a system to guarantee fairness, you are still responsible to detect and to mitigate fairness issues as much as possible. 
+
+When you are working with machine learning models, it is important to understand your models by means of assuring their interpretability and by assessing and mitigating unfairness.
+
+Let’s use the loan selection example to isolate the case to figure out each factor's level of impact on the prediction.
+
+## Assessment methods
+
+1. **Identify harms (and benefits)**. The first step is to identify harms and benefits. Think about how actions and decisions can affect both potential customers and a business itself.
+  
+1. **Identify the affected groups**. Once you understand what kind of harms or benefits that can occur, identify the groups that may be affected. Are these groups defined by gender, ethnicity, or social group?
+
+1. **Define fairness metrics**. Finally, define a metric so you have something to measure against in your work to improve the situation.
+
+### Identify harms (and benefits)
+
+What are the harms and benefits associated with lending? Think about false negatives and false positive scenarios: 
+
+**False negatives** (reject, but Y=1) - in this case, an applicant who will be capable of repaying a loan is rejected. This is an adverse event because the resources of the loans are withheld from qualified applicants.
+
+**False positives** (accept, but Y=0) - in this case, the applicant does get a loan but eventually defaults. As a result, the applicant's case will be sent to a debt collection agency which can affect their future loan applications.
+
+### Identify affected groups
+
+The next step is to determine which groups are likely to be affected. For example, in case of a credit card application, a model might determine that women should receive much lower credit limits compared with their spouses who share household assets. An entire demographic, defined by gender, is thereby affected.
+
+### Define fairness metrics
+ 
+You have identified harms and an affected group, in this case, delineated by gender. Now, use the quantified factors to disaggregate their metrics. For example, using the data below, you can see that women have the largest false positive rate and men have the smallest, and that the opposite is true for false negatives.
+
+✅ In a future lesson on Clustering, you will see how to build this 'confusion matrix' in code
+
+|            | False positive rate | False negative rate | count |
+| ---------- | ------------------- | ------------------- | ----- |
+| Women      | 0.37                | 0.27                | 54032 |
+| Men        | 0.31                | 0.35                | 28620 |
+| Non-binary | 0.33                | 0.31                | 1266  |
+
+ 
+This table tells us several things. First, we note that there are comparatively few non-binary people in the data. The data is skewed, so you need to be careful how you interpret these numbers.
+
+In this case, we have 3 groups and 2 metrics. When we are thinking about how our system affects the group of customers with their loan applicants, this may be sufficient, but when you want to define larger number of groups, you may want to distill this to smaller sets of summaries. To do that, you can add more metrics, such as the largest difference or smallest ratio of each false negative and false positive. 
+ 
+✅ Stop and Think: What other groups are likely to be affected for loan application? 
+ 
+## Mitigating unfairness 
+ 
+To mitigate unfairness, explore the model to generate various mitigated models and compare the tradeoffs it makes between accuracy and fairness to select the most fair model. 
+
+This introductory lesson does not dive deeply into the details of algorithmic unfairness mitigation, such as post-processing and reductions approach, but here is a tool that you may want to try. 
+
+### Fairlearn 
+ 
+[Fairlearn](https://fairlearn.github.io/) is an open-source Python package that allows you to assess your systems' fairness and mitigate unfairness.  
+
+The tool helps you to assesses how a model's predictions affect different groups, enabling you to compare multiple models by using fairness and performance metrics, and supplying a set of algorithms to mitigate unfairness in binary classification and regression. 
+
+- Learn how to use the different components by checking out the Fairlearn's [GitHub](https://github.com/fairlearn/fairlearn/)
+
+- Explore the [user guide](https://fairlearn.github.io/main/user_guide/index.html), [examples](https://fairlearn.github.io/main/auto_examples/index.html)
+
+- Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
+  
+- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning. 
+  
+- Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning. 
+
+---
+## 🚀 Challenge 
+ 
+To prevent biases from being introduced in the first place, we should: 
+
+- have a diversity of backgrounds and perspectives among the people working on systems 
+- invest in datasets that reflect the diversity of our society 
+- develop better methods for detecting and correcting bias when it occurs 
+
+Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider? 
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## Review & Self Study 
+ 
+In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.  
+ 
+Watch this workshop to dive deeper into the topics: 
+
+- YouTube: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik [Fairness-related harms in AI systems: Examples, assessment, and mitigation - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
+
+Also, read: 
+
+- Microsoft’s RAI resource center: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
+
+- Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
+
+Explore the Fairlearn toolkit
+
+[Fairlearn](https://fairlearn.org/)
+
+Read about Azure Machine Learning's tools to ensure fairness
+
+- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
+
+## Assignment
+
+[Explore Fairlearn](assignment.md) 
diff --git a/1-Introduction/README.ja.md b/1-Introduction/README.ja.md
index 7752ae312a..83f0ba6cb4 100644
--- a/1-Introduction/README.ja.md
+++ b/1-Introduction/README.ja.md
@@ -9,7 +9,7 @@
 
 1. [機械学習��導入](1-intro-to-ML/README.md)
 1. [機械学習�AI�歴�](2-history-of-ML/README.md)
-1. [公平性�機械学習](3-fairness/README.md)
+1. [機械学習���る公平�](3-fairness/README.md)
 1. [機械学習�技術](4-techniques-of-ML/README.md)
 ### Credits
 

From c6f90b6c8370053b9e6f8892e77c9e3ef6b92346 Mon Sep 17 00:00:00 2001
From: Vitor Mouro <49460655+VitorMouro@users.noreply.github.com>
Date: Sun, 4 Jul 2021 16:34:26 -0300
Subject: [PATCH 015/368] fix typo

---
 2-Regression/1-Tools/assignment.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/assignment.md b/2-Regression/1-Tools/assignment.md
index dd58a16970..de37856c51 100644
--- a/2-Regression/1-Tools/assignment.md
+++ b/2-Regression/1-Tools/assignment.md
@@ -2,7 +2,7 @@
 
 ## Instructions
 
-Take a look at the [Linnerud dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud) in Scikit-learn. This dataset has multiple [targets](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset): 'It consists of three excercise (data) and three physiological (target) variables collected from twenty middle-aged men in a fitness club'.
+Take a look at the [Linnerud dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud) in Scikit-learn. This dataset has multiple [targets](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset): 'It consists of three exercise (data) and three physiological (target) variables collected from twenty middle-aged men in a fitness club'.
 
 In your own words, describe how to create a Regression model that would plot the relationship between the waistline and how many situps are accomplished. Do the same for the other datapoints in this dataset.
 

From 6bb8b8eee824175f35259c40285faed8b027ede8 Mon Sep 17 00:00:00 2001
From: Peeeaje <74146834+Peeeaje@users.noreply.github.com>
Date: Mon, 5 Jul 2021 05:31:15 +0900
Subject: [PATCH 016/368] translate 1.3 fairness readme.md to Japanese

---
 .../3-fairness/translations/README.ja.md      | 103 ++++++++----------
 1 file changed, 48 insertions(+), 55 deletions(-)

diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
index 5794b23fca..3ac9418c81 100644
--- a/1-Introduction/3-fairness/translations/README.ja.md
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -105,107 +105,100 @@ AIや機械学習���る公平性��証���然���複雑�社
 
 ## モデルを�解��公平性を構築�る
  
-Although many aspects of fairness are not captured in quantitative fairness metrics, and it is not possible to fully remove bias from a system to guarantee fairness, you are still responsible to detect and to mitigate fairness issues as much as possible. 
+公平性�多�����定�的�指標����られ��公平性を�証�る���システム�ら�イアスを完全��り除������能����公平性��題を�能��り検出��軽減�る責任��り��。
 
-When you are working with machine learning models, it is important to understand your models by means of assuring their interpretability and by assessing and mitigating unfairness.
+機械学習モデルを扱�際���モデル�解釈�能性を�証���公平�を評価・軽減�る����モデルを�解�る�������。
 
-Let’s use the loan selection example to isolate the case to figure out each factor's level of impact on the prediction.
+�����ローン�択�例を使��ケースを切り分����素�予測�与�る影響�度��を把������ょ�。
 
-## Assessment methods
+## 評価方法
 
-1. **Identify harms (and benefits)**. The first step is to identify harms and benefits. Think about how actions and decisions can affect both potential customers and a business itself.
+1. **�害（�利益）を特定�る**。最��ステップ���害�利益を特定�る����。行動や決定��潜在的�顧客�ビジ�ス��も��両方���よ��影響を与�る�を考�����ょ�。
   
-1. **Identify the affected groups**. Once you understand what kind of harms or benefits that can occur, identify the groups that may be affected. Are these groups defined by gender, ethnicity, or social group?
+1. **影響を��るグループを特定�る**。��よ��害や利益�発生��る�を�解��ら�影響を��る�能性��るグループを特定���。�れら�グループ��性別�民�����社会的グループ�よ��定義�れる��ょ��。
 
-1. **Define fairness metrics**. Finally, define a metric so you have something to measure against in your work to improve the situation.
+1. **公正��測定基準を定義�る**。最後��状�を改善�る際�何を基準��る��指標を定義���。
 
-### Identify harms (and benefits)
+### 有害性（�よ�利益）を特定�る
+貸与�関連�る有害性�利益�何�？�陰性��陽性�シナリオ����考�����ょ�。
 
-What are the harms and benefits associated with lending? Think about false negatives and false positive scenarios: 
+**�陰性（�������Y=1）** - ��場��ローンを返済��る����申請者�拒��れ��。�れ���資�資格��る申請者���れ���る����利�事象��り��。
 
-**False negatives** (reject, but Y=1) - in this case, an applicant who will be capable of repaying a loan is rejected. This is an adverse event because the resources of the loans are withheld from qualified applicants.
+**�陽性（��入れる��Y=0）** - ��場��申請者��資を�����最終的��返済�能（デフォルト）��る。���果�申請者�事例�債権回�会社��られ�将��ローン申請�影響を与�る�能性��り��。
 
-**False positives** (accept, but Y=0) - in this case, the applicant does get a loan but eventually defaults. As a result, the applicant's case will be sent to a debt collection agency which can affect their future loan applications.
+### 影響を��るグループ�特定
+次�ステップ�����グループ�影響を��る�能性��る�を判断���。例���クレジットカード�申請�場��家計�資産を共有���る��者�比較���女性�与信�度��大幅�低������モデル�判断�る�も�れ��ん。�れ�より�ジェンダー�定義�れる層全体�影響を��る����り��。
 
-### Identify affected groups
+### 公正��測定基準を定義�る
+����有害性�影響を��るグループ（��場���性別�定義�れ��る）を�����特定����。次��定�化�れ��素を使�����評価基準を分解���。例���以下�データを使用�る��女性��陽性率�最も大���男性�最も�����������逆��陰性�場��当���る�����り��。
 
-The next step is to determine which groups are likely to be affected. For example, in case of a credit card application, a model might determine that women should receive much lower credit limits compared with their spouses who share household assets. An entire demographic, defined by gender, is thereby affected.
+✅ 今後�"クラスタリング"�レッスン�����"混�行列"をコード�構築�る方法を�紹介���。
 
-### Define fairness metrics
- 
-You have identified harms and an affected group, in this case, delineated by gender. Now, use the quantified factors to disaggregate their metrics. For example, using the data below, you can see that women have the largest false positive rate and men have the smallest, and that the opposite is true for false negatives.
-
-✅ In a future lesson on Clustering, you will see how to build this 'confusion matrix' in code
-
-|            | False positive rate | False negative rate | count |
+|            | �陽性率 | �陰性率 | サンプル数 |
 | ---------- | ------------------- | ------------------- | ----- |
-| Women      | 0.37                | 0.27                | 54032 |
-| Men        | 0.31                | 0.35                | 28620 |
-| Non-binary | 0.33                | 0.31                | 1266  |
+| 女性      | 0.37                | 0.27                | 54032 |
+| 男性        | 0.31                | 0.35                | 28620 |
+| ��ら�も属��� | 0.33                | 0.31                | 1266  |
 
- 
-This table tells us several things. First, we note that there are comparatively few non-binary people in the data. The data is skewed, so you need to be careful how you interpret these numbers.
+��表�ら�����������り��。���データ���れる男性�女性��ら�も��人�比較的少�������り��。従����データ�歪ん��り���数字を��解釈�る��注��必���。
 
-In this case, we have 3 groups and 2 metrics. When we are thinking about how our system affects the group of customers with their loan applicants, this may be sufficient, but when you want to define larger number of groups, you may want to distill this to smaller sets of summaries. To do that, you can add more metrics, such as the largest difference or smallest ratio of each false negative and false positive. 
+今回�場��3��グループ�2��指標��り��。��システム�ローン申請者��る�客様�グループ���よ��影響を与�る�を考�る�����れ��分�も�れ��ん。����より多��グループを定義���場����れをより����約����り�抽出�������も�れ��ん。��������陰性��陽性�れ�れ�最大値�差や最��比率���より多���素を追加�る�������。
  
-✅ Stop and Think: What other groups are likely to be affected for loan application? 
+✅ 一旦���考��������：ローン申請�際�影響を�����他�グループ�？
  
-## Mitigating unfairness 
+## �公平�緩和
  
-To mitigate unfairness, explore the model to generate various mitigated models and compare the tradeoffs it makes between accuracy and fairness to select the most fair model. 
+�公平を緩和�る�����モデルを探索��様々�緩和モデルを生���精度�公平性�間�行�トレードオフを比較���最も公平�モデルを�択���。
 
-This introductory lesson does not dive deeply into the details of algorithmic unfairness mitigation, such as post-processing and reductions approach, but here is a tool that you may want to try. 
+��入門編���後処�やリダクション�アプロー�����アルゴリズム�よる�公平�緩和�詳細�����深�触れ����ん��試������ツールを���紹介���。
 
 ### Fairlearn 
- 
-[Fairlearn](https://fairlearn.github.io/) is an open-source Python package that allows you to assess your systems' fairness and mitigate unfairness.  
+[Fairlearn](https://fairlearn.github.io/)�オープンソース�Pythonパッケージ��システム�公平性を評価���公平を緩和�る�������。
 
-The tool helps you to assesses how a model's predictions affect different groups, enabling you to compare multiple models by using fairness and performance metrics, and supplying a set of algorithms to mitigate unfairness in binary classification and regression. 
+��ツール��モデル�予測�異�るグループ���よ��影響を与�る�を評価��公平性�パフォーマンス�指標を用��複数�モデルを比較�る��を�能���二項分類(binary classification)�回帰(regression)���る�公平�を緩和�る���アルゴリズムを�供���。
 
-- Learn how to use the different components by checking out the Fairlearn's [GitHub](https://github.com/fairlearn/fairlearn/)
+- Fairlearn�[GitHub](https://github.com/fairlearn/fairlearn/)�����素�使用方法を紹介�����。
 
-- Explore the [user guide](https://fairlearn.github.io/main/user_guide/index.html), [examples](https://fairlearn.github.io/main/auto_examples/index.html)
+- [ユーザーガイド](https://fairlearn.github.io/main/user_guide/index.html)�[サンプル](https://fairlearn.github.io/main/auto_examples/index.html)を見る。
 
-- Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
+- [サンプルノートブック](https://github.com/fairlearn/fairlearn/tree/master/notebooks)を試�。
   
-- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning. 
+- Azure Machine Learning�機械学習モデル�[公平性評価を�能��る方法](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa)を学�。
   
-- Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning. 
+- Azure Machine Learning�[サンプルノートブック](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness)を�ェック���公平性評価��れを確��る。
 
 ---
 ## 🚀 Challenge 
  
-To prevent biases from being introduced in the first place, we should: 
+�も�も�り�生���よ���る�����次�よ�����必���。
 
-- have a diversity of backgrounds and perspectives among the people working on systems 
-- invest in datasets that reflect the diversity of our society 
-- develop better methods for detecting and correcting bias when it occurs 
+- システム���る人���背景や考�方を多様化�る。
+- 社会�多様性を�映��データセット�投資�る。
+- �イアス�発生�������れを検知��修正�る���より良�方法を開発�る。
 
-Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider? 
+モデル�構築や使用������公平�明ら���るよ���実�シナリオを考��������。他���よ����を考�る����ょ��？
 
 ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
 ## Review & Self Study 
  
-In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.  
+��レッスン���機械学習���る公平��公平�概念�基礎を学����。
  
-Watch this workshop to dive deeper into the topics: 
-
-- YouTube: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik [Fairness-related harms in AI systems: Examples, assessment, and mitigation - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
+��ワークショップを見��トピックをより深��解������：
 
-Also, read: 
+- YouTube: AIシステム���る公平性�関連��被害: Hanna Wallach�Miro Dudik�よる�事例�評価�緩和策����[AIシステム���る公平性�関連��被害: Hanna Wallach�Miro Dudik�よる�事例�評価�緩和策���� - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
 
-- Microsoft’s RAI resource center: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
+- Microsoft�RAIリソースセンター: [責任�るAIリソース – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
 
-- Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
+- Microsoft�FATE研究グループ: [AI���るFATE: Fairness（公平性）, Accountability（説明責任）, Transparency（�明性）, and Ethics（倫�）- Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
 
-Explore the Fairlearn toolkit
+Fairlearn�ツールキットを調�����ょ�
 
-[Fairlearn](https://fairlearn.org/)
+- [Fairlearn](https://fairlearn.org/)
 
-Read about Azure Machine Learning's tools to ensure fairness
+Azure Machine Learning�よる�公平性を確��る���ツール����読む
 
 - [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
 
-## Assignment
+## 課題
 
-[Explore Fairlearn](assignment.md) 
+[Fairlearnを調査�る](../assignment.md) 

From a742220e80b676d05c1a38771f714c19d661d9a8 Mon Sep 17 00:00:00 2001
From: Jun Hyuk Han <hanjun.1998@gmail.com>
Date: Mon, 5 Jul 2021 14:27:39 +0900
Subject: [PATCH 017/368] fix broken link and change wording

---
 1-Introduction/4-techniques-of-ML/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index ec42fe705e..7061821f27 100644
--- a/1-Introduction/4-techniques-of-ML/README.md
+++ b/1-Introduction/4-techniques-of-ML/README.md
@@ -35,7 +35,7 @@ Before starting to build your model, there are several tasks you need to complet
 To be able to answer your question with any kind of certainty, you need a good amount of data of the right type. There are two things you need to do at this point:
 
 - **Collect data**. Keeping in mind the previous lesson on fairness in data analysis, collect your data with care. Be aware of the sources of this data, any inherent biases it might have, and document its origin.
-- **Prepare data**. There are several steps in the data preparation process. You might need to collate data and normalize it if it comes from diverse sources. You can improve the data's quality and quantity through various methods such as converting strings to numbers (as we do in [Clustering](../../5-Clustering/1-Visualize/README.md)). You might also generate new data, based on the original (as we do in [Classification](../../4-Classification/1-Introduction/README.md)). You can clean and edit the data (as we did prior to the [Web App](../3-Web-App/README.md) lesson). Finally, you might also need to randomize it and shuffle it, depending on your training techniques.
+- **Prepare data**. There are several steps in the data preparation process. You might need to collate data and normalize it if it comes from diverse sources. You can improve the data's quality and quantity through various methods such as converting strings to numbers (as we do in [Clustering](../../5-Clustering/1-Visualize/README.md)). You might also generate new data, based on the original (as we do in [Classification](../../4-Classification/1-Introduction/README.md)). You can clean and edit the data (as we will prior to the [Web App](../../3-Web-App/README.md) lesson). Finally, you might also need to randomize it and shuffle it, depending on your training techniques.
 
 ✅ After collecting and processing your data, take a moment to see if its shape will allow you to address your intended question. It may be that the data will not perform well in your given task, as we discover in our [Clustering](../../5-Clustering/1-Visualize/README.md) lessons!
 

From 20c50a2c3f887c812908a6c527528f2c204842b8 Mon Sep 17 00:00:00 2001
From: Jun Hyuk Han <hanjun.1998@gmail.com>
Date: Mon, 5 Jul 2021 14:50:24 +0900
Subject: [PATCH 018/368] fix broken link

---
 1-Introduction/4-techniques-of-ML/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index 7061821f27..ae9d2c44be 100644
--- a/1-Introduction/4-techniques-of-ML/README.md
+++ b/1-Introduction/4-techniques-of-ML/README.md
@@ -53,7 +53,7 @@ Prior to training, you need to split your dataset into two or more parts of uneq
 
 - **Training**. This part of the dataset is fit to your model to train it. This set constitutes the majority of the original dataset.
 - **Testing**. A test dataset is an independent group of data, often gathered from the original data, that you use to confirm the performance of the built model.
-- **Validating**. A validation set is a smaller independent group of examples that you use to tune the model's hyperparameters, or architecture, to improve the model. Depending on your data's size and the question you are asking, you might not need to build this third set (as we note in [Time Series Forecasting](../7-TimeSeries/1-Introduction/README.md)).
+- **Validating**. A validation set is a smaller independent group of examples that you use to tune the model's hyperparameters, or architecture, to improve the model. Depending on your data's size and the question you are asking, you might not need to build this third set (as we note in [Time Series Forecasting](../../7-TimeSeries/1-Introduction/README.md)).
 
 ## Building a model
 

From 473566c61a197354d5c40371de961fe6e6edbd6b Mon Sep 17 00:00:00 2001
From: Rohan Hasabe <rohanhasabe8@pixelexperience.org>
Date: Mon, 5 Jul 2021 14:44:30 +0530
Subject: [PATCH 019/368] Fix typo in  2-Regression/1-Tools

* Also add image according to the change.

Signed-off-by: Rohan Hasabe <Rohanhasabe8@gmail.com>
---
 2-Regression/1-Tools/README.md           |   4 ++--
 2-Regression/1-Tools/images/notebook.png | Bin 151537 -> 28476 bytes
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index d18f529587..3d1fd89e9e 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -52,13 +52,13 @@ In this folder, you will find the file _notebook.ipynb_.
 
    Next, add some Python code. 
 
-1. Type **print("hello notebook'")** in the code block.
+1. Type **print("'hello notebook'")** in the code block.
 1. Select the arrow to run the code.
 
    You should see the printed statement:
 
     ```output
-    hello notebook
+    'hello notebook'
     ```
 
 ![VS Code with a notebook open](images/notebook.png)
diff --git a/2-Regression/1-Tools/images/notebook.png b/2-Regression/1-Tools/images/notebook.png
index 27a187118d9254d2e589fc6f13359ee7c084e04b..bea9e0e9ff26d6e98bca4ca677a1c8ccc46829e7 100644
GIT binary patch
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zHG=A{{0a*DySpM}qQa41ITjK7_aFZXMf>Ne|E$12EAY<>{Idf8tiV4j@c-KtSaBQA
Xkhv<!wtM~$_)k(?RxDdY%j5q5x35;D


From 545d138253569c64a788f7d980a25c9de3c882af Mon Sep 17 00:00:00 2001
From: zhanghai <zhanghai@wuhanins.com>
Date: Mon, 5 Jul 2021 18:16:57 +0800
Subject: [PATCH 020/368] translate

---
 .../translations/README.zh-cn.md              | 128 +++++++++---------
 1 file changed, 64 insertions(+), 64 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index 67c93dbf5f..ffa1eb26b8 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -1,116 +1,116 @@
-# History of machine learning
+# 机器学习的历� 
 
-![Summary of History of machine learning in a sketchnote](../../sketchnotes/ml-history.png)
-> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+![机器学习历�概述](../../sketchnotes/ml-history.png)
+> 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
 
-In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence.
+在本课中，我们将走过机器学习和人工智能历�上的主�里程碑。 
 
-The history of artificial intelligence, AI, as a field is intertwined with the history of machine learning, as the algorithms and computational advances that underpin ML fed into the development of AI. It is useful to remember that, while these fields as distinct areas of inquiry began to crystallize in the 1950s, important [algorithmical, statistical, mathematical, computational and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era. In fact, people have been thinking about these questions for [hundreds of years](https://wikipedia.org/wiki/History_of_artificial_intelligence): this article discusses the historical intellectual underpinnings of the idea of a 'thinking machine.'
+人工智能（AI）作为一个领域的历�与机器学习的历�交织在一起，因为支�机器学习的算法和计算能力的进步推动了AI的�展。记�，虽然这些领域作为��研究领域在20世纪50年代�开始具体化，但��的[算法�统计�数学�计算和技术�现](https://wikipedia.org/wiki/Timeline_of_machine_learning) �早于和��了这个时代。 事实上，[数百年�](https://wikipedia.org/wiki/History_of_artificial_intelligence)人们一直在�考这些问题：本文讨论了“�维机器�这一概念的历�知识基础。 
 
-## Notable discoveries
+## 主��现
 
-- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) and its predecessors. This theorem and its applications underlie inference, describing the probability of an event occurring based on prior knowledge.
-- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) by French mathematician Adrien-Marie Legendre. This theory, which you will learn about  in our Regression unit, helps in data fitting.
-- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) named after Russian mathematician Andrey Markov is used to describe a sequence of possible events based on a previous state.
-- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning.
-- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) is an algorithm originally designed to map routes. In an ML context it is used to  detect patterns.
-- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) is used to train [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network).
-- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) are artificial neural networks derived from feedforward neural networks that create temporal graphs.
+- 1763, 1812 [��斯定�](https://wikipedia.org/wiki/Bayes%27_theorem) �其�身。该定��其应用是推�的基础，�述了基于先验知识的事件�生的概率。
+- 1805 [最�二乘�论](https://wikipedia.org/wiki/Least_squares)由法国数学家Adrien-Marie Legendre�出。 您将在我们的回归�元中了解这一�论，它有助于数�拟�。
+- 1913 [马尔�夫链](https://wikipedia.org/wiki/Markov_chain)以俄罗斯数学家安德烈马尔�夫的�字命�，用于�述基于先�状�的一系列�能事件。
+- 1957 [感知器](https://wikipedia.org/wiki/Perceptron)是美国心�学家弗兰克·罗森布拉特�明的一�线性分类器，是深度学习�展的基础。
+- 1967 [最近邻](https://wikipedia.org/wiki/Nearest_neighbor)是一�最�设计用于映射路线的算法。 在ML中，它用于检测模�。
+- 1970 [��传播](https://wikipedia.org/wiki/Backpropagation)用于训练[�馈神�网络](https://wikipedia.org/wiki/Feedforward_neural_network)。
+- 1982 [循环神�网络](https://wikipedia.org/wiki/Recurrent_neural_network) 是�自产生时间图的�馈神�网络的人工神�网络。
 
-✅ Do a little research. What other dates stand out as pivotal in the history of ML and AI?
-## 1950: Machines that think
+✅ �点调查。在ML和AI的历�上，还有哪些日期是��的？
+## 1950: 会�考的机器 
 
-Alan Turing, a truly remarkable person who was voted [by the public in 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) as the greatest scientist of the 20th century, is credited as helping to lay the foundation for the concept of a 'machine that can think.' He grappled with naysayers and his own need for empirical evidence of this concept in part by creating the [Turing Test](https://www.bbc.com/news/technology-18475646), which you will explore in our NLP lessons.
+艾伦·图�，一个真正�出的人，[在2019年被公众投票选出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作为20世纪最伟大的科学家，他认为有助于为“会�考的机器�的概念打下基础。他通过创建 [图�测试](https://www.bbc.com/news/technology-18475646)�解决�对者和他自己对这一概念的�验��的需求，您将在我们的 NLP 课程中进行探索。
 
-## 1956: Dartmouth Summer Research Project
+## 1956: 达特茅斯�季研究项目
 
-"The Dartmouth Summer Research Project on artificial intelligence was a seminal event for artificial intelligence as a field," and it was here that the term 'artificial intelligence' was coined ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+“达特茅斯�季人工智能研究项目是人工智能领域的一个开创性事件，�正是在这里，人们创造了“人工智能�一�（[��](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth))。
 
-> Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.
+> 原则上，学习的�个方�或智能的任何其他特�都�以被精确地�述，以至于�以用机器�模拟它。 
 
-The lead researcher, mathematics professor John McCarthy, hoped "to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." The participants included another luminary in the field, Marvin Minsky.
+首席研究员�数学教授John McCarthy希望“基于这样一�猜想，�学习的�个方�或智能的任何其他特�原则上都�以如此精确地�述，以至于�以制造出一�机器�模拟它。� �与者包括该领域的�一��出人物Marvin Minsky。
 
-The workshop is credited with having initiated and encouraged several discussions including "the rise of symbolic methods, systems focussed on limited domains (early expert systems), and deductive systems versus inductive systems." ([source](https://wikipedia.org/wiki/Dartmouth_workshop)).
+研讨会被认为�起并鼓励了一些讨论，包括“符�方法的兴起�专注于有�领域的系统（早期专家系统），以�演绎系统与归纳系统的对比。�([��](https://wikipedia.org/wiki/Dartmouth_workshop))。
 
-## 1956 - 1974: "The golden years"
+## 1956 - 1974: “黄金�月�
 
-From the 1950s through the mid '70s, optimism ran high in the hope that AI could solve many problems. In 1967, Marvin Minsky stated confidently that "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+从20世纪50年代到70年代中期，�观情绪高涨，希望人工智能能够解决许多问题。1967年，Marvin Minsky自信地说，“一代人之内。。。创造‘人工智能’的问题将得到实质性的解决。�（Minsky，Marvin（1967），《计算：有�和无�机器》，新泽西州�格�德克利夫斯：Prentice Hall）
 
-natural language processing research flourished, search was refined and made more powerful, and the concept of 'micro-worlds' was created, where simple tasks were completed using plain language instructions.
+自然语言处�研究蓬勃�展，�索被�炼并�得更加强大，创造了“微观世界�的概念，在这个概念中，简�的任务是用简�的语言指令完�的。
 
-Research was well funded by government agencies, advances were made in computation and algorithms, and prototypes of intelligent machines were built. Some of these machines include:
+这项研究得到了政府机构的充分资助，在计算和算法方��得了进展，并建造了智能机器的原型。其中一些机器包括：
 
-* [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), who could maneuver and decide how to perform tasks 'intelligently'.
+* [机器人Shakey](https://wikipedia.org/wiki/Shakey_the_robot)，他们�以“�明地��纵和决定如何执行任务。
 
-    ![Shakey, an intelligent robot](images/shakey.jpg)
-    > Shakey in 1972
+    ![Shakey, 智能机器人](images/shakey.jpg)
+    > 1972 å¹´çš„Shakey
 
-* Eliza, an early 'chatterbot', could converse with people and act as a primitive 'therapist'. You'll learn more about Eliza in the NLP lessons.
+* Eliza，一个早期的“�天机器人�，�以与人交谈并充当原始的“治疗师�。 您将在NLP课程中了解有关Eliza的更多信�。 
 
-    ![Eliza, a bot](images/eliza.png)
-    > A version of Eliza, a chatbot
+    ![Eliza, 机器人](images/eliza.png)
+    > Eliza的一个版本，一个�天机器人 
 
-* "Blocks world" was an example of a micro-world where blocks could be stacked and sorted, and experiments in teaching machines to make decisions could be tested. Advances built with libraries such as [SHRDLU](https://wikipedia.org/wiki/SHRDLU) helped propel language processing forward.
+* “积木世界�是一个微观世界的例�，在那里积木�以堆�和分类，并且�以测试教机器�出决策的实验。 使用[SHRDLU](https://wikipedia.org/wiki/SHRDLU)等库构建的高级功能有助于推动语言处����展。
 
-    [![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
+    [![积木世界与SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "积木世界与SHRDLU")
     
-    > 🎥 Click the image above for a video: Blocks world with SHRDLU
+    > 🎥 点击上图观看视频： 积木世界与SHRDLU
 
-## 1974 - 1980: "AI Winter"
+## 1974 - 1980: AI的寒冬
 
-By the mid 1970s, it had become apparent that the complexity of making 'intelligent machines' had been understated and that its promise, given the available compute power, had been overblown. Funding dried up and confidence in the field slowed. Some issues that impacted confidence included:
+到了20世纪70年代中期，很明显制造“智能机器�的��性被低估了，而且考虑到�用的计算能力，它的�景被夸大了。资金枯竭，市场信心放缓。影�信心的一些问题包括：
 
-- **Limitations**. Compute power was too limited.
-- **Combinatorial explosion**. The amount of parameters needed to be trained grew exponentially as more was asked of computers, without a parallel evolution of compute power and capability.
-- **Paucity of data**. There was a paucity of data that hindered the process of testing, developing, and refining algorithms.
-- **Are we asking the right questions?**. The very questions that were being asked began to be questioned. Researchers began to field criticism about their approaches:
-  - Turing tests came into question by means, among other ideas, of the 'chinese room theory' which posited that, "programming a digital computer may make it appear to understand language but could not produce real understanding." ([source](https://plato.stanford.edu/entries/chinese-room/))
-  - The ethics of introducing artificial intelligences such as the "therapist" ELIZA into society was challenged.
+- **�制**。计算能力太有�了
+- **组�爆炸**。��对计算机的�求越�越高，需�训练的�数数�呈指数级增长，而计算能力�没有平行�展。
+- **缺�数�**。 缺�数�阻�了测试�开�和改进算法的过程。 
+- **我们是�在问正确的问题？**。 被问到的问题也开始�到质疑。 研究人员开始对他们的方法�出批评： 
+  - 图�测试�到质疑的方法之一是“中国房间�论�，该�论认为，“对数字计算机进行编程�能使其看起�能�解语言，但�能产生真正的�解。� ([��](https://plato.stanford.edu/entries/chinese-room/))
+  - 将“治疗师�ELIZA这样的人工智能引入社会的伦��到了挑战。
 
-At the same time, various AI schools of thought began to form. A dichotomy was established between ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) practices. _Scruffy_ labs tweaked programs for hours until they had the desired results. _Neat_ labs "focused on logic and formal problem solving". ELIZA and SHRDLU were well-known _scruffy_ systems. In the 1980s, as demand emerged to make ML systems reproducible, the _neat_ approach gradually took the forefront as its results are more explainable.
+与此�时，��人工智能学派开始形�。 在[“scruffy�与“neat AI�](https://wikipedia.org/wiki/Neats_and_scruffies)之间建立了二分法。 _Scruffy_ 实验室对程�进行了数�时的调整，直到获得所需的结果。 _Neat_ 实验室“专注于逻辑和形�问题的解决�。 ELIZA 和 SHRDLU 是众所周知的 _scruffy_ 系统。 在 1980 年代，��使 ML 系统��现的需求出现，_neat_ 方法��走上�沿，因为其结果更易于解释。
 
-## 1980s Expert systems
+## 1980s 专家系统
 
-As the field grew, its benefit to business became clearer, and in the 1980s so did the proliferation of 'expert systems'. "Expert systems were among the first truly successful forms of artificial intelligence (AI) software." ([source](https://wikipedia.org/wiki/Expert_system)).
+��这个领域的�展，它对商业的好处�得越�越明显，在20世纪80年代，‘专家系统’的泛滥也是如此。“专家系统是首批真正�功的人工智能 (AI) 软件形�之一。� ([��](https://wikipedia.org/wiki/Expert_system))。
 
-This type of system is actually _hybrid_, consisting partially of a rules engine defining business requirements, and an inference engine that leveraged the rules system to deduce new facts.
+这�类型的系统实际上是混�系统，部分由定义业务需求的规则引擎和利用规则系统推断新事实的推�引擎组�。
 
-This era also saw increasing attention paid to neural networks.
+在这个时代，神�网络也越�越�到�视。
 
-## 1987 - 1993: AI 'Chill'
+## 1987 - 1993: AI的冷�期
 
-The proliferation of specialized expert systems hardware had the unfortunate effect of becoming too specialized. The rise of personal computers also competed with these large, specialized, centralized systems. The democratization of computing had begun, and it eventually paved the way for the modern explosion of big data.
+专业的专家系统硬件的激增造�了过于专业化的�幸�果。个人电脑的兴起也与这些大型�专业化�集中化系统展开了竞争。计算机的平民化已�开始，它最终为大数�的现代爆炸铺平了�路。
 
 ## 1993 - 2011
 
-This epoch saw a new era for ML and AI to be able to solve some of the problems that had been caused earlier by the lack of data and compute power. The amount of data began to rapidly increase and become more widely available, for better and for worse, especially with the advent of the smartphone around 2007. Compute power expanded exponentially, and algorithms evolved alongside. The field began to gain maturity as the freewheeling days of the past began to crystallize into a true discipline.
+这个时代��了一个新的时代，ML和AI能够解决早期由于缺�数�和计算能力而导致的一些问题。数��开始迅速增加，�得越�越广泛，无论好�，尤其是2007年左�智能手机的出现，计算能力呈指数级增长，算法也�之�展。这个领域开始�得�熟，因为过去那些�心所欲的日�开始具体化为一�真正的纪律。
 
 ## Now
 
-Today, machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+今天，机器学习和人工智能几乎触�我们生活的�一个部分。这个时代�求仔细了解这些算法对人类生活的风险和潜在影�。正如微软的Brad Smith所言，“信�技术引�的问题触���和言论自由等基本人��护的核心。这些问题加�了制造这些产�的科技公�的责任。在我们看�，它们还呼�政府进行深�熟虑的监管，并围绕�接�的用途制定规范�([��](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/))。
 
-It remains to be seen what the future holds, but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
+未�的情况还有待观察，但了解这些计算机系统以�它们�行的软件和算法是很��的。我们希望这门课程能帮助你更好的�解，以便你自己决定。
 
-[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
-> 🎥 Click the image above for a video: Yann LeCun discusses the history of deep learning in this lecture
+[![深度学习的历� ](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "深度学习的历� ")
+> 🎥 点击上图观看视频：Yann LeCun 在本次讲座中讨论深度学习的历� 
 
 ---
-## 🚀Challenge
+## 🚀挑战
 
-Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
+深入了解这些历�时刻之一，并更多地了解它们背�的人。这里有许多引人入胜的人物，没有一项科学�现是在文化真空中创造出�的。你�现了什么？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
 
-## Review & Self Study
+## �习与自学
 
-Here are items to watch and listen to:
+以下是�观看和收�的节目：
 
-[This podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739)
+[这是Amy Boyd讨论人工智能进化的播客](http://runasradio.com/Shows/Show/739)
 
-[![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
+[![Amy Boyd的《人工智能�》](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Amy Boyd的《人工智能�》")
 
-## Assignment
+## 任务
 
-[Create a timeline](assignment.md)
+[创建时间线](assignment.md)

From 5240ecf85781bc65689ba36848292a9d2fbb2adb Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Mon, 5 Jul 2021 18:33:18 +0700
Subject: [PATCH 021/368] Add Indonesian translation for 1-introduction

---
 1-Introduction/translations/README.id.md | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 1-Introduction/translations/README.id.md

diff --git a/1-Introduction/translations/README.id.md b/1-Introduction/translations/README.id.md
new file mode 100644
index 0000000000..1f8fc7149c
--- /dev/null
+++ b/1-Introduction/translations/README.id.md
@@ -0,0 +1,23 @@
+# Pengantar Machine Learning
+
+Di bagian kurikulum ini, Kamu akan berkenalan dengan konsep yang mendasari bidang Machine Learning, apa itu Machine Learning, dan belajar mengenai
+sejarah serta teknik-teknik yang digunakan oleh para peneliti. Ayo jelajahi dunia baru Machine Learning bersama! 
+
+![bola dunia](images/globe.jpg)
+> Foto oleh <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> di <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+### Pelajaran
+
+1. [Pengantar Machine Learning](1-intro-to-ML/README.md)
+1. [Sejarah dari Machine Learning dan AI](2-history-of-ML/README.md)
+1. [Keadilan dan Machine Learning](3-fairness/README.md)
+1. [Teknik-Teknik Machine Learning](4-techniques-of-ML/README.md)
+### Credits
+
+"Pengantar Machine Learning" ditulis dengan ♥� oleh sebuah tim yang terdiri dari [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) dan [Jen Looper](https://twitter.com/jenlooper)
+
+"Sejarah dari Machine Learning dan AI" ditulis dengan ♥� oleh [Jen Looper](https://twitter.com/jenlooper) dan [Amy Boyd](https://twitter.com/AmyKateNicho)
+
+"Keadilan dan Machine Learning" ditulis dengan ♥� oleh [Tomomi Imura](https://twitter.com/girliemac) 
+
+"Teknik-Teknik Machine Learning" ditulis dengan ♥� oleh [Jen Looper](https://twitter.com/jenlooper) dan [Chris Noring](https://twitter.com/softchris) 
\ No newline at end of file

From 211d7cdfe05cce46f298055b6fb0a40834c1d656 Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Mon, 5 Jul 2021 18:50:45 +0700
Subject: [PATCH 022/368] Update 1-introduction translation

---
 1-Introduction/translations/README.id.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/translations/README.id.md b/1-Introduction/translations/README.id.md
index 1f8fc7149c..d2b0ae53a3 100644
--- a/1-Introduction/translations/README.id.md
+++ b/1-Introduction/translations/README.id.md
@@ -12,7 +12,7 @@ sejarah serta teknik-teknik yang digunakan oleh para peneliti. Ayo jelajahi duni
 1. [Sejarah dari Machine Learning dan AI](2-history-of-ML/README.md)
 1. [Keadilan dan Machine Learning](3-fairness/README.md)
 1. [Teknik-Teknik Machine Learning](4-techniques-of-ML/README.md)
-### Credits
+### Penghargaan
 
 "Pengantar Machine Learning" ditulis dengan ♥� oleh sebuah tim yang terdiri dari [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) dan [Jen Looper](https://twitter.com/jenlooper)
 

From d5d4b96277c5b2af7dd33a9e5b18aaf3acd92e30 Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Mon, 5 Jul 2021 19:18:06 +0700
Subject: [PATCH 023/368] Add Indonesian translation for 1-1 assignment

---
 .../1-intro-to-ML/translations/assignment.id.md          | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 1-Introduction/1-intro-to-ML/translations/assignment.id.md

diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.id.md b/1-Introduction/1-intro-to-ML/translations/assignment.id.md
new file mode 100644
index 0000000000..9de8a4bdd5
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.id.md
@@ -0,0 +1,9 @@
+# Persiapan
+
+## Instruksi
+
+Dalam tugas yang tidak dinilai ini, kamu akan mempelajari Python dan mempersiapkan *environment* kamu sehingga dapat digunakan untuk menjalankan *notebook*.
+
+Ambil [Jalur Belajar Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) ini, kemudian persiapkan sistem kamu dengan melihat video-video pengantar ini:
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6

From 2dbfbccafb07a05a6df2096027e11ba63694cae3 Mon Sep 17 00:00:00 2001
From: Rishit Dagli <rishit.dagli@gmail.com>
Date: Mon, 5 Jul 2021 14:12:45 +0000
Subject: [PATCH 024/368] Remove `maxAge` parameter from watchers badge

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1455d791d1..ba7e928a90 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 [![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
-[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch&maxAge=2592000)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
 [![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork&maxAge=2592000)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
 [![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 

From 6c424e4a6722268bcef6e94ecddcf9cca8f8f74e Mon Sep 17 00:00:00 2001
From: Rishit Dagli <rishit.dagli@gmail.com>
Date: Mon, 5 Jul 2021 14:13:03 +0000
Subject: [PATCH 025/368] Remove `maxAge` parameter from forks badge

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index ba7e928a90..e97709bb87 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
 [![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
-[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork&maxAge=2592000)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
 [![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
 # Machine Learning for Beginners - A Curriculum

From 4f9c8c42f50e7f20197d5b3db08c1d18a89f68ad Mon Sep 17 00:00:00 2001
From: Rishit Dagli <rishit.dagli@gmail.com>
Date: Mon, 5 Jul 2021 14:13:20 +0000
Subject: [PATCH 026/368] Remove `maxAge` parameter from stars badge

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e97709bb87..93dad0929f 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 
 [![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
 [![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
-[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star&maxAge=2592000)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
 
 # Machine Learning for Beginners - A Curriculum
 

From 4508dc32a62bcd7e01fd7dc2e93a3fed30992fc2 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Mon, 5 Jul 2021 23:33:28 +0800
Subject: [PATCH 027/368] translate

---
 .../3-fairness/translations/README.zh-cn.md   | 226 +++++++++---------
 1 file changed, 114 insertions(+), 112 deletions(-)

diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index 063c189813..bb43a027fa 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -1,212 +1,214 @@
-# Fairness in Machine Learning 
+# 机器学习中的公平性
  
-![Summary of Fairness in Machine Learning in a sketchnote](../../sketchnotes/ml-fairness.png)
-> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+![机器学习中的公平性概述](../../sketchnotes/ml-fairness.png)
+> 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
  
-## Introduction
+## 介�
 
-In this curriculum, you will start to discover how machine learning can and is impacting our everyday lives. Even now, systems and models are involved in daily decision-making tasks, such as health care diagnoses or detecting fraud. So it is important that these models work well in order to provide fair outcomes for everyone.
+在本课程中，您将开始了解机器学习如何影�我们的日常生活。截至目�，系统和模型已��与到日常决策任务中，例如医疗诊断或�现欺诈。因此，这些模型�行良好，并为�个人�供公平的结果�常��。
 
-Imagine what can happen when the data you are using to build these models lacks certain demographics, such as race, gender, political view, religion, or disproportionally represents such demographics. What about when the model's output is interpreted to favor some demographic? What is the consequence for the application? 
+想象一下，当您用于构建这些模型的数�缺少�些人�统计信�时会�生什么情况，例如���性别�政治观点�宗教，或者��比例地代表了这些人�统计信�。当模型的输出被解释为有利于�些人�统计学的时候呢？申请结果如何？
 
-In this lesson, you will:
+在本课中，您将：
 
-- Raise your awareness of the importance of fairness in machine learning.
-- Learn about fairness-related harms.
-- Learn about unfairness assessment and mitigation.
+- �高你对机器学习中公平的��性的认识。
+- 了解公平相关的�害。
+- 了解�公平评估和缓解措施。
 
-## Prerequisite
+## 先决�件
 
-As a prerequisite, please take the "Responsible AI Principles" Learn Path and watch the video below on the topic:
+作为先决�件，请选择“负责任的人工智能原则�学习路径并观看以下主题视频：
 
-Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
+按照此[学习路径](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)了解有关负责任 AI 的更多信�
 
-[![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
+[![微软对负责任人工智能的�法](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "微软对负责任人工智能的�法")
 
-> 🎥 Click the image above for a video: Microsoft's Approach to Responsible AI
+> 🎥 点击上图观看视频：微软对负责任人工智能的�法
 
-## Unfairness in data and algorithms
+## 数�和算法的�公平性
 
-> "If you torture the data long enough, it will confess to anything - Ronald Coase
+> “如果你折磨数�足够长的时间，它会�白一切� - Ronald Coase
 
-This statement sounds extreme, but it is true that data can be manipulated to support any conclusion. Such manipulation can sometimes happen unintentionally. As humans, we all have bias, and it's often difficult to consciously know when you are introducing bias in data.
+这�说法�起�很�端，但数�确实�以被�纵以支�任何结论。这��纵有时�能是无�中�生的。作为人类，我们都有��，当你在数�中引入��时，往往很难有�识地知�。
 
-Guaranteeing fairness in AI and machine learning remains a complex sociotechnical challenge. Meaning that it cannot be addressed from either purely social or technical perspectives.
+��人工智能和机器学习的公平性�然是一项��的社会技术挑战。这�味�它�能从纯粹的社会或技术角度�解决。
 
-### Fairness-related harms
+### 与公平相关的�害
 
-What do you mean by unfairness? "Unfairness" encompasses negative impacts, or "harms", for a group of people, such as those defined in terms of race, gender, age, or disability status.  
+你说的�公平是什么��？“�公平�包括对一群人的负�影�或“伤害�，例如根����性别�年龄或残疾状况定义的那些人。
 
-The main fairness-related harms can be classified as:
+与公平相关的主��害�分为：
 
-- **Allocation**, if a gender or ethnicity for example is favored over another.
-- **Quality of service**. If you train the data for one specific scenario but reality is much more complex, it leads to a poor performing service.
-- **Stereotyping**. Associating a given group with pre-assigned attributes.
-- **Denigration**. To unfairly criticize and label something or someone.
-- **Over- or under- representation**. The idea is that a certain group is not seen in a certain profession, and any service or function that keeps promoting that is contributing to harm.
+- **分�**，如果一个性别或��比�一个更���。
+- **�务质�**。 如果您针对一�特定场景训练数�，但实际情况���得多，则会导致�务性能�佳。
+- **刻��象**。 将给定的组与预先分�的属性相关�。
+- **诋�**。 �公平地批评和标记�事或�人。
+- **代表性过高或过低**。这�想法是，�个群体在�个行业中�被看到，而这个行业一直在��，这是造�伤害的原因。
 
-Let’s take a look at the examples.
+让我们�看看这些例�。
 
-### Allocation
+### 分�
 
-Consider a hypothetical system for screening loan applications. The system tends to pick white men as better candidates over other groups. As a result, loans are withheld from certain applicants.
+考虑一个用于筛选贷款申请的�设系统。该系统倾�于选择白人男性作为比其他群体更好的候选人。因此，�些申请人的贷款被拒。
 
-Another example would be an experimental hiring tool developed by a large corporation to screen candidates. The tool systemically discriminated against one gender by using the models were trained to prefer words associated with another. It resulted in penalizing candidates whose resumes contain words such as "women’s rugby team". 
+�一个例�是一家大型公�开�的一�实验性招�工具，用于筛选应�者。通过使用这些模型，该工具系统地歧视了一�性别，并被训练为更喜欢与�一�性别相关的�。这导致了对简历中�有“女�橄榄�队�等字样的候选人的�公正地对待。
 
-✅ Do a little research to find a real-world example of something like this
+✅ �一点研究，找出一个真实的例�
 
-### Quality of Service
+### �务质�
 
-Researchers found that several commercial gender classifiers had higher error rates around images of women with darker skin tones as opposed to images of men with lighter skin tones. [Reference](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/) 
+研究人员�现，与肤色较浅的男性相比，一些商业性的性别分类工具在肤色较深的女性图�上的错误率更高。[�考](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/) 
 
-Another infamous example is a hand soap dispenser that could not seem to be able to sense people with dark skin. [Reference](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+�一个臭�昭著的例�是洗手液分�器，它似乎无法感知皮肤�黑的人。[�考](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
 
-### Stereotyping
+### 刻��象
 
-Stereotypical gender view was found in machine translation. When translating “he is a nurse and she is a doctor� into Turkish, problems were encountered. Turkish is a genderless language which has one pronoun, “o� to convey a singular third person, but translating the sentence back from Turkish to English yields the stereotypical and incorrect as “she is a nurse and he is a doctor�.
+机器翻译中存在�刻�的性别观。在将“他是护士，她是医生�翻译�土耳其语时，�到了一些问题。土耳其语是一�无性别的语言，它有一个代�“o��表示�数第三人称，但把这个��从土耳其语翻译�英语，会产生“她是护士，他是医生�这样的刻��象和错误。
 
-![translation to Turkish](images/gender-bias-translate-en-tr.png)
+![翻译�土耳其语](images/gender-bias-translate-en-tr.png)
 
-![translation back to English](images/gender-bias-translate-tr-en.png)
+![翻译�英语](images/gender-bias-translate-tr-en.png)
 
-### Denigration
+### 诋�
 
-An image labeling technology infamously mislabeled images of dark-skinned people as gorillas. Mislabeling is harmful not just because the system made a mistake because it specifically applied a label that has a long history of being purposefully used to denigrate Black people.
+一�图�标记技术，臭�昭著地将深色皮肤的人的图�错误地标记为大猩猩。错误的标签是有害的，�仅仅是因为这个系统犯了一个错误，而且它还特别使用了一个长期以�被故�用�诋�黑人的标签。
 
-[![AI: Ain't I a Woman?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, Ain't I a Woman?")
-> 🎥 Click the image above for a video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
+[![AI: 我�是女人�？](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, 我�是女人�？")
+> 🎥 点击上图观看视频：AI，我�是女人� - 一场展示AI��主义诋�造�的伤害的表演
 
-### Over- or under- representation
+### 代表性过高或过低
  
-Skewed image search results can be a good example of this harm. When searching images of professions with an equal or higher percentage of men than women, such as engineering, or CEO, watch for results that are more heavily skewed towards a given gender.
+有倾�性的图��索结果就是一个很好的例�。在�索男性比例等于或高于女性的�业的图片时，比如工程或首席执行官，�注�那些更倾�于特定性别的结果。
 
-![Bing CEO search](images/ceos.png)
-> This search on Bing for 'CEO' produces pretty inclusive results
+![必应CEO�索](images/ceos.png)
+> 在Bing上�索“CEO�会得到�常全�的结果
 
-These five main types of harms are not mutually exclusive, and a single system can exhibit more than one type of harm. In addition, each case varies in its severity. For instance, unfairly labeling someone as a criminal is a much more severe harm than mislabeling an image. It's important, however, to remember that even relatively non-severe harms can make people feel alienated or singled out and the cumulative impact can be extremely oppressive. 
+这五�主�类型的�害�是相互排斥的，一个�一的系统�以表现出一�以上的�害。此外，�个案例的严�程度��相�。例如，�公平地给�人贴上罪犯的标签比给形象贴上错误的标签�严�得多。然而，��的是�记�，�使是相对�严�的伤害也会让人感到�远或被孤立，累积的影��能会�常压抑。
  
-✅ **Discussion**: Revisit some of the examples and see if they show different harms.  
+✅ **讨论**：�温一些例�，看看它们是�显示出��的�害。
 
-|                         | Allocation | Quality of service | Stereotyping | Denigration | Over- or under- representation |
+|                         | 分� | �务质� | 刻��象 | 诋� | 代表性过高或过低 |
 | ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| Automated hiring system |     x      |         x          |      x       |             |               x                |
-| Machine translation     |            |                    |              |             |                                |
-| Photo labeling          |            |                    |              |             |                                |
+| 自动招�系统 |     x      |         x          |      x       |             |               x                |
+| 机器翻译     |            |                    |              |             |                                |
+| 照片加标签          |            |                    |              |             |                                |
 
 
-## Detecting unfairness 
+## 检测�公平
 
-There are many reasons why a given system behaves unfairly. Social biases, for example, might be reflected in the datasets used to train them. For example, hiring unfairness might have been exacerbated by over reliance on historical data. By using the patterns in resumes submitted to the company over a 10-year period, the model determined that men were more qualified because the majority of resumes came from men, a reflection of past male dominance across the tech industry.
+给定系统行为�公平的原因有很多。例如，社会���能会�映在用于训练它们的数�集中。例如，过度�赖历�数��能会加剧招��公平。通过使用过去10年�交给公�的简历中的模�，该模型确定男性更�格，因为大多数简历�自男性，这�映了过去男性在整个科技行业的主导地�。
 
-Inadequate data about a certain group of people can be the reason for unfairness. For example, image classifiers a have higher rate of error for images of dark-skinned people because darker skin tones were underrepresented in the data.  
+关于特定人群的数��足�能是�公平的原因。例如，图�分类器对于深肤色人的图�具有较高的错误率，因为数�中没有充分代表较深的肤色。
 
-Wrong assumptions made during development cause unfairness too. For example, a facial analysis system intended to predict who is going to commit a crime based on images of people’s faces can lead to damaging assumptions. This could lead to substantial harms for people who are misclassified.
+开�过程中�出的错误�设也会导致�公平。例如，旨在根�人脸图�预测�将犯罪的�部分�系统�能会导致破�性�设。这�能会对错误分类的人造��大伤害。
 
-## Understand your models and build in fairness
+## 了解您的模型并建立公平性
  
-Although many aspects of fairness are not captured in quantitative fairness metrics, and it is not possible to fully remove bias from a system to guarantee fairness, you are still responsible to detect and to mitigate fairness issues as much as possible. 
+尽管公平性的许多方�都没有包�在�化公平性指标中，并且��能从系统中完全消除��以��公平性，但您�然有责任尽�能多地检测和缓解公平性问题。
 
-When you are working with machine learning models, it is important to understand your models by means of assuring their interpretability and by assessing and mitigating unfairness.
+当您使用机器学习模型时，通过确�模型的�解释性以�评估和�轻�公平性��解模型�常��。
 
-Let’s use the loan selection example to isolate the case to figure out each factor's level of impact on the prediction.
+让我们使用贷款选择示例�作为分�案例，以确定�个因素对预测的影�程度。
 
-## Assessment methods
+## 评价方法
 
-1. **Identify harms (and benefits)**. The first step is to identify harms and benefits. Think about how actions and decisions can affect both potential customers and a business itself.
+1. **识别�害（和好处）**。第一步是找出�害和好处。�考行动和决策如何影�潜在客户和�业本身。
   
-1. **Identify the affected groups**. Once you understand what kind of harms or benefits that can occur, identify the groups that may be affected. Are these groups defined by gender, ethnicity, or social group?
+2. **确定�影�的群体**。一旦你了解了什么样的伤害或好处�能会�生，找出�能�到影�的群体。这些群体是按性别���或社会群体界定的�？
 
-1. **Define fairness metrics**. Finally, define a metric so you have something to measure against in your work to improve the situation.
+3. **定义公平性度�**。最�，定义一个度�标准，这样你就�以在工作中衡�一些东西�改善这�情况。
 
-### Identify harms (and benefits)
+### 识别�害（和好处）
 
-What are the harms and benefits associated with lending? Think about false negatives and false positive scenarios: 
+与贷款相关的�害和好处是什么？想想�阴性和�阳性的情况：
 
-**False negatives** (reject, but Y=1) - in this case, an applicant who will be capable of repaying a loan is rejected. This is an adverse event because the resources of the loans are withheld from qualified applicants.
+**�阴性**（拒�，但Y=1）-在这�情况下，将拒�有能力�还贷款的申请人。这是一个�利的事件，因为贷款的资�是从�格的申请人扣留。
 
-**False positives** (accept, but Y=0) - in this case, the applicant does get a loan but eventually defaults. As a result, the applicant's case will be sent to a debt collection agency which can affect their future loan applications.
+**�阳性**（接�，但Y=0）-在这�情况下，申请人确实获得了贷款，但最终�约。因此，申请人的案件将被�往一个债务催收机构，这�能会影�他们未�的贷款申请。
 
-### Identify affected groups
+### 确定�影�的群体
 
-The next step is to determine which groups are likely to be affected. For example, in case of a credit card application, a model might determine that women should receive much lower credit limits compared with their spouses who share household assets. An entire demographic, defined by gender, is thereby affected.
+下一步是确定哪些群体�能�到影�。例如，在信用�申请的情况下，模型�能会确定女性应获得比共享家庭资产的��低得多的信用�度。因此，由性别定义的整个人�统计数�都会�到影�。
 
-### Define fairness metrics
+### 定义公平性度�
  
-You have identified harms and an affected group, in this case, delineated by gender. Now, use the quantified factors to disaggregate their metrics. For example, using the data below, you can see that women have the largest false positive rate and men have the smallest, and that the opposite is true for false negatives.
+你已�确定了伤害和�影�的群体，在本例中，是按性别划分的。现在，使用�化因��分解它们的度�。例如，使用下�的数�，你�以看到女性的�阳性率最大，男性的�阳性率最�，而对于�阴性则相�。
 
-✅ In a future lesson on Clustering, you will see how to build this 'confusion matrix' in code
+✅ 在以�关于�类的课程中，您将看到如何在代�中构建这个“混淆矩阵�
 
-|            | False positive rate | False negative rate | count |
+|            | �阳性率 | �阴性率 | 数� |
 | ---------- | ------------------- | ------------------- | ----- |
-| Women      | 0.37                | 0.27                | 54032 |
-| Men        | 0.31                | 0.35                | 28620 |
-| Non-binary | 0.33                | 0.31                | 1266  |
+| 女性      | 0.37                | 0.27                | 54032 |
+| 男性        | 0.31                | 0.35                | 28620 |
+| 未列出性别 | 0.33                | 0.31                | 1266  |
 
  
-This table tells us several things. First, we note that there are comparatively few non-binary people in the data. The data is skewed, so you need to be careful how you interpret these numbers.
+这张桌�告诉我们几件事。首先，我们注�到数�中的未列出性别的人相对较少。数�是有�差的，所以你需��心解释这些数字。
 
-In this case, we have 3 groups and 2 metrics. When we are thinking about how our system affects the group of customers with their loan applicants, this may be sufficient, but when you want to define larger number of groups, you may want to distill this to smaller sets of summaries. To do that, you can add more metrics, such as the largest difference or smallest ratio of each false negative and false positive. 
+在本例中，我们有3个组和2个度�。当我们考虑我们的系统如何影�贷款申请人的客户群时，这�能就足够了，但是当您想�定义更多的组时，您�能需�将其��到更�的摘�集。为此，您�以添加更多的度�，例如�个�阴性和�阳性的最大差异或最�比率。
  
-✅ Stop and Think: What other groups are likely to be affected for loan application? 
+✅ �下�想一想：还有哪些群体�能会�到贷款申请的影�？
  
-## Mitigating unfairness 
+## �轻�公平
  
-To mitigate unfairness, explore the model to generate various mitigated models and compare the tradeoffs it makes between accuracy and fairness to select the most fair model. 
+为了缓解�公平，探索模型生���缓解模型，并比较其在准确性和公平性之间的�衡，以选择最公平的模型。
 
-This introductory lesson does not dive deeply into the details of algorithmic unfairness mitigation, such as post-processing and reductions approach, but here is a tool that you may want to try. 
+这个介�性的课程并没有深入探讨算法�公平缓解的细节，比如�处�和�少方法，但是这里有一个你�能想�试的工具。
 
 ### Fairlearn 
  
-[Fairlearn](https://fairlearn.github.io/) is an open-source Python package that allows you to assess your systems' fairness and mitigate unfairness.  
+[Fairlearn](https://fairlearn.github.io/) 是一个开�Python包，�让您评估系统的公平性并�轻�公平性。
 
-The tool helps you to assesses how a model's predictions affect different groups, enabling you to compare multiple models by using fairness and performance metrics, and supplying a set of algorithms to mitigate unfairness in binary classification and regression. 
+该工具�帮助您评估模型的预测如何影���的组，使您能够通过使用公平性和性能指标�比较多个模型，并�供一组算法��轻二元分类和回归中的�公平性。
 
-- Learn how to use the different components by checking out the Fairlearn's [GitHub](https://github.com/fairlearn/fairlearn/)
+- 通过查看Fairlearn的[GitHub](https://github.com/fairlearn/fairlearn/)了解如何使用��的组件
 
-- Explore the [user guide](https://fairlearn.github.io/main/user_guide/index.html), [examples](https://fairlearn.github.io/main/auto_examples/index.html)
+- �览[用户指�](https://fairlearn.github.io/main/user_guide/index.html), [示例](https://fairlearn.github.io/main/auto_examples/index.html)
 
-- Try some [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
+- �试一些 [示例Notebook](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
   
-- Learn [how to enable fairness assessments](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) of machine learning models in Azure Machine Learning. 
+- 了解Azure机器学习中机器学习模型[如何�用公平性评估](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa)。
   
-- Check out these [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning. 
+- 看看这些[示例Notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness)了解Azure机器学习中的更多公平性评估场景。
 
 ---
-## 🚀 Challenge 
+## 🚀 挑战 
  
-To prevent biases from being introduced in the first place, we should: 
+为了防止首先引入��，我们应该：
 
-- have a diversity of backgrounds and perspectives among the people working on systems 
-- invest in datasets that reflect the diversity of our society 
-- develop better methods for detecting and correcting bias when it occurs 
+- 在系统工作人员中有��的背景和观点
 
-Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider? 
+- 获��映我们社会多样性的数�集
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
-## Review & Self Study 
- 
-In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.  
+- 开�更好的方法�检测和纠正�差
+
+想想现实生活中的场景，在模型构建和使用中明显存在�公平。我们还应该考虑什么？
+
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## �习与自学
  
-Watch this workshop to dive deeper into the topics: 
+在本课中，您学习了机器学习中公平和�公平概念的一些基础知识。
+
+观看本次研讨会，深入探讨以下主题：
 
-- YouTube: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik [Fairness-related harms in AI systems: Examples, assessment, and mitigation - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
+- YouTube:人工智能系统中与公平相关的�害：示例�评估和缓解Hanna Wallach和Miro Dudik[人工智能系统中与公平相关的�害：示例�评估和缓解-YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
 
-Also, read: 
+�外，请阅读：
 
-- Microsoft’s RAI resource center: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
+- 微软RAI资�中心：[负责人工智能资�-微软人工智能](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
 
-- Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
+- 微软FATE研究�组：[FATE：AI 中的公平�问责��明和�德-微软研究院](https://www.microsoft.com/research/theme/fate/) 
 
-Explore the Fairlearn toolkit
+探索Fairlearn工具箱
 
 [Fairlearn](https://fairlearn.org/)
 
-Read about Azure Machine Learning's tools to ensure fairness
+了解Azure机器学习的工具以确�公平性
 
-- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
+- [Azure机器学习](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
 
-## Assignment
+## 任务
 
-[Explore Fairlearn](assignment.md) 
+[探索Fairlearn](assignment.md) 

From 91985bbb1a5b18d868098006edf16d53a5795cd6 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Mon, 5 Jul 2021 12:08:26 -0400
Subject: [PATCH 028/368] Update 2-Regression/1-Tools/README.md

Co-authored-by: Rishit Dagli <rishit.dagli@gmail.com>
---
 2-Regression/1-Tools/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index 3d1fd89e9e..349b216383 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -52,7 +52,7 @@ In this folder, you will find the file _notebook.ipynb_.
 
    Next, add some Python code. 
 
-1. Type **print("'hello notebook'")** in the code block.
+1. Type **print('hello notebook')** in the code block.
 1. Select the arrow to run the code.
 
    You should see the printed statement:

From 07396ed1dd6d8a7d505c0035262ab62e873cb195 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Mon, 5 Jul 2021 12:08:37 -0400
Subject: [PATCH 029/368] Update 2-Regression/1-Tools/README.md

Co-authored-by: Rishit Dagli <rishit.dagli@gmail.com>
---
 2-Regression/1-Tools/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index 349b216383..e36c34fe68 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -58,7 +58,7 @@ In this folder, you will find the file _notebook.ipynb_.
    You should see the printed statement:
 
     ```output
-    'hello notebook'
+    hello notebook
     ```
 
 ![VS Code with a notebook open](images/notebook.png)

From 8217fb11d6a4f6addf58350ffaaacbc8cc7f3e15 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Mon, 5 Jul 2021 20:55:47 +0200
Subject: [PATCH 030/368] Chapter 1 Readme;Section 1 and 2 Reame and
 assignments

---
 .../1-intro-to-ML/translations/README.it.md   | 108 ++++++++++++++++
 .../translations/assignment.it.md             |   9 ++
 .../2-history-of-ML/translations/README.it.md | 118 ++++++++++++++++++
 .../translations/assignment.it.md             |  11 ++
 1-Introduction/translations/README.it.md      |  22 ++++
 5 files changed, 268 insertions(+)
 create mode 100644 1-Introduction/1-intro-to-ML/translations/README.it.md
 create mode 100644 1-Introduction/1-intro-to-ML/translations/assignment.it.md
 create mode 100644 1-Introduction/2-history-of-ML/translations/README.it.md
 create mode 100644 1-Introduction/2-history-of-ML/translations/assignment.it.md
 create mode 100644 1-Introduction/translations/README.it.md

diff --git a/1-Introduction/1-intro-to-ML/translations/README.it.md b/1-Introduction/1-intro-to-ML/translations/README.it.md
new file mode 100644
index 0000000000..7d9fe43aed
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/README.it.md
@@ -0,0 +1,108 @@
+# Introduzione a machine learning
+
+[![ML, AI, deep learning: qual è la differenza?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning: qual è la differenza?")
+
+> 🎥 Fare clic sull'immagine sopra per un video che illustra la differenza tra machine learning, intelligenza artificiale (AI) e deep learning.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+
+### Introduzione
+
+Benvenuti in questo corso su machine learning classico per principianti! Che si sia completamente nuovo su questo argomento, o un professionista esperto di ML che cerca di rispolverare un'area, è un piacere avervi con noi! Si vuole creare un punto di partenza amichevole per lo studio di ML e saremo lieti di valutare, rispondere e incorporare il vostro [feedback](https://github.com/microsoft/ML-For-Beginners/discussions).
+
+[![Introduzione a ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 " Introduzione a ML")
+
+> 🎥 Fare clic sull'immagine sopra per un video: John Guttag del MIT introduce machine learning
+
+### Iniziare con machine learning
+
+Prima di iniziare con questo programma di studi, è necessario che il computer sia configurato e pronto per eseguire i notebook in locale.
+
+- **Si configuri la propria macchina con l'aiuto di questi video**. Si scopra di più su come configurare la propria macchina in questa [serie di video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6).
+- **Imparare Python**. Si consiglia inoltre di avere una conoscenza di base di [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), un linguaggio di programmazione utile per i data scientist che si utilizzerà in questo corso.
+- **Imparare Node.js e JavaScript**. Talvolta in questo corso si usa anche JavaScript durante la creazione di app web, quindi sarà necessario disporre di [node](https://nodejs.org) e [npm](https://www.npmjs.com/) installati, oltre a [Visual Studio Code](https://code.visualstudio.com/) disponibile sia per lo sviluppo Python che JavaScript.
+- **Creare un account GitHub**. E' probabile che si [](https://github.com)disponga già di un account GitHub, ma in caso contrario occorre crearne uno e poi eseguire il fork di questo programma di studi per utilizzarlo autonomamente. (Sentitevi liberi di darci anche una stella 😊)
+- **Esplorare Scikit-learn**. Familiarizzare con Scikit-learn,[]([https://scikit-learn.org/stable/user_guide.html) un insieme di librerie ML a cui si farà riferimento in queste lezioni.
+
+### Che cos'è machine learning?
+
+Il termine "machine learning" è uno dei termini più popolari e usati di oggi. C'è una buona possibilità che si abbia sentito questo termine almeno una volta se si ha una sorta di familiarità con la tecnologia, indipendentemente dal campo in cui si lavora. I meccanismi di machine learning, tuttavia, sono un mistero per la maggior parte delle persone. Per un principiante di machine learning l'argomento a volte può sembrare soffocante. Pertanto, è importante capire cos'è effettivamente machine learning e impararlo passo dopo passo, attraverso esempi pratici.
+
+![ml curva di hype](../images/hype.png)
+
+> Google Trends mostra la recente "curva di hype" del termine "machine learning"
+
+Si vive in un universo pieno di misteri affascinanti. Grandi scienziati come Stephen Hawking, Albert Einstein e molti altri hanno dedicato la loro vita alla ricerca di informazioni significative che svelino i misteri del mondo circostante. Questa è la condizione umana dell'apprendimento: un bambino impara cose nuove e scopre la struttura del suo mondo anno dopo anno mentre cresce fino all'età adulta.
+
+Il cervello e i sensi di un bambino percepiscono i fatti dell'ambiente circostante e apprendono gradualmente i modelli di vita nascosti che aiutano il bambino a creare regole logiche per identificare i modelli appresi. Il processo di apprendimento del cervello umano rende l'essere umano la creatura vivente più sofisticata di questo mondo. Imparare continuamente scoprendo schemi nascosti e poi innovare su questi schemi ci consente di migliorarsi sempre di più per tutta la vita. Questa capacità di apprendimento e capacità di evoluzione è correlata a un concetto chiamato [plasticità cerebrale](https://www.simplypsychology.org/brain-plasticity.html). Superficialmente, si possono tracciare alcune somiglianze motivazionali tra il processo di apprendimento del cervello umano e i concetti di machine learning.
+
+Il [cervello umano](https://www.livescience.com/29365-human-brain.html) percepisce le cose dal mondo reale, elabora le informazioni percepite, prende decisioni razionali ed esegue determinate azioni in base alle circostanze. Questo è ciò che viene chiamato comportarsi in modo intelligente. Quando si programma un facsimile del processo comportamentale intelligente su una macchina, si parla di intelligenza artificiale (AI).
+
+Sebbene i termini possano essere confusi, machine learning (ML) è un importante sottoinsieme dell'intelligenza artificiale. **Machine learning si occupa di utilizzare algoritmi specializzati per scoprire informazioni significative e trovare modelli nascosti dai dati percepiti per corroborare il processo decisionale razionale**.
+
+![AI, machine learning, deep learning, data science](../images/ai-ml-ds.png)
+
+> Un diagramma che mostra le relazioni tra intelligenza artificiale (AI), machine learning, deep learning e data science. Infografica di [Jen Looper](https://twitter.com/jenlooper) ispirata a [questa grafica](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
+
+## Ecco cosa si imparerà in questo corso
+
+In questo programma di studi, saranno tratteti solo i concetti fondamentali di machine learning che un principiante deve conoscere. Si tratterà di ciò che viene chiamato "machine learning classico" principalmente utilizzando Scikit-learn, una eccellente libreria che molti studenti usano per apprendere le basi. Per comprendere concetti più ampi di intelligenza artificiale o deep learning, è indispensabile una forte conoscenza fondamentale di machine learning, e quindi la si vorrebbe offrire qui.
+
+In questo corso si imparerà:
+
+- concetti fondamentali di machine learning
+- la storia di ML
+- ML e correttezza
+- tecniche di regressione ML
+- tecniche di classificazione ML
+- tecniche di clustering ML
+- tecniche di elaborazione del linguaggio naturale ML
+- tecniche ML di previsione delle serie temporali
+- reinforcement learning
+- applicazioni del mondo reale per ML
+## Cosa non verrà trattato
+
+- deep learning
+- reti neurali
+- AI (intelligenza artificiale)
+
+Per rendere l'esperienza di apprendimento migliore, si eviteranno le complessità delle reti neurali, del "deep learning" (costruzione di modelli a più livelli utilizzando le reti neurali) e dell'AI, di cui si tratterà in un altro programma di studi. Si offrirà anche un prossimo programma di studi di data science per concentrarsi su quell'aspetto di questo campo più ampio.
+## Perché studiare machine learning?
+
+Machine learning, dal punto di vista dei sistemi, è definito come la creazione di sistemi automatizzati in grado di apprendere modelli nascosti dai dati per aiutare a prendere decisioni intelligenti.
+
+Questa motivazione è vagamente ispirata dal modo in cui il cervello umano apprende determinate cose in base ai dati che percepisce dal mondo esterno.
+
+✅ Si pensi per un minuto al motivo per cui un'azienda dovrebbe provare a utilizzare strategie di machine learning rispetto alla creazione di un motore cablato a codice basato su regole codificate.
+
+### Applicazioni di machine learning
+
+Le applicazioni di machine learning sono ormai quasi ovunque e sono onnipresenti come i dati che circolano nelle società, generati dagli smartphone, dispositivi connessi e altri sistemi. Considerando l'immenso potenziale degli algoritmi di machine learning all'avanguardia, i ricercatori hanno esplorato la loro capacità di risolvere problemi multidimensionali e multidisciplinari della vita reale con grandi risultati positivi.
+
+**Si può utilizzare machine learning in molti modi**:
+
+- Per prevedere la probabilità di malattia dall'anamnesi o dai rapporti di un paziente.
+- Per sfruttare i dati meteorologici per prevedere gli eventi meteorologici.
+- Per comprendere il sentimento di un testo.
+- Per rilevare notizie false per fermare la diffusione della propaganda.
+
+La finanza, l'economia, le scienze della terra, l'esplorazione spaziale, l'ingegneria biomedica, le scienze cognitive e persino i campi delle scienze umanistiche hanno adattato machine learning per risolvere gli ardui problemi di elaborazione dati del proprio campo.
+
+Machine learning automatizza il processo di individuazione dei modelli trovando approfondimenti significativi dal mondo reale o dai dati generati. Si è dimostrato di grande valore in applicazioni aziendali, sanitarie e finanziarie, tra le altre.
+
+Nel prossimo futuro, comprendere le basi di machine learning sarà un must per le persone in qualsiasi campo a causa della sua adozione diffusa.
+
+---
+## 🚀 Sfida
+
+Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excalidraw.com/), la propria comprensione delle differenze tra AI, ML, deep learning e data science. Aggiungere alcune idee sui problemi che ciascuna di queste tecniche è in grado di risolvere.
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+
+## Revisione e Auto Apprendimento
+
+Per saperne di più su come si può lavorare con gli algoritmi ML nel cloud, si segua questo [percorso di apprendimento](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
+
+## Compito
+
+[Tempi di apprendimento brevi](assignment.it.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.it.md b/1-Introduction/1-intro-to-ML/translations/assignment.it.md
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+# Tempi di apprendimento brevi
+
+## Istruzioni
+
+In questo compito senza valutazione, si dovrebbe rispolverare Python e rendere il proprio ambiente attivo e funzionante, in grado di eseguire notebook.
+
+Si segua questo [percorso di apprendimento di Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) e quindi si configurino i propri sistemi seguendo questi video introduttivi:
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
diff --git a/1-Introduction/2-history-of-ML/translations/README.it.md b/1-Introduction/2-history-of-ML/translations/README.it.md
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+# Storia di machine learning
+
+![Riepilogo della storia di machine learning in uno sketchnote](../../../sketchnotes/ml-history.png)
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+
+In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale.
+
+La storia dell'intelligenza artificiale, AI, come campo è intrecciata con la storia di machine learning, poiché gli algoritmi e i progressi computazionali alla base di machine learning hanno contribuito allo sviluppo dell'intelligenza artificiale. È utile ricordare che, mentre questi campi come distinte aree di indagine hanno cominciato a cristallizzarsi negli anni '50, importanti [scoperte algoritmiche, statistiche, matematiche, computazionali e tecniche](https://wikipedia.org/wiki/Timeline_of_machine_learning) hanno preceduto e si sono sovrapposte a questa era. In effetti, le persone hanno riflettuto su queste domande per [centinaia di anni](https://wikipedia.org/wiki/History_of_artificial_intelligence): questo articolo discute le basi intellettuali storiche dell'idea di una "macchina pensante".
+
+## Scoperte rilevanti
+
+- 1763, 1812 [Teorema di Bayes](https://it.wikipedia.org/wiki/Teorema_di_Bayes) e suoi predecessori. Questo teorema e le sue applicazioni sono alla base dell'inferenza, descrivendo la probabilità che un evento si verifichi in base alla conoscenza precedente.
+- 1805 [Metodo dei Minimi Quadrati](https://it.wikipedia.org/wiki/Metodo_dei_minimi_quadrati) del matematico francese Adrien-Marie Legendre. Questa teoria, che verrà trattata nell'unità Regressione, aiuta nell'adattamento dei dati.
+- 1913 [Processo Markoviano](https://it.wikipedia.org/wiki/Processo_markoviano) dal nome del matematico russo Andrey Markov è usato per descrivere una sequenza di possibili eventi basati su uno stato precedente.
+- 1957 [Percettrone](https://it.wikipedia.org/wiki/Percettrone) è un tipo di classificatore lineare inventato dallo psicologo americano Frank Rosenblatt che sta alla base dei progressi nel deep learning.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) è un algoritmo originariamente progettato per mappare i percorsi. In un contesto ML viene utilizzato per rilevare i modelli.
+- 1970 [La Retropropagazione dell'Errore](https://it.wikipedia.org/wiki/Retropropagazione_dell'errore) viene utilizzata per addestrare [le reti neurali feed-forward](https://it.wikipedia.org/wiki/Rete_neurale_feed-forward).
+- Le [Reti Neurali Ricorrenti](https://it.wikipedia.org/wiki/Rete_neurale_ricorrente) del 1982 sono reti neurali artificiali derivate da reti neurali feedforward che creano grafici temporali.
+
+✅ Fare una piccola ricerca. Quali altre date si distinguono come fondamentali nella storia del machine learning e dell'intelligenza artificiale?
+## 1950: Macchine che pensano
+
+Alan Turing, una persona davvero notevole che è stata votata [dal pubblico nel 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) come il più grande scienziato del XX secolo, è accreditato per aver contribuito a gettare le basi per il concetto di "macchina in grado di pensare". Ha affrontato gli oppositori e il suo stesso bisogno di prove empiriche di questo concetto in parte creando il [Test di Turing](https://www.bbc.com/news/technology-18475646), che verrà esplorato nelle lezioni di NLP (elaborazione del linguaggio naturale).
+
+## 1956: Progetto di Ricerca Estivo Dartmouth
+
+"Il Dartmouth Summer Research Project sull'intelligenza artificiale è stato un evento seminale per l'intelligenza artificiale come campo", qui è stato coniato il termine "intelligenza artificiale" ([fonte](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> In linea di principio, ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza può essere descritto in modo così preciso che si può costruire una macchina per simularlo.
+
+Il ricercatore capo, il professore di matematica John McCarthy, sperava "di procedere sulla base della congettura che ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza possa in linea di principio essere descritta in modo così preciso che si possa costruire una macchina per simularlo". I partecipanti includevano un altro luminare nel campo, Marvin Minsky.
+
+Il workshop è accreditato di aver avviato e incoraggiato diverse discussioni tra cui "l'ascesa di metodi simbolici, sistemi focalizzati su domini limitati (primi sistemi esperti) e sistemi deduttivi contro sistemi induttivi". ([fonte](https://wikipedia.org/wiki/Dartmouth_workshop)).
+
+## 1956 - 1974: "Gli anni d'oro"
+
+Dagli anni '50 fino alla metà degli anni '70, l'ottimismo era alto nella speranza che l'AI potesse risolvere molti problemi. Nel 1967, Marvin Minsky dichiarò con sicurezza che "Entro una generazione... il problema della creazione di 'intelligenza artificiale' sarà sostanzialmente risolto". (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+La ricerca sull'elaborazione del linguaggio naturale è fiorita, la ricerca è stata perfezionata e resa più potente ed è stato creato il concetto di "micro-mondi", in cui compiti semplici sono stati completati utilizzando istruzioni in linguaggio semplice.
+
+La ricerca è stata ben finanziata dalle agenzie governative, sono stati fatti progressi nel calcolo e negli algoritmi e sono stati costruiti prototipi di macchine intelligenti. Alcune di queste macchine includono:
+
+* [Shakey il robot](https://wikipedia.org/wiki/Shakey_the_robot), che poteva manovrare e decidere come eseguire i compiti "intelligentemente".
+
+   ![Shakey, un robot intelligente](../images/shakey.jpg)
+   > Shakey nel 1972
+
+* Eliza, una delle prime "chatterbot", poteva conversare con le persone e agire come una "terapeuta" primitiva. Si Imparerà di più su Eliza nelle lezioni di NLP.
+
+   ![Eliza, un bot](../images/eliza.png)
+   > Una versione di Eliza, un chatbot
+
+* Il "mondo dei blocchi" era un esempio di un micromondo in cui i blocchi potevano essere impilati e ordinati e si potevano testare esperimenti su macchine per insegnare a prendere decisioni. I progressi realizzati con librerie come [SHRDLU](https://it.wikipedia.org/wiki/SHRDLU) hanno contribuito a far progredire l'elaborazione del linguaggio.
+
+    [![Il mondo dei blocchi con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Il mondo dei blocchi con SHRDLU")
+
+   > 🎥 Fare clic sull'immagine sopra per un video: Blocks world con SHRDLU
+
+## 1974 - 1980: "L'inverno dell'AI"
+
+Verso la metà degli anni '70, era diventato evidente che la complessità della creazione di "macchine intelligenti" era stata sottovalutata e che la sua promessa, data la potenza di calcolo disponibile, era stata esagerata. I finanziamenti si sono prosciugati e la fiducia nel settore è rallentata. Alcuni problemi che hanno influito sulla fiducia includono:
+
+- **Limitazioni**. La potenza di calcolo era troppo limitata.
+- **Esplosione combinatoria**. La quantità di parametri necessari per essere addestrati è cresciuta in modo esponenziale man mano che veniva chiesto di più ai computer, senza un'evoluzione parallela della potenza e delle capacità di calcolo.
+- **Scarsità di dati**. C'era una scarsità di dati che ostacolava il processo di test, sviluppo e perfezionamento degli algoritmi.
+- **Stiamo facendo le domande giuste?**. Le stesse domande che venivano poste cominciarono ad essere messe in discussione. I ricercatori hanno iniziato a criticare i loro approcci:
+   - I test di Turing furono messi in discussione attraverso, tra le altre idee, la "teoria della stanza cinese" che postulava che "la programmazione di un computer digitale può far sembrare che capisca il linguaggio ma non potrebbe produrre una vera comprensione". ([fonte](https://plato.stanford.edu/entries/chinese-room/))
+   - L'etica dell'introduzione di intelligenze artificiali come la "terapeuta" ELIZA nella società è stata messa in discussione.
+
+Allo stesso tempo, iniziarono a formarsi varie scuole di pensiero sull'AI. È stata stabilita una dicotomia tra pratiche ["scruffy" contro "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) ;. I laboratori _scruffy_ ottimizzavano i programmi per ore fino a quando non ottenevano i risultati desiderati. I laboratori _Neat_ "si focalizzavano sulla logica e sulla risoluzione formale dei problemi". ELIZA e SHRDLU erano ben noti _sistemi scruffy_. Negli anni '80, quando è emersa la richiesta di rendere riproducibili i sistemi ML, l'_approccio neat_ ha gradualmente preso il sopravvento in quanto i suoi risultati sono più spiegabili.
+
+## Sistemi esperti degli anni '80
+
+Man mano che il settore cresceva, i suoi vantaggi per le imprese diventavano più chiari e negli anni '80 lo stesso accadeva con la proliferazione di "sistemi esperti". "I sistemi esperti sono stati tra le prime forme di software di intelligenza artificiale (AI) di vero successo". ([fonte](https://wikipedia.org/wiki/Expert_system)).
+
+Questo tipo di sistema è in realtà _ibrido_, costituito in parte da un motore di regole che definisce i requisiti aziendali e un motore di inferenza che sfrutta il sistema di regole per dedurre nuovi fatti.
+
+Questa era ha visto anche una crescente attenzione rivolta alle reti neurali.
+
+## 1987 - 1993: AI 'Chill'
+
+La proliferazione di hardware specializzato per sistemi esperti ha avuto lo sfortunato effetto di diventare troppo specializzato. L'ascesa dei personal computer ha anche gareggiato con questi grandi sistemi centralizzati specializzati. La democratizzazione dell'informatica era iniziata e alla fine ha spianato la strada alla moderna esplosione dei big data.
+
+## 1993 - 2011
+
+Questa epoca ha visto una nuova era per ML e AI per essere in grado di risolvere alcuni dei problemi che erano stati causati in precedenza dalla mancanza di dati e potenza di calcolo. La quantità di dati ha iniziato ad aumentare rapidamente e a diventare più ampiamente disponibile, nel bene e nel male, soprattutto con l'avvento degli smartphone intorno al 2007. La potenza di calcolo si è ampliata in modo esponenziale e gli algoritmi si sono evoluti di pari passo. Il campo ha iniziato a maturare quando i giorni a ruota libera del passato hanno iniziato a cristallizzarsi in una vera disciplina.
+
+## Adesso
+
+Oggi, machine learning e intelligenza artificiale toccano quasi ogni parte della nostra vita. Questa era richiede un'attenta comprensione dei rischi e dei potenziali effetti di questi algoritmi sulle vite umane. Come ha affermato Brad Smith di Microsoft, "La tecnologia dell'informazione solleva questioni che vanno al cuore delle protezioni fondamentali dei diritti umani come la privacy e la libertà di espressione. Questi problemi aumentano la responsabilità delle aziende tecnologiche che creano questi prodotti. A nostro avviso, richiedono anche un'attenta regolamentazione del governo e lo sviluppo di norme sugli usi accettabili" ([fonte](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi informatici e il software e gli algoritmi che eseguono. Ci si augura che questo programma di studi aiuti ad acquisire una migliore comprensione in modo che si possa decidere in autonomia.
+
+[![La storia del deeplearningLa](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 " storia del deep learning")
+> 🎥 Fare clic sull'immagine sopra per un video: Yann LeCun discute la storia del deep learning in questa lezione
+
+---
+
+## 🚀 Sfida
+
+Approfondire uno di questi momenti storici e scoprire
+ di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+
+## Revisione e Auto Apprendimento
+
+Ecco gli elementi da guardare e ascoltare:
+
+[Questo podcast in cui Amy Boyd discute l'evoluzione dell'AI](http://runasradio.com/Shows/Show/739)
+
+[![La storia dell'AI di Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La storia dell'AI di Amy Boyd")
+
+## Compito
+
+[Creare una sequenza temporale](assignment.it.md)
diff --git a/1-Introduction/2-history-of-ML/translations/assignment.it.md b/1-Introduction/2-history-of-ML/translations/assignment.it.md
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+# Creare una sequenza temporale
+
+## Istruzioni
+
+Usando [questo repository](https://github.com/Digital-Humanities-Toolkit/timeline-builder), si crei una sequenza temporale di alcuni aspetti della storia di algoritmi, matematica, statistica, AI o ML, o una combinazione di questi. Ci si può concentrare su una persona, un'idea o un lungo lasso di tempo di pensiero. Ci si assicuri di aggiungere elementi multimediali.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- |
+|          | Una sequenza temporale distribuita viene presentata come una pagina GitHub | Il codice è incompleto e non è stato distribuito | La sequenza temporale è incompleta, non ben studiata e non implementata |
diff --git a/1-Introduction/translations/README.it.md b/1-Introduction/translations/README.it.md
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+++ b/1-Introduction/translations/README.it.md
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+# Introduzione a machine learning
+
+In questa sezione del curriculum, verranno presentati i concetti di base sottostanti machine learning, di cosa si tratta, e si imparerà la sua storia e le tecniche utilizzate dai ricercatori per lavorarci. Si esplorerà insieme questo nuovo mondo di ML!
+
+![globo](../images/globe.jpg)
+> Foto di <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> su <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+### Lezioni
+
+1. [Introduzione a machine learning](../1-intro-to-ML/translations/README.it.md)
+1. [La storia di machine learning e dell'AI](../2-history-of-ML/translations/README.it.md)
+1. [Equità e machine learning](../3-fairness/translations/README.it.md)
+1. [Tecniche di machine learning](../4-techniques-of-ML/translations/README.it.md)
+### Crediti
+
+"Introduzione a Machine Learning" è stato scritto con ♥� da un team di persone tra cui [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) e [Jen Looper](https://twitter.com/jenlooper)
+
+"La Storia di Machine Learning" è stato scritto con ♥� da [Jen Looper](https://twitter.com/jenlooper) e [Amy Boyd](https://twitter.com/AmyKateNicho)
+
+"Equità e Machine Learning" è stato scritto con ♥� da [Tomomi Imura](https://twitter.com/girliemac)
+
+"Tecniche di Machine Learning" è stato scritto con ♥� da [Jen Looper](https://twitter.com/jenlooper) e [Chris Noring](https://twitter.com/softchris)
\ No newline at end of file

From 4d5dca2a0c86d20a4ed74e159043d3e1362c1a55 Mon Sep 17 00:00:00 2001
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+# Pengantar Machine Learning
+
+[![ML, AI, deep learning - Apa perbedaannya?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - Apa perbedaannya?")
+
+> 🎥 Klik gambar diatas untuk melihat video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning.
+
+## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+
+### Pengantar
+
+Selamat datang di pelajaran Machine Learning klasik untuk pemula! Baik kamu yang masih benar-benar baru, atau seorang praktisi ML berpengalaman yang ingin meningkatkan kemampuan kamu, kami senang kamu ikut bersama kami! Kami ingin membuat sebuah titik mulai yang ramah untuk pembelajaran ML kamu dan akan sangat senang untuk mengevaluasi, merespon, dan memasukkan [umpan balik](https://github.com/microsoft/ML-For-Beginners/discussions) kamu.
+
+[![Pengantar Machine Learning](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Pengantar Machine Learning")
+
+> 🎥 Klik gambar diatas untuk melihat video: John Guttag dari MIT yang memberikan pengantar Machine Learning.
+### Memulai Machine Learning
+
+Sebelum memulai kurikulum ini, kamu perlu memastikan komputer kamu sudah dipersiapkan untuk menjalankan *notebook* secara lokal.
+
+- **Konfigurasi komputer kamu dengan video-video ini**. Pelajari bagaimana menyiapkan komputer kamu dalam [video-video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) ini.
+- **Belajar Python**. Disarankan juga untuk memiliki pemahaman dasar dari [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), sebuah bahasa pemrograman yang digunakan oleh data scientist yang juga akan kita gunakan dalam pelajaran ini. 
+- **Belajar Node.js dan JavaScript**. Kita juga menggunakan JavaScript beberapa kali dalam pelajaran ini ketika membangun aplikasi web, jadi kamu perlu menginstal [node](https://nodejs.org) dan [npm](https://www.npmjs.com/), serta [Visual Studio Code](https://code.visualstudio.com/) yang tersedia untuk pengembangan Python dan JavaScript.
+- **Buat akun GitHub**. Karena kamu menemukan kami di [GitHub](https://github.com), kamu mungkin sudah punya akun, tapi jika belum, silahkan buat akun baru kemudian *fork* kurikulum ini untuk kamu pergunakan sendiri. (Jangan ragu untuk memberikan kami bintang juga 😊)
+- **Jelajahi Scikit-learn**. Buat diri kamu familiar dengan [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html), seperangkat *library* ML yang kita acu dalam pelajaran-pelajaran ini.
+
+### Apa itu Machine Learning?
+
+Istilah 'Machine Learning' merupakan salah satu istilah yang paling populer dan paling sering digunakan saat ini. Ada kemungkinan kamu pernah mendengar istilah ini paling tidak sekali jika kamu familiar dengan teknologi. Tetapi untuk mekanisme Machine Learning sendiri, merupakan sebuah misteri bagi sebagian besar orang. Karena itu, penting untuk memahami sebenarnya apa itu Machine Learning, dan mempelajarinya langkah demi langkah melalui contoh praktis.
+
+![kurva tren ml](images/hype.png)
+
+> Google Trends memperlihatkan 'kurva tren' dari istilah 'Machine Learning' belakangan ini.
+
+Kita hidup di sebuah alam semesta yang penuh dengan misteri yang menarik. Ilmuwan-ilmuwan besar seperti Stephen Hawking, Albert Einstein, dan banyak lagi telah mengabdikan hidup mereka untuk mencari informasi yang berarti yang mengungkap misteri dari dunia disekitar kita. Ini adalah kondisi belajar manusia: seorang anak manusia belajar hal-hal baru dan mengungkap struktur dari dunianya tahun demi tahun saat mereka tumbuh dewasa. 
+
+Otak dan indera seorang anak memahami fakta-fakta di sekitarnya dan secara bertahap mempelajari pola-pola kehidupan yang tersembunyi yang membantu anak untuk menyusun aturan-aturan logis untuk mengidentifikasi pola-pola yang dipelajari. Proses pembelajaran otak manusia ini menjadikan manusia sebagai makhluk hidup paling canggih di dunia ini. Belajar terus menerus dengan menemukan pola-pola tersembunyi dan kemudian berinovasi pada pola-pola itu memungkinkan kita untuk terus menjadikan diri kita lebih baik sepanjang hidup. Kapasitas belajar dan kemampuan berkembang ini terkait dengan konsep yang disebut dengan *[brain plasticity](https://www.simplypsychology.org/brain-plasticity.html)*. Secara sempit, kita dapat menarik beberapa kesamaan motivasi antara proses pembelajaran otak manusia dan konsep Machine Learning.
+
+[Otak manusia](https://www.livescience.com/29365-human-brain.html) menerima banyak hal dari dunia nyata, memproses informasi yang diterima, membuat keputusan rasional, dan melakukan aksi-aksi tertentu berdasarkan keadaan. Inilah yang kita sebut dengan berperilaku cerdas. Ketika kita memprogram sebuah salinan dari proses perilaku cerdas ke sebuah mesin, ini dinamakan kecerdasan buatan atau Artificial Intelligence (AI).
+
+Meskipun istilah-stilahnya bisa membingungkan, Machine Learning (ML) adalah bagian penting dari Artificial Intelligence. **ML berkaitan dengan menggunakan algoritma-algoritma terspesialisasi untuk mengungkap informasi yang berarti dan mencari pola-pola tersembunyi dari data yang diterima untuk mendukung proses pembuatan keputusan rasional**.
+
+![AI, ML, deep learning, data science](images/ai-ml-ds.png)
+
+> Sebuah diagram yang memperlihatkan hubungan antara AI, ML, Deep Learning, dan Data Science. Infografis oleh [Jen Looper](https://twitter.com/jenlooper) terinspirasi dari [infografis ini](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
+
+## Apa yang akan kamu pelajari
+
+Dalam kurikulum ini, kita hanya akan membahas konsep inti dari Machine Learning yang harus diketahui oleh seorang pemula. Kita membahas apa yang kami sebut sebagai 'Machine Learning klasik' utamanya menggunakan Scikit-learn, sebuah *library* luar biasa yang banyak digunakan para siswa untuk belajar dasarnya. Untuk memahami konsep Artificial Intelligence atau Deep Learning yang lebih luas, pengetahuan dasar yang kuat tentang Machine Learning sangat diperlukan, itulah yang ingin kami tawarkan di sini. 
+
+Kamu akan belajar:
+
+- Konsep inti ML
+- Sejarah dari ML
+- Keadilan dan ML
+- Teknik regresi ML
+- Teknik klasifikasi ML
+- Teknik *clustering* ML
+- Teknik *natural language processing* ML
+- Teknik *time series forecasting* ML
+- *Reinforcement learning*
+- Penerapan nyata dari ML
+## Yang tidak akan kita bahas
+
+- *deep learning*
+- *neural networks*
+- AI
+
+Untuk membuat pengalaman belajar yang lebih baik, kita akan menghindari kerumitan dari *neural network*, *deep learning* - membangun *many-layered model* menggunakan *neural network* - dan AI, yang mana akan kita bahas dalam kurikulum yang berbeda. Kami juga akan menawarkan kurikulum *data science* yang berfokus pada aspek bidang tersebut. 
+## Kenapa belajar Machine Learning?
+
+Machine Learning, dari perspektif sistem, didefinisikan sebagai pembuatan sistem otomatis yang dapat mempelajari pola-pola tersembunyi dari data untuk membantu membuat keputusan cerdas. 
+
+Motivasi ini secara bebas terinspirasi dari bagaimana otak manusia mempelajari hal-hal tertentu berdasarkan data yang diterimanya dari dunia luar. 
+
+✅ Pikirkan sejenak mengapa sebuah bisnis ingin mencoba menggunakan strategi Machine Learning dibandingkan membuat sebuah mesin berbasis aturan yang tertanam (*hard-coded*). 
+
+### Penerapan Machine Learning
+
+Penerapan Machine Learning saat ini hampir ada di mana-mana, seperti data yang mengalir di sekitar kita, yang dihasilkan oleh ponsel pintar, perangkat yang terhubung, dan sistem lainnya. Mempertimbangkan potensi besar dari algoritma Machine Learning terkini, para peneliti telah mengeksplorasi kemampuan Machine Learning untuk memecahkan masalah kehidupan nyata multi-dimensi dan multi-disiplin dengan hasil positif yang luar biasa. 
+
+**Kamu bisa menggunakan Machine Learning dalam banyak hal**:
+
+- Untuk memprediksi kemungkinan penyakit berdasarkan riwayat atau laporan medis pasien.
+- Untuk memanfaatkan data cuaca untuk memprediksi peristiwa cuaca.
+- Untuk memahami sentimen sebuah teks.
+- Untuk mendeteksi berita palsu untuk menghentikan penyebaran propaganda.
+
+Keuangan, ekonomi, geosains, eksplorasi ruang angkasa, teknik biomedis, ilmu kognitif, dan bahkan bidang humaniora telah mengadaptasi Machine Learning untuk memecahkan masalah sulit pemrosesan data di bidang mereka. 
+
+Machine Learning mengotomatiskan proses penemuan pola dengan menemukan wawasan yang berarti dari dunia nyata atau dari data yang dihasilkan. Machine Learning terbukti sangat berharga dalam penerapannya di berbagai bidang, diantaranya adalah bidang bisnis, kesehatan, dan keuangan.
+
+Dalam waktu dekat, memahami dasar-dasar Machine Learning akan menjadi suatu keharusan bagi orang-orang dari bidang apa pun karena adopsinya yang luas. 
+
+---
+## 🚀 Tantangan
+
+Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https://excalidraw.com/), mengenai pemahaman kamu tentang perbedaan antara AI, ML, Deep Learning, dan Data Science. Tambahkan beberapa ide masalah yang cocok diselesaikan masing-masing teknik.
+
+## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+
+## Ulasan & Belajar Mandiri
+
+Untuk mempelajari lebih lanjut tentang bagaimana kamu dapat menggunakan algoritma ML di cloud, ikuti [Jalur Belajar](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa) ini. 
+
+## Tugas
+
+[Persiapan](assignment.md)

From 0cf74b4ced81fed56a31ac55a5983130b8fcc1aa Mon Sep 17 00:00:00 2001
From: Jun Hyuk Han <hanjun.1998@gmail.com>
Date: Tue, 6 Jul 2021 16:36:12 +0900
Subject: [PATCH 032/368] fix typo

---
 2-Regression/2-Data/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 2f36b15a89..479ff99250 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -187,7 +187,7 @@ To get charts to display useful data, you usually need to group the data somehow
 
 ## 🚀Challenge
 
-Explore the different types of visualization that M Matplotlib offers. Which types are most appropriate for regression problems?
+Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
 
 ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
 

From d225e4f0a7f13b1a41968c1d62da63dda942e605 Mon Sep 17 00:00:00 2001
From: Vishvanathan K <vishvanathank@outlook.in>
Date: Tue, 6 Jul 2021 15:48:29 +0530
Subject: [PATCH 033/368] Fixed a Typo

Removed an a statement which was repeated twice
---
 2-Regression/3-Linear/README.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md
index 9e0b950271..2a60658b2e 100644
--- a/2-Regression/3-Linear/README.md
+++ b/2-Regression/3-Linear/README.md
@@ -81,7 +81,6 @@ Since you'll use Scikit-learn, there's no reason to do this by hand (although yo
 ```python
 from sklearn.preprocessing import LabelEncoder
 
-new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
 new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
 ```
 
@@ -329,4 +328,4 @@ In this lesson we learned about Linear Regression. There are other important typ
 
 ## Assignment 
 
-[Build a Model](assignment.md)
\ No newline at end of file
+[Build a Model](assignment.md)

From 3d3abad19a040209b984d04284d260825fff34ba Mon Sep 17 00:00:00 2001
From: iBlueDust <35223375+iBlueDust@users.noreply.github.com>
Date: Tue, 6 Jul 2021 19:28:37 +0700
Subject: [PATCH 034/368] docs: translate 1-Tools (WIP)

---
 .../1-Tools/translations/README.id.md         | 207 ++++++++++++++++++
 1 file changed, 207 insertions(+)
 create mode 100644 2-Regression/1-Tools/translations/README.id.md

diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
new file mode 100644
index 0000000000..ca32b820ee
--- /dev/null
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -0,0 +1,207 @@
+# Get started with Python and Scikit-learn for regression models
+
+![Ringkisan regresi dalam sebuah catatan sketsa](../../sketchnotes/ml-regression.png)
+
+> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Kuis Pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## Pembukaan
+
+Dalam keempat pelajaran ini, kamu akan belajar bagaimana membangun model regresi. Kita akan berdiskusi apa fungsi model tersebut dalam sejenak. Tetapi sebelum kamu melakukan apapun, pastikan bahwa kamu sudah mempunyai alat-alat yang diperlukan untuk memulai!
+
+Dalam pelajaran ini, kamu akan belajar bagaimana untuk:
+
+- Konfigurasi komputermu untuk tugas pembelajaran.
+- Bekerja dengan Jupyter notebooks.
+- Menggunakan Scikit-learn, termasuk instalasi.
+- Menjelajahi regresi linear dengan latihan *hands-on*.
+
+
+## Instalasi dan konfigurasi
+
+[![Menggunakan Python dalam Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Menggunakan Python dalam Visual Studio Code")
+
+> 🎥 Klik foto di atas untuk sebuah video: menggunakan Python dalam VS Code
+
+1. **Pasang Python**. Pastikan bahwa [Python](https://www.python.org/downloads/) telah dipasang di komputermu. Kamu akan menggunakan Python untuk banyak tugas *data science* dan *machine learning*. Kebanyakan sistem komputer sudah diinstal dengan Python. Adapula *[Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa)* yang berguna untuk membantu proses pemasangan untuk beberapa pengguna.
+
+   Beberapa penggunaan Python memerlukan satu versi perangkat lunak tersebut, sedangkan beberapa penggunaan lainnya mungkin memerlukan versi Python yang beda lagi. Oleh sebab itulah akan sangat berguna untuk bekerja dalam sebuah *[virtual environment](https://docs.python.org/3/library/venv.html)* (lingkungan virtual).
+
+2. **Pasang Visual Studio Code**. Pastikan kamu sudah memasangkan Visual Studio Code di komputermu. Ikuti instruksi-instruksi ini untuk [memasangkan Visual Studio Code](https://code.visualstudio.com/) untuk instalasi dasar. Kamu akan menggunakan Python dalam Visual Studio Code dalam kursus ini, jadi kamu mungkin akan ingin mencari tahu cara [mengkonfigurasi Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) untuk menggunakan Python.
+
+   > Nyamankan diri dengan Python dengan mengerjakan [koleksi modul pembelajaran ini](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
+
+3. **Pasang Scikit-learn**, dengan mengikuti [instruksi di sini](https://scikit-learn.org/stable/install.html). Karena harus dipastikan bahwa kamu sedang menggunakan Python 3, kami anjurkan kamu menggunakan sebuah *virtual environment*. Ingatlah juga bahwa jika kamu ingin memasangkan ini di sebuah M1 Mac, ada instruksi khusus dalam laman yang ditautkan di atas.
+
+4. **Pasang Jupyter Notebook**. Kamu akan harus [memasang paket Jupyter](https://pypi.org/project/jupyter/). 
+
+## Lingkungan penulisan ML-mu
+
+Kamu akan menggunakan ***notebooks*** untuk bekerja dengan kode Python-mu dan membuat model *machine learning*-mu. Jenis file ini adalah alat yang sering digunakan *data scientists* dan dapat diidentifikasikan dengan akhiran/ekstensi `.ipynb`. 
+
+Notebooks are an interactive environment that allow the developer to both code and add notes and write documentation around the code which is quite helpful for experimental or research-oriented projects.
+
+### Exercise - work with a notebook
+
+In this folder, you will find the file _notebook.ipynb_. 
+
+1. Open _notebook.ipynb_ in Visual Studio Code.
+
+   A Jupyter server will start with Python 3+ started. You will find areas of the notebook that can be `run`, pieces of code. You can run a code block, by selecting the icon that looks like a play button.
+
+1. Select the `md` icon and add a bit of markdown, and the following text **# Welcome to your notebook**.
+
+   Next, add some Python code. 
+
+1. Type **print('hello notebook')** in the code block.
+1. Select the arrow to run the code.
+
+   You should see the printed statement:
+
+    ```output
+    hello notebook
+    ```
+
+![VS Code with a notebook open](images/notebook.png)
+
+You can interleaf your code with comments to self-document the notebook.
+
+✅ Think for a minute how different a web developer's working environment is versus that of a data scientist.
+
+## Up and running with Scikit-learn
+
+Now that Python is set up in your local environment, and you are comfortable with Jupyter notebooks, let's get equally comfortable with Scikit-learn (pronounce it `sci` as in `science`). Scikit-learn provides an [extensive API](https://scikit-learn.org/stable/modules/classes.html#api-ref) to help you perform ML tasks.
+
+According to their [website](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities."
+
+In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. 
+
+Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let's explore the process of loading prepackaged data and using a built in estimator  first ML model with Scikit-learn with some basic data.
+
+## Exercise - your first Scikit-learn notebook
+
+> This tutorial was inspired by the [linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) on Scikit-learn's web site.
+
+In the _notebook.ipynb_ file associated to this lesson, clear out all the cells by pressing the 'trash can' icon.
+
+In this section, you will work with a small dataset about diabetes that is built into Scikit-learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
+
+✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use linear regression, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use logistic regression. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
+
+Let's get started on this task.
+
+### Import libraries
+
+For this task we will import some libraries:
+
+- **matplotlib**. It's a useful [graphing tool](https://matplotlib.org/) and we will use it to create a line plot.
+- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) is a useful library for handling numeric data in Python.
+- **sklearn**. This is the Scikit-learn library.
+
+Import some libraries to help with your tasks.
+
+1. Add imports by typing the following code:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   from sklearn import datasets, linear_model, model_selection
+   ```
+
+   Above you are importing `matplottlib`, `numpy` and you are importing `datasets`, `linear_model` and `model_selection` from `sklearn`. `model_selection` is used for splitting data into training and test sets.
+
+### The diabetes dataset
+
+The built-in [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) includes 442 samples of data around diabetes, with 10 feature variables, some of which include:
+
+age: age in years
+bmi: body mass index
+bp: average blood pressure
+s1 tc: T-Cells (a type of white blood cells)
+
+✅ This dataset includes the concept of 'sex' as a feature variable important to research around diabetes. Many medical datasets include this type of binary classification. Think a bit about how categorizations such as this might exclude certain parts of a population from treatments.
+
+Now, load up the X and y data.
+
+> 🎓 Remember, this is supervised learning, and we need a named 'y' target.
+
+In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The input `return_X_y=True` signals that `X` will be a data matrix, and `y` will be the regression target. 
+
+1. Add some print commands to show the shape of the data matrix and its first element:
+
+    ```python
+    X, y = datasets.load_diabetes(return_X_y=True)
+    print(X.shape)
+    print(X[0])
+    ```
+
+    What you are getting back as a response, is a tuple. What you are doing is to assign the two first values of the tuple to `X` and `y` respectively. Learn more [about tuples](https://wikipedia.org/wiki/Tuple).
+
+    You can see that this data has 442 items shaped in arrays of 10 elements:
+
+    ```text
+    (442, 10)
+    [ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076
+    -0.04340085 -0.00259226  0.01990842 -0.01764613]
+    ```
+
+    ✅ Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target?
+
+2. Next, select a portion of this dataset to plot by arranging it into a new array using numpy's `newaxis` function. We are going to use linear regression to generate a line between values in this data, according to a pattern it determines.
+
+   ```python
+   X = X[:, np.newaxis, 2]
+   ```
+
+   ✅ At any time, print out the data to check its shape.
+
+3. Now that you have data ready to be plotted, you can see if a machine can help determine a logical split between the numbers in this dataset. To do this, you need to split both the data (X) and the target (y) into test and training sets. Scikit-learn has a straightforward way to do this; you can split your test data at a given point.
+
+   ```python
+   X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
+   ```
+
+4. Now you are ready to train your model! Load up the linear regression model and train it with your X and y training sets using `model.fit()`:
+
+    ```python
+    model = linear_model.LinearRegression()
+    model.fit(X_train, y_train)
+    ```
+
+    ✅ `model.fit()` is a function you'll see in many ML libraries such as TensorFlow
+
+5. Then, create a prediction using test data, using the function `predict()`. This will be used to draw the line between data groups
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+6. Now it's time to show the data in a plot. Matplotlib is a very useful tool for this task. Create a scatterplot of all the X and y test data, and use the prediction to draw a line in the most appropriate place, between the model's data groupings.
+
+    ```python
+    plt.scatter(X_test, y_test,  color='black')
+    plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.show()
+    ```
+
+   ![a scatterplot showing datapoints around diabetes](./images/scatterplot.png)
+
+   ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.
+
+Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!
+
+---
+## 🚀Challenge
+
+Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+
+## Review & Self Study
+
+In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+
+Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) to deepen your understanding.
+
+## Assignment 
+
+[A different dataset](assignment.md)

From b084ad214acd0aa5f299e46f1d0b514e1a5e0823 Mon Sep 17 00:00:00 2001
From: iBlueDust <35223375+iBlueDust@users.noreply.github.com>
Date: Tue, 6 Jul 2021 19:28:37 +0700
Subject: [PATCH 035/368] Add Indonesian translation for 2-Regression/1-Tools
 (WIP)

---
 .../1-Tools/translations/README.id.md         | 207 ++++++++++++++++++
 1 file changed, 207 insertions(+)
 create mode 100644 2-Regression/1-Tools/translations/README.id.md

diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
new file mode 100644
index 0000000000..ca32b820ee
--- /dev/null
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -0,0 +1,207 @@
+# Get started with Python and Scikit-learn for regression models
+
+![Ringkisan regresi dalam sebuah catatan sketsa](../../sketchnotes/ml-regression.png)
+
+> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Kuis Pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## Pembukaan
+
+Dalam keempat pelajaran ini, kamu akan belajar bagaimana membangun model regresi. Kita akan berdiskusi apa fungsi model tersebut dalam sejenak. Tetapi sebelum kamu melakukan apapun, pastikan bahwa kamu sudah mempunyai alat-alat yang diperlukan untuk memulai!
+
+Dalam pelajaran ini, kamu akan belajar bagaimana untuk:
+
+- Konfigurasi komputermu untuk tugas pembelajaran.
+- Bekerja dengan Jupyter notebooks.
+- Menggunakan Scikit-learn, termasuk instalasi.
+- Menjelajahi regresi linear dengan latihan *hands-on*.
+
+
+## Instalasi dan konfigurasi
+
+[![Menggunakan Python dalam Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Menggunakan Python dalam Visual Studio Code")
+
+> 🎥 Klik foto di atas untuk sebuah video: menggunakan Python dalam VS Code
+
+1. **Pasang Python**. Pastikan bahwa [Python](https://www.python.org/downloads/) telah dipasang di komputermu. Kamu akan menggunakan Python untuk banyak tugas *data science* dan *machine learning*. Kebanyakan sistem komputer sudah diinstal dengan Python. Adapula *[Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa)* yang berguna untuk membantu proses pemasangan untuk beberapa pengguna.
+
+   Beberapa penggunaan Python memerlukan satu versi perangkat lunak tersebut, sedangkan beberapa penggunaan lainnya mungkin memerlukan versi Python yang beda lagi. Oleh sebab itulah akan sangat berguna untuk bekerja dalam sebuah *[virtual environment](https://docs.python.org/3/library/venv.html)* (lingkungan virtual).
+
+2. **Pasang Visual Studio Code**. Pastikan kamu sudah memasangkan Visual Studio Code di komputermu. Ikuti instruksi-instruksi ini untuk [memasangkan Visual Studio Code](https://code.visualstudio.com/) untuk instalasi dasar. Kamu akan menggunakan Python dalam Visual Studio Code dalam kursus ini, jadi kamu mungkin akan ingin mencari tahu cara [mengkonfigurasi Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) untuk menggunakan Python.
+
+   > Nyamankan diri dengan Python dengan mengerjakan [koleksi modul pembelajaran ini](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
+
+3. **Pasang Scikit-learn**, dengan mengikuti [instruksi di sini](https://scikit-learn.org/stable/install.html). Karena harus dipastikan bahwa kamu sedang menggunakan Python 3, kami anjurkan kamu menggunakan sebuah *virtual environment*. Ingatlah juga bahwa jika kamu ingin memasangkan ini di sebuah M1 Mac, ada instruksi khusus dalam laman yang ditautkan di atas.
+
+4. **Pasang Jupyter Notebook**. Kamu akan harus [memasang paket Jupyter](https://pypi.org/project/jupyter/). 
+
+## Lingkungan penulisan ML-mu
+
+Kamu akan menggunakan ***notebooks*** untuk bekerja dengan kode Python-mu dan membuat model *machine learning*-mu. Jenis file ini adalah alat yang sering digunakan *data scientists* dan dapat diidentifikasikan dengan akhiran/ekstensi `.ipynb`. 
+
+Notebooks are an interactive environment that allow the developer to both code and add notes and write documentation around the code which is quite helpful for experimental or research-oriented projects.
+
+### Exercise - work with a notebook
+
+In this folder, you will find the file _notebook.ipynb_. 
+
+1. Open _notebook.ipynb_ in Visual Studio Code.
+
+   A Jupyter server will start with Python 3+ started. You will find areas of the notebook that can be `run`, pieces of code. You can run a code block, by selecting the icon that looks like a play button.
+
+1. Select the `md` icon and add a bit of markdown, and the following text **# Welcome to your notebook**.
+
+   Next, add some Python code. 
+
+1. Type **print('hello notebook')** in the code block.
+1. Select the arrow to run the code.
+
+   You should see the printed statement:
+
+    ```output
+    hello notebook
+    ```
+
+![VS Code with a notebook open](images/notebook.png)
+
+You can interleaf your code with comments to self-document the notebook.
+
+✅ Think for a minute how different a web developer's working environment is versus that of a data scientist.
+
+## Up and running with Scikit-learn
+
+Now that Python is set up in your local environment, and you are comfortable with Jupyter notebooks, let's get equally comfortable with Scikit-learn (pronounce it `sci` as in `science`). Scikit-learn provides an [extensive API](https://scikit-learn.org/stable/modules/classes.html#api-ref) to help you perform ML tasks.
+
+According to their [website](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities."
+
+In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. 
+
+Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let's explore the process of loading prepackaged data and using a built in estimator  first ML model with Scikit-learn with some basic data.
+
+## Exercise - your first Scikit-learn notebook
+
+> This tutorial was inspired by the [linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) on Scikit-learn's web site.
+
+In the _notebook.ipynb_ file associated to this lesson, clear out all the cells by pressing the 'trash can' icon.
+
+In this section, you will work with a small dataset about diabetes that is built into Scikit-learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
+
+✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use linear regression, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use logistic regression. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
+
+Let's get started on this task.
+
+### Import libraries
+
+For this task we will import some libraries:
+
+- **matplotlib**. It's a useful [graphing tool](https://matplotlib.org/) and we will use it to create a line plot.
+- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) is a useful library for handling numeric data in Python.
+- **sklearn**. This is the Scikit-learn library.
+
+Import some libraries to help with your tasks.
+
+1. Add imports by typing the following code:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   from sklearn import datasets, linear_model, model_selection
+   ```
+
+   Above you are importing `matplottlib`, `numpy` and you are importing `datasets`, `linear_model` and `model_selection` from `sklearn`. `model_selection` is used for splitting data into training and test sets.
+
+### The diabetes dataset
+
+The built-in [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) includes 442 samples of data around diabetes, with 10 feature variables, some of which include:
+
+age: age in years
+bmi: body mass index
+bp: average blood pressure
+s1 tc: T-Cells (a type of white blood cells)
+
+✅ This dataset includes the concept of 'sex' as a feature variable important to research around diabetes. Many medical datasets include this type of binary classification. Think a bit about how categorizations such as this might exclude certain parts of a population from treatments.
+
+Now, load up the X and y data.
+
+> 🎓 Remember, this is supervised learning, and we need a named 'y' target.
+
+In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The input `return_X_y=True` signals that `X` will be a data matrix, and `y` will be the regression target. 
+
+1. Add some print commands to show the shape of the data matrix and its first element:
+
+    ```python
+    X, y = datasets.load_diabetes(return_X_y=True)
+    print(X.shape)
+    print(X[0])
+    ```
+
+    What you are getting back as a response, is a tuple. What you are doing is to assign the two first values of the tuple to `X` and `y` respectively. Learn more [about tuples](https://wikipedia.org/wiki/Tuple).
+
+    You can see that this data has 442 items shaped in arrays of 10 elements:
+
+    ```text
+    (442, 10)
+    [ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076
+    -0.04340085 -0.00259226  0.01990842 -0.01764613]
+    ```
+
+    ✅ Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target?
+
+2. Next, select a portion of this dataset to plot by arranging it into a new array using numpy's `newaxis` function. We are going to use linear regression to generate a line between values in this data, according to a pattern it determines.
+
+   ```python
+   X = X[:, np.newaxis, 2]
+   ```
+
+   ✅ At any time, print out the data to check its shape.
+
+3. Now that you have data ready to be plotted, you can see if a machine can help determine a logical split between the numbers in this dataset. To do this, you need to split both the data (X) and the target (y) into test and training sets. Scikit-learn has a straightforward way to do this; you can split your test data at a given point.
+
+   ```python
+   X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
+   ```
+
+4. Now you are ready to train your model! Load up the linear regression model and train it with your X and y training sets using `model.fit()`:
+
+    ```python
+    model = linear_model.LinearRegression()
+    model.fit(X_train, y_train)
+    ```
+
+    ✅ `model.fit()` is a function you'll see in many ML libraries such as TensorFlow
+
+5. Then, create a prediction using test data, using the function `predict()`. This will be used to draw the line between data groups
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+6. Now it's time to show the data in a plot. Matplotlib is a very useful tool for this task. Create a scatterplot of all the X and y test data, and use the prediction to draw a line in the most appropriate place, between the model's data groupings.
+
+    ```python
+    plt.scatter(X_test, y_test,  color='black')
+    plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.show()
+    ```
+
+   ![a scatterplot showing datapoints around diabetes](./images/scatterplot.png)
+
+   ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.
+
+Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!
+
+---
+## 🚀Challenge
+
+Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+
+## Review & Self Study
+
+In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+
+Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) to deepen your understanding.
+
+## Assignment 
+
+[A different dataset](assignment.md)

From d553016ce088d4aeca1c5f2274e5edd029885ee2 Mon Sep 17 00:00:00 2001
From: iBlueDust <35223375+iBlueDust@users.noreply.github.com>
Date: Tue, 6 Jul 2021 21:14:18 +0700
Subject: [PATCH 036/368] Add Indonesian translation for 2-Regression/1-Tools

---
 .../1-Tools/translations/README.id.md         | 135 +++++++++---------
 1 file changed, 68 insertions(+), 67 deletions(-)

diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
index ca32b820ee..b29301e2a4 100644
--- a/2-Regression/1-Tools/translations/README.id.md
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -1,4 +1,4 @@
-# Get started with Python and Scikit-learn for regression models
+# Memulai dengan Python dan Scikit-learn untuk model regresi
 
 ![Ringkisan regresi dalam sebuah catatan sketsa](../../sketchnotes/ml-regression.png)
 
@@ -23,7 +23,7 @@ Dalam pelajaran ini, kamu akan belajar bagaimana untuk:
 
 > 🎥 Klik foto di atas untuk sebuah video: menggunakan Python dalam VS Code
 
-1. **Pasang Python**. Pastikan bahwa [Python](https://www.python.org/downloads/) telah dipasang di komputermu. Kamu akan menggunakan Python untuk banyak tugas *data science* dan *machine learning*. Kebanyakan sistem komputer sudah diinstal dengan Python. Adapula *[Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa)* yang berguna untuk membantu proses pemasangan untuk beberapa pengguna.
+1. **Pasang Python**. Pastikan bahwa [Python](https://www.python.org/downloads/) telah dipasang di komputermu. Kamu akan menggunakan Python untuk banyak tugas *data science* dan *machine learning*. Python sudah dipasang di kebanyakan sistem komputer. Adapula *[Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa)* yang berguna untuk membantu proses pemasangan untuk beberapa pengguna.
 
    Beberapa penggunaan Python memerlukan satu versi perangkat lunak tersebut, sedangkan beberapa penggunaan lainnya mungkin memerlukan versi Python yang beda lagi. Oleh sebab itulah akan sangat berguna untuk bekerja dalam sebuah *[virtual environment](https://docs.python.org/3/library/venv.html)* (lingkungan virtual).
 
@@ -39,68 +39,70 @@ Dalam pelajaran ini, kamu akan belajar bagaimana untuk:
 
 Kamu akan menggunakan ***notebooks*** untuk bekerja dengan kode Python-mu dan membuat model *machine learning*-mu. Jenis file ini adalah alat yang sering digunakan *data scientists* dan dapat diidentifikasikan dengan akhiran/ekstensi `.ipynb`. 
 
-Notebooks are an interactive environment that allow the developer to both code and add notes and write documentation around the code which is quite helpful for experimental or research-oriented projects.
+*Notebook* adalah sebuah lingkungan interaktif yang memungkinkan seorang *developer* untuk menulis kode, catatan, dan dokumentasi mengenai kode tersebut sehingga menjadi sangat berguna untuk proyek eksperimental ataupun riset.
 
-### Exercise - work with a notebook
+### Latihan - bekerja dengan sebuah *notebook*
 
-In this folder, you will find the file _notebook.ipynb_. 
+Dalam folder ini, kamu akan menemukan file _notebook.ipynb_.
 
-1. Open _notebook.ipynb_ in Visual Studio Code.
+1. Buka _notebook.ipynb_ dalam Visual Studio Code.
 
-   A Jupyter server will start with Python 3+ started. You will find areas of the notebook that can be `run`, pieces of code. You can run a code block, by selecting the icon that looks like a play button.
+   Sebuah server Jupyter akan mulai dengan Python 3+. Kamu akan menemukan bagian-bagian *notebook* yang dapat di-`run` (dijalankan). Bagian-bagian tersebut adalah carikan-carikan kode. Kamu bisa menjalankan secarik kode dengan mengklik tombol â–¶.
 
-1. Select the `md` icon and add a bit of markdown, and the following text **# Welcome to your notebook**.
+2. Pilih ikon `md` dan tambahlah sedikit *markdown*: **# Selamat datang di *notebook* saya!**
 
-   Next, add some Python code. 
+   Lalu, tambahlah sedikit kode Python.
 
-1. Type **print('hello notebook')** in the code block.
-1. Select the arrow to run the code.
+3. Ketik **print('hello notebook')** dalam blok kode.
 
-   You should see the printed statement:
+4. Klik â–¶ untuk menjalankan kode.
+
+   Hasilnya harusnya ini:
 
     ```output
     hello notebook
     ```
 
-![VS Code with a notebook open](images/notebook.png)
+![Sebuah *notebook* yang dibuka di VS Code](images/notebook.png)
+
+Kamu bisa menyelipkan catatan-catatan antara kodemu untuk mendokumentasi *notebook*-nya.
 
-You can interleaf your code with comments to self-document the notebook.
+✅ Pikirkanlah sejenak bagaimana lingkungan seorang *web developer* berbeda dengan lingkungan seorang *data scientist*.
 
-✅ Think for a minute how different a web developer's working environment is versus that of a data scientist.
+## Berjalan dengan Scikit-learn
 
-## Up and running with Scikit-learn
+Sekarang, Python sudah siap dalam lingkungan lokalmu, dan kamu sudah nyaman bekerja dengan *Jupyter notebook*. Marilah membiasakan diri dengan Scikit-learn (dilafalkan `saikit lern`; huruf `e` dalam `lern` seperti `e` dalam kata `Perancis`). Scikit-learn menyediakan sebuah [API ekstensif](https://scikit-learn.org/stable/modules/classes.html#api-ref) untuk membantu kamu mengerjakan tugas ML.
 
-Now that Python is set up in your local environment, and you are comfortable with Jupyter notebooks, let's get equally comfortable with Scikit-learn (pronounce it `sci` as in `science`). Scikit-learn provides an [extensive API](https://scikit-learn.org/stable/modules/classes.html#api-ref) to help you perform ML tasks.
+Berdasarkan [situs mereka](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn" adalah sebuah *open-source library* untuk *machine-learning* yang dapat digunakan untuk *supervised* dan *unsupervised learning*. Scikit-learn juga menyediakan beragam alat untuk *model-fitting*, *data preprocessing*, seleksi dan evaluasi model, dll.
 
-According to their [website](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities."
+Dalam kursus ini, kamu akan menggunakan Scikit-learn dan beberapa alat lainnya untuk membangun model *machine-learning* untuk mengerjakan apa yang kami panggil tugas '*machine-learning* tradisional'. Kami sengaja menghindari *neural networks* dan *deep learning* sebab mereka akan dibahas dengan lebih lengkap dalam kurikulum 'AI untuk pemula' nanti.
 
-In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. 
+Scikit-learn memudahkan pembangunan dan evaluasi model-model. Dia terutama fokus pada menggunakan data numerik dan mempunyai beberapa *dataset* yang siap sedia untuk digunakan sebagai alat belajar. Dia juga mempunyai model yang sudah dibangun untuk murid-murid untuk langsung coba. Mari menjelajahi proses memuat data yang sudah disiapkan dan menggunakan model ML pengestimasian pertama menggunakan Scikit-learn dengan data sederhana.
 
-Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let's explore the process of loading prepackaged data and using a built in estimator  first ML model with Scikit-learn with some basic data.
 
-## Exercise - your first Scikit-learn notebook
+## Latihan - Scikit-learn *notebook* pertamamu
 
-> This tutorial was inspired by the [linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) on Scikit-learn's web site.
+> Tutorial ini terinspirasi [contoh regresi linear ini](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) di situs Scikit-learn.
 
-In the _notebook.ipynb_ file associated to this lesson, clear out all the cells by pressing the 'trash can' icon.
+Dalam file _notebook.ipynb_ dari pelajaran ini, kosongkan semua sel dengan mengklik tombol berlambang 'tempat sampah'. 
 
-In this section, you will work with a small dataset about diabetes that is built into Scikit-learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
+Dalam bagian ini, kamu akan bekerja dengan sebuah *dataset* kecil tentang diabetes yang datang bersama dengan Scikit-learn yang dimaksud sebagai bahan ajaran. Bayangkan bahwa kamu ingin mencoba sebuah cara pengobatan untuk pasien diabetes. Model *machine learning* mungkin dapat membantu kamu menentukan pasien mana merespon lebih baik pada pengobatan tersebut berdasarkan kombinasi-kombinasi variabel. Bahkan sebuah model regresi yang sederhana, saat divisualisasikan, dapat menunjukkanmu informasi tentang variabel-variabel yang dapat membantu kamu mengorganisasikan uji-uji klinis teoritismu.
 
-✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use linear regression, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use logistic regression. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
+✅ Ada banyak macam metode regresi, dan yang mana yang kamu pilih tergantung pada jawaban yang kamu sedang cari. Kalau kamu ingin memprediksi tinggi badan seseorang dari usianya, kamu bisa menggunakan regresi linear, karena kamu mencari sebuah **nilai numerik**. Kalau kamu tertarik pada apa sebuah jenis kuliner sebaiknya dianggap sebagai vegan atau tidak, kamu sedang mencari sebuah **kategorisasi/klasifikasi**; kamu bisa menggunakan regresi logistik. Kamu akan belajar lebih banyak tentang regresi logistik nanti. Pikirkan dahulu beberapa pertanyaan yang kamu bisa tanyakan dari data yang ada dan metode yang mana akan paling cocok.
 
-Let's get started on this task.
+Mari mulai mengerjakan tugas ini.
 
-### Import libraries
+### Impor *library*
 
-For this task we will import some libraries:
+Untuk tugas ini, kita akan mengimpor beberapa *library*:
 
-- **matplotlib**. It's a useful [graphing tool](https://matplotlib.org/) and we will use it to create a line plot.
-- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) is a useful library for handling numeric data in Python.
-- **sklearn**. This is the Scikit-learn library.
+- **matplotlib**. Sebuah [alat untuk membuat grafik](https://matplotlib.org/) yang kita akan gunakan untuk membuat sebuah grafik garis.
+- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) adalah sebuah *library* berguna untuk menangani data numerik di Python.
+- **sklearn**. Ini adalah *library* Scikit-learn.
 
-Import some libraries to help with your tasks.
+Imporlah *library-library* yang akan membantu dengan tugasmu.
 
-1. Add imports by typing the following code:
+1. Tambahlah impor dengan mengetik kode berikut:
 
    ```python
    import matplotlib.pyplot as plt
@@ -108,36 +110,35 @@ Import some libraries to help with your tasks.
    from sklearn import datasets, linear_model, model_selection
    ```
 
-   Above you are importing `matplottlib`, `numpy` and you are importing `datasets`, `linear_model` and `model_selection` from `sklearn`. `model_selection` is used for splitting data into training and test sets.
-
-### The diabetes dataset
+   Di atas, kamu sedang mengimpor `matplottlib`, `numpy`. Selain itu, kamu juga sedang mengimpor `datasets`, `linear_model` dan `model_selection` dari `sklearn`. `model_selection` digunakan untuk memisahkan data menjadi set latihan dan set ujian.
 
-The built-in [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) includes 442 samples of data around diabetes, with 10 feature variables, some of which include:
+### *Dataset* diabetes
 
-age: age in years
-bmi: body mass index
-bp: average blood pressure
-s1 tc: T-Cells (a type of white blood cells)
+[*Dataset* diabetes](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) mencakupi 442 sampel data mengenai diabetes dengan 10 variabel utama, termasuk:
 
-✅ This dataset includes the concept of 'sex' as a feature variable important to research around diabetes. Many medical datasets include this type of binary classification. Think a bit about how categorizations such as this might exclude certain parts of a population from treatments.
+- age: usia dalam tahun
+- bmi: *body mass index*
+- bp: tekanan darah rata-rata
+- s1 tc: Sel T (sejenis cel darah putih)
 
-Now, load up the X and y data.
+✅ *Dataset* ini juga mempunyai konsep 'jenis kelamin' sebagai sebuah variabel utama yang penting dalam riset diabetes. Banyak *dataset* medis mempunyai klasifikasi binari ini. Pikirkan sejenak bagaimana kategorisasi seperti yang ini dapat mengecualikan bagian tertentu dari sebuah populasi dari pengobatan.
 
-> 🎓 Remember, this is supervised learning, and we need a named 'y' target.
+Sekarang, muatkan data X dan y.
 
-In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The input `return_X_y=True` signals that `X` will be a data matrix, and `y` will be the regression target. 
+> 🎓 Ingatlah, ini adalah *supervised learning*, jadi kita perlu sebuah target (dinamakan 'y').
 
-1. Add some print commands to show the shape of the data matrix and its first element:
+Dalam sebuah sel kode yang baru, muatkan *dataset* diabetes dengan memanggil `load_diabetes()`. Input `return_X_y=True` menunjukkan bahwa `X` adalah sebuah matriks data dan `y` adalah target regresi.
 
+1. Tambah beberapa instruksi *print* untuk menunjukkan ukuran data matriks dan elemen pertama matriks tersebut.
     ```python
     X, y = datasets.load_diabetes(return_X_y=True)
     print(X.shape)
     print(X[0])
     ```
 
-    What you are getting back as a response, is a tuple. What you are doing is to assign the two first values of the tuple to `X` and `y` respectively. Learn more [about tuples](https://wikipedia.org/wiki/Tuple).
+    Respon yang didapati adalah sebuah *tuple*. Kamu sedang menetapkan kedua nilai pertama dalam *tuple* itu ke dalam `X` dan `y` secara berturut. Pelajari lebih banyak [tentant *tuples*](https://wikipedia.org/wiki/Tuple).
 
-    You can see that this data has 442 items shaped in arrays of 10 elements:
+    Kamu bisa melihat bahwa data ini berupa 422 nilai yang disusun menjadi beberapa `array` dengan 10 elemen:
 
     ```text
     (442, 10)
@@ -145,38 +146,38 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The
     -0.04340085 -0.00259226  0.01990842 -0.01764613]
     ```
 
-    ✅ Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target?
+    ✅ Pikirkan sejenak tentang hubungan antara data dan target regresi. Apa kamu bisa menemukan [targetnya](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) untuk *database* diabetes dalam dokumentasinya? Mengetahui targetnya, apa yang *dataset* ini sedang mendemonstrasikan?
 
-2. Next, select a portion of this dataset to plot by arranging it into a new array using numpy's `newaxis` function. We are going to use linear regression to generate a line between values in this data, according to a pattern it determines.
+2. Lalu, pilih sebuah porsi dataset ini untuk digambarkan dengan menyusuninya menjadi sebuah `array` baru dengan fungsi `newaxis` dari `numpy`. Kita akan menggunakan regresi linear untuk menggambar sebuah garis antara nilai-nilai dalam data ini sesuai dengan pola yang ditentukannya.
 
    ```python
    X = X[:, np.newaxis, 2]
    ```
 
-   ✅ At any time, print out the data to check its shape.
+   ✅ Kapanpun, *print*-lah datanya untuk memeriksa bentuknya.
 
-3. Now that you have data ready to be plotted, you can see if a machine can help determine a logical split between the numbers in this dataset. To do this, you need to split both the data (X) and the target (y) into test and training sets. Scikit-learn has a straightforward way to do this; you can split your test data at a given point.
+3. Sekarang datanya sudah siap untuk digambar. Kamu bisa melihat jikalau sebuah mesin dapat menentukan sebuah perpisahan logika dari nomor-nomor *dataset* ini. Untuk melakukan itu, kamu harus memisahkan data (X) dan target (y) menjadi set latihan dan set ujian. Scikit-learn juga memberi cara untuk melakukan itu; kamu bisa memisahkan data ujianmu pada titik tertentu.
 
    ```python
    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
    ```
 
-4. Now you are ready to train your model! Load up the linear regression model and train it with your X and y training sets using `model.fit()`:
+4. Sekarang, kamu sudah siap untuk melatihkan modelmu! Muatkan dahulu data regresi linear dan latihkan modelmu dengan set X dan y-mu dengan `model.fit()`:
 
     ```python
     model = linear_model.LinearRegression()
     model.fit(X_train, y_train)
     ```
 
-    ✅ `model.fit()` is a function you'll see in many ML libraries such as TensorFlow
+    ✅ `model.fit()` adalah sebuah fungsi yang akan kamu temukan dalam banyak *library* ML seperti TensorFlow.
 
-5. Then, create a prediction using test data, using the function `predict()`. This will be used to draw the line between data groups
+5. Lalu, buatlah sebuah prediksi dengan data ujianmu dengan fungsi `predict()`. Ini akan digunakan untuk menggambar sebuah garis antara grup-grup data.
 
     ```python
     y_pred = model.predict(X_test)
     ```
 
-6. Now it's time to show the data in a plot. Matplotlib is a very useful tool for this task. Create a scatterplot of all the X and y test data, and use the prediction to draw a line in the most appropriate place, between the model's data groupings.
+6. Sekarang waktunya untuk menggambar data dalam sebuah grafik. Matplotlib adalah sebuah alat yang sangat berguna untuk melakukan itu. Buatlah sebuah petak sebar dari semua X dan y dari set ujian dan gunakan prediksi yang dihasilkan untuk menggambar sebuah garis di tempat yang paling cocok antara grup-grup data modelnya.
 
     ```python
     plt.scatter(X_test, y_test,  color='black')
@@ -184,24 +185,24 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The
     plt.show()
     ```
 
-   ![a scatterplot showing datapoints around diabetes](./images/scatterplot.png)
+   ![sebuah petak sebar yang menunjukkan poin-poin data sekitar diabetes](./images/scatterplot.png)
 
-   ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.
+   ✅ Pikirkan sejenak tentang apa yang sedang terjadi di sini. Sebuah garis lurus membentang tengah-tengah titik-titik kecil data. Tetapi apa yang sedang garis itu lakukan? Apa kamu bisa melihat bagaimana kamu bisa menggunakan garis ini untuk memprediksi di mana sebuah data poin baru yang tidak pernah dilihat sebelumnya kemungkinan besar akan terletak berhubungan dengan sumbu y grafik ini? Coba jelaskan dalam kata-kata kegunaan praktis model ini.
 
-Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!
+Selamat, kamu telah membangun model regresi linear pertamamu, membuat sebuah prediksi darinya, dan menunjukkannya dalam sebuah grafik!
 
 ---
-## 🚀Challenge
+## Tantangan
 
-Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+Gambarkan sebuah variabel yang beda dari *dataset* ini. Petunjuk: edit baris ini: `X = X[:, np.newaxis, 2]`. Mengetahui target *dataset* ini, apa yang kamu bisa menemukan tentang kemajuan diabetes sebagai sebuah penyakit?
+## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
 
-## Review & Self Study
+## Review & Pembelajaran Mandiri
 
-In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+Dalam tutorial ini, kamu bekerja dengan sebuah model regresi linear yang sederhana daripada regresi linear univariat atau berganda. Bacalah sedikit tentang perbedaan antara metode-metode ini atau tontonlah [video ini](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef).
 
-Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) to deepen your understanding.
+Bacalah lebih banyak tentang konsep regresi dan pikirkanlah tentang jenis pertanyaan apa saja yang bisa dijawab teknik ini. Cobalah [tutorial ini](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) untuk memperdalam pemahamanmu.
 
-## Assignment 
+## Tugas 
 
-[A different dataset](assignment.md)
+[*Dataset* yang beda](assignment.md)

From 37bc5cac5c68c97282a8edd19c403aab8dbb5b4b Mon Sep 17 00:00:00 2001
From: iBlueDust <35223375+iBlueDust@users.noreply.github.com>
Date: Tue, 6 Jul 2021 21:29:47 +0700
Subject: [PATCH 037/368] Add Indonesian translations for 2-Regression/Preface

---
 2-Regression/translations/README.id.md | 33 ++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 2-Regression/translations/README.id.md

diff --git a/2-Regression/translations/README.id.md b/2-Regression/translations/README.id.md
new file mode 100644
index 0000000000..a1085fc84e
--- /dev/null
+++ b/2-Regression/translations/README.id.md
@@ -0,0 +1,33 @@
+# Model regresi untuk *machine learning*
+## Topik regional: Model regresi untuk harga labu di Amerika Utara 🎃
+
+Di Amerika Utara, labu seringkali diukir menjadi muka-muka seram untuk Halloween. Mari mencari tahu lebih banyak tentang sayur menarik ini!
+
+![jack-o-lantern](./images/jack-o-lanterns.jpg)
+> Foto oleh <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> di <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## Apa yang kamu akan pelajari
+
+Pelajaran-pelajaran dalam seksi ini mencakupi jenis-jenis regresi dalam konteks *machine learning*. Model regresi dapat membantu menentukan _hubungan_ antara variabel-variabel. Model jenis ini dapat memprediksi nilai-nilai seperti panjang, temperatur, atau usia, sehingga mengemukakan hubungan-hubungan antara variabel dengan menganalisis poin-poin data.
+
+Dalam seri pelajaran ini, kamu akan menemukan perbedaan antara regresi linear dan logistik, dan kapan untuk menggunakan satu atau yang lainnya.
+
+Selain itu, kamu akan disiapkan untuk mulai mengerjakan tugas *machine learning*, termasuk mengkonfigurasi Visual Studio Code untuk mengelola *notebook*, lingkungan wajar untuk *data scientist*. Kamu akan menemukan Scikit-learn, sebuah *library* untuk *machine learning*, dan kamu akan membangun model pertamamu dengan memfokus pada model regresi dalam bab ini.
+
+> Ada alat-alat *low-code* yang dapat membantumu belajar tentang bekerja dengan model regresi. Cobalah [Azure ML untuk tugas ini](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa).
+
+### Pelajaran
+
+1. [Alat-alat seorang *data scientist*](1-Tools/README.md)
+2. [Mengelola data](2-Data/README.md)
+3. [Regresi linear dan polinomial](3-Linear/README.md)
+4. [Regresi logistik](4-Logistic/README.md)
+
+---
+### Kredit
+
+"ML with regression" (ML dengan regresi) ditulis dari ♥� oleh [Jen Looper](https://twitter.com/jenlooper)
+
+♥� Kontributor kuis termasuk: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) dan [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+*Dataset* labu disarankan [proyek ini di Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) dan datanya disumberkan dari [Specialty Crops Terminal Markets Standard Reports (Laporan Standar Pasar Terminal Tanaman Khusus)](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) yang didistribusikan Departemen Agrikultur Amerika Serikat. Kami telah menambahkan beberapa poin tentang warna berdasarkan jenis labu untuk menormalisasi distribusi data. Data ini terbuka untuk umum (*public domain*).

From 0b9f85da77545fcb40bd18b6fd77d934424b33ca Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Tue, 6 Jul 2021 22:15:41 +0700
Subject: [PATCH 038/368] Add Indonesian translation for 1-2 assignment

---
 .../2-history-of-ML/translations/assignment.id.md     | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/2-history-of-ML/translations/assignment.id.md

diff --git a/1-Introduction/2-history-of-ML/translations/assignment.id.md b/1-Introduction/2-history-of-ML/translations/assignment.id.md
new file mode 100644
index 0000000000..0ee7c0096c
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.id.md
@@ -0,0 +1,11 @@
+# Membuat sebuah *timeline*
+
+## Instruksi
+
+Menggunakan [repo ini](https://github.com/Digital-Humanities-Toolkit/timeline-builder), buatlah sebuah *timeline* dari beberapa aspek sejarah algoritma, matematika, statistik, AI, atau ML, atau kombinasi dari semuanya. Kamu dapat fokus pada satu orang, satu ide, atau rentang waktu pemikiran yang panjang. Pastikan untuk menambahkan elemen multimedia. 
+
+## Rubrik
+
+| Kriteria | Sangat Bagus                                         | Cukup                                | Perlu Peningkatan                                                |
+| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- |
+|          | *Timeline* yang dideploy disajikan sebagai halaman GitHub | Kode belum lengkap dan belum dideploy | *Timeline* belum lengkap, belum diriset dengan baik dan belum dideploy |
\ No newline at end of file

From a04c1add96d80443561ef462eb24e3a65dab4b6b Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Tue, 6 Jul 2021 17:22:15 +0200
Subject: [PATCH 039/368] Chapter 1, Section 3 Readme and assignments

---
 .../3-fairness/translations/README.it.md      | 212 ++++++++++++++++++
 .../3-fairness/translations/assignment.it.md  |  11 +
 2 files changed, 223 insertions(+)
 create mode 100644 1-Introduction/3-fairness/translations/README.it.md
 create mode 100644 1-Introduction/3-fairness/translations/assignment.it.md

diff --git a/1-Introduction/3-fairness/translations/README.it.md b/1-Introduction/3-fairness/translations/README.it.md
new file mode 100644
index 0000000000..2b822c88bf
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/README.it.md
@@ -0,0 +1,212 @@
+# Equità e machine learning
+
+![Riepilogo dell'equità in machine learning in uno sketchnote](../../../sketchnotes/ml-fairness.png)
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+
+## Introduzione
+
+In questo programma di studi, si inizierà a scoprire come machine learning può e sta influenzando la  vita quotidiana. Anche ora, sistemi e modelli sono coinvolti nelle attività decisionali quotidiane, come le diagnosi sanitarie o l'individuazione di frodi. Quindi è importante che questi modelli funzionino bene per fornire risultati equi per tutti.
+
+Si immagini cosa può accadere quando i dati che si stanno utilizzando per costruire questi modelli mancano di determinati dati demografici, come razza, genere, visione politica, religione, o rappresentano tali dati demografici in modo sproporzionato. E quando il risultato del modello viene interpretato per favorire alcuni gruppi demografici? Qual è la conseguenza per l'applicazione?
+
+In questa lezione, si dovrà:
+
+- Aumentare la propria consapevolezza sull'importanza dell'equità nel machine learning.
+- Informarsi sui danni legati all'equità.
+- Apprendere ulteriori informazioni sulla valutazione e la mitigazione dell'ingiustizia.
+
+## Prerequisito
+
+Come prerequisito, si segua il percorso di apprendimento "Principi di IA responsabile" e si guardi il video qui sotto sull'argomento:
+
+Si scopra di più sull'AI Responsabile seguendo questo [percorso di apprendimento](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
+
+[![L'approccio di Microsoft all'AI responsabileL'](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "approccio di Microsoft all'AI Responsabile")
+
+> 🎥 Fare clic sull'immagine sopra per un video: L'approccio di Microsoft all'AI Responsabile
+
+## Iniquità nei dati e negli algoritmi
+
+> "Se si torturano i dati abbastanza a lungo, essi confesseranno qualsiasi cosa" - Ronald Coase
+
+Questa affermazione suona estrema, ma è vero che i dati possono essere manipolati per supportare qualsiasi conclusione. Tale manipolazione a volte può avvenire involontariamente. Come esseri umani, abbiamo tutti dei pregiudizi, ed è spesso difficile sapere consapevolmente quando si introduce un pregiudizio nei dati.
+
+Garantire l'equità nell'intelligenza artificiale e machine learning rimane una sfida socio-tecnica complessa. Ciò significa che non può essere affrontata da prospettive puramente sociali o tecniche.
+
+### Danni legati all'equità
+
+Cosa si intende per ingiustizia? L'"ingiustizia" comprende gli impatti negativi, o "danni", per un gruppo di persone, come quelli definiti in termini di razza, genere, età o stato di disabilità.
+
+I principali danni legati all'equità possono essere classificati come:
+
+- **Allocazione**, se un genere o un'etnia, ad esempio, sono preferiti a un altro.
+- **Qualità di servizio** Se si addestrano i dati per uno scenario specifico, ma la realtà è molto più complessa, si ottiene un servizio scadente.
+- **Stereotipi**. Associazione di un dato gruppo con attributi preassegnati.
+- **Denigrazione**. Criticare ed etichettare ingiustamente qualcosa o qualcuno.
+- **Sovra o sotto rappresentazione**. L'idea è che un certo gruppo non è visto in una certa professione, e qualsiasi servizio o funzione che continua a promuovere ciò contribuisce al danno.
+
+Si dia un'occhiata agli esempi.
+
+### Allocazione
+
+Si consideri un ipotetico sistema per la scrematura delle domande di prestito. Il sistema tende a scegliere gli uomini bianchi come candidati migliori rispetto ad altri gruppi. Di conseguenza, i prestiti vengono trattenuti da alcuni richiedenti.
+
+Un altro esempio potrebbe essere uno strumento sperimentale di assunzione sviluppato da una grande azienda per selezionare i candidati. Lo strumento discrimina sistematicamente un genere utilizzando i modelli che sono stati addestrati a preferire parole associate con altro. Ha portato a penalizzare i candidati i cui curricula contengono parole come "squadra di rugby femminile".
+
+✅ Si compia una piccola ricerca per trovare un esempio reale di qualcosa del genere
+
+### Qualità di Servizio
+
+I ricercatori hanno scoperto che diversi classificatori di genere commerciali avevano tassi di errore più elevati intorno alle immagini di donne con tonalità della pelle più scura rispetto alle immagini di uomini con tonalità della pelle più chiare. [Riferimento](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/)
+
+Un altro esempio infamante è un distributore di sapone per le mani che sembrava non essere in grado di percepire le persone con la pelle scura. [Riferimento](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+
+### Stereotipi
+
+La visione di genere stereotipata è stata trovata nella traduzione automatica. Durante la traduzione in turco "he is a nurse and she is a doctor" (lui è un'infermiere e lei un medico), sono stati riscontrati problemi. Il turco è una lingua senza genere che ha un pronome, "o" per trasmettere una terza persona singolare, ma tradurre la frase dal turco all'inglese produce lo stereotipo e scorretto come "she is a nurse and he is a doctor" (lei è un'infermiera e lui è un medico).
+
+![traduzione in turco](../images/gender-bias-translate-en-tr.png)
+
+![Traduzione in inglese](../images/gender-bias-translate-tr-en.png)
+
+### Denigrazione
+
+Una tecnologia di etichettatura delle immagini ha contrassegnato in modo infamante le immagini di persone dalla pelle scura come gorilla. L'etichettatura errata è dannosa non solo perché il sistema ha commesso un errore, ma anche perché ha applicato specificamente un'etichetta che ha una lunga storia di essere intenzionalmente utilizzata per denigrare i neri.
+
+[![AI: Non sono una donna?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, non sono una donna?")
+> 🎥 Cliccare sull'immagine sopra per un video: AI, Ain't I a Woman - una performance che mostra il danno causato dalla denigrazione razzista da parte dell'AI
+
+### Sovra o sotto rappresentazione
+
+I risultati di ricerca di immagini distorti possono essere un buon esempio di questo danno. Quando si cercano immagini di professioni con una percentuale uguale o superiore di uomini rispetto alle donne, come l'ingegneria o CEO, si osserva che i risultati sono più fortemente distorti verso un determinato genere.
+
+![Ricerca CEO di Bing](../images/ceos.png)
+> Questa ricerca su Bing per "CEO" produce risultati piuttosto inclusivi
+
+Questi cinque principali tipi di danno non si escludono a vicenda e un singolo sistema può presentare più di un tipo di danno. Inoltre, ogni caso varia nella sua gravità. Ad esempio, etichettare ingiustamente qualcuno come criminale è un danno molto più grave che etichettare erroneamente un'immagine. È importante, tuttavia, ricordare che anche danni relativamente non gravi possono far sentire le persone alienate o emarginate e l'impatto cumulativo può essere estremamente opprimente.
+
+✅ **Discussione**: rivisitare alcuni degli esempi e vedere se mostrano danni diversi.
+
+|                         | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
+| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
+| Sistema di assunzione automatizzato |     x      |         x          |      x       |             |               x                |
+| Traduzione automatica     |            |                    |              |             |                                |
+| Eitchettatura foto          |            |                    |              |             |                                |
+
+## Rilevare l'ingiustizia
+
+Ci sono molte ragioni per cui un dato sistema si comporta in modo scorretto. I pregiudizi sociali, ad esempio, potrebbero riflettersi nell'insieme di dati utilizzati per addestrarli. Ad esempio, l'ingiustizia delle assunzioni potrebbe essere stata esacerbata dall'eccessivo affidamento sui dati storici. Utilizzando i modelli nei curricula inviati all'azienda per un periodo di 10 anni, il modello ha determinato che gli uomini erano più qualificati perché la maggior parte dei curricula proveniva da uomini, un riflesso del passato dominio maschile nell'industria tecnologica.
+
+Dati inadeguati su un determinato gruppo di persone possono essere motivo di ingiustizia. Ad esempio, i classificatori di immagini hanno un tasso di errore più elevato per le immagini di persone dalla pelle scura perché le tonalità della pelle più scure sono sottorappresentate nei dati.
+
+Anche le ipotesi errate fatte durante lo sviluppo causano iniquità. Ad esempio, un sistema di analisi facciale destinato a prevedere chi commetterà un crimine basato sulle immagini dei volti delle persone può portare a ipotesi dannose. Ciò potrebbe portare a danni sostanziali per le persone classificate erroneamente.
+
+## Si comprendano i propri modelli e si costruiscano in modo onesto
+
+Sebbene molti aspetti dell'equità non vengano catturati nelle metriche di equità quantitativa e non sia possibile rimuovere completamente i pregiudizi da un sistema per garantire l'equità, si è comunque responsabili di rilevare e mitigare il più possibile i problemi di equità.
+
+Quando si lavora con modelli di machine learning, è importante comprendere i propri modelli assicurandone l'interpretabilità e valutando e mitigando l'ingiustizia.
+
+Si utilizza l'esempio di selezione del prestito per isolare il caso e determinare il livello di impatto di ciascun fattore sulla previsione.
+
+## Metodi di valutazione
+
+1. **Identificare i danni (e benefici)**. Il primo passo è identificare danni e benefici. Si pensi a come azioni e decisioni possono influenzare sia i potenziali clienti che un'azienda stessa.
+
+1. **Identificare i gruppi interessati**. Una volta compreso il tipo di danni o benefici che possono verificarsi, identificare i gruppi che potrebbero essere interessati. Questi gruppi sono definiti per genere, etnia o gruppo sociale?
+
+1. **Definire le metriche di equità**. Infine, si definisca una metrica in modo da avere qualcosa su cui misurare il proprio lavoro per migliorare la situazione.
+
+### **Identificare danni (e benefici)**
+
+Quali sono i danni e i benefici associati al prestito? Si pensi agli scenari di falsi negativi e falsi positivi:
+
+**Falsi negativi** (rifiutato, ma Y=1) - in questo caso viene rifiutato un richiedente che sarà in grado di rimborsare un prestito. Questo è un evento avverso perché le risorse dei prestiti sono trattenute da richiedenti qualificati.
+
+**Falsi positivi** (accettato, ma Y=0) - in questo caso, il richiedente ottiene un prestito ma alla fine fallisce. Di conseguenza, il caso del richiedente verrà inviato a un'agenzia di recupero crediti che può influire sulle sue future richieste di prestito.
+
+### **Identificare i gruppi interessati**
+
+Il passo successivo è determinare quali gruppi potrebbero essere interessati. Ad esempio, nel caso di una richiesta di carta di credito, un modello potrebbe stabilire che le donne dovrebbero ricevere limiti di credito molto più bassi rispetto ai loro coniugi che condividono i beni familiari. Un intero gruppo demografico, definito in base al genere, è così interessato.
+
+### **Definire le metriche di equità**
+
+Si sono identificati i danni e un gruppo interessato, in questo caso, delineato per genere. Ora, si usino i fattori quantificati per disaggregare le loro metriche. Ad esempio, utilizzando i dati di seguito, si può vedere che le donne hanno il più alto tasso di falsi positivi e gli uomini il più piccolo, e che è vero il contrario per i falsi negativi.
+
+✅ In una futura lezione sul Clustering, si vedrà come costruire questa 'matrice di confusione' nel codice
+
+|            | percentuale di falsi positivi | Percentuale di falsi negativi |conteggio |
+| ---------- | ------------------- | ------------------- | ----- |
+| Donna | 0,37 | 0,27 | 54032 |
+| Uomo | 0,31 | 0.35 | 28620 |
+| Non binario | 0,33 | 0,31 | 1266 |
+
+Questa tabella ci dice diverse cose. Innanzitutto, si nota che ci sono relativamente poche persone non binarie nei dati. I dati sono distorti, quindi si deve fare attenzione a come si interpretano  questi numeri.
+
+In questo caso, ci sono 3 gruppi e 2 metriche. Quando si pensa a come il nostro sistema influisce sul gruppo di clienti con i loro richiedenti di prestito, questo può essere sufficiente, ma quando si desidera definire un numero maggiore di gruppi, è possibile distillare questo in insiemi più piccoli di riepiloghi. Per fare ciò, si possono aggiungere più metriche, come la differenza più grande o il rapporto più piccolo di ogni falso negativo e falso positivo.
+
+✅ Ci si fermi a pensare: quali altri gruppi potrebbero essere interessati dalla richiesta di prestito?
+
+## Mitigare l'ingiustizia
+
+Per mitigare l'ingiustizia, si esplorari il modello per generare vari modelli mitigati e si confrontino i compromessi tra accuratezza ed equità per selezionare il modello più equo.
+
+Questa lezione introduttiva non approfondisce i dettagli dell'algoritmo della mitigazione dell'ingiustizia, come l'approccio di post-elaborazione e riduzione, ma ecco uno strumento che si potrebbe voler provare.
+
+### Fairlearn
+
+[Fairlearn](https://fairlearn.github.io/) è un pacchetto Python open source che consente di valutare l'equità dei propri sistemi e mitigare l'ingiustizia.
+
+Lo strumento consente di valutare in che modo le previsioni di un modello influiscono su diversi gruppi, consentendo di confrontare più modelli utilizzando metriche di equità e prestazioni e fornendo una serie di algoritmi per mitigare l'ingiustizia nella classificazione binaria e nella regressione.
+
+- Si scopra come utilizzare i diversi componenti controllando il GitHub di [Fairlearn](https://github.com/fairlearn/fairlearn/)
+
+- Si esplori la [guida per l'utente](https://fairlearn.github.io/main/user_guide/index.html), e gli [esempi](https://fairlearn.github.io/main/auto_examples/index.html)
+
+- Si provino alcuni [notebook di esempio](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
+
+- Si scopra [come abilitare le valutazioni dell'equità](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dei modelli di Machine Learning in Azure Machine Learning.
+
+- Si dia un'occhiata a questi [notebook di esempio](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) per ulteriori scenari di valutazione dell'equità in Azure Machine Learning.
+
+---
+
+## 🚀 Sfida
+
+Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe:
+
+- avere una diversità di background e prospettive tra le persone che lavorano sui sistemi
+- investire in insiemi di dati che riflettano le diversità della società
+- sviluppare metodi migliori per rilevare e correggere i pregiudizi quando si verificano
+
+Si pensi a scenari di vita reale in cui l'ingiustizia è evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+
+## Revisione e Auto Apprendimento
+
+In questa lezione si sono apprese alcune nozioni di base sui concetti di equità e ingiustizia in machine learning.
+
+Si guardi questo workshop per approfondire gli argomenti:
+
+- YouTube: Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione di Hanna Wallach e Miro Dudik [Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k)
+
+Si legga anche:
+
+- Centro risorse RAI di Microsoft: [risorse AI responsabili – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4)
+
+- Gruppo di ricerca FATE di Microsoft[: FATE: equità, responsabilità, trasparenza ed etica nell'intelligenza artificiale - Microsoft Research](https://www.microsoft.com/research/theme/fate/)
+
+Si esplori il toolkit Fairlearn
+
+[Fairlearn](https://fairlearn.org/)
+
+Si scoprano gli strumenti di Azure Machine Learning per garantire l'equità
+
+- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa)
+
+## Compito
+
+[Esplorare Fairlearn](assignment.it.md)
diff --git a/1-Introduction/3-fairness/translations/assignment.it.md b/1-Introduction/3-fairness/translations/assignment.it.md
new file mode 100644
index 0000000000..e2151bdf3c
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Esplora Fairlearn
+
+## Istruzioni
+
+In questa lezione si è appreso di Fairlearn, un "progetto open source guidato dalla comunità per aiutare i data scientist a migliorare l'equità dei sistemi di intelligenza artificiale". Per questo compito, esplorare uno dei [notebook](https://fairlearn.org/v0.6.2/auto_examples/index.html) di Fairlearn e riportare i propri risultati in un documento o in una presentazione.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------- | -------- | ----------------- |
+|          | Viene presentato un documento o una presentazione powerpoint in cui si discutono i sistemi di Fairlearn, il notebook che è stato eseguito e le conclusioni tratte dall'esecuzione | Viene presentato un documento senza conclusioni | Non viene presentato alcun documento |

From 6fa46d10ffd67d0ada97e728c77e7085a4df0555 Mon Sep 17 00:00:00 2001
From: iBlueDust <35223375+iBlueDust@users.noreply.github.com>
Date: Tue, 6 Jul 2021 22:55:20 +0700
Subject: [PATCH 040/368] Add Indonesian translation for 2-Regression/2-Data

---
 2-Regression/2-Data/translations/README.id.md | 202 ++++++++++++++++++
 1 file changed, 202 insertions(+)
 create mode 100644 2-Regression/2-Data/translations/README.id.md

diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md
new file mode 100644
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+# Membangun sebuah model regresi dengan Scikit-learn: siapkan dan visualisasikan data
+
+> ![Infografik visualisasi data](./images/data-visualization.png)
+> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+
+## Pembukaan
+
+Karena sekarang kamu sudah siap dengan alat-alat yang akan diperlukan untuk mulai melampiaskan pembangunan model *machine learning* dengan Scikit-learn, kamu juga siap untuk mulai membuat pertanyaan dari datamu. Selagi kamu bekerja dengan data dan mengaplikasikan solusi ML, sangatlah penting untuk mengerti bagaimana menanyakan cara yang benar dan tepat untuk mengemukakan potensial *dataset*-mu.
+
+Dalam pelajaran ini, kamu akan belajar:
+
+- Cara mempersiapkan datamu untuk pembangunan model.
+- Cara menggunakan Matplotlib untuk memvisualisasikan data.
+
+## Menanyakan pertanyaan yang tepat dari datamu
+
+Pertanyaan yang perlu dijawab akan menentukan jenis algoritma ML yang kamu akan memanfaatkan. Lalu, kualitas jawaban yang kamu akan dapat sangat tergantung pada sifat datamu (*the nature of your data*).
+
+Lihatlah [data](../data/US-pumpkins.csv) yang disiapkan untuk pelajaran ini. Kamu bisa membuka file .csv ini di VS Code. Membaca dengan cepat, *dataset* ini ada yang kosong dan ada yang campuran data *string* dan data numerik. Adapula sebuah kolom 'Package' yang aneh dan mengandung data antara 'sacks', 'bins', dll. Terus terang, data ini amburadul.
+
+Faktanya adalah tidak sering kita dihadiahkan sebuah *dataset* yang langsung bisa digunakan untuk membuat sebuah model ML. Dalam pelajaran ini, kamu akan belajar bagaimana menyiapkan sebuah *dataset* 'mentah' menggunakan *library* standar Python. Kamu juga akan belajar aneka teknik untuk memvisualisasikan datanya.
+
+## Studi kasus: 'pasar labu'
+
+Dalam folder ini kamu akan menemukan sebuah file .csv dalam folder `data` bernama [US-pumpkins.csv](../data/US-pumpkins.csv) yang mempunyai 1757 baris data tentang pasar labu disortir dalam pengelompokkan berdasarkan kota. Ini adalah data mentah yang diambil dari [Specialty Crops Terminal Markets Standard Reports (Laporan Standar Pasar Terminal Tanaman Khusus)](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) yang didistribusi Departemen Agrikultur Amerika Serikat.
+
+### Menyiapkan data
+
+Data ini terbuka untuk umum (*publik domain*) dan bisa diunduh sebagai banyak file terpisah berdasarkan kota dari situs internet Departemen Agrikultur Amerika Serikat. Supaya tidak berurusan dengan terlalu banyak file, kami telah menggabungkan data dari semua kota menjadi satu *spreadsheet* (file Excel). Jadi kamu sudah _menyiapkan_ datanya sedikit. Selanjutnya, mari kita lihat datanya.
+
+### Data labu - kesimpulan-kesimpulan awal
+
+Apa yang kamu cermati tentang data ini? Kamu sudah melihat bahwa ada campuran *string*, nomor, kekosongan, dan nilai-nilai aneh yang harus diartikan.
+
+Pertanyaan apa yang kamu bisa tanyakan dari data data ini menggunakan teknik regresi? Kalau "Prediksikan harga jual sebuah labu pada bulan tertentu" bagaimana? Melihat datanya sekali lagi, ada beberapa perubahan yang kamu harus terapkan untuk membuat struktur data yang diperlukan untuk tugas ini.
+
+## Latihan - analisiskan data labu
+
+Mari menggunakan [Pandas](https://pandas.pydata.org/) (singkatan dari `Python Data Analysis`), sebuah alat yang sangat beruna untuk membentuk, menganalisis, dan menyiapkan data labu ini.
+
+### Pertama, carilah tanggal yang hilang
+
+Kamu harus mengambil langkah untuk mencari tanggal-tanggal yang hilang terlebih dahulu:
+
+1. Konversi tanggal-tanggalnya menjadi format bulan (tanggal-tanggal ini dalam format Amerika Serikat, yaitu `BULAN/TANGGAL/TAHUN`).
+2. Jadikan data bulan menjadi kolom baru
+
+Buka file _notebook.ipynb_ dalam Visual Studio Code dan impor *spreadsheet*-nya menjadi sebuah *dataframe* Pandas.
+
+1. Gunakan fungsi `head()` untuk melihat lima baris pertama.
+
+    ```python
+    import pandas as pd
+    pumpkins = pd.read_csv('../../data/US-pumpkins.csv')
+    pumpkins.head()
+    ```
+
+    ✅ Fungsi apa yang akan kamu gunakan untuk melihat lima baris **terakhir**?
+
+2. Periksa apa ada data yang hilang dalam *dataframe* ini:
+
+    ```python
+    pumpkins.isnull().sum()
+    ```
+
+    Ada data yang hilang, namun mungkin tidak akan diperlukan untuk tugas ini.
+
+3. Untuk menjadikan *dataframe* kamu lebih mudah untuk digunakan, buanglah beberapa kolom menggunakan `drop()` dan simpanlah kolom-kolom yang diperlukan saja:
+
+    ```python
+    new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
+    pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    ```
+
+### Kedua, tentukan harga rata-rata labu
+
+Pikirkan bagaimana caranya menentukan harga rata-rata sebuah labu pada bulan tertentu. Kamu akan pilih kolom apa saja untuk tugas ini? Petunjuk: kamu akan perlu 3 kolom.
+
+Solusi: Ambil rata-rata kolom `Low Price` dan `High Price` untuk mengisi kolom `Price` yang baru. Terus, konversikan kolom `Date` untuk hanya menunjukkan bulan saja. Untungnya, berdasarkan pemeriksaan di atas, tidak ada data tanggal atau harga yang hilang.
+
+1. Untuk mengkalkulasi rata-rata, tambahlah kode berikut:
+
+    ```python
+    price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
+    
+    month = pd.DatetimeIndex(pumpkins['Date']).month
+    
+    ```
+
+   ✅ Jangan ragu untuk mem-*print* data apapun yang kamu ingin periksa menggunakan `print(month)`.
+
+2. Sekarang, salinlah data yang telah dikonversi ke sebuah *dataframe* Pandas yang baru:
+
+    ```python
+    new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
+    ```
+
+    Jika *dataframe* baru ini di-*print*, kamu akan lihat sebuah *dataset* yang rapih darimana kamu bisa membangun model regresi barumu.
+
+### But wait! There's something odd here
+### Tunggu! Ada yang aneh di sini
+
+Kalau kamu lihat kolom `Package`, labu dijual dalam berbagai konfigurasi. Beberapa dijual dalam satuan '1 1/9 bushel', beberapa per labu, beberapa per pon, dan beberapa dalam dus-dus besar dengan kelebaran yang berbeda-beda.
+
+> Kelihatannya susah untuk menimbang labu secara konsisten
+
+Menggali data orisinal lebih dalam, sangatlah menarik untuk melihat apapun dengan `Unit of Sale` (satuan penjualan) yang sama dengan 'EACH' atau 'PER BIN' akan mempunyai jenis `Package` yang per inci, per bin, atau 'each'. Kelihatannya susah untuk menimbang labu secara konsisten, jadi mari memilah datanya dengan hanya memilih labu yang kolom `Package`-nya sama dengan *string* 'bushel'.
+
+1. Tambah sebuah filter di atas file tetapi dibawah impor .csv yang di awal
+
+    ```python
+    pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
+    ```
+    
+	Kalau kamu *print* datanya sekarang, kamu bisa lihat bahwa kamu hanya mendapatkan sekitar 415 baris data yang mengandung data labu per bushel.
+
+### Tunggu! Masih ada satu lagi
+
+Apa kamu sadar bahwa jumlah bushel berbeda-beda per baris? Kamu harus menormalisasi harganya supaya kamu menunjukkan harga per bushel. Gunakanlah sedikit matematika untuk menstandarisasinya.
+
+1. Tambahlah beberapa baris ini setelah blok yang membuat *dataframe* new_pumpkins:
+
+    ```python
+    new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
+    
+    new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
+    ```
+
+✅ Berdasarkan [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), berat satu bushel tergantung jenis hasil bumi sebab bushel adalah satuan volume. "Satu bushel tomat, sebagai contoh, seharusnya seberat 56 pon (25.4 kg)... Dedaunan mengambil lebih banyak ruang tetapi lebih ringan, jadi satu bushel bayam hanya seberat 20 pon (9.1 kg)" (diterjemah). Lumayan rumit ya! Kita tidak usah mengkonversi bushel ke pon saja bagaimana, jadi kita gunakan satuan harga per bushel? Namun, semua riset ini tentang bushel labu menunjukkan sebagaimana pentingnya untuk mengerti sifat datamu!
+
+Sekarang, kamu bisa meneliti harga per satuan berdasarkan hitungan bushel mereka. Jika kamu *print* datanya sekali lagi, kamu bisa lihat bagaimana telah distandarisasi.
+
+✅ Apa kamu sadar bahwa labu yang dijual per setengah bushel sangat mahal? Kira-kira mengapa ya? Petunjuk: labu kecil jauh lebih mahal daripada labu besar, mungkin karena ada lebih banyak per bushel, apalagi mengingat pula bahwa satu labu besar mempunyai rongga kosong yang besar di dalamnya.
+
+## Strategi Visualisasi
+
+Sebagian dari peran seorang *data scientist* adalah untuk mendemonstrasikan kualitas dan sifat data yang sedang digunakan. Untuk melakukan ini, mereka seringkali membuat visualisasi-visualisasi atau grafik menarik yang menunjukkan aspek-aspek berbeda tentang datanya. Dengan cara ini, mereka dapat menunjukkan hubungan-hubungan dan celah-celah secara visual. Kalau tidak secara visual, akan susah untuk menemukan pola-pola tersebut.
+
+Visualisasi juga bisa membantu menentukan teknik *machine learning* yang palingn cocok untuk datanya. Sebagai contoh, sebuah petak sebar yang kelihatannya mengikuti sebuah garis mengindikasikan bahwa data ini adalah kandidat baik untuk latihan regresi linear.
+
+Satu *library* visualisasi data yang bekerja dengan baik dalam sebuah *Jupyter notebook* adalah [Matplotlib](https://matplotlib.org/) (yang kamu juga lihat dalam pelajaran sebelumnya).
+
+> Carilah pengalaman dalam memvisualisasi data dengan [tutorial-tutorial ini](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
+
+## Latihan - sebuah experimen dengan Matplotlib
+
+Coba membuat beberapa grafik sederhana untuk menunjukkan *dataframe* baru yang baru kamu buat. Kira-kira, sebuah plot garis akan menunjukkan apa ya?
+
+1. Impor Matplotlib di atas file tetapi di bawah impor Pandas:
+
+    ```python
+    import matplotlib.pyplot as plt
+    ```
+
+2. Jalankan ulang keseluruhan *notebook*-nya.
+3. Di bagian bawah *notebook*-nya, tambahkan sebuah sel untuk menggambarkan datanya sebagai sebuah kotak.
+
+    ```python
+    price = new_pumpkins.Price
+    month = new_pumpkins.Month
+    plt.scatter(price, month)
+    plt.show()
+    ```
+
+    ![Sebuah petak sebar yang menunjukkan hubungan antara harga dan bulan](./images/scatterplot.png)
+
+    Apakah grafik ini berguna? Apa ada yang mengejutkanmu?
+
+    Sebenarnya tidak terlalu berguna karena dia hanya menunjukkan datamu sebagai sebuah penyebaran poin pada bulan tertentu.
+
+### Jadikan berguna
+
+Untuk menjadikan sebuah grafik menjadi berguna, biasanya datanya harus dikelompokkan dengan suatu cara. Kita coba membuat suatu plot di mana sumbu y menunjukkan bulan dan datanya mendemonstrasikan distribusi data, yuk!
+
+1. Tambah sebuah sel untuk membuat sebuah diagram batang berkelompok:
+
+    ```python
+    new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
+    plt.ylabel("Pumpkin Price")
+    ```
+
+    ![Sebuah diagram batang yang menunjukkan hubungan antara harga dan bulan](./images/barchart.png)
+
+    Nah, ini lebih berguna! Kelihatannya visualisasi ini mengindikasi bahwa labu itu paling mahal pada bulan September dan Oktober. Apa itu sesuai ekspektasimu? Mengapa?
+
+---
+
+## 🚀Tantangan
+
+Jelajahi jenis-jenis visualisasi yang beda dan yang disediakan Matplotlib. Jenis mana yang paling cocok untuk kasus regresi?
+
+## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+
+## Review & Pembelajaran Mandiri
+
+Lihatlah beragam cara memvisualisasi data. Buatlah sebuah daftar dari aneka *library* yang tersedia dan catatlah yang mana yang paling baik untuk jenis-jenis tugas tertentu. Sebagai contoh, bagaimana dengan visualisasi 2D vs. 3D? Apa yang kamu temukan?
+
+## Tugas
+
+[Menjelajahi visualisasi](assignment.md)

From 8665a5313e40a18ebe5f62e8382d85e827fbed9f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tomomi=20=E2=9D=A4=20Imura?= <tomomi.i@gmail.com>
Date: Tue, 6 Jul 2021 07:52:08 -1000
Subject: [PATCH 041/368] Add LICENSE.md for the full info

---
 sketchnotes/LICENSE.md | 427 +++++++++++++++++++++++++++++++++++++++++
 sketchnotes/README.md  |   3 +-
 2 files changed, 428 insertions(+), 2 deletions(-)
 create mode 100644 sketchnotes/LICENSE.md

diff --git a/sketchnotes/LICENSE.md b/sketchnotes/LICENSE.md
new file mode 100644
index 0000000000..3b7b82d0da
--- /dev/null
+++ b/sketchnotes/LICENSE.md
@@ -0,0 +1,427 @@
+Attribution-ShareAlike 4.0 International
+
+=======================================================================
+
+Creative Commons Corporation ("Creative Commons") is not a law firm and
+does not provide legal services or legal advice. Distribution of
+Creative Commons public licenses does not create a lawyer-client or
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diff --git a/sketchnotes/README.md b/sketchnotes/README.md
index 30eec9b505..a227b22b8b 100644
--- a/sketchnotes/README.md
+++ b/sketchnotes/README.md
@@ -4,5 +4,4 @@ All the curriculum's sketchnotes can be downloaded here.
 
 🎨 Created by: [Tomomi Imura](https://github.com/girliemac) (Twitter: [@girlie_mac](https://twitter.com/girlie_mac))
 
----
-⚖� License: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+[![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]

From 1a44131eb93babf8ef8dcb82fef85a149f3537e8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tomomi=20=E2=9D=A4=20Imura?= <tomomi.i@gmail.com>
Date: Tue, 6 Jul 2021 11:01:22 -0700
Subject: [PATCH 042/368] Fix license badge link

---
 sketchnotes/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sketchnotes/README.md b/sketchnotes/README.md
index a227b22b8b..c73a735597 100644
--- a/sketchnotes/README.md
+++ b/sketchnotes/README.md
@@ -4,4 +4,4 @@ All the curriculum's sketchnotes can be downloaded here.
 
 🎨 Created by: [Tomomi Imura](https://github.com/girliemac) (Twitter: [@girlie_mac](https://twitter.com/girlie_mac))
 
-[![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa]
+[![CC BY-SA 4.0](https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-sa/4.0/)

From cd33b7d8a75fc9b8ff025bfbf2baf15a0088b96c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tomomi=20=E2=9D=A4=20Imura?= <tomomi.i@gmail.com>
Date: Tue, 6 Jul 2021 16:55:28 -0700
Subject: [PATCH 043/368] fix image path

---
 1-Introduction/translations/README.ja.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/translations/README.ja.md b/1-Introduction/translations/README.ja.md
index 83f0ba6cb4..04127043cc 100644
--- a/1-Introduction/translations/README.ja.md
+++ b/1-Introduction/translations/README.ja.md
@@ -2,7 +2,7 @@
 
 ��セクション���機械学習�分野�基礎��る概念�機械学習��何�を紹介����歴�や研究者�機械学習を扱�際�使用�る技術����学���。 新��ML�世界を一緒�探求������ょ��
 
-![地�](images/globe.jpg)
+![地�](../images/globe.jpg)
 > <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>�<a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a>�よる写真
   
 ### Lessons
@@ -19,4 +19,4 @@
 
 "公平性�機械学習"�[Tomomi Imura](https://twitter.com/girliemac) �よ��制作�れ���。
 
-"機械学習�技術"�[Jen Looper](https://twitter.com/jenlooper)�[Chris Noring](https://twitter.com/softchris) �よ��制作�れ���。
\ No newline at end of file
+"機械学習�技術"�[Jen Looper](https://twitter.com/jenlooper)�[Chris Noring](https://twitter.com/softchris) �よ��制作�れ���。

From 610b04038814b5eeda06087347607870f4ca867d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tomomi=20=E2=9D=A4=20Imura?= <tomomi.i@gmail.com>
Date: Tue, 6 Jul 2021 17:00:57 -0700
Subject: [PATCH 044/368] Fix links

---
 1-Introduction/translations/README.ja.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/1-Introduction/translations/README.ja.md b/1-Introduction/translations/README.ja.md
index 04127043cc..f2f64c2c63 100644
--- a/1-Introduction/translations/README.ja.md
+++ b/1-Introduction/translations/README.ja.md
@@ -7,10 +7,10 @@
   
 ### Lessons
 
-1. [機械学習��導入](1-intro-to-ML/README.md)
-1. [機械学習�AI�歴�](2-history-of-ML/README.md)
-1. [機械学習���る公平�](3-fairness/README.md)
-1. [機械学習�技術](4-techniques-of-ML/README.md)
+1. [機械学習��導入](../1-intro-to-ML/translations/README.ja.md)
+1. [機械学習�AI�歴�](../2-history-of-ML/translations/README.ja.md)
+1. [機械学習���る公平�](../3-fairness/translations/README.ja.md)
+1. [機械学習�技術](../4-techniques-of-ML/translations/README.ja.md)
 ### Credits
 
 "機械学習��導入 "��[Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan)�[Ornella Altunyan](https://twitter.com/ornelladotcom)�[Jen Looper](https://twitter.com/jenlooper)����ーム�よ��制作�れ���。

From ccba26a7ce35d5600946a56012fa3115d106a068 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tomomi=20=E2=9D=A4=20Imura?= <tomomi.i@gmail.com>
Date: Tue, 6 Jul 2021 17:11:31 -0700
Subject: [PATCH 045/368] Update README.ja.md

---
 1-Introduction/3-fairness/translations/README.ja.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
index 3ac9418c81..e844835909 100644
--- a/1-Introduction/3-fairness/translations/README.ja.md
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -82,7 +82,7 @@ AIや機械学習���る公平性��証���然���複雑�社
 異常�画�検索��果����題�良�例��。エンジニアやCEO���男性�女性�割������れ以上��業�画�を検索�る����ら��性別�大������果�表示�れる��注��必���。
 
 ![Bing�CEO�検索](../images/ceos.png)
-> This search on Bing for 'CEO' produces pretty inclusive results
+> Bing ��「CEO��検索�果�包摂的��果�表示�れ����
 
 �れら�5��主��タイプ��題��相互�排他的�も������1��システム�複数�タイプ�害を示���も�り��。�ら���れ�れ�ケース������大性�異�り��。例����る人��当�犯罪者�レッテルを貼る����画�を誤��表示�る��よりも�る��深刻��題��。����比較的深刻����被害����も�人々�疎外感を感��り�特別視�れ��る�感��り�る����り���累�的�影響��常�抑圧的�も���り�る��を覚�����������ょ�。
  

From 32f520a10c73969325784ad14d3dc419ba4ebeb3 Mon Sep 17 00:00:00 2001
From: zhanghai <zhanghai@wuhanins.com>
Date: Wed, 7 Jul 2021 13:10:42 +0800
Subject: [PATCH 046/368] translate

---
 .../translations/README.zh-cn.md              | 120 +++++++++---------
 1 file changed, 61 insertions(+), 59 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index ec42fe705e..7656259dc1 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -1,105 +1,107 @@
-# Techniques of Machine Learning
+# 机器学习技术
 
-The process of building, using, and maintaining machine learning models and the data they use is a very different process from many other development workflows. In this lesson, we will demystify the process, and outline the main techniques you need to know. You will:
+构建�使用和维护机器学习模型�其使用的数�的过程与许多其他开�工作�程截然��。 在本课中，我们将�开该过程的神秘�纱，并概述您需�了解的主�技术。 您会： 
 
-- Understand the processes underpinning machine learning at a high level.
-- Explore base concepts such as 'models', 'predictions', and 'training data'.
+- 在高层次上�解支�机器学习的过程。 
+- 探索基本概念，例如“模型��“预测�和“训练数��。 
   
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
-## Introduction
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## 介�
 
-On a high level, the craft of creating machine learning (ML) processes is comprised of a number of steps:
+在较高的层次上，创建机器学习（ML）过程的工艺包括许多步骤：
 
-1. **Decide on the question**. Most ML processes start by asking a question that cannot be answered by a simple conditional program or rules-based engine. These questions often revolve around predictions based on a collection of data.
-2. **Collect and prepare data**. To be able to answer your question, you need data. The quality and, sometimes, quantity of your data will determine how well you can answer your initial question. Visualizing data is an important aspect of this phase. This phase also includes splitting the data into a training and testing group to build a model.
-3. **Choose a training method**. Depending on your question and the nature of your data, you need to choose how you want to train a model to best reflect your data and make accurate predictions against it. This is the part of your ML process that requires specific expertise and, often, a considerable amount of experimentation.
-4. **Train the model**. Using your training data, you'll use various algorithms to train a model to recognize patterns in the data. The model might leverage internal weights that can be adjusted to privilege certain parts of the data over others to build a better model.
-5. **Evaluate the model**. You use never before seen data (your testing data) from your collected set to see how the model is performing.
-6. **Parameter tuning**. Based on the performance of your model, you can redo the process using different parameters, or variables, that control the behavior of the algorithms used to train the model.
-7. **Predict**. Use new inputs to test the accuracy of your model.
+1. **决定问题**。 大多数机器学习过程都是从�出一个简�的�件程�或基于规则的引擎无法回答的问题开始的。 这些问题通常围绕基于数�集�的预测展开。 
+2. **收集和准备数�**。为了能够回答您的问题，您需�数�。数�的质�（有时是数�）将决定您回答最�问题的能力。�视化数�是这个阶段的一个��方�。此阶段还包括将数�拆分为训练和测试组以构建模型。 
+3. **选择一�训练方法**。根�您的问题和数�的性质，您需�选择如何训练模型以最好地�映您的数�并对其进行准确预测。这是您的ML过程的一部分，需�特定的专业知识，并且通常需�大�的实验。 
+4. **训练模型**。使用您的训练数�，您将使用��算法�训练模型以识别数�中的模�。该模型�能会利用�以调整的内部���使数�的�些部分优于其他部分，从而构建更好的模型。 
+5. **评估模型**。您使用收集到的集�中从未�过的数�（您的测试数�）�查看模型的性能。 
+6. **�数调整**。根�模型的性能，您�以使用��的�数或����该过程，这些�数或��控制用于训练模型的算法的行为。 
+7. **预测**。使用新输入�测试模型的准确性。 
 
-## What question to ask
+## �问什么问题 
 
-Computers are particularly skilled at discovering hidden patterns in data. This utility is very helpful for researchers who have questions about a given domain that cannot be easily answered by creating a conditionally-based rules engine. Given an actuarial task, for example, a data scientist might be able to construct handcrafted rules around the mortality of smokers vs non-smokers.
+计算机特别擅长�现数�中的��模�。此实用程�对于对给定领域有疑问的研究人员�常有帮助，这些问题无法通过创建基于�件的规则引擎�轻�回答。例如，给定一项精算任务，数�科学家�能能够围绕�烟者与��烟者的死亡率构建手工规则。 
 
-When many other variables are brought into the equation, however, a ML model might prove more efficient to predict future mortality rates based on past health history. A more cheerful example might be making weather predictions for the month of April in a given location based on data that includes latitude, longitude, climate change, proximity to the ocean, patterns of the jet stream, and more.
+然而，当将许多其他��纳入等�时，ML模型�能会更有效地根�过去的�康�预测未�的死亡率。一个更令人愉快的例��能是根�包括纬度��度�气候�化�与海洋的接近程度�急�模�等在内的数�对给定�置的4月份进行天气预报。 
 
-✅ This [slide deck](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) on weather models offers a historical perspective for using ML in weather analysis.  
+✅ 这个关于天气模型的[幻�片](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf)为在天气分�中使用机器学习�供了一个历�视角。
 
-## Pre-building tasks
+## 预构建任务
 
-Before starting to build your model, there are several tasks you need to complete. To test your question and form a hypothesis based on a model's predictions, you need to identify and configure several elements.
+在开始构建模型之�，您需�完�多项任务。�测试您的问题并根�模型的预测形��设，您需�识别和�置多个元素。 
 
 ### Data
 
-To be able to answer your question with any kind of certainty, you need a good amount of data of the right type. There are two things you need to do at this point:
+为了能够确定地回答您的问题，您需�大�正确类型的数�。 此时您需��两件事： 
 
-- **Collect data**. Keeping in mind the previous lesson on fairness in data analysis, collect your data with care. Be aware of the sources of this data, any inherent biases it might have, and document its origin.
-- **Prepare data**. There are several steps in the data preparation process. You might need to collate data and normalize it if it comes from diverse sources. You can improve the data's quality and quantity through various methods such as converting strings to numbers (as we do in [Clustering](../../5-Clustering/1-Visualize/README.md)). You might also generate new data, based on the original (as we do in [Classification](../../4-Classification/1-Introduction/README.md)). You can clean and edit the data (as we did prior to the [Web App](../3-Web-App/README.md) lesson). Finally, you might also need to randomize it and shuffle it, depending on your training techniques.
+- **收集数�**。记�之�关于数�分�公平性的课程，�心收集数�。请注�此数�的���它�能具有的任何固有��，并记录其��。 
+- **准备数�**。数�准备过程有几个步骤。如果数��自��的��，您�能需�整�数�并对其进行标准化。您�以通过��方法�高数�的质�和数�，例如将字符串转�为数字（就�我们在[�类](../../5-Clustering/1-Visualize/README.md)中所�的那样）。您还�以根�原始数�生�新数�（正如我们在[分类](../../4-Classification/1-Introduction/README.md)中所�的那样）。您�以清�和编辑数�（就�我们在 [Web App](../3-Web-App/README.md)课程之�所�的那样）。最�，您�能还需�对其进行�机化和打乱，具体�决于您的训练技术。
 
-✅ After collecting and processing your data, take a moment to see if its shape will allow you to address your intended question. It may be that the data will not perform well in your given task, as we discover in our [Clustering](../../5-Clustering/1-Visualize/README.md) lessons!
+✅ 在收集和处�您的数��，花点时间看看它的形状是�能让您解决您的预期问题。正如我们在[�类](../../5-Clustering/1-Visualize/README.md)课程中�现的那样，数��能在您的给定任务中表现�佳�
 
-### Selecting your feature variable
+### 选择特���
 
-A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**?
+[特�](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection)是数�的�衡�属性。在许多数�集中，它表示为列标题，如“日期��“大��或“颜色�。您的特���（通常在代�中表示为`y`）代表您试图对数��出的问题的答案：在12月，哪�**颜色**的�瓜最便宜？在旧金山，哪些街区的房地产**价格**最好？
 
-🎓 **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." ([source](https://wikipedia.org/wiki/Feature_selection))
-### Visualize your data
+🎓 **特�选择和特���** 构建模型时如何知�选择哪个��？您�能会�历一个特�选择或特���的过程，以便为性能最好的模型选择正确的��。然而，它们�是一回事：“特���是从基于原始特�的函数中创建新特�，而特�选择返回特�的一个�集。�（[��](https://wikipedia.org/wiki/Feature_selection)）
+### �视化数�
 
-An important aspect of the data scientist's toolkit is the power to  visualize data using several excellent libraries such as Seaborn or MatPlotLib. Representing your data visually might allow you to uncover hidden correlations that you can leverage. Your visualizations might also help you to uncover bias or unbalanced data (as we discover in [Classification](../../4-Classification/2-Classifiers-1/README.md)).
-### Split your dataset
+数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示您的数��能会让您�现�以利用的��关�。 您的�视化还�以帮助您�现��或�平衡的数�（正如我们在 [分类](../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
+### 拆分数�集
 
-Prior to training, you need to split your dataset into two or more parts of unequal size that still represent the data well.
+在训练之�，您需�将数�集拆分为两个或多个大��等但�能很好地代表数�的部分。
 
-- **Training**. This part of the dataset is fit to your model to train it. This set constitutes the majority of the original dataset.
-- **Testing**. A test dataset is an independent group of data, often gathered from the original data, that you use to confirm the performance of the built model.
-- **Validating**. A validation set is a smaller independent group of examples that you use to tune the model's hyperparameters, or architecture, to improve the model. Depending on your data's size and the question you are asking, you might not need to build this third set (as we note in [Time Series Forecasting](../7-TimeSeries/1-Introduction/README.md)).
+- **训练**。这部分数�集适�您的模型进行训练。这个集�构�了原始数�集的大部分。
+- **测试**。测试数�集是一组独立的数�，通常从原始数�中收集，用于确认构建模型的性能。
+- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�您的数�大�和您�出的问题，您�能�需�构建第三组（正如我们在[时间�列预测](../7-TimeSeries/1-Introduction/README.md)中所述）。 
 
-## Building a model
+## 建立模型 
 
-Using your training data, your goal is to build a model, or a statistical representation of your data, using various algorithms to **train** it. Training a model exposes it to data and allows it to make assumptions about perceived patterns it discovers, validates, and accepts or rejects.
+使用您的训练数�，您的目标是构建模型或数�的统计表示，并使用��算法对其进行**训练**。训练模型将其暴露给数�，并�许它对其�现�验�和接�或拒�的感知模��出�设。 
 
-### Decide on a training method
+### 决定一�训练方法
 
-Depending on your question and the nature of your data, your will choose a method to train it. Stepping through [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html) - which we use in this course - you can explore many ways to train a model. Depending on your experience, you might have to try several different methods to build the best model. You are likely to go through a process whereby data scientists evaluate the performance of a model by feeding it unseen data, checking for accuracy, bias, and other quality-degrading issues, and selecting the most appropriate training method for the task at hand.
-### Train a model
+根�您的问题和数�的性质，您将选择一�方法�训练它。�步完� [Scikit-learn的文档](https://scikit-learn.org/stable/user_guide.html) - 我们在本课程中使用 - 您�以探索多�训练模型的方法。 根�您的�验，您�能需��试多���的方法�构建最佳模型。您�能会�历一个过程，在该过程中，数�科学家通过�供看��的数��评估模型的性能，检查准确性��差和其他�低质�的问题，并为手头的任务选择最�适的训练方法。
 
-Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your data as an array of values (usually 'X') and a feature variable (usually 'y').
-### Evaluate the model
+### 训练模型
 
-Once the training process is complete (it can take many iterations, or 'epochs', to train a large model), you will be able to evaluate the model's quality by using test data to gauge its performance. This data is a subset of the original data that the model has not previously analyzed. You can print out a table of metrics about your model's quality.
+有了您的训练数�，您就�以“拟��它以创建模型。您会注�到，在许多ML库中，您会找到代�'model.fit'——此时您将数�作为值数组（通常为'X'）和特���（通常为'y'）��）。 
 
-🎓 **Model fitting**
+### 评估模型 
 
-In the context of machine learning, model fitting refers to the accuracy of the model's underlying function as it attempts to analyze data with which it is not familiar.
+训练过程完��（训练大型模型�能需�多次迭代或“时期�），您将能够通过使用测试数��衡�模型的性能�评估模型的质�。此数�是模型先�未分�的原始数�的�集。 您�以打�出有关模型质�的指标表。 
 
-🎓 **Underfitting** and **overfitting** are common problems that degrade the quality of the model, as the model fits either not well enough or too well. This causes the model to make predictions either too closely aligned or too loosely aligned with its training data. An overfit model predicts training data too well because it has learned the data's details and noise too well. An underfit model is not accurate as it can neither accurately analyze its training data nor data it has not yet 'seen'.
+🎓 **模型拟� **
 
-![overfitting model](images/overfitting.png)
-> Infographic by [Jen Looper](https://twitter.com/jenlooper)
+在机器学习的背景下，模型拟�是指模型在�试分��熟悉的数�时其底层功能的准确性。 
 
-## Parameter tuning
+🎓 **欠拟�**和**过拟�**是�低模型质�的常�问题，因为模型拟�得�够好或太好。这会导致模型�出与其训练数�过于紧密对�或过于�散对�的预测。 过拟�模型对训练数�的预测太好，因为它已�很好地了解了数�的细节和噪声。欠拟�模型并�准确，因为它既�能准确分�其训练数�，也�能准确分�尚未“看到�的数�。
 
-Once your initial training is complete, observe the quality of the model and consider improving it by tweaking its 'hyperparameters'. Read more about the process [in the documentation](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+![过拟�模型 ](images/overfitting.png)
+> 作者[Jen Looper](https://twitter.com/jenlooper)
 
-## Prediction
+## �数调优 
 
-This is the moment where you can use completely new data to test your model's accuracy. In an 'applied' ML setting, where you are building web assets to use the model in production, this process might involve gathering user input (a button press, for example) to set a variable and send it to the model for inference, or evaluation.
+�始训练完��，观察模型的质�并考虑通过调整其“超�数��改进它。[在此文档中](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa)阅读有关该过程的更多信�。
 
-In these lessons, you will discover how to use these steps to prepare, build, test, evaluate, and predict - all the gestures of a data scientist and more, as you progress in your journey to become a 'full stack' ML engineer.
+## 预测
+
+这是您�以使用全新数��测试模型准确性的时刻。在“应用�ML设置中，您正在构建Web资�以在生产中使用模型，此过程�能涉�收集用户输入（例如按下按钮）以设置��并将其��到模型进行推�，或者评估。
+
+在这些课程中，您将了解如何使用这些步骤�准备�构建�测试�评估和预测—所有这些都是数�科学家的姿�，而且��您在�为一�“全栈�ML工程师的旅程中�得进展，您将了解更多。 
 
 ---
 
-## 🚀Challenge
+## 🚀挑战
 
-Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you?
+画一个�程图，�映ML的步骤。在这个过程中，您认为自己现在在哪里？您预测您在哪里会�到困难？什么对您�说很容易？ 
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [阅读�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
 
-## Review & Self Study
+## �习与自学 
 
-Search online for interviews with data scientists who discuss their daily work. Here is [one](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+在线�索对讨论日常工作的数�科学家的采访。 这是[其中之一](https://www.youtube.com/watch?v=Z3IjgbbCEfs)。
 
-## Assignment
+## 任务
 
-[Interview a data scientist](assignment.md)
+[采访一�数�科学家](assignment.md)

From ec04381b37cb87ec537383190723223460c475ff Mon Sep 17 00:00:00 2001
From: Ted <45612704+tddschn@users.noreply.github.com>
Date: Wed, 7 Jul 2021 17:44:05 +0800
Subject: [PATCH 047/368] Fix csv file path in the code snippet

---
 2-Regression/2-Data/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 479ff99250..2c7f23adcd 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -52,7 +52,7 @@ Open the _notebook.ipynb_ file in Visual Studio Code and import the spreadsheet
 
     ```python
     import pandas as pd
-    pumpkins = pd.read_csv('../../data/US-pumpkins.csv')
+    pumpkins = pd.read_csv('../data/US-pumpkins.csv')
     pumpkins.head()
     ```
 

From 39835af2d4d92e4c09df57be571ae18a450ed814 Mon Sep 17 00:00:00 2001
From: labFareb <68812909+labFareb@users.noreply.github.com>
Date: Wed, 7 Jul 2021 15:55:19 +0530
Subject: [PATCH 048/368] Fix typo in broken link

---
 1-Introduction/1-intro-to-ML/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md
index 8c1e30d080..e18a003617 100644
--- a/1-Introduction/1-intro-to-ML/README.md
+++ b/1-Introduction/1-intro-to-ML/README.md
@@ -21,7 +21,7 @@ Before starting with this curriculum, you need to have your computer set up and
 - **Learn Python**. It's also recommended to have a basic understanding of [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), a programming language useful for data scientists that we use in this course.
 - **Learn Node.js and JavaScript**. We also use JavaScript a few times in this course when building web apps, so you will need to have [node](https://nodejs.org) and [npm](https://www.npmjs.com/) installed, as well as [Visual Studio Code](https://code.visualstudio.com/) available for both Python and JavaScript development.
 - **Create a GitHub account**. Since you found us here on [GitHub](https://github.com), you might already have an account, but if not, create one and then fork this curriculum to use on your own. (Feel free to give us a star, too 😊)
-- **Explore Scikit-learn**. Familiarize yourself with [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html), a set of ML libraries that we reference in these lessons.
+- **Explore Scikit-learn**. Familiarize yourself with [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), a set of ML libraries that we reference in these lessons.
 
 ### What is machine learning?
 

From 94e846920dd5fb8959fcde730a7f2874bfcbe830 Mon Sep 17 00:00:00 2001
From: Vishvanathan K <vishvanathank@outlook.in>
Date: Wed, 7 Jul 2021 17:51:40 +0530
Subject: [PATCH 049/368] Changed .pk1 to .pkl

---
 3-Web-App/1-Web-App/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index f848d80786..6150aece78 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -167,7 +167,7 @@ Now you can build a Flask app to call your model and return similar results, but
         css/
         templates/
     notebook.ipynb
-    ufo-model.pk1
+    ufo-model.pkl
     ``` 
 
    ✅ Refer to the solution folder for a view of the finished app

From b8d2284b65bf98045cc857aea5e5713593081baf Mon Sep 17 00:00:00 2001
From: Vishvanathan K <vishvanathank@outlook.in>
Date: Wed, 7 Jul 2021 18:11:55 +0530
Subject: [PATCH 050/368] Change pip to !pip

---
 4-Classification/4-Applied/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index 0e20f6c37f..773271a1e4 100644
--- a/4-Classification/4-Applied/README.md
+++ b/4-Classification/4-Applied/README.md
@@ -31,7 +31,7 @@ First, train a classification model using the cleaned cuisines dataset we used.
 1. Start by importing useful libraries:
 
     ```python
-    pip install skl2onnx
+    !pip install skl2onnx
     import pandas as pd 
     ```
 

From c6e484bc37db40031cdf6a9b8d44493d9df6ab5f Mon Sep 17 00:00:00 2001
From: Vishvanathan K <vishvanathank@outlook.in>
Date: Wed, 7 Jul 2021 18:21:43 +0530
Subject: [PATCH 051/368] change !pip to pip

---
 5-Clustering/1-Visualize/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md
index 12ac7c7b11..8453c45129 100644
--- a/5-Clustering/1-Visualize/README.md
+++ b/5-Clustering/1-Visualize/README.md
@@ -104,7 +104,7 @@ Clustering as a technique is greatly aided by proper visualization, so let's get
 1. Import the `Seaborn` package for good data visualization.
 
     ```python
-    pip install seaborn
+    !pip install seaborn
     ```
 
 1. Append the song data from _nigerian-songs.csv_. Load up a dataframe with some data about the songs. Get ready to explore this data by importing the libraries and dumping out the data:

From 57ff3626e5f7929399f1242b44ae67d947d7e9cb Mon Sep 17 00:00:00 2001
From: Vishvanathan K <vishvanathank@outlook.in>
Date: Wed, 7 Jul 2021 19:38:40 +0530
Subject: [PATCH 052/368] changed to !pip

---
 4-Classification/4-Applied/solution/notebook.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/4-Classification/4-Applied/solution/notebook.ipynb b/4-Classification/4-Applied/solution/notebook.ipynb
index b96820489a..5ed9da5203 100644
--- a/4-Classification/4-Applied/solution/notebook.ipynb
+++ b/4-Classification/4-Applied/solution/notebook.ipynb
@@ -64,7 +64,7 @@
     }
    ],
    "source": [
-    "pip install skl2onnx"
+    "!pip install skl2onnx"
    ]
   },
   {

From adf07f636f8ac65e10078d480c776af3e9b490b8 Mon Sep 17 00:00:00 2001
From: Vishvanathan K <vishvanathank@outlook.in>
Date: Wed, 7 Jul 2021 19:51:14 +0530
Subject: [PATCH 053/368] Changed pip to !pip

---
 5-Clustering/1-Visualize/solution/notebook.ipynb | 2 +-
 7-TimeSeries/2-ARIMA/solution/notebook.ipynb     | 5 ++---
 2 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/5-Clustering/1-Visualize/solution/notebook.ipynb b/5-Clustering/1-Visualize/solution/notebook.ipynb
index 1542549cec..44c15495e8 100644
--- a/5-Clustering/1-Visualize/solution/notebook.ipynb
+++ b/5-Clustering/1-Visualize/solution/notebook.ipynb
@@ -61,7 +61,7 @@
     }
    ],
    "source": [
-    "pip install seaborn"
+    "!pip install seaborn"
    ]
   },
   {
diff --git a/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
index 62ebccaafe..4914ab705f 100644
--- a/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
+++ b/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -41,7 +41,7 @@
     }
    ],
    "source": [
-    "pip install statsmodels"
+    "!pip install statsmodels"
    ]
   },
   {
@@ -354,7 +354,6 @@
     "print(results.summary())\n"
    ]
   },
-
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -741,4 +740,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file

From c1b470ce6405a8d752c766c10db7a9546d15f0a5 Mon Sep 17 00:00:00 2001
From: Vishvanathan K <vishvanathank@outlook.in>
Date: Wed, 7 Jul 2021 19:57:09 +0530
Subject: [PATCH 054/368] Change the code to match the style in repo

---
 8-Reinforcement/2-Gym/solution/notebook.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/8-Reinforcement/2-Gym/solution/notebook.ipynb b/8-Reinforcement/2-Gym/solution/notebook.ipynb
index 49e1c43eda..b6338c8962 100644
--- a/8-Reinforcement/2-Gym/solution/notebook.ipynb
+++ b/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -57,7 +57,7 @@
    ],
    "source": [
     "import sys\n",
-    "!{sys.executable} -m pip install gym \n",
+    "!pip install gym \n",
     "\n",
     "import gym\n",
     "import matplotlib.pyplot as plt\n",

From 15b34964e75e228accf1a8155744c0e9fc1344f4 Mon Sep 17 00:00:00 2001
From: iBlueDust <35223375+iBlueDust@users.noreply.github.com>
Date: Wed, 7 Jul 2021 22:40:35 +0700
Subject: [PATCH 055/368] Fix Indonesian translation of 'datapoint'

---
 2-Regression/1-Tools/translations/README.id.md | 4 ++--
 2-Regression/translations/README.id.md         | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
index b29301e2a4..4345460ab3 100644
--- a/2-Regression/1-Tools/translations/README.id.md
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -185,9 +185,9 @@ Dalam sebuah sel kode yang baru, muatkan *dataset* diabetes dengan memanggil `lo
     plt.show()
     ```
 
-   ![sebuah petak sebar yang menunjukkan poin-poin data sekitar diabetes](./images/scatterplot.png)
+   ![sebuah petak sebar yang menunjukkan titik-titik data sekitar diabetes](./images/scatterplot.png)
 
-   ✅ Pikirkan sejenak tentang apa yang sedang terjadi di sini. Sebuah garis lurus membentang tengah-tengah titik-titik kecil data. Tetapi apa yang sedang garis itu lakukan? Apa kamu bisa melihat bagaimana kamu bisa menggunakan garis ini untuk memprediksi di mana sebuah data poin baru yang tidak pernah dilihat sebelumnya kemungkinan besar akan terletak berhubungan dengan sumbu y grafik ini? Coba jelaskan dalam kata-kata kegunaan praktis model ini.
+   ✅ Pikirkan sejenak tentang apa yang sedang terjadi di sini. Sebuah garis lurus membentang tengah-tengah titik-titik kecil data. Tetapi apa yang sedang garis itu lakukan? Apa kamu bisa melihat bagaimana kamu bisa menggunakan garis ini untuk memprediksi di mana sebuah titik data baru yang tidak pernah dilihat sebelumnya kemungkinan besar akan terletak berhubungan dengan sumbu y grafik ini? Coba jelaskan dalam kata-kata kegunaan praktis model ini.
 
 Selamat, kamu telah membangun model regresi linear pertamamu, membuat sebuah prediksi darinya, dan menunjukkannya dalam sebuah grafik!
 
diff --git a/2-Regression/translations/README.id.md b/2-Regression/translations/README.id.md
index a1085fc84e..6b228c1474 100644
--- a/2-Regression/translations/README.id.md
+++ b/2-Regression/translations/README.id.md
@@ -8,7 +8,7 @@ Di Amerika Utara, labu seringkali diukir menjadi muka-muka seram untuk Halloween
   
 ## Apa yang kamu akan pelajari
 
-Pelajaran-pelajaran dalam seksi ini mencakupi jenis-jenis regresi dalam konteks *machine learning*. Model regresi dapat membantu menentukan _hubungan_ antara variabel-variabel. Model jenis ini dapat memprediksi nilai-nilai seperti panjang, temperatur, atau usia, sehingga mengemukakan hubungan-hubungan antara variabel dengan menganalisis poin-poin data.
+Pelajaran-pelajaran dalam seksi ini mencakupi jenis-jenis regresi dalam konteks *machine learning*. Model regresi dapat membantu menentukan _hubungan_ antara variabel-variabel. Model jenis ini dapat memprediksi nilai-nilai seperti panjang, temperatur, atau usia, sehingga mengemukakan hubungan-hubungan antara variabel dengan menganalisis titik-titik data.
 
 Dalam seri pelajaran ini, kamu akan menemukan perbedaan antara regresi linear dan logistik, dan kapan untuk menggunakan satu atau yang lainnya.
 

From 8035013f364da7a7c031e10bf74dfdea3235fd81 Mon Sep 17 00:00:00 2001
From: iBlueDust <35223375+iBlueDust@users.noreply.github.com>
Date: Wed, 7 Jul 2021 23:53:47 +0700
Subject: [PATCH 056/368] Add Indonesian translation for 2-Regression/3-Linear

---
 .../3-Linear/translations/README.id.md        | 335 ++++++++++++++++++
 1 file changed, 335 insertions(+)
 create mode 100644 2-Regression/3-Linear/translations/README.id.md

diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md
new file mode 100644
index 0000000000..548d2030a3
--- /dev/null
+++ b/2-Regression/3-Linear/translations/README.id.md
@@ -0,0 +1,335 @@
+# Bangun sebuah model regresi dengan Scikit-learn: regresi dua arah
+
+![Infografik regresi linear vs polinomial](./images/linear-polynomial.png)
+> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
+## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+### Pembukaan
+
+Selama ini kamu telah menjelajahi apa regresi itu dengan data contoh yang dikumpulkan dari *dataset* harga labu yang kita akan gunakan terus sepanjang pelajaran ini. Kamu juga telah memvisualisasikannya dengan Matplotlib.
+
+Sekarang kamu sudah siap terjun ke dalam regresi untuk ML. Dalam pelajaran ini, kamu akan belajar lebih tentang dua jenis regresi: _regresi linear sederhana_ dan _regresi polinomial_ serta sedikit matematika yang mendasari kedua teknik ini.
+
+> Sepanjang kurikulum ini, kami mengasumsi kamu punya pengetahuan matematika yang minim dan ingin tetap membuat pelajaran ini terjangkau bagi murid-murid dari bidang-bidang lain. Jadi perhatikan catatan, 🧮 info, diagram, dan alat-alat belajar lainnya untuk membantu pemahaman.
+
+### Prasyarat
+
+Kamu harusnya sudah terbiasa sekarang dengan struktur data labu yang kita sedang teliti. Datanya harusnya sudah dimuat dan dibersihkan dalam file _notebook.ipynb_ pelajaran ini. Dalam file ini, harga labu ditampilkan per bushel dalam *dataframe* yang bari. Pastikan kamu bisa menjalankan *notebook-notebook* ini di *kernels* di Visual Studio Code.
+
+### Persiapan
+
+Ingat, kamu sedang memuat data ini untuk menanyakan pertanyaan darinya.
+
+- Kapankah waktu terbaik untuk membeli labu?
+- Saya kira-kira harus bayar berapa untuk satu kotak labu mini?
+- Apa saya sebaiknya membelinya dalam keranjang-keranjang setengah bushel atau kardus-kardus 1 1/9 bushel?
+Ayo terjun lebih lagi ke dalam data ini.
+
+Dalam pelajaran sebelumnya, kamu membuat sebuah *dataframe* Pandas, mengisinya dengan sebagian *dataset* orisinal, dan menstandarisasi harganya per bushel. Tetapi, dengan begitu, kamu hanya dapat mengumpul sekitar 400 titik data dan itupun hanya untuk bulan-bulan musim gugur.
+
+Lihatlah data yang kita sudah muat dalam *notebook* yang terlampir pelajaran ini. Data telah di muat dan sebuah petak sebar inisial telah digambar untuk menunjukkan data per bulan. Mungkin kita bisa dapat lebih banyak detail tentang sifat datanya dengan membersih-bersihkannya lebih lagi.
+
+## Sebuah garis regresi linear
+
+Seperti yang kamu telah belajar dalam Pelajaran 1, tujuan sebuah latihan regresi linear adalah untuk dapat menggambar sebuah garis untuk:
+
+- **Menunjukkan hubungan antar-variabel**. Menunjukkan hubungan antara variabel-variabel.
+- **Membuat prediksi**. Membuat prediksi akurat tentang di mana sebuah titik data baru akan terletak berhubungan dengan garis tersebut.
+ 
+Dalam kasus **Regresi Kuadrat Terkecil (_Least-Squares Regression_)**, biasanya garis seperti ini digambar. Istilah 'kuadrat terkecil' berarti semua titik data yang mengitari garis regresi dikuadratkan dan dijumlahkan. Secara ideal, harusnya jumlah akhirnya sekecil mungkin, karena kita ingin kesalahan (error) terkecil, alias `kuadrat terkecil`.
+
+Kita melakukan itu sebab kita ingin memodelkan sebuah garis yang jarak kumulatifnya dari semua titik data itu sekecil mungkin. Kita juga mengkuadratkan setiap suku sebelum dijumlahkan karena kita fokus pada besarannya daripada arahnya.
+
+> **🧮 Tunjukkan matematikanya kepadaku** 
+> 
+> Garis ini, dipanggil _garis yang paling cocok_, dapat diekspresikan dalam [sebuah persamaan](https://en.wikipedia.org/wiki/Simple_linear_regression): 
+> 
+> ```
+> Y = a + bX
+> ```
+>
+> `X` adalah 'variabel penerang'. `Y` adalah 'variabel dependen'. Gradien garisnya adalah `b`, dan `a` adalah titik potong sumbu y yaitu nilai `Y` saat `X = 0`. 
+>
+>![hitunglah gradiennya](images/slope.png)
+>
+> Pertama, hitunglah gradien `b`. Infografik oleh [Jen Looper](https://twitter.com/jenlooper)
+>
+> Dalam kata lain, dan berhubungan pula dengan pertanyaan awal data labu kita "prediksikan harga satu bushel labu setiap bulan", `X` merujuk pada harganya, sedangkan `Y` akan merujuk pada bulan penjualan.
+>
+>![lengkapilah persamaan ini](images/calculation.png)
+>
+> Hitunglah nilai Y. Kalau harganya $4, artinya pasti April! Infografik oleh [Jen Looper](https://twitter.com/jenlooper)
+>
+> Matematika yang mengkalkulasi garis ini harus mendemonstrasikan gradien garisnya yang juga tergantung pada titik potongnya pada sumbu y, alias apa `Y`-nya saat `X = 0`.
+>
+> Kamu bisa melihat metode menghitung nilai-nilai ini di situs internet [*Math is Fun* (Matematika Itu Menyenangkan)](https://www.mathsisfun.com/data/least-squares-regression.html). Kunjungi [kalkulator kuadrat terkecil ini](https://www.mathsisfun.com/data/least-squares-calculator.html) juga untuk melihat bagaimana nomor-nomor ini mengubah garisnya.
+
+## Korelasi
+
+Satu lagi yang harus dipahami adalah **Koefisien Korelasi** antara variabel X dan Y yang tersedia. Menggunakan sebuah petak sebar, kamu bisa memvisualisasi korelasi ini dengan cepat. Sebuah grafik dengan titik-titik data yang tersebar rapi seperti sebuah garis mempunyai korelasi yang tinggi. Namun, sebuah grafik dengan titik-titik data yang tersebar di mana-mana antara X dan Y mempunyai korelasi yang rendah.
+
+Sebuah model regresi linear yang bagus akan mempunyai Koefisien Korelasi yang tinggi (lebih dekat ke 1 daripada ke 0) menggunakan metode Regresi Kuadrat Terkecil dengan sebuah garis regresi.
+
+✅ Jalankan *notebook* yang terlampir dalam pelajaran ini dan lihatlah petak sebar City (Kota) ke Price (Harga). Apa data yang menghubungkan City ke Price untuk penjualan labu mempunyai korelasi yang tinggi atau rendah kelihatannya?
+
+
+## Siapkan datamu untuk regresi
+
+Sekarang dengan pemahamanmu mengenai matematika di balik latihan ini, buatlah sebuah model regresi untuk melihat apa kamu bisa memprediksi paket labu yang mana yang harganya paling baik. Seorang pembeli labu akan ingin mengetahui informasi ini untuk mengoptimasi pembelian labu mereka.
+
+Karena kamu akan menggunakan Scikit-learn, tidak usah mengerjakan ini dengan tangan (walaupun bisa sih!). Dalam blok memrosesan data utama *notebook*-mu untuk pelajaran ini, tambahlah sebuah *library* dari Scikit-learn untuk mengkonversi semua data *string* menjadi nomor secara otomatis:
+
+```python
+from sklearn.preprocessing import LabelEncoder
+
+new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
+new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
+```
+
+Kalau kamu sekarang simak *dataframe* new_punkins, kamu akan lihat bahwa semua *string* sudah dijadikan nomor. Ini lebih susah untuk kita baca, tetapi jauh lebih mudah untuk Scikit-learn! 
+Sekarang kamu bisa membuat lebih banyak keputusan berakal (tidak hanya tebak-tebak dari petak sebarnya) tentang data yang paling cocok untuk regresi.
+
+Coba cari sebuah korelasi bagus antara dua titik data yang berpotensi untuk membangun sebuah model prediksi yang baik. Ternyata, hanya ada korelasi yang lemah antara City dan Price:
+
+```python
+print(new_pumpkins['City'].corr(new_pumpkins['Price']))
+0.32363971816089226
+```
+
+Meskipun begitu, ada korelasi yang sedikit lebih baik antara Package (Paket) dan Price (Harga). Masuk akal juga kan? Biasanya, lebih besar kardusnya, lebih mahal harganya.
+
+```python
+print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
+0.6061712937226021
+```
+
+Sebuah pertanyaan bagus untuk ditanyakan dari data ini adalah "Kira-kira harga sebuah paket labu berapa?"
+
+Mari membangun sebuah model regresi
+
+## Membangun sebuah model linear
+
+Sebelum membangun modelmu, rapikanlah datamu sekali lagi. Buanglah sebuah data nil (null) dan periksalah sekali lagi datanya kelihatannya seperti apa.
+
+```python
+new_pumpkins.dropna(inplace=True)
+new_pumpkins.info()
+```
+
+Lalu, buatlah sebuah *dataframe* baru dari set minimal ini dan *print*:
+
+```python
+new_columns = ['Package', 'Price']
+lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+lin_pumpkins
+```
+
+```output
+	Package	Price
+70	0	13.636364
+71	0	16.363636
+72	0	16.363636
+73	0	15.454545
+74	0	13.636364
+...	...	...
+1738	2	30.000000
+1739	2	28.750000
+1740	2	25.750000
+1741	2	24.000000
+1742	2	24.000000
+415 rows × 2 columns
+```
+
+1. Sekarang kamu bisa menetapkan data koordinat X dan y-mu:
+
+   ```python
+   X = lin_pumpkins.values[:, :1]
+   y = lin_pumpkins.values[:, 1:2]
+   ```
+✅ Apa yang sedang terjadi di sini? Kamu sedang menggunakan [notasi perpotongan Python (*Python slice notation*)](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295) untuk membuat dua *array* untuk mengisi `X` dan `y`.
+
+1. Selanjutnya, mulailah rutin pembangunan model:
+
+   ```python
+   from sklearn.linear_model import LinearRegression
+   from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+   from sklearn.model_selection import train_test_split
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   lin_reg = LinearRegression()
+   lin_reg.fit(X_train,y_train)
+
+   pred = lin_reg.predict(X_test)
+
+   accuracy_score = lin_reg.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   Karena korelasinya tidak begitu baik, model yang didapatkan tidak terlalu akurat.
+
+   ```output
+   Model Accuracy:  0.3315342327998987
+   ```
+
+2. Kamu bisa memvisualisasi garis yang digambarkan dalam proses ini:
+
+   ```python
+   plt.scatter(X_test, y_test,  color='black')
+   plt.plot(X_test, pred, color='blue', linewidth=3)
+
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+
+   plt.show()
+   ```
+   ![Sebuah petak sebar yang menunjukkan hubungan antara paket dan harga](./images/linear.png)
+
+3. Ujilah modelnya dengan sebuah jenis labu hipotetis:
+
+   ```python
+   lin_reg.predict( np.array([ [2.75] ]) )
+   ```
+   
+   Harga yang dihasilkan untuk jenis labu mitologis ini adalah:
+
+   ```output
+   array([[33.15655975]])
+   ```
+
+Nomor itu masuk akal jikalau logika garis regresinya benar.
+
+🎃 Selamat, kamu baru saja membuat sebuah model yang bisa membantu memprediksi harga beberapa jenis labu. Namun, kamu masih bisa membuatnya lebih baik!
+
+## Regresi polinomial
+
+Jenis lain regresi linear adalah regresi polinomial. Walaupun kadangkali ada hubungan linear antara variabel-variabel — lebih besar volume labunya, lebih tinggi harganya — kadangkali hubungan-hubungan ini tidak bisa digambarkan sebagai sebuah bidang atau garis lurus.
+
+✅ Ini ada [beberapa contoh data lain](https://online.stat.psu.edu/stat501/lesson/9/9.8) yang bisa menggunakan regresi polinomial
+
+Tengok kembali hubungan antara Variety (Jenis) dan Price (Harga) dalam grafik sebelumnya. Apa petak sebar ini terlihat seperti harus dianalisis dengan sebuah garis lurus? Mungkin tidak. Kali ini, kamu bisa mencoba regresi polinomial.
+
+✅ Polinomial adalah sebuah pernyataan matematika yang mempunyai satu atau lebih variabel dan koefisien disusun menjadi suku-suku.
+
+Regresi polinomial menghasilkan sebuah garis lengkung supaya lebih cocok dengan data non-linear.
+
+1. Mari kita membuat sebuah *dataframe* yang diisi sebuah segmen dari data orisinal labu:
+
+   ```python
+   new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
+   poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+   poly_pumpkins
+   ```
+
+Sebuah cara bagus untuk memvisualisasi korelasi-korelasi antara data dalam *dataframe-dataframe* adalah untuk menampilkannya dalam sebuah peta '*coolwarm*' (panas-dingin):
+
+2. Gunakan fungsi `Background_gradient()` dengan `coolwarm` sebagai argumennya:
+
+   ```python
+   corr = poly_pumpkins.corr()
+   corr.style.background_gradient(cmap='coolwarm')
+   ```
+   This code creates a heatmap:
+   Kode ini membuat sebuah peta panas
+   ![Sebuah peta panas yang menunjukkan korelasi data](./images/heatmap.png)
+
+Melihat peta ini, kamu bisa memvisualisasikan korelasi yang baik antara Package dan Price. Jadi kamu seharusnya bisa membuat sebuah model yang lebih baik daripada yang sebelumnya.
+
+### Buatlah sebuah *pipeline*
+
+Scikit-learn mempunyai sebuah API yang berguna untuk membangun model regresi polinomial — [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline) `make_pipeline`. Sebuah '*pipeline*' adalah sebuah rantai penaksir. Dalam kasus ini, *pipeline* ini mempunyai fitur-fitur polinomial, atau prediksi-prediksi yang membuat garis non-linear.
+
+1. Bangunlah kolom X dan y:
+
+   ```python
+   X=poly_pumpkins.iloc[:,3:4].values
+   y=poly_pumpkins.iloc[:,4:5].values
+   ```
+
+2. Buatlah *pipeline*-nya dengan fungsi `make_pipeline()`:
+
+   ```python
+   from sklearn.preprocessing import PolynomialFeatures
+   from sklearn.pipeline import make_pipeline
+
+   pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+   pipeline.fit(np.array(X_train), y_train)
+
+   y_pred=pipeline.predict(X_test)
+   ```
+
+### Buatlah sebuah barisan
+
+Di sini, kamu harus membuat sebuah *dataframe* baru dengan data yang _berurutan_ supaya *pipeline*-nya bisa membuat sebuah barisan.
+
+Tambahlah kode ini:
+
+   ```python
+   df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})
+   df.sort_values(by='x',inplace = True)
+   points = pd.DataFrame(df).to_numpy()
+
+   plt.plot(points[:, 0], points[:, 1],color="blue", linewidth=3)
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+   plt.scatter(X,y, color="black")
+   plt.show()
+   ```
+
+Kamu membuat sebuah *dataframe* baru dengan fungsi `pd.DataFrame`. Lalu kamu mengurutkan isinya dengan fungsi `sort_values()`. Akhirnya kamu membuat sebuah bagan polinomial:
+
+![Sebuah bagan polinomial yang menunjukkan hubungan antara paket dan harga](./images/polynomial.png)
+
+Kamu bisa melihat garis lengkungnya yang lebih cocok terhadap datamu.
+
+Ayo periksa akurasi modelnya:
+
+   ```python
+   accuracy_score = pipeline.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   Nah!
+
+   ```output
+   Model Accuracy:  0.8537946517073784
+   ```
+
+Itu bagus! Coba memprediksi harga:
+
+### Buatlah sebuah prediksi
+
+Apa kita bisa memberi input dan dapat sebuah prediksi?
+
+Pakai fungsi `predict()` untuk membuat prediksi:
+ 
+   ```python
+   pipeline.predict( np.array([ [2.75] ]) )
+   ```
+   Kamu diberi prediksi ini:
+
+   ```output
+   array([[46.34509342]])
+   ```
+
+Itu sangat masuk akal dengan bagan sebelumnya! Selain itu, jika ini model lebih baik daripada yang sebelumnya dengan data ini, kamu bisa siap untuk labu-labu yang lebih mahal ini!
+
+� Mantap sekali! Kamu membuat dua model regresi dalam satu pelajaran. Dalam bagian terakhir mengenai regresi, kamu akan belajar tentang regresi logistik untuk pengkategorisasian.
+
+---
+## 🚀 Tantangan
+
+Coba-cobalah variabel-variabel yang lain di *notebook* ini untuk melihat bagaimana korelasi berhubungan dengan akurasi model.
+
+## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+
+## Review & Pembelajaran Mandiri
+
+Dalam pelajaran ini kita belajar tentang regresi linear. Ada banyak jenis regresi lain yang penting pula. Bacalah tentang teknik *Stepwise*, *Ridge*, *Lasso*, dan *Elasticnet*. [Kursus Pembelajaran Statistik Stanford](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning) juga baik untuk belajar lebih lanjut.
+
+## Tugas 
+
+[Buatlah sebuah model](assignment.md)
\ No newline at end of file

From 5b1422fca2e4361feafcb87299a62f5152f4e353 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 8 Jul 2021 02:41:58 +0900
Subject: [PATCH 057/368] translate 2-Regression/README.md to Japanese

---
 2-Regression/translations/README.ja.md | 32 ++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 2-Regression/translations/README.ja.md

diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md
new file mode 100644
index 0000000000..bd021b8817
--- /dev/null
+++ b/2-Regression/translations/README.ja.md
@@ -0,0 +1,32 @@
+# 機械学習����回帰モデル
+## トピック: 北米�カボ�ャ価格�回帰モデル 🎃
+
+In North America, pumpkins are often carved into scary faces for Halloween. Let's discover more about these fascinating vegetables!
+
+![jack-o-lanterns](./images/jack-o-lanterns.jpg)
+> <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a>�よ��<a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>�投稿�れ�写真
+
+## 今回学���
+��章�レッスン���機械学習�文脈���る回帰�種類����説明���。回帰モデル�変数間�"関係"を決定�る��役立���。��タイプ�モデル��長��温度�年齢���値を予測��データ�イント�分�を�る���変数間�関係性を明ら�����。
+
+今回�レッスン���線形回帰�ロジスティック回帰���や��よ��使�分�る�を説明���。
+
+データサイエンティスト�共通開発環境��るノートブックを管��る���Visual Studio Code�構�や機械学習�タスクを開始�る���準備を行���。���機械学習用�ライブラリ��るScikit-learnを利用�最��モデルを構築���。��章��回帰モデル�焦点を当���。
+
+> 回帰モデルを学習�る��役立�ローコードツール��り��。��[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)を使��������。
+
+### レッスン
+
+1. [商売�具](1-Tools/README.md)
+2. [データ管�](2-Data/README.md)
+3. [線形回帰�多項�回帰](3-Linear/README.md)
+4. [ロジスティック回帰](4-Logistic/README.md)
+
+---
+### クレジット
+
+"機械学習�回帰"��[Jen Looper](https://twitter.com/jenlooper)�よ��制作�れ���。
+
+クイズ�貢献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan)�[Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+pumpkin dataset��[��ら�Kaggleプロジェクト](https://www.kaggle.com/usda/a-year-of-pumpkin-prices)��案�れ����。��データ��アメリカ�衆国農務���布���る[Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)�元�������。�����分布を正�化�る���多様性を元�色��������追加を行�����。��データ�パブリックドメイン��。

From 5a48cbd025aa578ad37975bf3d907d94f8a7aa81 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 8 Jul 2021 02:45:49 +0900
Subject: [PATCH 058/368] translate 2-Regression/README.md to Japanese

---
 2-Regression/translations/README.ja.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md
index bd021b8817..f0e4bc6bcc 100644
--- a/2-Regression/translations/README.ja.md
+++ b/2-Regression/translations/README.ja.md
@@ -1,7 +1,7 @@
 # 機械学習����回帰モデル
-## トピック: 北米�カボ�ャ価格�回帰モデル 🎃
+## トピック: 北米�カボ�ャ価格�関�る回帰モデル 🎃
 
-In North America, pumpkins are often carved into scary faces for Halloween. Let's discover more about these fascinating vegetables!
+北米����ロウィン����カボ�ャ�よ�怖�顔�彫られ����。�ん�魅力的�野�����も��知り��ょ��
 
 ![jack-o-lanterns](./images/jack-o-lanterns.jpg)
 > <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a>�よ��<a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>�投稿�れ�写真

From e88c3d7b2d5378482226b1cba844f653e5651273 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 8 Jul 2021 02:47:15 +0900
Subject: [PATCH 059/368] translate 2-Regression/README.md to Japanese

---
 2-Regression/translations/README.ja.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md
index f0e4bc6bcc..214cfc12a2 100644
--- a/2-Regression/translations/README.ja.md
+++ b/2-Regression/translations/README.ja.md
@@ -3,7 +3,7 @@
 
 北米����ロウィン����カボ�ャ�よ�怖�顔�彫られ����。�ん�魅力的�野�����も��知り��ょ��
 
-![jack-o-lanterns](./images/jack-o-lanterns.jpg)
+![jack-o-lanterns](../images/jack-o-lanterns.jpg)
 > <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a>�よ��<a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>�投稿�れ�写真
 
 ## 今回学���

From 5f123752965a6be9a395b3f791d154969d532f78 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 8 Jul 2021 02:48:51 +0900
Subject: [PATCH 060/368] translate 2-Regression/README.md to Japanese

---
 2-Regression/translations/README.ja.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md
index 214cfc12a2..dd58ff9a48 100644
--- a/2-Regression/translations/README.ja.md
+++ b/2-Regression/translations/README.ja.md
@@ -17,10 +17,10 @@
 
 ### レッスン
 
-1. [商売�具](1-Tools/README.md)
-2. [データ管�](2-Data/README.md)
-3. [線形回帰�多項�回帰](3-Linear/README.md)
-4. [ロジスティック回帰](4-Logistic/README.md)
+1. [商売�具](../1-Tools/README.md)
+2. [データ管�](../2-Data/README.md)
+3. [線形回帰�多項�回帰](../3-Linear/README.md)
+4. [ロジスティック回帰](../4-Logistic/README.md)
 
 ---
 ### クレジット

From 076339f2375564ce3570c7c76a70f4d7ff2ed7a3 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Thu, 8 Jul 2021 13:00:31 -0400
Subject: [PATCH 061/368] adding IoT!

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 93dad0929f..34c09b5706 100644
--- a/README.md
+++ b/README.md
@@ -115,4 +115,5 @@ Would you like to contribute a translation? Please read our [translation guideli
 Our team produces other curricula! Check out:
 
 - [Web Dev for Beginners](https://aka.ms/webdev-beginners)
+- [IoT for Beginners](https://aka.ms/iot-beginners)
 

From 42155e19c439ca215827782cd38b058e6a0c95ca Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Thu, 8 Jul 2021 20:05:33 +0200
Subject: [PATCH 062/368] Chapter 1, Section 4 Readme and assignment

---
 .../translations/README.it.md                 | 110 ++++++++++++++++++
 .../translations/assignment.it.md             |  11 ++
 2 files changed, 121 insertions(+)
 create mode 100644 1-Introduction/4-techniques-of-ML/translations/README.it.md
 create mode 100644 1-Introduction/4-techniques-of-ML/translations/assignment.it.md

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.it.md b/1-Introduction/4-techniques-of-ML/translations/README.it.md
new file mode 100644
index 0000000000..a3bcda3c44
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md
@@ -0,0 +1,110 @@
+# Tecniche di Machine Learning
+
+Il processo di creazione, utilizzo e mantenimento dei modelli di machine learning e dei dati che utilizzano è un processo molto diverso da molti altri flussi di lavoro di sviluppo. In questa lezione si demistifica il processo, e si delineano le principali tecniche che occorre conoscere. Si dovrà:
+
+- Comprendere i processi ad alto livello alla base di machine learning.
+- Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento".
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+
+## Introduzione
+
+Ad alto livello, il mestiere di creare processi di apprendimento automatico (ML) comprende una serie di passaggi:
+
+1. **Decidere circa la domanda**. La maggior parte dei processi ML inizia ponendo una domanda alla quale non è possibile ottenere risposta da un semplice programma condizionale o da un motore basato su regole. Queste domande spesso ruotano attorno a previsioni basate su una raccolta di dati.
+2. **Raccogliere e preparare i dati**. Per poter rispondere alla domanda, servono dati. La qualità e, a volte, la quantità dei dati determineranno quanto bene sarà possibile rispondere alla domanda iniziale. La visualizzazione dei dati è un aspetto importante di questa fase. Questa fase include anche la suddivisione dei dati in un gruppo di addestramento (training) e test per costruire un modello.
+3. **Scegliere un metodo di addestramento**. A seconda della domanda e della natura dei dati, è necessario scegliere come si desidera addestrare un modello per riflettere al meglio i dati e fare previsioni accurate su di essi. Questa è la parte del processo di ML che richiede competenze specifiche e, spesso, una notevole quantità di sperimentazione.
+4. **Addestrare il modello**. Usando i dati di addestramento, si utilizzeranno vari algoritmi per addestrare un modello a riconoscere modelli nei dati. Il modello potrebbe sfruttare pesi interni che possono essere regolati per privilegiare alcune parti dei dati rispetto ad altre per costruire un modello migliore.
+5. **Valutare il modello**. Si utilizzano dati mai visti prima (i dati di test) da quelli raccolti per osservare le prestazioni del modello.
+6. **Regolazione dei parametri**. In base alle prestazioni del modello, si può ripetere il processo utilizzando parametri differenti, o variabili, che controllano il comportamento degli algoritmi utilizzati per addestrare il modello.
+7. **Prevedere** Usare nuovi input per testare la precisione del modello.
+
+## Che domanda fare
+
+I computer sono particolarmente abili nello scoprire modelli nascosti nei dati. Questa caratteristica è molto utile per i ricercatori che hanno domande su un determinato campo a cui non è possibile rispondere facilmente creando un motore di regole basato su condizioni. Dato un compito attuariale, ad esempio, un data scientist potrebbe essere in grado di costruire manualmente regole sulla mortalità dei fumatori rispetto ai non fumatori.
+
+Quando molte altre variabili vengono introdotte nell'equazione, tuttavia, un modello ML potrebbe rivelarsi più efficiente per prevedere i tassi di mortalità futuri in base alla storia sanitaria passata. Un esempio più allegro potrebbe essere fare previsioni meteorologiche per il mese di aprile in una determinata località sulla base di dati che includono latitudine, longitudine, cambiamento climatico, vicinanza all'oceano, modelli della corrente a getto e altro ancora.
+
+✅ Questa [presentazione](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) sui modelli meteorologici offre una prospettiva storica per l'utilizzo di ML nell'analisi meteorologica.
+
+## Attività di pre-costruzione
+
+Prima di iniziare a costruire il proprio modello, ci sono diverse attività da completare. Per testare la domanda e formare un'ipotesi basata sulle previsioni di un modello, occorre identificare e configurare diversi elementi.
+
+### Dati
+
+Per poter rispondere con sicurezza alla domanda, serve una buona quantità di dati del tipo giusto. Ci sono due cose da fare a questo punto:
+
+- **Raccogliere dati**. Tenendo presente la lezione precedente sull'equità nell'analisi dei dati, si raccolgano i dati con cura. Ci sia consapevolezza delle fonti di questi dati, di eventuali pregiudizi intrinseci che potrebbero avere e si documenti la loro origine.
+- **Preparare i dati**. Ci sono diversi passaggi nel processo di preparazione dei dati. Potrebbe essere necessario raccogliere i dati e normalizzarli se provengono da fonti diverse. Si può migliorare la qualità e la quantità dei dati attraverso vari metodi come la conversione di stringhe in numeri (come si fa in [Clustering](../../../5-Clustering/1-Visualize/transaltions/README.it.md)). Si potrebbero anche generare nuovi dati, basati sull'originale (come si fa in [Classificazione](../../../4-Classification/1-Introduction/translations/README.it.md)). Si possono pulire e modificare i dati (come verrà fatto prima della lezione sull'[app Web](../../../3-Web-App/translations/README.it.md) ). Infine, si potrebbe anche aver bisogno di renderli casuali e mescolarli, a seconda delle proprie tecniche di addestramento.
+
+✅ Dopo aver raccolto ed elaborato i propri dati, si prenda un momento per vedere se la loro forma consentirà di rispondere alla domanda prevista. Potrebbe essere che i dati non funzionino bene nello svolgere il compito assegnato, come si scopre nelle lezioni di [Clustering](../../../5-Clustering/1-Visualize/translations/README.it.md)!
+
+### Selezione della variabile caratteristica
+
+Una [caratteristica](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) è una proprietà misurabile dei propri dati. In molti insiemi di dati è espressa come un'intestazione di colonna come "data", "dimensione" o "colore". La propria variabile caratteristica, solitamente rappresentata come `y` nel codice, rappresenta la risposta alla domanda che si sta cercando di porre ai propri dati: a dicembre, di che **colore** saranno le zucche più economiche? A San Francisco, quali quartieri avranno il miglior **prezzo** immobiliare?
+
+🎓 **Selezione ed estrazione della caratteristica** Come si fa a sapere quale variabile scegliere quando si costruisce un modello? Probabilmente si dovrà passare attraverso un processo di selezione o estrazione delle caratteristiche per scegliere le variabili giuste per il modello più efficace. Tuttavia, non è la stessa cosa: "L'estrazione delle caratteristiche crea nuove caratteristiche dalle funzioni delle caratteristiche originali, mentre la selezione delle caratteristiche restituisce un sottoinsieme delle caratteristiche". ([fonte](https://it.wikipedia.org/wiki/Selezione_delle_caratteristiche))
+
+### Visualizzare i dati
+
+Un aspetto importante del bagaglio del data scientist è la capacità di visualizzare i dati utilizzando diverse eccellenti librerie come Seaborn o MatPlotLib. Rappresentare visivamente i propri dati potrebbe consentire di scoprire correlazioni nascoste che si possono sfruttare. Le visualizzazioni potrebbero anche aiutare a scoprire pregiudizi o dati sbilanciati (come si scopre in [Classificazione](../../../4-Classification/2-Classifiers-1/translations/README.it.md)).
+
+### Dividere l'insieme di dati
+
+Prima dell'addestramento, è necessario dividere l'insieme di dati in due o più parti di dimensioni diverse che rappresentano comunque bene i dati.
+
+- **Addestramento**. Questa parte dell'insieme di dati è adatta al proprio modello per addestrarlo. Questo insieme costituisce la maggior parte dell'insieme di dati originale.
+- **Test**. Un insieme di dati di test è un gruppo indipendente di dati, spesso raccolti dai dati originali, che si utilizzano per confermare le prestazioni del modello creato.
+- **Convalida**. Un insieme di convalida è un gruppo indipendente più piccolo di esempi da usare per ottimizzare gli iperparametri, o architettura, del modello per migliorarlo. A seconda delle dimensioni dei propri dati e della domanda che si sta ponendo, si potrebbe non aver bisogno di creare questo terzo insieme (come si nota in [Previsione delle Serie Temporali](../../../7-TimeSeries/1-Introduction/translations/README.it.md)).
+
+## Costruire un modello
+
+Utilizzando i dati di addestramento, l'obiettivo è costruire un modello o una rappresentazione statistica dei propri dati, utilizzando vari algoritmi per **addestrarlo** . L'addestramento di un modello lo espone ai dati e consente di formulare ipotesi sui modelli percepiti che scopre, convalida e accetta o rifiuta.
+
+### Decidere un metodo di addestramento
+
+A seconda della domanda e della natura dei dati, si sceglierà un metodo per addestrarlo. Passando attraverso [la documentazione di Scikit-learn](https://scikit-learn.org/stable/user_guide.html), che si usa in questo corso, si possono esplorare molti modi per addestrare un modello. A seconda della propria esperienza, si potrebbe dover provare diversi metodi per creare il modello migliore. È probabile che si attraversi un processo in cui i data scientist valutano le prestazioni di un modello fornendogli dati non visti, verificandone l'accuratezza, i pregiudizi e altri problemi che degradano la qualità e selezionando il metodo di addestramento più appropriato per l'attività da svolgere.
+
+### Allenare un modello
+
+Occorre armarsi dei propri dati di allenamento, per essere pronti per "adattarli" per creare un modello. Si noterà che in molte librerie ML si trova il codice "model.fit" - è in questo momento che si inviano i propri dati come un vettore di valori (di solito "X") e una variabile di caratteristica (di solito "y" ).
+
+### Valutare il modello
+
+Una volta completato il processo di addestramento (potrebbero essere necessarie molte iterazioni, o "epoche", per addestrare un modello di grandi dimensioni), si sarà in grado di valutare la qualità del modello utilizzando i dati di test per valutarne le prestazioni. Questi dati sono un sottoinsieme dei dati originali che il modello non ha analizzato in precedenza. Si può stampare una tabella di metriche sulla qualità del proprio modello.
+
+**Adattamento del modello**
+
+Nel contesto di machine learning, l'adattamento del modello si riferisce all'accuratezza della funzione sottostante del modello mentre tenta di analizzare dati con cui non ha familiarità.
+
+? **Inadeguatezza** o **sovraadattamento** sono problemi comuni che degradano la qualità del modello, poiché il modello non si adatta abbastanza bene o troppo bene. Ciò fa sì che il modello esegua previsioni troppo allineate o troppo poco allineate con i suoi dati di addestramento. Un modello overfit (sovraaddestrato) prevede troppo bene i dati di addestramento perché ha appreso troppo bene i dettagli e il rumore dei dati. Un modello underfit (inadeguato) non è accurato in quanto non può né analizzare accuratamente i suoi dati di allenamento né i dati che non ha ancora "visto".
+
+![modello sovraaddestrato](../images/overfitting.png)
+> Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+## Sintonia dei parametri
+
+Una volta completato l'addestramento iniziale, si osservi la qualità del modello e si valuti di migliorarlo modificando i suoi "iperparametri". Maggiori informazioni sul processo [nella documentazione](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+
+## Previsione
+
+Questo è il momento in cui si possono utilizzare dati completamente nuovi per testare l'accuratezza del proprio modello. In un'impostazione ML "applicata", in cui si creano risorse Web per utilizzare il modello in produzione, questo processo potrebbe comportare la raccolta dell'input dell'utente (ad esempio, la pressione di un pulsante) per impostare una variabile e inviarla al modello per l'inferenza, oppure valutazione.
+
+In queste lezioni si scoprirà come utilizzare questi passaggi per preparare, costruire, testare, valutare e prevedere - tutti gesti di un data scientist e altro ancora, mentre si avanza nel proprio viaggio per diventare un ingegnere ML "full stack".
+
+---
+
+## 🚀 Sfida
+
+Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficoltà? Cosa sembra facile?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+
+## Revisione e Auto Apprendimento
+
+Cercare online le interviste con i data scientist che discutono del loro lavoro quotidiano. Eccone [una](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+
+## Compito
+
+[Intervista a un data scientist](assignment.it.md)
diff --git a/1-Introduction/4-techniques-of-ML/translations/assignment.it.md b/1-Introduction/4-techniques-of-ML/translations/assignment.it.md
new file mode 100644
index 0000000000..41c597f40e
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Intervista a un data scientist
+
+## Istruzioni
+
+Nella propria azienda, in un gruppo di utenti, o tra amici o compagni di studio, si parli con qualcuno che lavora professionalmente come data scientist. Si scriva un breve documento (500 parole) sulle loro occupazioni quotidiane. Sono specialisti o lavorano "full stack"?
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------ | --------------------- |
+|          | Un saggio della lunghezza corretta, con fonti attribuite, è presentato come file .doc | Il saggio è attribuito male o più corto della lunghezza richiesta | Non viene presentato alcun saggio |

From dc46af12b90e7d14879580df7c5f0419a8badd14 Mon Sep 17 00:00:00 2001
From: iBlueDust <35223375+iBlueDust@users.noreply.github.com>
Date: Fri, 9 Jul 2021 12:52:54 +0700
Subject: [PATCH 063/368] Add Indonesian translation for
 2-Regression/4-Logistic

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 .../4-Logistic/translations/README.id.md      | 301 ++++++++++++++++++
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diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
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+# Regresi logistik untuk memprediksi kategori-kategori
+
+![Infografik regresi logistik vs. linear](./images/logistic-linear.png)
+> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
+## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+
+## Pembukaan
+
+Dalam pelajaran regresi terakhir, salah satu teknik ML _klasik_ dan sederhana adalah regresi logistik. Teknik ini digunakan untuk mengemukakan pola-pola untuk memprediksi kategori binari. Apa ini sebuah permen coklat atau tidak? Apa penyakit ini menular tidak? Apa pelanggan ini akan memilih produk ini tidak?
+
+Dalam pelajaran ini, kamu akan belajar:
+
+- Sebuah *library* baru untuk pemvisualisasian data
+- Teknik-teknik untuk regresi logistik
+
+✅ Perdalamkan pemahamanmu dalam bekerja dengan regresi jenis ini dalam [modul pembelajaran ini](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
+## Prasyarat
+
+Setelah bekerja dengan data labu, kita sekarang sudah terbiasa dengannya untuk menyadari bahwa adapula sebuah kategori binari yang kita dapat menggunakan: `Color` (warna).
+
+Mari membangun sebuah model regresi logistik untuk memprediksi _kemungkinannya labu ini warnanya apa_ berdasarkan beberapa variabel (oranye 🎃 atau putih 👻).
+
+> Mengapa kita berbicara tentang klasifikasi binary dalam seri pelajaran tentang regresi? Hanya untuk kemudahan linguistik, regresi logistik juga [sebenarnya sebuah metode klasifikasi](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), namun satu yang berdasarkan garis linear. Pelajari lebih lanjut tentang cara-cara lain untuk mengklasifikasi data dalam seri pelajaran berikutnya.
+
+## Tentukan pertanyaannya
+
+Untuk keperluan kita, kita akan mengekspresikannya sebagai pilihan binari 'Orange' atau 'Not Orange' (oranye atau bukan oranye). Adapula kategori 'striped' (belang-belang) dalam dataset kita, tetapi tidak banyak titik datanya, jadi kita tidak akan menggunakannya. Lagipula, kategori itu hilang begitu kita buang nilai-nilai nil (null) dari datasetnya.
+
+> 🎃 Tahukah tidak? Kita kadangkali memanggil labu putih labu 'hantu'. Mereka tidak mudah diukir, jadi mereka tidak sepopuler yang oranye pada Halloween. Tetapi mereka keren juga ya!
+
+## Tentang regresi logistik
+
+Regresi logistik berbeda dari regresi linear, jenis regresi yang kamu pelajari sebelumnya, dalam beberapa askpek penting.
+
+### Klasifikasi binari
+
+Regresi logistik tidak mempunyai beberapa fitur regresi linear. Regresi logistik menyediakan sebuah prediksi tentang sebuah kategori binari (seperti "oranye atau bukan oranye"), sedangkan yang lainnya dapat memprediksi nilai-nilai kontinu. Contohnya, dengan mengetahui dari mana labu ini dan kapan dipanennya, regresi linear dapat memprediksi _berapa harganya akan naik_, namun regresi logistik tidak bisa.
+
+![Model klasifikasi labu](./images/pumpkin-classifier.png)
+> Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
+### Klasifikasi lain
+
+Ditambah itu, ada banyak jenis regresi logistik, termasuk jenis multinomial dan ordinal:
+
+- **Multinomial** memperlibatkan lebih dari satu kategori - "Oranye, Putih, dan Belang-belang".
+- **Ordinal** memperlibatkan kategori-kategori berurut. Biasanya berguna jika kita inging mengurutkan hasil kita secara logikal, seperti labu-useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
+
+![Multinomial vs ordinal regression](./images/multinomial-ordinal.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+### Eh, masih linear ya?
+
+Walaupun jenis regresi ini semuanya tentang 'prediksi kategori', jenis ini masih paling efektif jika ada hubungan linear antara variabel dependen (warna) dan independen (sisa *dataset*-nya, seperti kota dan ukuran). Jadi baik juga untuk mencari tahu dahulu apa ada hubungan linear antara variabel-variabel ini.
+
+### Variabel-variabel TIDAK HARUS berkorelasi
+
+Ingat bagaimana regresi linear bekerja lebih baik dengan variabel berkorelasi? Regresi logistik itu kebalikannya: variabel-variabelnya tidak harus berjejer menjadi garis. Artinya, regresi ini bekerja untuk data ini yang korelasinya lumayan lemah.
+
+### Perlu banyak data rapi
+
+Regresi logistik akan memberi hasil lebih akurat jika kamu menggunakan data lebih banyak; *dataset* kecil kita tidak optimal untuk tugas ini, ingatlah itu.
+
+✅ Pikirkan tentang jenis-jenis data yang akan bekerja baik dengan regresi logistik
+
+## Latihan - rapikan data
+
+Pertama, rapikanlah datanya sedikit. Buanglah nilai-nilai nil (null) dan pilihlah beberapa kolom:
+
+1. Tambahlah kode di bawah ini:
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']
+    
+    new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    
+    new_pumpkins.dropna(inplace=True)
+    
+    new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
+    ```
+
+    Kamu selalu bisa mengintip kedalam *dataframe*-mu:
+
+    ```python
+    new_pumpkins.info
+    ```
+
+### Visualisasi - *grid* berdampingan (*side-by-side grid*)
+
+Sekarang kamu sudah memuat [*notebook* starter](./notebook.ipynb) dengan data labunya sekali lagi dan merapikannya untuk mempertahankan sebuah *dataset* dengan beberapa variabel, termasuk `Color`. Mari memvisualisasi *dataframe*-nya dengan *library* yang beda: [Seaborn](https://seaborn.pydata.org/index.html) yang dibangun di atas Matplotlib yang kita gunakan sebelumnya.
+
+Seaborn menyediakan beberapa cara keren untuk memvisualisasi datamu. Contohnya, kamu bisa membandungkan distribusi datanya untuk setiap titik data dalam sebuah *grid* berdampingan.
+
+1. Buatlah sebuah *grid* dengan meng-*instantiate* sebuah `PairGrid` menggunakan data labu kita `new_pumpkins` diikuti memanggil fungsi `map()`:
+
+    ```python
+    import seaborn as sns
+    
+    g = sns.PairGrid(new_pumpkins)
+    g.map(sns.scatterplot)
+    ```
+
+    ![Sebuah visualisasi *grid* data](images/grid.png)
+
+    Dengan mengobservasi datanya secara berdampingan, kamu bisa lihat bagaimana data warnanya berhubungan dengan kolom-kolom lainnya.
+
+    ✅ Dengan petak sebar ini, pendalaman menarik apa saja yang kamu bisa membayangkan?
+
+### Gunakan sebuah bagan kawanan (*swarm plot*)
+
+Karena warna adalah sebuah kategori binari (oranye atau bukan oranye), warna disebut 'data kategorikal' dan memerlukan 'sebuah [pendekatan khusus](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) untuk memvisualisasi'. Ada beberapa cara lain untuk memvisualisasi hubungan antara kategori ini dengan variabel-variabel lainnya.
+
+Kamu bisa memvisualisasikan variabel-variabel secara berdampingan dengan bagan-bagan Seaborn.
+
+1. Cobalah sebuah bagan kawanan untuk menunjukkan distribusi nilai:
+
+    ```python
+    sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
+    ```
+
+    ![Sekawanan data yang divisualisasi](images/swarm.png)
+
+### Bagan biola
+
+Sebuah bagan 'biola' itu berguna sebab kamu bisa memvisualisasi bagaimana data dalam kedua kategori itu terdistribusi dengan mudah. Bagan viola tidak efektif dengan *dataset* yang lebih kecil sebab distribusinya ditampilkan sebagai lebih 'mulus'.
+
+1. Gunakan fungsi `catplot()` dengan parameter `x=Color` dan `kind="violin"`:
+
+    ```python
+    sns.catplot(x="Color", y="Item Size",
+                kind="violin", data=new_pumpkins)
+    ```
+
+    ![sebuah bagan biola](images/violin.png)
+
+    ✅ Cobalah membuat bagan ini dan jenis-jenis bagan Seaborn lainnya dengan variabel-variabel lainnya.
+
+Sekarang kita sudah dapat bayangan hubungan antara kedua kategori binary warna dan ukuran. Ayo menjelajahi regresi logistik untuk memprediksi warna sebuah labu tertentu.
+
+> **🧮 Perlihatkanlah Matematikanya Kepada Saya**
+>
+> Ingat bagaiaman regresi linear seringkali menggunakan metode kuadrat terkecil untuk tiba pada sebuah nilai? Regresi logistik tergantung pada konsep 'kemungkinan terbesar' menggunakan [fungsi sigmoid](https://wikipedia.org/wiki/Sigmoid_function). Sebuah 'fungsi Sigmoid' terlihat seperti huruf 'S' dalam sistem koordinat Kartesius. Fungsi ini mengambil sebuah nilai dan 'mencorongkannya' menjadi sebuah nomor antara 0 dan 1. Kurva ini juga dipanggil sebuah 'kurva logistik'. Formulanya seperti ini:
+>
+> ![Fungsi logistic](images/sigmoid.png)
+>
+> Titik tengah sigmoidnya terletak di sumbu X. L adalah nilai maksimum kurvanya. k adalah terjalnya kurvanya. Jika hasil fungsinya lebih dari 0.5, nilai yang diberikan kepada fungsi tersebut akan diklasifikasikan sebagai '1'. Kalau tidak, nilai itu akan diklasifikasikan sebagai '0'.
+
+## Bangunlah modelmu
+
+Scikit-learn membuat membangun model klasifikasi binary sangat mudah.
+
+1. Pilihlah variabel-variabel yang kamu ingin gunakan dalam model klasifikasimu dan bagilah datanya menjadi set latihan dan set ujian dengan fungsi `train_test_split()`:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Origin','Item Size','Variety','City Name','Package']
+    
+    X = new_pumpkins[Selected_features]
+    y = new_pumpkins['Color']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    
+    ```
+
+2. Sekarang kamu bisa melatihkan modelmu dengan fungsi `fit()` dengan data latihanmu. *Print* hasilnya:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.linear_model import LogisticRegression
+    
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+    Lihatlah *scoreboard* modelmu. Tidak buruk, apalagi hanya dengan 1000 baris data:
+
+    ```output
+                       precision    recall  f1-score   support
+    
+               0       0.85      0.95      0.90       166
+               1       0.38      0.15      0.22        33
+    
+        accuracy                           0.82       199
+       macro avg       0.62      0.55      0.56       199
+    weighted avg       0.77      0.82      0.78       199
+    
+    Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+     0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 1 0 1 0 0 1 0 0 0 1 0]
+    ```
+
+## Pemahaman lebih baik via sebuah 'matriks kebingungan'
+
+Walaupun kamu bisa membuat sebuah *scoreboard* melaporkan [istilah-istilah](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) dengan mem-*print* yang di atas, kamu mungkin bisa memahami modelmu dengan lebih mudah dengan sebuah [matriks kebingungan](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix) untuk membantu kita lebih paham akan performa modelnya.
+
+> 🎓 Sebuah '[matriks kebingungan](https://wikipedia.org/wiki/Confusion_matrix)' (atau 'matriks kesalahan') adalah sebuah tabel yang mengekspresikan positif benar vs. positif palsu modelmu sehingga mengukur akurasi prediksi=prediksinya.
+
+1. Untuk menggunakan sebuah matriks kebingungan, gunakan fungsi `confusin_matrix()`:
+
+    ```python
+    from sklearn.metrics import confusion_matrix
+    confusion_matrix(y_test, predictions)
+    ```
+
+    Lihatlah matriks kebingungan modelmu:
+
+    ```output
+    array([[162,   4],
+           [ 33,   0]])
+    ```
+
+Apa yang sedang terjadi di sini? Mari kita asumsi dulu bahwa model kita ditanyakan untuk mengklasifikasi antara dua kategori binari: 'labu' dan 'bukan labu'.
+
+- Kalau modelmu memprediksi sesuatu sebagai sebuah labu dan memang benar sesuatu itu adalah sebuah labu, itu disebut positif benar yang diindikasi angka di pojok kiri atas. 
+- Kalau modelmu memprediksi sesuatu sebagai bukan sebuah labu tetapi sesuatu itu sebenarnya sebuah labu, itu disebut positif palsu yang diindikasi angka di pojok kanan atas.
+- Kalau modelmu memprediksi sesuati sebagai sebuah labu tetapi sebenarnya bukan sebuah labu, itu disebut negatif palsu yang diindikasi angka di pojok kiri bawah.
+- Kalau modelmu memprediksi sesuati sebagai bukan sebuah labu dan memang benar sesuatu itu bukan sebuah labu, itu disebut negatif benar yang diindikasi angka di pojok kanan bawah.
+
+![Matriks Kebingungan](images/confusion-matrix.png)
+
+> Infografik oleh [Jen Looper](https://twitter.com/jenlooper)
+
+Sebagaimana kamu mungkin sudah pikirkan, lebih baik dapat banyak positif benar dan negatif benar dan sedikit positif palsu dan negatif palsu. Implikasinya adalah performa modelnya bagus.
+
+✅ Pertanyaan: Berdasarkan matriks kebingungan, modelnya baik tidak? Jawaban: Tidak buruk; ada banyak positif benar dan sedikit negatif palsu.
+
+Mari kita lihat kembali istilah-istilah yang kita lihat tadi dengan bantuan matriks kebingungan:
+
+> PB: Positif benar
+> PP: Positif palsu
+> NB: Negatif benar
+> NP: Negatif palsu
+
+🎓 Presisi: PB/(PB + NP) Rasio titik data relevan antara semua titik data (seperti data mana yang benar dilabelkannya)
+
+🎓 *Recall*: PB/(PB + PP) Rasio titk data relevan yang digunakan, maupun labelnya benar atau tidak.
+
+🎓 *f1-score*: (2 * Presisi * *Recall*)/(Presisi + *Recall*) Sebuah rata-rata tertimbang antara presisi dan *recall*. 1 itu baik dan 0 itu buruk.
+
+🎓 Dukungan: Jumlah kejadian per label
+
+🎓 Akurasi: (PB + NB)/(PB + PS + NB + NS) Persentase label yang diprediksi dengan benar untuk sebuah sampel.
+
+🎓 Rata-rata Makro: Hitungan rata-rata sederhana (non-tertimbang) metrik setiap label tanpa menghiraukan ketidakseimbangan label.
+
+🎓 Rata-rata Tertimbang: Hitungan rata-rata metrik setiap label dengan mempertimbangkan ketidakseimbangan label. Rata-ratanya tertimbang nilai Dukungan (jumlah kejadian dalam realita) setiap label.
+
+✅ Apa kamu bisa tebak metrik apa yang harus dipantau untuk mengurangi jumlah negatif palsu modelmu?
+## Visualisasikan kurva ROC model ini
+
+Ini bukanlah sebuah model buruk. Akurasinya sekitar 80%, jadi sebenarnya bisa digunakan untuk memprediksi warna sebuah labu berdasarkan beberapa variabel.
+
+Mari kita memvisualisasikan datanya sekali lagi untuk melihat nilai ROC ini:
+
+```python
+from sklearn.metrics import roc_curve, roc_auc_score
+
+y_scores = model.predict_proba(X_test)
+# calculate ROC curve
+fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
+sns.lineplot([0, 1], [0, 1])
+sns.lineplot(fpr, tpr)
+```
+Menggunakan Seaborn lagi, gambarlah [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) (ROC) model ini. Kurva ROC seringkali digunakan untuk menunjukkan output sebuah pembuat klasifikasi berdasarkan jumlah positif benar dan positif palsunya. "Kurva ROC biasanya menetapkan persentase positif benar di sumbu Y dan positif palsunya di sumbu X" (diterjemahkan). Maka, terjalnya kurva ini dan ruang antara garis titik tengah dan kurvanya penting: kamu mau sebuah kurva yang naik ke atas garisnya secepat mungkin. Dalam kasus ini, ada positif palsu di awal, terus kurvanya naik di atas garisnya dengan benar:
+
+![ROC](./images/ROC.png)
+
+Akhirnya, gunakanlah [API `roc_auc_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) Scikit-learn untuk menghitung 'Area Di Bawah Kurva'-nya (ADBK) secara persis:
+
+```python
+auc = roc_auc_score(y_test,y_scores[:,1])
+print(auc)
+```
+Hasilnya adalah `0.6976998904709748`. Mengingat bahwa ADBK itu antara 0 dan 1, lebih besar ADBK-nya lebih baik sebab ADBK model yang 100% benar terus adalah 1; dalam kasus ini, modelnya _lumayan bagus_. 
+
+Nanti dalam pelajaran lebih lanjut tentang klasifikasi, kamu akan belajar bagaimana mengulang untuk membuat nilai-nilai modelmu lebih baik. Tetapi sekian dulu. Selamat! Kamu selesai pelajaran-pelajaran regresi ini!
+
+---
+## 🚀 Tantangan
+
+Masih ada banyak tentang regresi logistik! Tetapi cara paling baik adalah untuk bereksperimen. Carilah sebuah *dataset* yang bisa diteliti seperti ini dan bangunlah sebuah model darinya. Apa yang kamu pelajari? Petunjuk: Coba [Kaggle](https://kaggle.com) untuk *dataset-dataset* menarik.
+## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+
+## Review & Pembelajaran mandiri
+
+Bacalah beberapa halaman pertama [makalah ini dari Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) tentang beberapa penggunaan praktis regresi logistik. Pikirkan tentang tugas-tugas yang lebih baik untuk suatu jenis regresi atau jenis-jenis lainnya yang kita telah pelajari sampai kini. Apa yang akan bekerja paling baik?
+
+## Tugas 
+
+[Coba lagi regresi ini](assignment.md)

From b3e678374fb17409140d829e305f6ebb09505740 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 14:34:40 +0800
Subject: [PATCH 064/368] Update README.zh-cn.md

---
 1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index 7656259dc1..156639c3f9 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -61,7 +61,7 @@
 
 ### 决定一�训练方法
 
-根�您的问题和数�的性质，您将选择一�方法�训练它。�步完� [Scikit-learn的文档](https://scikit-learn.org/stable/user_guide.html) - 我们在本课程中使用 - 您�以探索多�训练模型的方法。 根�您的�验，您�能需��试多���的方法�构建最佳模型。您�能会�历一个过程，在该过程中，数�科学家通过�供看��的数��评估模型的性能，检查准确性��差和其他�低质�的问题，并为手头的任务选择最�适的训练方法。
+根�您的问题和数�的性质，您将选择一�方法�训练它。�步完� [Scikit-learn的文档](https://scikit-learn.org/stable/user_guide.html) - 我们在本课程中使用 - 您�以探索多�训练模型的方法。 根�您的�验，您�能需��试多���的方法�构建最佳模型。您�能会�历一个过程，在该过程中，数�科学家通过�供未�过的数��评估模型的性能，检查准确性��差和其他�低质�的问题，并为手头的任务选择最�适的训练方法。
 
 ### 训练模型
 

From be839fe1e35cdd60efa4e70b9ad7ec334131e841 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 15:05:06 +0800
Subject: [PATCH 065/368] Update README.zh-cn.md

---
 .../2-history-of-ML/translations/README.zh-cn.md     | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index ffa1eb26b8..0491f865a8 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -13,8 +13,8 @@
 
 - 1763, 1812 [��斯定�](https://wikipedia.org/wiki/Bayes%27_theorem) �其�身。该定��其应用是推�的基础，�述了基于先验知识的事件�生的概率。
 - 1805 [最�二乘�论](https://wikipedia.org/wiki/Least_squares)由法国数学家Adrien-Marie Legendre�出。 您将在我们的回归�元中了解这一�论，它有助于数�拟�。
-- 1913 [马尔�夫链](https://wikipedia.org/wiki/Markov_chain)以俄罗斯数学家安德烈马尔�夫的�字命�，用于�述基于先�状�的一系列�能事件。
-- 1957 [感知器](https://wikipedia.org/wiki/Perceptron)是美国心�学家弗兰克·罗森布拉特�明的一�线性分类器，是深度学习�展的基础。
+- 1913 [马尔�夫链](https://wikipedia.org/wiki/Markov_chain)以俄罗斯数学家Andrey Markov的�字命�，用于�述基于先�状�的一系列�能事件。
+- 1957 [感知器](https://wikipedia.org/wiki/Perceptron)是美国心�学家Frank Rosenblatt�明的一�线性分类器，是深度学习�展的基础。
 - 1967 [最近邻](https://wikipedia.org/wiki/Nearest_neighbor)是一�最�设计用于映射路线的算法。 在ML中，它用于检测模�。
 - 1970 [��传播](https://wikipedia.org/wiki/Backpropagation)用于训练[�馈神�网络](https://wikipedia.org/wiki/Feedforward_neural_network)。
 - 1982 [循环神�网络](https://wikipedia.org/wiki/Recurrent_neural_network) 是�自产生时间图的�馈神�网络的人工神�网络。
@@ -22,7 +22,7 @@
 ✅ �点调查。在ML和AI的历�上，还有哪些日期是��的？
 ## 1950: 会�考的机器 
 
-艾伦·图�，一个真正�出的人，[在2019年被公众投票选出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作为20世纪最伟大的科学家，他认为有助于为“会�考的机器�的概念打下基础。他通过创建 [图�测试](https://www.bbc.com/news/technology-18475646)�解决�对者和他自己对这一概念的�验��的需求，您将在我们的 NLP 课程中进行探索。
+Alan Turing，一个真正�出的人，[在2019年被公众投票选出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作为20世纪最伟大的科学家，他认为有助于为“会�考的机器�的概念打下基础。他通过创建 [图�测试](https://www.bbc.com/news/technology-18475646)�解决�对者和他自己对这一概念的�验��的需求，您将在我们的 NLP 课程中进行探索。
 
 ## 1956: 达特茅斯�季研究项目
 
@@ -87,14 +87,14 @@
 
 这个时代��了一个新的时代，ML和AI能够解决早期由于缺�数�和计算能力而导致的一些问题。数��开始迅速增加，�得越�越广泛，无论好�，尤其是2007年左�智能手机的出现，计算能力呈指数级增长，算法也�之�展。这个领域开始�得�熟，因为过去那些�心所欲的日�开始具体化为一�真正的纪律。
 
-## Now
+## 现在
 
 今天，机器学习和人工智能几乎触�我们生活的�一个部分。这个时代�求仔细了解这些算法对人类生活的风险和潜在影�。正如微软的Brad Smith所言，“信�技术引�的问题触���和言论自由等基本人��护的核心。这些问题加�了制造这些产�的科技公�的责任。在我们看�，它们还呼�政府进行深�熟虑的监管，并围绕�接�的用途制定规范�([��](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/))。
 
 未�的情况还有待观察，但了解这些计算机系统以�它们�行的软件和算法是很��的。我们希望这门课程能帮助你更好的�解，以便你自己决定。
 
-[![深度学习的历� ](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "深度学习的历� ")
-> 🎥 点击上图观看视频：Yann LeCun 在本次讲座中讨论深度学习的历� 
+[![深度学习的历�](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "深度学习的历�")
+> 🎥 点击上图观看视频：Yann LeCun在本次讲座中讨论深度学习的历� 
 
 ---
 ## 🚀挑战

From ebbee0c2070d8a148025f094a3865cc707ee1d06 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Fri, 9 Jul 2021 22:20:32 +0900
Subject: [PATCH 066/368] first translation

---
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 1 file changed, 201 insertions(+)
 create mode 100644 2-Regression/2-Data/translations/README.ja.md

diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
new file mode 100644
index 0000000000..122d5badd2
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+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -0,0 +1,201 @@
+# Scikit-learnを用��回帰モデル�構築: データ�準備��視化
+
+> ![Data visualization infographic](../images/data-visualization.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
+
+## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+
+## イントロダクション
+
+Now that you are set up with the tools you need to start tackling machine learning model building with Scikit-learn, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.
+
+
+��レッスン���以下���を学���。
+
+- モデルを構築�る���データ処�方法����
+- データ��視化���るMatplotlib�使�方����
+
+## Asking the right question of your data
+
+The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.
+
+Take a look at the [data](../data/US-pumpkins.csv) provided for this lesson. You can open this .csv file in VS Code. A quick skim immediately shows that there are blanks and a mix of strings and numeric data. There's also a strange column called 'Package' where the data is a mix between 'sacks', 'bins' and other values. The data, in fact, is a bit of a mess.
+
+In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. In this lesson, you will learn how to prepare a raw dataset using standard Python libraries. You will also learn various techniques to visualize the data.
+
+## Case study: 'the pumpkin market'
+
+In this folder you will find a .csv file in the root `data` folder called [US-pumpkins.csv](../data/US-pumpkins.csv) which includes 1757 lines of data about the market for pumpkins, sorted into groupings by city. This is raw data extracted from the [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distributed by the United States Department of Agriculture.
+
+### Preparing data
+
+This data is in the public domain. It can be downloaded in many separate files, per city, from the USDA web site. To avoid too many separate files, we have concatenated all the city data into one spreadsheet, thus we have already _prepared_ the data a bit. Next, let's take a closer look at the data.
+
+### The pumpkin data - early conclusions
+
+What do you notice about this data? You already saw that there is a mix of strings, numbers, blanks and strange values that you need to make sense of.
+
+What question can you ask of this data, using a Regression technique? What about "Predict the price of a pumpkin for sale during a given month". Looking again at the data, there are some changes you need to make to create the data structure necessary for the task. 
+## Exercise - analyze the pumpkin data
+
+Let's use [Pandas](https://pandas.pydata.org/), (the name stands for `Python Data Analysis`) a tool very useful for shaping data, to analyze and prepare this pumpkin data.
+
+### First, check for missing dates
+
+You will first need to take steps to check for missing dates:
+
+1. Convert the dates to a month format (these are US dates, so the format is `MM/DD/YYYY`).
+2. Extract the month to a new column.
+
+Open the _notebook.ipynb_ file in Visual Studio Code and import the spreadsheet in to a new Pandas dataframe.
+
+1. Use the `head()` function to view the first five rows.
+
+    ```python
+    import pandas as pd
+    pumpkins = pd.read_csv('../data/US-pumpkins.csv')
+    pumpkins.head()
+    ```
+
+    ✅ What function would you use to view the last five rows?
+
+1. Check if there is missing data in the current dataframe:
+
+    ```python
+    pumpkins.isnull().sum()
+    ```
+
+    There is missing data, but maybe it won't matter for the task at hand.
+
+1. To make your dataframe easier to work with, drop several of its columns, using `drop()`, keeping only the columns you need: 
+
+    ```python
+    new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
+    pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    ```
+
+### Second, determine average price of pumpkin
+
+Think about how to determine the average price of a pumpkin in a given month. What columns would you pick for this task? Hint: you'll need 3 columns.
+
+Solution: take the average of the `Low Price` and `High Price` columns to populate the new Price column, and convert the Date column to only show the month. Fortunately, according to the check above, there is no missing data for dates or prices.
+
+1. To calculate the average, add the following code:
+
+    ```python
+    price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
+    
+    month = pd.DatetimeIndex(pumpkins['Date']).month
+    
+    ```
+
+   ✅ Feel free to print any data you'd like to check using `print(month)`.
+
+2. Now, copy your converted data into a fresh Pandas dataframe:
+
+    ```python
+    new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
+    ```
+
+    Printing out your dataframe will show you a clean, tidy dataset on which you can build your new regression model.
+
+### But wait! There's something odd here
+
+If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in '1 1/9 bushel' measures, and some in '1/2 bushel' measures, some per pumpkin, some per pound, and some in big boxes with varying widths.
+
+> Pumpkins seem very hard to weigh consistently
+
+Digging into the original data, it's interesting that anything with `Unit of Sale` equalling 'EACH' or 'PER BIN' also have the `Package` type per inch, per bin, or 'each'. Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string 'bushel' in their `Package` column.
+
+1. Add a filter at the top of the file, under the initial .csv import:
+
+    ```python
+    pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
+    ```
+
+    If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel.
+
+### But wait! There's one more thing to do
+
+Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, so do some math to standardize it.
+
+1. Add these lines after the block creating the new_pumpkins dataframe:
+
+    ```python
+    new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
+    
+    new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
+    ```
+
+✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!
+
+Now, you can analyze the pricing per unit based on their bushel measurement. If you print out the data one more time, you can see how it's standardized.
+
+✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.
+
+## Visualization Strategies
+
+Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover. 
+
+Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.
+
+One data visualization libary that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson).
+
+> Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
+
+## Exercise - experiment with Matplotlib
+
+Try to create some basic plots to display the new dataframe you just created. What would a basic line plot show?
+
+1. Import Matplotlib at the top of the file, under the Pandas import:
+
+    ```python
+    import matplotlib.pyplot as plt
+    ```
+
+1. Rerun the entire notebook to refresh.
+1. At the bottom of the notebook, add a cell to plot the data as a box:
+
+    ```python
+    price = new_pumpkins.Price
+    month = new_pumpkins.Month
+    plt.scatter(price, month)
+    plt.show()
+    ```
+
+    ![A scatterplot showing price to month relationship](../images/scatterplot.png)
+
+    Is this a useful plot? Does anything about it surprise you?
+
+    It's not particularly useful as all it does is display in your data as a spread of points in a given month.
+
+### Make it useful
+
+To get charts to display useful data, you usually need to group the data somehow. Let's try creating a plot where the y axis shows the months and the data demonstrates the distribution of data.
+
+1. Add a cell to create a grouped bar chart:
+
+    ```python
+    new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
+    plt.ylabel("Pumpkin Price")
+    ```
+
+    ![A bar chart showing price to month relationship](../images/barchart.png)
+
+    This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?
+
+---
+
+## 🚀Challenge
+
+Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+
+## Review & Self Study
+
+Take a look at the many ways to visualize data. Make a list of the various libraries available and note which are best for given types of tasks, for example 2D visualizations vs. 3D visualizations. What do you discover?
+
+## Assignment
+
+[Exploring visualization](assignment.md)

From f131b675cad86915d2fb3bff2e1b6794c3124695 Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Fri, 9 Jul 2021 20:41:03 +0700
Subject: [PATCH 067/368] Add Indonesian translation for 1-2 README

---
 .../2-history-of-ML/translations/README.id.md | 116 ++++++++++++++++++
 1 file changed, 116 insertions(+)
 create mode 100644 1-Introduction/2-history-of-ML/translations/README.id.md

diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
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index 0000000000..4053785b3f
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@@ -0,0 +1,116 @@
+# Sejarah Machine Learning
+
+![Ringkasan dari Sejarah Machine Learning dalam sebuah catatan sketsa](../../sketchnotes/ml-history.png)
+> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+
+Dalam pelajaran ini, kita akan membahas tonggak utama dalam sejarah Machine Learning dan Artificial Intelligence. 
+
+Sejarah Artifical Intelligence, AI, sebagai bidang terkait dengan sejarah Machine Learning, karena algoritma dan kemajuan komputasi yang mendukung ML dimasukkan ke dalam pengembangan AI. Penting untuk diingat bahwa, meski bidang-bidang ini sebagai bidang-bidang penelitian yang berbeda mulai terbentuk pada 1950-an, [algoritmik, statistik, matematik, komputasi dan penemuan teknis](https://wikipedia.org/wiki/Timeline_of_machine_learning) penting sudah ada sebelumnya, dan saling tumpang tindih di era ini. Faktanya, orang-orang telah memikirkan pertanyaan-pertanyaan ini selama [ratusan tahun](https://wikipedia.org/wiki/History_of_artificial_intelligence): artikel ini membahas dasar-dasar intelektual historis dari gagasan 'mesin yang berpikir'. 
+
+## Penemuan penting
+
+- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) dan para pendahulu. Teorema ini dan penerapannya mendasari inferensi, mendeskripsikan kemungkinan suatu peristiwa terjadi berdasarkan pengetahuan sebelumnya. 
+- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) oleh matematikawan Perancis Adrien-Marie Legendre. Teori ini yang akan kamu pelajari di unit Regresi, ini membantu dalam *data fitting*. 
+- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) dinamai setelah matematikawan Rusia, Andrey Markov, digunakan untuk mendeskripsikan sebuah urutan dari kejadian-kejadian yang mungkin terjadi berdasarkan kondisi sebelumnya.
+- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) adalah sebuah tipe dari *linear classifier* yang ditemukan oleh psikolog Amerika, Frank Rosenblatt, yang mendasari kemajuan dalam *Deep Learning*.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) adalah sebuah algoritma yang pada awalnya didesain untuk memetakan rute. Dalam konteks ML, ini digunakan untuk mendeteksi berbagai pola.
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) digunakan untuk men-*train* [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network). 
+- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) adalah *artificial neural networks* yang berasal dari *feedforward neural networks* yang membuat grafik sementara. 
+
+✅ Lakukan sebuah riset kecil. Tanggal berapa lagi yang merupakan tanggal penting dalam sejarah ML dan AI?
+## 1950: Mesin yang berpikir
+
+Alan Turing, merupakan orang luar biasa yang terpilih oleh [publik di tahun 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) sebagai ilmuwan terhebat di abad 20, diberikan penghargaan karena membantu membuat fondasi dari sebuah konsep 'mesin yang bisa berpikir', Dia berjuang menghadapi orang-orang yang menentangnya dan keperluannya sendiri untuk bukti empiris dari konsep ini dengan membuat [Turing Test](https://www.bbc.com/news/technology-18475646), yang mana akan kamu jelajahi di pelajaran NLP kami.
+
+## 1956: Proyek Riset Musim Panas Dartmouth
+
+"Proyek Riset Musim Panas Dartmouth pada *artificial intelligence* merupakan sebuah acara penemuan untuk *artificial intelligence* sebagai sebuah bidang," dan dari sinilah istilah '*artificial intelligence*' diciptakan ([sumber](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> Setiap aspek pembelajaran atau fitur kecerdasan lainnya pada prinsipnya dapat dideskripsikan dengan sangat tepat sehingga sebuah mesin dapat dibuat untuk mensimulasikannya. 
+
+Ketua peneliti, profesor matematika John McCarthy, berharap "untuk meneruskan dasar dari dugaan bahwa setiap aspek pembelajaran atau fitur kecerdasan lainnya pada prinsipnya dapat dideskripsikan dengan sangat tepat sehingga mesin dapat dibuat untuk mensimulasikannya." Marvin Minsky, seorang tokoh terkenal di bidang ini juga termasuk sebagai peserta penelitian.
+
+Workshop ini dipuji karena telah memprakarsai dan mendorong beberapa diskusi termasuk "munculnya metode simbolik, sistem yang berfokus pada domain terbatas (sistem pakar awal), dan sistem deduktif versus sistem induktif." ([sumber](https://wikipedia.org/wiki/Dartmouth_workshop)).
+
+## 1956 - 1974: "Tahun-tahun Emas"
+
+Dari tahun 1950-an hingga pertengahan 70-an, optimisme memuncak dengan harapan bahwa AI dapat memecahkan banyak masalah. Pada tahun 1967, Marvin Minsky dengan yakin menyatakan bahwa "Dalam satu generasi ... masalah menciptakan '*artificial intelligence*' secara substansial akan terpecahkan." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+penelitian *natural language processing* berkembang, pencarian disempurnakan dan dibuat lebih kuat, dan konsep '*micro-worlds*' diciptakan, di mana tugas-tugas sederhana diselesaikan menggunakan instruksi bahasa sederhana. 
+
+Penelitian didanai dengan baik oleh lembaga pemerintah, banyak kemajuan dibuat dalam komputasi dan algoritma, dan prototipe mesin cerdas dibangun. Beberapa mesin tersebut antara lain: 
+
+* [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), yang bisa bermanuver dan memutuskan bagaimana melakukan tugas-tugas secara 'cerdas'. 
+
+    ![Shakey, an intelligent robot](images/shakey.jpg)
+    > Shakey pada 1972
+
+* Eliza, sebuah 'chatterbot' awal, dapat mengobrol dengan orang-orang dan bertindak sebagai 'terapis' primitif. Kamu akan belajar lebih banyak tentang Eliza dalam pelajaran NLP. 
+
+    ![Eliza, a bot](images/eliza.png)
+    > Sebuah versi dari Eliza, sebuah *chatbot*
+
+* "Blocks world" adalah contoh sebuah *micro-world* dimana balok dapat ditumpuk dan diurutkan, dan pengujian eksperimen mesin pengajaran untuk membuat keputusan dapat dilakukan. Kemajuan yang dibuat dengan *library-library* seperti [SHRDLU](https://wikipedia.org/wiki/SHRDLU) membantu mendorong kemajuan pemrosesan bahasa.
+
+    [![blocks world dengan SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world dengan SHRDLU")
+    
+    > 🎥 Klik gambar diatas untuk melihat video: Blocks world with SHRDLU
+
+## 1974 - 1980: "Musim Dingin AI"
+
+Pada pertengahan 1970-an, semakin jelas bahwa kompleksitas pembuatan 'mesin cerdas' telah diremehkan dan janjinya, mengingat kekuatan komputasi yang tersedia, telah dilebih-lebihkan. Pendanaan telah habis dan kepercayaan dalam bidang ini menurun. Beberapa masalah yang memengaruhi kepercayaan diri termasuk: 
+
+- **Keterbatasan**. Kekuatan komputasi terlalu terbatas.
+- **Ledakan kombinatorial**. Jumlah parameter yang perlu di-*train* bertambah secara eksponensial karena lebih banyak hal yang diminta dari komputer, tanpa evolusi paralel dari kekuatan dan kemampuan komputasi. 
+- **Kekurangan data**. Adanya kekurangan data yang menghalangi proses pengujian, pengembangan, dan penyempurnaan algoritma.
+- **Apakah kita menanyakan pertanyaan yang tepat?**. Pertanyaan-pertanyaan yang diajukan pun mulai dipertanyakan kembali. Para peneliti mulai melontarkan kritik tentang pendekatan mereka 
+  - Tes Turing mulai dipertanyakan, di antara ide-ide lain, dari 'teori ruang Cina' yang mengemukakan bahwa, "memprogram komputer digital mungkin membuatnya tampak memahami bahasa tetapi tidak dapat menghasilkan pemahaman yang sebenarnya." ([sumber](https://plato.stanford.edu/entries/chinese-room/))
+  - Etika memperkenalkan kecerdasan buatan seperti "terapis" ELIZA ke dalam masyarakat telah ditantang. 
+
+Pada saat yang sama, berbagai aliran pemikiran AI mulai terbentuk. Sebuah dikotomi didirikan antara praktek ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). Lab _Scruffy_ mengubah program selama berjam-jam sampai mendapat hasil yang diinginkan. Lab _Neat_ "berfokus pada logika dan penyelesaian masalah formal". ELIZA dan SHRDLU adalah sistem _scruffy_ yang terkenal. Pada tahun 1980-an, karena perkembangan permintaan untuk membuat sistem ML yang dapat direproduksi, pendekatan _neat_ secara bertahap menjadi yang terdepan karena hasilnya lebih dapat dijelaskan.
+
+## 1980s Sistem Pakar
+
+Seiring berkembangnya bidang ini, manfaatnya bagi bisnis menjadi lebih jelas, dan begitu pula dengan menjamurnya 'sistem pakar' pada tahun 1980-an. "Sistem pakar adalah salah satu bentuk perangkat lunak artificial intelligence (AI) pertama yang benar-benar sukses."  ([sumber](https://wikipedia.org/wiki/Expert_system)).
+
+Tipe sistem ini sebenarnya adalah _hybrid_, sebagian terdiri dari mesin aturan yang mendefinisikan *business requirement*, dan mesin inferensi yang memanfaatkan sistem aturan untuk menyimpulkan fakta baru. 
+
+Pada era ini juga terlihat adanya peningkatan perhatian pada jaringan saraf. 
+
+## 1987 - 1993: AI 'Chill'
+
+Perkembangan perangkat keras sistem pakar terspesialisasi memiliki efek yang tidak menguntungkan karena menjadi terlalu terspesialiasasi. Munculnya komputer pribadi juga bersaing dengan sistem yang besar, terspesialisasi, dan terpusat ini. Demokratisasi komputasi telah dimulai, dan pada akhirnya membuka jalan untuk ledakan modern dari *big data*. 
+
+## 1993 - 2011
+
+Pada zaman ini memperlihatkan era baru bagi ML dan AI untuk dapat menyelesaikan beberapa masalah yang sebelumnya disebabkan oleh kurangnya data dan daya komputasi. Jumlah data mulai meningkat dengan cepat dan tersedia secara luas, terlepas dari baik dan buruknya, terutama dengan munculnya *smartphone* sekitar tahun 2007. Daya komputasi berkembang secara eksponensial, dan algoritma juga berkembang saat itu. Bidang ini mulai mengalami kedewasaan karena hari-hari yang tidak beraturan di masa lalu mulai terbentuk menjadi disiplin yang sebenarnya. 
+
+## Sekarang
+
+Saat ini, *machine learning* dan AI hampir ada di setiap bagian dari kehidupan kita. Era ini menuntut pemahaman yang cermat tentang risiko dan efek potensi dari algoritma ini pada kehidupan manusia. Seperti yang telah dinyatakan oleh Brad Smith dari Microsoft, "Teknologi informasi mengangkat isu-isu yang menjadi inti dari perlindungan hak asasi manusia yang mendasar seperti privasi dan kebebasan berekspresi. Masalah-masalah ini meningkatkan tanggung jawab bagi perusahaan teknologi yang menciptakan produk-produk ini. Dalam pandangan kami, mereka juga menyerukan peraturan pemerintah yang bijaksana dan untuk pengembangan norma-norma seputar penggunaan yang wajar" ([sumber](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk memahami sistem komputer dan perangkat lunak serta algoritma yang dijalankannya. Kami berharap kurikulum ini akan membantu kamu untuk mendapatkan pemahaman yang lebih baik sehingga kamu dapat memutuskan sendiri. 
+
+[![Sejarah Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "Sejarah Deep Learning")
+> 🎥 Klik gambar diatas untuk melihat video: Yann LeCun mendiskusikan sejarah dari Deep Learning dalam pelajaran ini
+
+---
+## 🚀Tantangan
+
+Gali salah satu momen bersejarah ini dan pelajari lebih lanjut tentang orang-orang di baliknya. Ada karakter yang menarik, dan tidak ada penemuan ilmiah yang pernah dibuat dalam kekosongan budaya. Apa yang kamu temukan? 
+
+## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+
+## Ulasan & Belajar Mandiri
+
+Berikut adalah item untuk ditonton dan didengarkan: 
+
+[Podcast dimana Amy Boyd mendiskusikan evolusi dari AI](http://runasradio.com/Shows/Show/739)
+
+[![Sejarah AI oleh Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Sejarah AI oleh Amy Boyd")
+
+## Tugas
+
+[Membuat sebuah *timeline*](assignment.md)

From 9d6028f32ccaa03b98bfc28ceceb312d5184c38c Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Fri, 9 Jul 2021 15:52:09 +0200
Subject: [PATCH 068/368] Italian Translation - Chapter 1 - Final

---
 .../2-history-of-ML/translations/README.it.md | 236 +++++-----
 .../3-fairness/translations/README.it.md      | 424 +++++++++---------
 .../3-fairness/translations/assignment.it.md  |  22 +-
 .../translations/README.it.md                 | 220 ++++-----
 1-Introduction/translations/README.it.md      |  44 +-
 5 files changed, 473 insertions(+), 473 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.it.md b/1-Introduction/2-history-of-ML/translations/README.it.md
index 74caa19d7b..c7e59fd77e 100644
--- a/1-Introduction/2-history-of-ML/translations/README.it.md
+++ b/1-Introduction/2-history-of-ML/translations/README.it.md
@@ -1,118 +1,118 @@
-# Storia di machine learning
-
-![Riepilogo della storia di machine learning in uno sketchnote](../../../sketchnotes/ml-history.png)
-> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
-
-In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale.
-
-La storia dell'intelligenza artificiale, AI, come campo è intrecciata con la storia di machine learning, poiché gli algoritmi e i progressi computazionali alla base di machine learning hanno contribuito allo sviluppo dell'intelligenza artificiale. È utile ricordare che, mentre questi campi come distinte aree di indagine hanno cominciato a cristallizzarsi negli anni '50, importanti [scoperte algoritmiche, statistiche, matematiche, computazionali e tecniche](https://wikipedia.org/wiki/Timeline_of_machine_learning) hanno preceduto e si sono sovrapposte a questa era. In effetti, le persone hanno riflettuto su queste domande per [centinaia di anni](https://wikipedia.org/wiki/History_of_artificial_intelligence): questo articolo discute le basi intellettuali storiche dell'idea di una "macchina pensante".
-
-## Scoperte rilevanti
-
-- 1763, 1812 [Teorema di Bayes](https://it.wikipedia.org/wiki/Teorema_di_Bayes) e suoi predecessori. Questo teorema e le sue applicazioni sono alla base dell'inferenza, descrivendo la probabilità che un evento si verifichi in base alla conoscenza precedente.
-- 1805 [Metodo dei Minimi Quadrati](https://it.wikipedia.org/wiki/Metodo_dei_minimi_quadrati) del matematico francese Adrien-Marie Legendre. Questa teoria, che verrà trattata nell'unità Regressione, aiuta nell'adattamento dei dati.
-- 1913 [Processo Markoviano](https://it.wikipedia.org/wiki/Processo_markoviano) dal nome del matematico russo Andrey Markov è usato per descrivere una sequenza di possibili eventi basati su uno stato precedente.
-- 1957 [Percettrone](https://it.wikipedia.org/wiki/Percettrone) è un tipo di classificatore lineare inventato dallo psicologo americano Frank Rosenblatt che sta alla base dei progressi nel deep learning.
-- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) è un algoritmo originariamente progettato per mappare i percorsi. In un contesto ML viene utilizzato per rilevare i modelli.
-- 1970 [La Retropropagazione dell'Errore](https://it.wikipedia.org/wiki/Retropropagazione_dell'errore) viene utilizzata per addestrare [le reti neurali feed-forward](https://it.wikipedia.org/wiki/Rete_neurale_feed-forward).
-- Le [Reti Neurali Ricorrenti](https://it.wikipedia.org/wiki/Rete_neurale_ricorrente) del 1982 sono reti neurali artificiali derivate da reti neurali feedforward che creano grafici temporali.
-
-✅ Fare una piccola ricerca. Quali altre date si distinguono come fondamentali nella storia del machine learning e dell'intelligenza artificiale?
-## 1950: Macchine che pensano
-
-Alan Turing, una persona davvero notevole che è stata votata [dal pubblico nel 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) come il più grande scienziato del XX secolo, è accreditato per aver contribuito a gettare le basi per il concetto di "macchina in grado di pensare". Ha affrontato gli oppositori e il suo stesso bisogno di prove empiriche di questo concetto in parte creando il [Test di Turing](https://www.bbc.com/news/technology-18475646), che verrà esplorato nelle lezioni di NLP (elaborazione del linguaggio naturale).
-
-## 1956: Progetto di Ricerca Estivo Dartmouth
-
-"Il Dartmouth Summer Research Project sull'intelligenza artificiale è stato un evento seminale per l'intelligenza artificiale come campo", qui è stato coniato il termine "intelligenza artificiale" ([fonte](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
-
-> In linea di principio, ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza può essere descritto in modo così preciso che si può costruire una macchina per simularlo.
-
-Il ricercatore capo, il professore di matematica John McCarthy, sperava "di procedere sulla base della congettura che ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza possa in linea di principio essere descritta in modo così preciso che si possa costruire una macchina per simularlo". I partecipanti includevano un altro luminare nel campo, Marvin Minsky.
-
-Il workshop è accreditato di aver avviato e incoraggiato diverse discussioni tra cui "l'ascesa di metodi simbolici, sistemi focalizzati su domini limitati (primi sistemi esperti) e sistemi deduttivi contro sistemi induttivi". ([fonte](https://wikipedia.org/wiki/Dartmouth_workshop)).
-
-## 1956 - 1974: "Gli anni d'oro"
-
-Dagli anni '50 fino alla metà degli anni '70, l'ottimismo era alto nella speranza che l'AI potesse risolvere molti problemi. Nel 1967, Marvin Minsky dichiarò con sicurezza che "Entro una generazione... il problema della creazione di 'intelligenza artificiale' sarà sostanzialmente risolto". (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
-
-La ricerca sull'elaborazione del linguaggio naturale è fiorita, la ricerca è stata perfezionata e resa più potente ed è stato creato il concetto di "micro-mondi", in cui compiti semplici sono stati completati utilizzando istruzioni in linguaggio semplice.
-
-La ricerca è stata ben finanziata dalle agenzie governative, sono stati fatti progressi nel calcolo e negli algoritmi e sono stati costruiti prototipi di macchine intelligenti. Alcune di queste macchine includono:
-
-* [Shakey il robot](https://wikipedia.org/wiki/Shakey_the_robot), che poteva manovrare e decidere come eseguire i compiti "intelligentemente".
-
-   ![Shakey, un robot intelligente](../images/shakey.jpg)
-   > Shakey nel 1972
-
-* Eliza, una delle prime "chatterbot", poteva conversare con le persone e agire come una "terapeuta" primitiva. Si Imparerà di più su Eliza nelle lezioni di NLP.
-
-   ![Eliza, un bot](../images/eliza.png)
-   > Una versione di Eliza, un chatbot
-
-* Il "mondo dei blocchi" era un esempio di un micromondo in cui i blocchi potevano essere impilati e ordinati e si potevano testare esperimenti su macchine per insegnare a prendere decisioni. I progressi realizzati con librerie come [SHRDLU](https://it.wikipedia.org/wiki/SHRDLU) hanno contribuito a far progredire l'elaborazione del linguaggio.
-
-    [![Il mondo dei blocchi con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Il mondo dei blocchi con SHRDLU")
-
-   > 🎥 Fare clic sull'immagine sopra per un video: Blocks world con SHRDLU
-
-## 1974 - 1980: "L'inverno dell'AI"
-
-Verso la metà degli anni '70, era diventato evidente che la complessità della creazione di "macchine intelligenti" era stata sottovalutata e che la sua promessa, data la potenza di calcolo disponibile, era stata esagerata. I finanziamenti si sono prosciugati e la fiducia nel settore è rallentata. Alcuni problemi che hanno influito sulla fiducia includono:
-
-- **Limitazioni**. La potenza di calcolo era troppo limitata.
-- **Esplosione combinatoria**. La quantità di parametri necessari per essere addestrati è cresciuta in modo esponenziale man mano che veniva chiesto di più ai computer, senza un'evoluzione parallela della potenza e delle capacità di calcolo.
-- **Scarsità di dati**. C'era una scarsità di dati che ostacolava il processo di test, sviluppo e perfezionamento degli algoritmi.
-- **Stiamo facendo le domande giuste?**. Le stesse domande che venivano poste cominciarono ad essere messe in discussione. I ricercatori hanno iniziato a criticare i loro approcci:
-   - I test di Turing furono messi in discussione attraverso, tra le altre idee, la "teoria della stanza cinese" che postulava che "la programmazione di un computer digitale può far sembrare che capisca il linguaggio ma non potrebbe produrre una vera comprensione". ([fonte](https://plato.stanford.edu/entries/chinese-room/))
-   - L'etica dell'introduzione di intelligenze artificiali come la "terapeuta" ELIZA nella società è stata messa in discussione.
-
-Allo stesso tempo, iniziarono a formarsi varie scuole di pensiero sull'AI. È stata stabilita una dicotomia tra pratiche ["scruffy" contro "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) ;. I laboratori _scruffy_ ottimizzavano i programmi per ore fino a quando non ottenevano i risultati desiderati. I laboratori _Neat_ "si focalizzavano sulla logica e sulla risoluzione formale dei problemi". ELIZA e SHRDLU erano ben noti _sistemi scruffy_. Negli anni '80, quando è emersa la richiesta di rendere riproducibili i sistemi ML, l'_approccio neat_ ha gradualmente preso il sopravvento in quanto i suoi risultati sono più spiegabili.
-
-## Sistemi esperti degli anni '80
-
-Man mano che il settore cresceva, i suoi vantaggi per le imprese diventavano più chiari e negli anni '80 lo stesso accadeva con la proliferazione di "sistemi esperti". "I sistemi esperti sono stati tra le prime forme di software di intelligenza artificiale (AI) di vero successo". ([fonte](https://wikipedia.org/wiki/Expert_system)).
-
-Questo tipo di sistema è in realtà _ibrido_, costituito in parte da un motore di regole che definisce i requisiti aziendali e un motore di inferenza che sfrutta il sistema di regole per dedurre nuovi fatti.
-
-Questa era ha visto anche una crescente attenzione rivolta alle reti neurali.
-
-## 1987 - 1993: AI 'Chill'
-
-La proliferazione di hardware specializzato per sistemi esperti ha avuto lo sfortunato effetto di diventare troppo specializzato. L'ascesa dei personal computer ha anche gareggiato con questi grandi sistemi centralizzati specializzati. La democratizzazione dell'informatica era iniziata e alla fine ha spianato la strada alla moderna esplosione dei big data.
-
-## 1993 - 2011
-
-Questa epoca ha visto una nuova era per ML e AI per essere in grado di risolvere alcuni dei problemi che erano stati causati in precedenza dalla mancanza di dati e potenza di calcolo. La quantità di dati ha iniziato ad aumentare rapidamente e a diventare più ampiamente disponibile, nel bene e nel male, soprattutto con l'avvento degli smartphone intorno al 2007. La potenza di calcolo si è ampliata in modo esponenziale e gli algoritmi si sono evoluti di pari passo. Il campo ha iniziato a maturare quando i giorni a ruota libera del passato hanno iniziato a cristallizzarsi in una vera disciplina.
-
-## Adesso
-
-Oggi, machine learning e intelligenza artificiale toccano quasi ogni parte della nostra vita. Questa era richiede un'attenta comprensione dei rischi e dei potenziali effetti di questi algoritmi sulle vite umane. Come ha affermato Brad Smith di Microsoft, "La tecnologia dell'informazione solleva questioni che vanno al cuore delle protezioni fondamentali dei diritti umani come la privacy e la libertà di espressione. Questi problemi aumentano la responsabilità delle aziende tecnologiche che creano questi prodotti. A nostro avviso, richiedono anche un'attenta regolamentazione del governo e lo sviluppo di norme sugli usi accettabili" ([fonte](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
-
-Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi informatici e il software e gli algoritmi che eseguono. Ci si augura che questo programma di studi aiuti ad acquisire una migliore comprensione in modo che si possa decidere in autonomia.
-
-[![La storia del deeplearningLa](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 " storia del deep learning")
-> 🎥 Fare clic sull'immagine sopra per un video: Yann LeCun discute la storia del deep learning in questa lezione
-
----
-
-## 🚀 Sfida
-
-Approfondire uno di questi momenti storici e scoprire
- di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto?
-
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
-
-## Revisione e Auto Apprendimento
-
-Ecco gli elementi da guardare e ascoltare:
-
-[Questo podcast in cui Amy Boyd discute l'evoluzione dell'AI](http://runasradio.com/Shows/Show/739)
-
-[![La storia dell'AI di Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La storia dell'AI di Amy Boyd")
-
-## Compito
-
-[Creare una sequenza temporale](assignment.it.md)
+# Storia di machine learning
+
+![Riepilogo della storia di machine learning in uno sketchnote](../../../sketchnotes/ml-history.png)
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+
+In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale.
+
+La storia dell'intelligenza artificiale, AI, come campo è intrecciata con la storia di machine learning, poiché gli algoritmi e i progressi computazionali alla base di machine learning hanno contribuito allo sviluppo dell'intelligenza artificiale. È utile ricordare che, mentre questi campi come distinte aree di indagine hanno cominciato a cristallizzarsi negli anni '50, importanti [scoperte algoritmiche, statistiche, matematiche, computazionali e tecniche](https://wikipedia.org/wiki/Timeline_of_machine_learning) hanno preceduto e si sono sovrapposte a questa era. In effetti, le persone hanno riflettuto su queste domande per [centinaia di anni](https://wikipedia.org/wiki/History_of_artificial_intelligence); questo articolo discute le basi intellettuali storiche dell'idea di una "macchina pensante".
+
+## Scoperte rilevanti
+
+- 1763, 1812 [Teorema di Bayes](https://it.wikipedia.org/wiki/Teorema_di_Bayes) e suoi predecessori. Questo teorema e le sue applicazioni sono alla base dell'inferenza, descrivendo la probabilità che un evento si verifichi in base alla conoscenza precedente.
+- 1805 [Metodo dei Minimi Quadrati](https://it.wikipedia.org/wiki/Metodo_dei_minimi_quadrati) del matematico francese Adrien-Marie Legendre. Questa teoria, che verrà trattata nell'unità Regressione, aiuta nell'adattamento dei dati.
+- 1913 [Processo Markoviano](https://it.wikipedia.org/wiki/Processo_markoviano) dal nome del matematico russo Andrey Markov è usato per descrivere una sequenza di possibili eventi basati su uno stato precedente.
+- 1957 [Percettrone](https://it.wikipedia.org/wiki/Percettrone) è un tipo di classificatore lineare inventato dallo psicologo americano Frank Rosenblatt che sta alla base dei progressi nel deep learning.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) è un algoritmo originariamente progettato per mappare i percorsi. In un contesto ML viene utilizzato per rilevare i modelli.
+- 1970 [La Retropropagazione dell'Errore](https://it.wikipedia.org/wiki/Retropropagazione_dell'errore) viene utilizzata per addestrare [le reti neurali feed-forward](https://it.wikipedia.org/wiki/Rete_neurale_feed-forward).
+- Le [Reti Neurali Ricorrenti](https://it.wikipedia.org/wiki/Rete_neurale_ricorrente) del 1982 sono reti neurali artificiali derivate da reti neurali feedforward che creano grafici temporali.
+
+✅ Fare una piccola ricerca. Quali altre date si distinguono come fondamentali nella storia del machine learning e dell'intelligenza artificiale?
+## 1950: Macchine che pensano
+
+Alan Turing, una persona davvero notevole che è stata votata [dal pubblico nel 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) come il più grande scienziato del XX secolo, è accreditato per aver contribuito a gettare le basi per il concetto di "macchina in grado di pensare". Ha affrontato gli oppositori e il suo stesso bisogno di prove empiriche di questo concetto in parte creando il [Test di Turing](https://www.bbc.com/news/technology-18475646), che verrà esplorato nelle lezioni di NLP (elaborazione del linguaggio naturale).
+
+## 1956: Progetto di Ricerca Estivo Dartmouth
+
+"Il Dartmouth Summer Research Project sull'intelligenza artificiale è stato un evento seminale per l'intelligenza artificiale come campo", qui è stato coniato il termine "intelligenza artificiale" ([fonte](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> In linea di principio, ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza può essere descritto in modo così preciso che si può costruire una macchina per simularlo.
+
+Il ricercatore capo, il professore di matematica John McCarthy, sperava "di procedere sulla base della congettura che ogni aspetto dell'apprendimento o qualsiasi altra caratteristica dell'intelligenza possa in linea di principio essere descritta in modo così preciso che si possa costruire una macchina per simularlo". I partecipanti includevano un altro luminare nel campo, Marvin Minsky.
+
+Il workshop è accreditato di aver avviato e incoraggiato diverse discussioni tra cui "l'ascesa di metodi simbolici, sistemi focalizzati su domini limitati (primi sistemi esperti) e sistemi deduttivi contro sistemi induttivi". ([fonte](https://wikipedia.org/wiki/Dartmouth_workshop)).
+
+## 1956 - 1974: "Gli anni d'oro"
+
+Dagli anni '50 fino alla metà degli anni '70, l'ottimismo era alto nella speranza che l'AI potesse risolvere molti problemi. Nel 1967, Marvin Minsky dichiarò con sicurezza che "Entro una generazione... il problema della creazione di 'intelligenza artificiale' sarà sostanzialmente risolto". (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+La ricerca sull'elaborazione del linguaggio naturale è fiorita, la ricerca è stata perfezionata e resa più potente ed è stato creato il concetto di "micro-mondi", in cui compiti semplici sono stati completati utilizzando istruzioni in linguaggio semplice.
+
+La ricerca è stata ben finanziata dalle agenzie governative, sono stati fatti progressi nel calcolo e negli algoritmi e sono stati costruiti prototipi di macchine intelligenti. Alcune di queste macchine includono:
+
+* [Shakey il robot](https://wikipedia.org/wiki/Shakey_the_robot), che poteva manovrare e decidere come eseguire i compiti "intelligentemente".
+
+   ![Shakey, un robot intelligente](../images/shakey.jpg)
+   > Shakey nel 1972
+
+* Eliza, una delle prime "chatterbot", poteva conversare con le persone e agire come una "terapeuta" primitiva. Si Imparerà di più su Eliza nelle lezioni di NLP.
+
+   ![Eliza, un bot](../images/eliza.png)
+   > Una versione di Eliza, un chatbot
+
+* Il "mondo dei blocchi" era un esempio di un micromondo in cui i blocchi potevano essere impilati e ordinati e si potevano testare esperimenti su macchine per insegnare a prendere decisioni. I progressi realizzati con librerie come [SHRDLU](https://it.wikipedia.org/wiki/SHRDLU) hanno contribuito a far progredire l'elaborazione del linguaggio.
+
+    [![Il mondo dei blocchi con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Il mondo dei blocchi con SHRDLU")
+
+   > 🎥 Fare clic sull'immagine sopra per un video: Blocks world con SHRDLU
+
+## 1974 - 1980: "L'inverno dell'AI"
+
+Verso la metà degli anni '70, era diventato evidente che la complessità della creazione di "macchine intelligenti" era stata sottovalutata e che la sua promessa, data la potenza di calcolo disponibile, era stata esagerata. I finanziamenti si sono prosciugati e la fiducia nel settore è rallentata. Alcuni problemi che hanno influito sulla fiducia includono:
+
+- **Limitazioni**. La potenza di calcolo era troppo limitata.
+- **Esplosione combinatoria**. La quantità di parametri necessari per essere addestrati è cresciuta in modo esponenziale man mano che veniva chiesto di più ai computer, senza un'evoluzione parallela della potenza e delle capacità di calcolo.
+- **Scarsità di dati**. C'era una scarsità di dati che ostacolava il processo di test, sviluppo e perfezionamento degli algoritmi.
+- **Stiamo facendo le domande giuste?**. Le stesse domande che venivano poste cominciarono ad essere messe in discussione. I ricercatori hanno iniziato a criticare i loro approcci:
+   - I test di Turing furono messi in discussione attraverso, tra le altre idee, la "teoria della stanza cinese" che postulava che "la programmazione di un computer digitale può far sembrare che capisca il linguaggio ma non potrebbe produrre una vera comprensione". ([fonte](https://plato.stanford.edu/entries/chinese-room/))
+   - L'etica dell'introduzione di intelligenze artificiali come la "terapeuta" ELIZA nella società è stata messa in discussione.
+
+Allo stesso tempo, iniziarono a formarsi varie scuole di pensiero sull'AI. È stata stabilita una dicotomia tra pratiche ["scruffy" contro "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). I laboratori _scruffy_ ottimizzavano i programmi per ore fino a quando non ottenevano i risultati desiderati. I laboratori _Neat_ "si focalizzavano sulla logica e sulla risoluzione formale dei problemi". ELIZA e SHRDLU erano ben noti _sistemi scruffy_. Negli anni '80, quando è emersa la richiesta di rendere riproducibili i sistemi ML, l'_approccio neat_ ha gradualmente preso il sopravvento in quanto i suoi risultati sono più spiegabili.
+
+## Sistemi esperti degli anni '80
+
+Man mano che il settore cresceva, i suoi vantaggi per le imprese diventavano più chiari e negli anni '80 lo stesso accadeva con la proliferazione di "sistemi esperti". "I sistemi esperti sono stati tra le prime forme di software di intelligenza artificiale (AI) di vero successo". ([fonte](https://wikipedia.org/wiki/Expert_system)).
+
+Questo tipo di sistema è in realtà _ibrido_, costituito in parte da un motore di regole che definisce i requisiti aziendali e un motore di inferenza che sfrutta il sistema di regole per dedurre nuovi fatti.
+
+Questa era ha visto anche una crescente attenzione rivolta alle reti neurali.
+
+## 1987 - 1993: AI 'Chill'
+
+La proliferazione di hardware specializzato per sistemi esperti ha avuto lo sfortunato effetto di diventare troppo specializzato. L'ascesa dei personal computer ha anche gareggiato con questi grandi sistemi centralizzati specializzati. La democratizzazione dell'informatica era iniziata e alla fine ha spianato la strada alla moderna esplosione dei big data.
+
+## 1993 - 2011
+
+Questa epoca ha visto una nuova era per ML e AI per essere in grado di risolvere alcuni dei problemi che erano stati causati in precedenza dalla mancanza di dati e potenza di calcolo. La quantità di dati ha iniziato ad aumentare rapidamente e a diventare più ampiamente disponibile, nel bene e nel male, soprattutto con l'avvento degli smartphone intorno al 2007. La potenza di calcolo si è ampliata in modo esponenziale e gli algoritmi si sono evoluti di pari passo. Il campo ha iniziato a maturare quando i giorni a ruota libera del passato hanno iniziato a cristallizzarsi in una vera disciplina.
+
+## Adesso
+
+Oggi, machine learning e intelligenza artificiale toccano quasi ogni parte della nostra vita. Questa era richiede un'attenta comprensione dei rischi e dei potenziali effetti di questi algoritmi sulle vite umane. Come ha affermato Brad Smith di Microsoft, "La tecnologia dell'informazione solleva questioni che vanno al cuore delle protezioni fondamentali dei diritti umani come la privacy e la libertà di espressione. Questi problemi aumentano la responsabilità delle aziende tecnologiche che creano questi prodotti. A nostro avviso, richiedono anche un'attenta regolamentazione del governo e lo sviluppo di norme sugli usi accettabili" ([fonte](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi informatici e il software e gli algoritmi che eseguono. Ci si augura che questo programma di studi aiuti ad acquisire una migliore comprensione in modo che si possa decidere in autonomia.
+
+[![La storia del deeplearningLa](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 " storia del deep learning")
+> 🎥 Fare clic sull'immagine sopra per un video: Yann LeCun discute la storia del deep learning in questa lezione
+
+---
+
+## 🚀 Sfida
+
+Approfondire uno di questi momenti storici e scoprire
+ di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+
+## Revisione e Auto Apprendimento
+
+Ecco gli elementi da guardare e ascoltare:
+
+[Questo podcast in cui Amy Boyd discute l'evoluzione dell'AI](http://runasradio.com/Shows/Show/739)
+
+[![La storia dell'AI di Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La storia dell'AI di Amy Boyd")
+
+## Compito
+
+[Creare una sequenza temporale](assignment.it.md)
diff --git a/1-Introduction/3-fairness/translations/README.it.md b/1-Introduction/3-fairness/translations/README.it.md
index 2b822c88bf..3c167f096f 100644
--- a/1-Introduction/3-fairness/translations/README.it.md
+++ b/1-Introduction/3-fairness/translations/README.it.md
@@ -1,212 +1,212 @@
-# Equità e machine learning
-
-![Riepilogo dell'equità in machine learning in uno sketchnote](../../../sketchnotes/ml-fairness.png)
-> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
-
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
-
-## Introduzione
-
-In questo programma di studi, si inizierà a scoprire come machine learning può e sta influenzando la  vita quotidiana. Anche ora, sistemi e modelli sono coinvolti nelle attività decisionali quotidiane, come le diagnosi sanitarie o l'individuazione di frodi. Quindi è importante che questi modelli funzionino bene per fornire risultati equi per tutti.
-
-Si immagini cosa può accadere quando i dati che si stanno utilizzando per costruire questi modelli mancano di determinati dati demografici, come razza, genere, visione politica, religione, o rappresentano tali dati demografici in modo sproporzionato. E quando il risultato del modello viene interpretato per favorire alcuni gruppi demografici? Qual è la conseguenza per l'applicazione?
-
-In questa lezione, si dovrà:
-
-- Aumentare la propria consapevolezza sull'importanza dell'equità nel machine learning.
-- Informarsi sui danni legati all'equità.
-- Apprendere ulteriori informazioni sulla valutazione e la mitigazione dell'ingiustizia.
-
-## Prerequisito
-
-Come prerequisito, si segua il percorso di apprendimento "Principi di IA responsabile" e si guardi il video qui sotto sull'argomento:
-
-Si scopra di più sull'AI Responsabile seguendo questo [percorso di apprendimento](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
-
-[![L'approccio di Microsoft all'AI responsabileL'](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "approccio di Microsoft all'AI Responsabile")
-
-> 🎥 Fare clic sull'immagine sopra per un video: L'approccio di Microsoft all'AI Responsabile
-
-## Iniquità nei dati e negli algoritmi
-
-> "Se si torturano i dati abbastanza a lungo, essi confesseranno qualsiasi cosa" - Ronald Coase
-
-Questa affermazione suona estrema, ma è vero che i dati possono essere manipolati per supportare qualsiasi conclusione. Tale manipolazione a volte può avvenire involontariamente. Come esseri umani, abbiamo tutti dei pregiudizi, ed è spesso difficile sapere consapevolmente quando si introduce un pregiudizio nei dati.
-
-Garantire l'equità nell'intelligenza artificiale e machine learning rimane una sfida socio-tecnica complessa. Ciò significa che non può essere affrontata da prospettive puramente sociali o tecniche.
-
-### Danni legati all'equità
-
-Cosa si intende per ingiustizia? L'"ingiustizia" comprende gli impatti negativi, o "danni", per un gruppo di persone, come quelli definiti in termini di razza, genere, età o stato di disabilità.
-
-I principali danni legati all'equità possono essere classificati come:
-
-- **Allocazione**, se un genere o un'etnia, ad esempio, sono preferiti a un altro.
-- **Qualità di servizio** Se si addestrano i dati per uno scenario specifico, ma la realtà è molto più complessa, si ottiene un servizio scadente.
-- **Stereotipi**. Associazione di un dato gruppo con attributi preassegnati.
-- **Denigrazione**. Criticare ed etichettare ingiustamente qualcosa o qualcuno.
-- **Sovra o sotto rappresentazione**. L'idea è che un certo gruppo non è visto in una certa professione, e qualsiasi servizio o funzione che continua a promuovere ciò contribuisce al danno.
-
-Si dia un'occhiata agli esempi.
-
-### Allocazione
-
-Si consideri un ipotetico sistema per la scrematura delle domande di prestito. Il sistema tende a scegliere gli uomini bianchi come candidati migliori rispetto ad altri gruppi. Di conseguenza, i prestiti vengono trattenuti da alcuni richiedenti.
-
-Un altro esempio potrebbe essere uno strumento sperimentale di assunzione sviluppato da una grande azienda per selezionare i candidati. Lo strumento discrimina sistematicamente un genere utilizzando i modelli che sono stati addestrati a preferire parole associate con altro. Ha portato a penalizzare i candidati i cui curricula contengono parole come "squadra di rugby femminile".
-
-✅ Si compia una piccola ricerca per trovare un esempio reale di qualcosa del genere
-
-### Qualità di Servizio
-
-I ricercatori hanno scoperto che diversi classificatori di genere commerciali avevano tassi di errore più elevati intorno alle immagini di donne con tonalità della pelle più scura rispetto alle immagini di uomini con tonalità della pelle più chiare. [Riferimento](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/)
-
-Un altro esempio infamante è un distributore di sapone per le mani che sembrava non essere in grado di percepire le persone con la pelle scura. [Riferimento](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
-
-### Stereotipi
-
-La visione di genere stereotipata è stata trovata nella traduzione automatica. Durante la traduzione in turco "he is a nurse and she is a doctor" (lui è un'infermiere e lei un medico), sono stati riscontrati problemi. Il turco è una lingua senza genere che ha un pronome, "o" per trasmettere una terza persona singolare, ma tradurre la frase dal turco all'inglese produce lo stereotipo e scorretto come "she is a nurse and he is a doctor" (lei è un'infermiera e lui è un medico).
-
-![traduzione in turco](../images/gender-bias-translate-en-tr.png)
-
-![Traduzione in inglese](../images/gender-bias-translate-tr-en.png)
-
-### Denigrazione
-
-Una tecnologia di etichettatura delle immagini ha contrassegnato in modo infamante le immagini di persone dalla pelle scura come gorilla. L'etichettatura errata è dannosa non solo perché il sistema ha commesso un errore, ma anche perché ha applicato specificamente un'etichetta che ha una lunga storia di essere intenzionalmente utilizzata per denigrare i neri.
-
-[![AI: Non sono una donna?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, non sono una donna?")
-> 🎥 Cliccare sull'immagine sopra per un video: AI, Ain't I a Woman - una performance che mostra il danno causato dalla denigrazione razzista da parte dell'AI
-
-### Sovra o sotto rappresentazione
-
-I risultati di ricerca di immagini distorti possono essere un buon esempio di questo danno. Quando si cercano immagini di professioni con una percentuale uguale o superiore di uomini rispetto alle donne, come l'ingegneria o CEO, si osserva che i risultati sono più fortemente distorti verso un determinato genere.
-
-![Ricerca CEO di Bing](../images/ceos.png)
-> Questa ricerca su Bing per "CEO" produce risultati piuttosto inclusivi
-
-Questi cinque principali tipi di danno non si escludono a vicenda e un singolo sistema può presentare più di un tipo di danno. Inoltre, ogni caso varia nella sua gravità. Ad esempio, etichettare ingiustamente qualcuno come criminale è un danno molto più grave che etichettare erroneamente un'immagine. È importante, tuttavia, ricordare che anche danni relativamente non gravi possono far sentire le persone alienate o emarginate e l'impatto cumulativo può essere estremamente opprimente.
-
-✅ **Discussione**: rivisitare alcuni degli esempi e vedere se mostrano danni diversi.
-
-|                         | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| Sistema di assunzione automatizzato |     x      |         x          |      x       |             |               x                |
-| Traduzione automatica     |            |                    |              |             |                                |
-| Eitchettatura foto          |            |                    |              |             |                                |
-
-## Rilevare l'ingiustizia
-
-Ci sono molte ragioni per cui un dato sistema si comporta in modo scorretto. I pregiudizi sociali, ad esempio, potrebbero riflettersi nell'insieme di dati utilizzati per addestrarli. Ad esempio, l'ingiustizia delle assunzioni potrebbe essere stata esacerbata dall'eccessivo affidamento sui dati storici. Utilizzando i modelli nei curricula inviati all'azienda per un periodo di 10 anni, il modello ha determinato che gli uomini erano più qualificati perché la maggior parte dei curricula proveniva da uomini, un riflesso del passato dominio maschile nell'industria tecnologica.
-
-Dati inadeguati su un determinato gruppo di persone possono essere motivo di ingiustizia. Ad esempio, i classificatori di immagini hanno un tasso di errore più elevato per le immagini di persone dalla pelle scura perché le tonalità della pelle più scure sono sottorappresentate nei dati.
-
-Anche le ipotesi errate fatte durante lo sviluppo causano iniquità. Ad esempio, un sistema di analisi facciale destinato a prevedere chi commetterà un crimine basato sulle immagini dei volti delle persone può portare a ipotesi dannose. Ciò potrebbe portare a danni sostanziali per le persone classificate erroneamente.
-
-## Si comprendano i propri modelli e si costruiscano in modo onesto
-
-Sebbene molti aspetti dell'equità non vengano catturati nelle metriche di equità quantitativa e non sia possibile rimuovere completamente i pregiudizi da un sistema per garantire l'equità, si è comunque responsabili di rilevare e mitigare il più possibile i problemi di equità.
-
-Quando si lavora con modelli di machine learning, è importante comprendere i propri modelli assicurandone l'interpretabilità e valutando e mitigando l'ingiustizia.
-
-Si utilizza l'esempio di selezione del prestito per isolare il caso e determinare il livello di impatto di ciascun fattore sulla previsione.
-
-## Metodi di valutazione
-
-1. **Identificare i danni (e benefici)**. Il primo passo è identificare danni e benefici. Si pensi a come azioni e decisioni possono influenzare sia i potenziali clienti che un'azienda stessa.
-
-1. **Identificare i gruppi interessati**. Una volta compreso il tipo di danni o benefici che possono verificarsi, identificare i gruppi che potrebbero essere interessati. Questi gruppi sono definiti per genere, etnia o gruppo sociale?
-
-1. **Definire le metriche di equità**. Infine, si definisca una metrica in modo da avere qualcosa su cui misurare il proprio lavoro per migliorare la situazione.
-
-### **Identificare danni (e benefici)**
-
-Quali sono i danni e i benefici associati al prestito? Si pensi agli scenari di falsi negativi e falsi positivi:
-
-**Falsi negativi** (rifiutato, ma Y=1) - in questo caso viene rifiutato un richiedente che sarà in grado di rimborsare un prestito. Questo è un evento avverso perché le risorse dei prestiti sono trattenute da richiedenti qualificati.
-
-**Falsi positivi** (accettato, ma Y=0) - in questo caso, il richiedente ottiene un prestito ma alla fine fallisce. Di conseguenza, il caso del richiedente verrà inviato a un'agenzia di recupero crediti che può influire sulle sue future richieste di prestito.
-
-### **Identificare i gruppi interessati**
-
-Il passo successivo è determinare quali gruppi potrebbero essere interessati. Ad esempio, nel caso di una richiesta di carta di credito, un modello potrebbe stabilire che le donne dovrebbero ricevere limiti di credito molto più bassi rispetto ai loro coniugi che condividono i beni familiari. Un intero gruppo demografico, definito in base al genere, è così interessato.
-
-### **Definire le metriche di equità**
-
-Si sono identificati i danni e un gruppo interessato, in questo caso, delineato per genere. Ora, si usino i fattori quantificati per disaggregare le loro metriche. Ad esempio, utilizzando i dati di seguito, si può vedere che le donne hanno il più alto tasso di falsi positivi e gli uomini il più piccolo, e che è vero il contrario per i falsi negativi.
-
-✅ In una futura lezione sul Clustering, si vedrà come costruire questa 'matrice di confusione' nel codice
-
-|            | percentuale di falsi positivi | Percentuale di falsi negativi |conteggio |
-| ---------- | ------------------- | ------------------- | ----- |
-| Donna | 0,37 | 0,27 | 54032 |
-| Uomo | 0,31 | 0.35 | 28620 |
-| Non binario | 0,33 | 0,31 | 1266 |
-
-Questa tabella ci dice diverse cose. Innanzitutto, si nota che ci sono relativamente poche persone non binarie nei dati. I dati sono distorti, quindi si deve fare attenzione a come si interpretano  questi numeri.
-
-In questo caso, ci sono 3 gruppi e 2 metriche. Quando si pensa a come il nostro sistema influisce sul gruppo di clienti con i loro richiedenti di prestito, questo può essere sufficiente, ma quando si desidera definire un numero maggiore di gruppi, è possibile distillare questo in insiemi più piccoli di riepiloghi. Per fare ciò, si possono aggiungere più metriche, come la differenza più grande o il rapporto più piccolo di ogni falso negativo e falso positivo.
-
-✅ Ci si fermi a pensare: quali altri gruppi potrebbero essere interessati dalla richiesta di prestito?
-
-## Mitigare l'ingiustizia
-
-Per mitigare l'ingiustizia, si esplorari il modello per generare vari modelli mitigati e si confrontino i compromessi tra accuratezza ed equità per selezionare il modello più equo.
-
-Questa lezione introduttiva non approfondisce i dettagli dell'algoritmo della mitigazione dell'ingiustizia, come l'approccio di post-elaborazione e riduzione, ma ecco uno strumento che si potrebbe voler provare.
-
-### Fairlearn
-
-[Fairlearn](https://fairlearn.github.io/) è un pacchetto Python open source che consente di valutare l'equità dei propri sistemi e mitigare l'ingiustizia.
-
-Lo strumento consente di valutare in che modo le previsioni di un modello influiscono su diversi gruppi, consentendo di confrontare più modelli utilizzando metriche di equità e prestazioni e fornendo una serie di algoritmi per mitigare l'ingiustizia nella classificazione binaria e nella regressione.
-
-- Si scopra come utilizzare i diversi componenti controllando il GitHub di [Fairlearn](https://github.com/fairlearn/fairlearn/)
-
-- Si esplori la [guida per l'utente](https://fairlearn.github.io/main/user_guide/index.html), e gli [esempi](https://fairlearn.github.io/main/auto_examples/index.html)
-
-- Si provino alcuni [notebook di esempio](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
-
-- Si scopra [come abilitare le valutazioni dell'equità](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dei modelli di Machine Learning in Azure Machine Learning.
-
-- Si dia un'occhiata a questi [notebook di esempio](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) per ulteriori scenari di valutazione dell'equità in Azure Machine Learning.
-
----
-
-## 🚀 Sfida
-
-Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe:
-
-- avere una diversità di background e prospettive tra le persone che lavorano sui sistemi
-- investire in insiemi di dati che riflettano le diversità della società
-- sviluppare metodi migliori per rilevare e correggere i pregiudizi quando si verificano
-
-Si pensi a scenari di vita reale in cui l'ingiustizia è evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare?
-
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
-
-## Revisione e Auto Apprendimento
-
-In questa lezione si sono apprese alcune nozioni di base sui concetti di equità e ingiustizia in machine learning.
-
-Si guardi questo workshop per approfondire gli argomenti:
-
-- YouTube: Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione di Hanna Wallach e Miro Dudik [Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k)
-
-Si legga anche:
-
-- Centro risorse RAI di Microsoft: [risorse AI responsabili – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4)
-
-- Gruppo di ricerca FATE di Microsoft[: FATE: equità, responsabilità, trasparenza ed etica nell'intelligenza artificiale - Microsoft Research](https://www.microsoft.com/research/theme/fate/)
-
-Si esplori il toolkit Fairlearn
-
-[Fairlearn](https://fairlearn.org/)
-
-Si scoprano gli strumenti di Azure Machine Learning per garantire l'equità
-
-- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa)
-
-## Compito
-
-[Esplorare Fairlearn](assignment.it.md)
+# Equità e machine learning
+
+![Riepilogo dell'equità in machine learning in uno sketchnote](../../../sketchnotes/ml-fairness.png)
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+
+## Introduzione
+
+In questo programma di studi, si inizierà a scoprire come machine learning può e sta influenzando la  vita quotidiana. Anche ora, sistemi e modelli sono coinvolti nelle attività decisionali quotidiane, come le diagnosi sanitarie o l'individuazione di frodi. Quindi è importante che questi modelli funzionino bene per fornire risultati equi per tutti.
+
+Si immagini cosa può accadere quando i dati che si stanno utilizzando per costruire questi modelli mancano di determinati dati demografici, come razza, genere, visione politica, religione, o rappresentano tali dati demografici in modo sproporzionato. E quando il risultato del modello viene interpretato per favorire alcuni gruppi demografici? Qual è la conseguenza per l'applicazione?
+
+In questa lezione, si dovrà:
+
+- Aumentare la propria consapevolezza sull'importanza dell'equità nel machine learning.
+- Informarsi sui danni legati all'equità.
+- Apprendere ulteriori informazioni sulla valutazione e la mitigazione dell'ingiustizia.
+
+## Prerequisito
+
+Come prerequisito, si segua il percorso di apprendimento "Principi di AI Responsabile" e si guardi il video qui sotto sull'argomento:
+
+Si scopra di più sull'AI Responsabile seguendo questo [percorso di apprendimento](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
+
+[![L'approccio di Microsoft all'AI responsabileL'](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "approccio di Microsoft all'AI Responsabile")
+
+> 🎥 Fare clic sull'immagine sopra per un video: L'approccio di Microsoft all'AI Responsabile
+
+## Iniquità nei dati e negli algoritmi
+
+> "Se si torturano i dati abbastanza a lungo, essi confesseranno qualsiasi cosa" - Ronald Coase
+
+Questa affermazione suona estrema, ma è vero che i dati possono essere manipolati per supportare qualsiasi conclusione. Tale manipolazione a volte può avvenire involontariamente. Come esseri umani, abbiamo tutti dei pregiudizi, ed è spesso difficile sapere consapevolmente quando si introduce un pregiudizio nei dati.
+
+Garantire l'equità nell'intelligenza artificiale e machine learning rimane una sfida socio-tecnica complessa. Ciò significa che non può essere affrontata da prospettive puramente sociali o tecniche.
+
+### Danni legati all'equità
+
+Cosa si intende per ingiustizia? L'"ingiustizia" comprende gli impatti negativi, o "danni", per un gruppo di persone, come quelli definiti in termini di razza, genere, età o stato di disabilità.
+
+I principali danni legati all'equità possono essere classificati come:
+
+- **Allocazione**, se un genere o un'etnia, ad esempio, sono preferiti a un altro.
+- **Qualità di servizio** Se si addestrano i dati per uno scenario specifico, ma la realtà è molto più complessa, si ottiene un servizio scadente.
+- **Stereotipi**. Associazione di un dato gruppo con attributi preassegnati.
+- **Denigrazione**. Criticare ed etichettare ingiustamente qualcosa o qualcuno.
+- **Sovra o sotto rappresentazione**. L'idea è che un certo gruppo non è visto in una certa professione, e qualsiasi servizio o funzione che continua a promuovere ciò, contribuisce al danno.
+
+Si dia un'occhiata agli esempi.
+
+### Allocazione
+
+Si consideri un ipotetico sistema per la scrematura delle domande di prestito. Il sistema tende a scegliere gli uomini bianchi come candidati migliori rispetto ad altri gruppi. Di conseguenza, i prestiti vengono negati ad alcuni richiedenti.
+
+Un altro esempio potrebbe essere uno strumento sperimentale di assunzione sviluppato da una grande azienda per selezionare i candidati. Lo strumento discrimina sistematicamente un genere utilizzando i modelli che sono stati addestrati a preferire parole associate con altro. Ha portato a penalizzare i candidati i cui curricula contengono parole come "squadra di rugby femminile".
+
+✅ Si compia una piccola ricerca per trovare un esempio reale di qualcosa del genere
+
+### Qualità di Servizio
+
+I ricercatori hanno scoperto che diversi classificatori di genere commerciali avevano tassi di errore più elevati intorno alle immagini di donne con tonalità della pelle più scura rispetto alle immagini di uomini con tonalità della pelle più chiare. [Riferimento](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/)
+
+Un altro esempio infamante è un distributore di sapone per le mani che sembrava non essere in grado di percepire le persone con la pelle scura. [Riferimento](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+
+### Stereotipi
+
+La visione di genere stereotipata è stata trovata nella traduzione automatica. Durante la traduzione in turco "he is a nurse and she is a doctor" (lui è un'infermiere e lei un medico), sono stati riscontrati problemi. Il turco è una lingua senza genere che ha un pronome, "o" per trasmettere una terza persona singolare, ma tradurre la frase dal turco all'inglese produce lo stereotipo e scorretto come "she is a nurse and he is a doctor" (lei è un'infermiera e lui è un medico).
+
+![traduzione in turco](../images/gender-bias-translate-en-tr.png)
+
+![Traduzione in inglese](../images/gender-bias-translate-tr-en.png)
+
+### Denigrazione
+
+Una tecnologia di etichettatura delle immagini ha contrassegnato in modo infamante le immagini di persone dalla pelle scura come gorilla. L'etichettatura errata è dannosa non solo perché il sistema ha commesso un errore, ma anche perché ha applicato specificamente un'etichetta che ha una lunga storia di essere intenzionalmente utilizzata per denigrare i neri.
+
+[![AI: Non sono una donna?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, non sono una donna?")
+> 🎥 Cliccare sull'immagine sopra per un video: AI, Ain't I a Woman - una performance che mostra il danno causato dalla denigrazione razzista da parte dell'AI
+
+### Sovra o sotto rappresentazione
+
+I risultati di ricerca di immagini distorti possono essere un buon esempio di questo danno. Quando si cercano immagini di professioni con una percentuale uguale o superiore di uomini rispetto alle donne, come l'ingegneria o CEO, si osserva che i risultati sono più fortemente distorti verso un determinato genere.
+
+![Ricerca CEO di Bing](../images/ceos.png)
+> Questa ricerca su Bing per "CEO" produce risultati piuttosto inclusivi
+
+Questi cinque principali tipi di danno non si escludono a vicenda e un singolo sistema può presentare più di un tipo di danno. Inoltre, ogni caso varia nella sua gravità. Ad esempio, etichettare ingiustamente qualcuno come criminale è un danno molto più grave che etichettare erroneamente un'immagine. È importante, tuttavia, ricordare che anche danni relativamente non gravi possono far sentire le persone alienate o emarginate e l'impatto cumulativo può essere estremamente opprimente.
+
+✅ **Discussione**: rivisitare alcuni degli esempi e vedere se mostrano danni diversi.
+
+|                         | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
+| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
+| Sistema di assunzione automatizzato |     x      |         x          |      x       |             |               x                |
+| Traduzione automatica     |            |                    |              |             |                                |
+| Eitchettatura foto          |            |                    |              |             |                                |
+
+## Rilevare l'ingiustizia
+
+Ci sono molte ragioni per cui un dato sistema si comporta in modo scorretto. I pregiudizi sociali, ad esempio, potrebbero riflettersi nell'insieme di dati utilizzati per addestrarli. Ad esempio, l'ingiustizia delle assunzioni potrebbe essere stata esacerbata dall'eccessivo affidamento sui dati storici. Utilizzando i modelli nei curricula inviati all'azienda per un periodo di 10 anni, il modello ha determinato che gli uomini erano più qualificati perché la maggior parte dei curricula proveniva da uomini, un riflesso del passato dominio maschile nell'industria tecnologica.
+
+Dati inadeguati su un determinato gruppo di persone possono essere motivo di ingiustizia. Ad esempio, i classificatori di immagini hanno un tasso di errore più elevato per le immagini di persone dalla pelle scura perché le tonalità della pelle più scure sono sottorappresentate nei dati.
+
+Anche le ipotesi errate fatte durante lo sviluppo causano iniquità. Ad esempio, un sistema di analisi facciale destinato a prevedere chi commetterà un crimine basato sulle immagini dei volti delle persone può portare a ipotesi dannose. Ciò potrebbe portare a danni sostanziali per le persone classificate erroneamente.
+
+## Si comprendano i propri modelli e si costruiscano in modo onesto
+
+Sebbene molti aspetti dell'equità non vengano catturati nelle metriche di equità quantitativa e non sia possibile rimuovere completamente i pregiudizi da un sistema per garantire l'equità, si è comunque responsabili di rilevare e mitigare il più possibile i problemi di equità.
+
+Quando si lavora con modelli di machine learning, è importante comprendere i propri modelli assicurandone l'interpretabilità e valutando e mitigando l'ingiustizia.
+
+Si utilizza l'esempio di selezione del prestito per isolare il caso e determinare il livello di impatto di ciascun fattore sulla previsione.
+
+## Metodi di valutazione
+
+1. **Identificare i danni (e benefici)**. Il primo passo è identificare danni e benefici. Si pensi a come azioni e decisioni possono influenzare sia i potenziali clienti che un'azienda stessa.
+
+1. **Identificare i gruppi interessati**. Una volta compreso il tipo di danni o benefici che possono verificarsi, identificare i gruppi che potrebbero essere interessati. Questi gruppi sono definiti per genere, etnia o gruppo sociale?
+
+1. **Definire le metriche di equità**. Infine, si definisca una metrica in modo da avere qualcosa su cui misurare il proprio lavoro per migliorare la situazione.
+
+### **Identificare danni (e benefici)**
+
+Quali sono i danni e i benefici associati al prestito? Si pensi agli scenari di falsi negativi e falsi positivi:
+
+**Falsi negativi** (rifiutato, ma Y=1) - in questo caso viene rifiutato un richiedente che sarà in grado di rimborsare un prestito. Questo è un evento avverso perché le risorse dei prestiti non sono erogate a richiedenti qualificati.
+
+**Falsi positivi** (accettato, ma Y=0) - in questo caso, il richiedente ottiene un prestito ma alla fine fallisce. Di conseguenza, il caso del richiedente verrà inviato a un'agenzia di recupero crediti che può influire sulle sue future richieste di prestito.
+
+### **Identificare i gruppi interessati**
+
+Il passo successivo è determinare quali gruppi potrebbero essere interessati. Ad esempio, nel caso di una richiesta di carta di credito, un modello potrebbe stabilire che le donne dovrebbero ricevere limiti di credito molto più bassi rispetto ai loro coniugi che condividono i beni familiari. Un intero gruppo demografico, definito in base al genere, è così interessato.
+
+### **Definire le metriche di equità**
+
+Si sono identificati i danni e un gruppo interessato, in questo caso, delineato per genere. Ora, si usino i fattori quantificati per disaggregare le loro metriche. Ad esempio, utilizzando i dati di seguito, si può vedere che le donne hanno il più alto tasso di falsi positivi e gli uomini il più piccolo, e che è vero il contrario per i falsi negativi.
+
+✅ In una futura lezione sul Clustering, si vedrà come costruire questa 'matrice di confusione' nel codice
+
+|            | percentuale di falsi positivi | Percentuale di falsi negativi |conteggio |
+| ---------- | ------------------- | ------------------- | ----- |
+| Donna | 0,37 | 0,27 | 54032 |
+| Uomo | 0,31 | 0.35 | 28620 |
+| Non binario | 0,33 | 0,31 | 1266 |
+
+Questa tabella ci dice diverse cose. Innanzitutto, si nota che ci sono relativamente poche persone non binarie nei dati. I dati sono distorti, quindi si deve fare attenzione a come si interpretano  questi numeri.
+
+In questo caso, ci sono 3 gruppi e 2 metriche. Quando si pensa a come il nostro sistema influisce sul gruppo di clienti con i loro richiedenti di prestito, questo può essere sufficiente, ma quando si desidera definire un numero maggiore di gruppi, è possibile distillare questo in insiemi più piccoli di riepiloghi. Per fare ciò, si possono aggiungere più metriche, come la differenza più grande o il rapporto più piccolo di ogni falso negativo e falso positivo.
+
+✅ Ci si fermi a pensare: quali altri gruppi potrebbero essere interessati dalla richiesta di prestito?
+
+## Mitigare l'ingiustizia
+
+Per mitigare l'ingiustizia, si esplori il modello per generare vari modelli mitigati e si confrontino i compromessi tra accuratezza ed equità per selezionare il modello più equo.
+
+Questa lezione introduttiva non approfondisce i dettagli dell'algoritmo della mitigazione dell'ingiustizia, come l'approccio di post-elaborazione e riduzione, ma ecco uno strumento che si potrebbe voler provare.
+
+### Fairlearn
+
+[Fairlearn](https://fairlearn.github.io/) è un pacchetto Python open source che consente di valutare l'equità dei propri sistemi e mitigare l'ingiustizia.
+
+Lo strumento consente di valutare in che modo le previsioni di un modello influiscono su diversi gruppi, consentendo di confrontare più modelli utilizzando metriche di equità e prestazioni e fornendo una serie di algoritmi per mitigare l'ingiustizia nella classificazione binaria e nella regressione.
+
+- Si scopra come utilizzare i diversi componenti controllando il GitHub di [Fairlearn](https://github.com/fairlearn/fairlearn/)
+
+- Si esplori la [guida per l'utente](https://fairlearn.github.io/main/user_guide/index.html), e gli [esempi](https://fairlearn.github.io/main/auto_examples/index.html)
+
+- Si provino alcuni [notebook di esempio](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
+
+- Si scopra [come abilitare le valutazioni dell'equità](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dei modelli di Machine Learning in Azure Machine Learning.
+
+- Si dia un'occhiata a questi [notebook di esempio](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) per ulteriori scenari di valutazione dell'equità in Azure Machine Learning.
+
+---
+
+## 🚀 Sfida
+
+Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe:
+
+- avere una diversità di background e prospettive tra le persone che lavorano sui sistemi
+- investire in insiemi di dati che riflettano le diversità della società
+- sviluppare metodi migliori per rilevare e correggere i pregiudizi quando si verificano
+
+Si pensi a scenari di vita reale in cui l'ingiustizia è evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+
+## Revisione e Auto Apprendimento
+
+In questa lezione si sono apprese alcune nozioni di base sui concetti di equità e ingiustizia in machine learning.
+
+Si guardi questo workshop per approfondire gli argomenti:
+
+- YouTube: Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione di Hanna Wallach e Miro Dudik [Danni correlati all'equità nei sistemi di IA: esempi, valutazione e mitigazione - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k)
+
+Si legga anche:
+
+- Centro risorse RAI di Microsoft: [risorse AI responsabili – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4)
+
+- Gruppo di ricerca FATE di Microsoft[: FATE: equità, responsabilità, trasparenza ed etica nell'intelligenza artificiale - Microsoft Research](https://www.microsoft.com/research/theme/fate/)
+
+Si esplori il toolkit Fairlearn
+
+[Fairlearn](https://fairlearn.org/)
+
+Si scoprano gli strumenti di Azure Machine Learning per garantire l'equità
+
+- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa)
+
+## Compito
+
+[Esplorare Fairlearn](assignment.it.md)
diff --git a/1-Introduction/3-fairness/translations/assignment.it.md b/1-Introduction/3-fairness/translations/assignment.it.md
index e2151bdf3c..4523fbb3d9 100644
--- a/1-Introduction/3-fairness/translations/assignment.it.md
+++ b/1-Introduction/3-fairness/translations/assignment.it.md
@@ -1,11 +1,11 @@
-# Esplora Fairlearn
-
-## Istruzioni
-
-In questa lezione si è appreso di Fairlearn, un "progetto open source guidato dalla comunità per aiutare i data scientist a migliorare l'equità dei sistemi di intelligenza artificiale". Per questo compito, esplorare uno dei [notebook](https://fairlearn.org/v0.6.2/auto_examples/index.html) di Fairlearn e riportare i propri risultati in un documento o in una presentazione.
-
-## Rubrica
-
-| Criteri | Ottimo | Adeguato | Necessita miglioramento |
-| -------- | --------- | -------- | ----------------- |
-|          | Viene presentato un documento o una presentazione powerpoint in cui si discutono i sistemi di Fairlearn, il notebook che è stato eseguito e le conclusioni tratte dall'esecuzione | Viene presentato un documento senza conclusioni | Non viene presentato alcun documento |
+# Esplorare Fairlearn
+
+## Istruzioni
+
+In questa lezione si è appreso di Fairlearn, un "progetto open source guidato dalla comunità per aiutare i data scientist a migliorare l'equità dei sistemi di intelligenza artificiale". Per questo compito, esplorare uno dei [notebook](https://fairlearn.org/v0.6.2/auto_examples/index.html) di Fairlearn e riportare i propri risultati in un documento o in una presentazione.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------- | -------- | ----------------- |
+|          | Viene presentato un documento o una presentazione powerpoint in cui si discutono i sistemi di Fairlearn, il notebook che è stato eseguito e le conclusioni tratte dall'esecuzione | Viene presentato un documento senza conclusioni | Non viene presentato alcun documento |
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.it.md b/1-Introduction/4-techniques-of-ML/translations/README.it.md
index a3bcda3c44..2fe5794b70 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.it.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md
@@ -1,110 +1,110 @@
-# Tecniche di Machine Learning
-
-Il processo di creazione, utilizzo e mantenimento dei modelli di machine learning e dei dati che utilizzano è un processo molto diverso da molti altri flussi di lavoro di sviluppo. In questa lezione si demistifica il processo, e si delineano le principali tecniche che occorre conoscere. Si dovrà:
-
-- Comprendere i processi ad alto livello alla base di machine learning.
-- Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento".
-
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
-
-## Introduzione
-
-Ad alto livello, il mestiere di creare processi di apprendimento automatico (ML) comprende una serie di passaggi:
-
-1. **Decidere circa la domanda**. La maggior parte dei processi ML inizia ponendo una domanda alla quale non è possibile ottenere risposta da un semplice programma condizionale o da un motore basato su regole. Queste domande spesso ruotano attorno a previsioni basate su una raccolta di dati.
-2. **Raccogliere e preparare i dati**. Per poter rispondere alla domanda, servono dati. La qualità e, a volte, la quantità dei dati determineranno quanto bene sarà possibile rispondere alla domanda iniziale. La visualizzazione dei dati è un aspetto importante di questa fase. Questa fase include anche la suddivisione dei dati in un gruppo di addestramento (training) e test per costruire un modello.
-3. **Scegliere un metodo di addestramento**. A seconda della domanda e della natura dei dati, è necessario scegliere come si desidera addestrare un modello per riflettere al meglio i dati e fare previsioni accurate su di essi. Questa è la parte del processo di ML che richiede competenze specifiche e, spesso, una notevole quantità di sperimentazione.
-4. **Addestrare il modello**. Usando i dati di addestramento, si utilizzeranno vari algoritmi per addestrare un modello a riconoscere modelli nei dati. Il modello potrebbe sfruttare pesi interni che possono essere regolati per privilegiare alcune parti dei dati rispetto ad altre per costruire un modello migliore.
-5. **Valutare il modello**. Si utilizzano dati mai visti prima (i dati di test) da quelli raccolti per osservare le prestazioni del modello.
-6. **Regolazione dei parametri**. In base alle prestazioni del modello, si può ripetere il processo utilizzando parametri differenti, o variabili, che controllano il comportamento degli algoritmi utilizzati per addestrare il modello.
-7. **Prevedere** Usare nuovi input per testare la precisione del modello.
-
-## Che domanda fare
-
-I computer sono particolarmente abili nello scoprire modelli nascosti nei dati. Questa caratteristica è molto utile per i ricercatori che hanno domande su un determinato campo a cui non è possibile rispondere facilmente creando un motore di regole basato su condizioni. Dato un compito attuariale, ad esempio, un data scientist potrebbe essere in grado di costruire manualmente regole sulla mortalità dei fumatori rispetto ai non fumatori.
-
-Quando molte altre variabili vengono introdotte nell'equazione, tuttavia, un modello ML potrebbe rivelarsi più efficiente per prevedere i tassi di mortalità futuri in base alla storia sanitaria passata. Un esempio più allegro potrebbe essere fare previsioni meteorologiche per il mese di aprile in una determinata località sulla base di dati che includono latitudine, longitudine, cambiamento climatico, vicinanza all'oceano, modelli della corrente a getto e altro ancora.
-
-✅ Questa [presentazione](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) sui modelli meteorologici offre una prospettiva storica per l'utilizzo di ML nell'analisi meteorologica.
-
-## Attività di pre-costruzione
-
-Prima di iniziare a costruire il proprio modello, ci sono diverse attività da completare. Per testare la domanda e formare un'ipotesi basata sulle previsioni di un modello, occorre identificare e configurare diversi elementi.
-
-### Dati
-
-Per poter rispondere con sicurezza alla domanda, serve una buona quantità di dati del tipo giusto. Ci sono due cose da fare a questo punto:
-
-- **Raccogliere dati**. Tenendo presente la lezione precedente sull'equità nell'analisi dei dati, si raccolgano i dati con cura. Ci sia consapevolezza delle fonti di questi dati, di eventuali pregiudizi intrinseci che potrebbero avere e si documenti la loro origine.
-- **Preparare i dati**. Ci sono diversi passaggi nel processo di preparazione dei dati. Potrebbe essere necessario raccogliere i dati e normalizzarli se provengono da fonti diverse. Si può migliorare la qualità e la quantità dei dati attraverso vari metodi come la conversione di stringhe in numeri (come si fa in [Clustering](../../../5-Clustering/1-Visualize/transaltions/README.it.md)). Si potrebbero anche generare nuovi dati, basati sull'originale (come si fa in [Classificazione](../../../4-Classification/1-Introduction/translations/README.it.md)). Si possono pulire e modificare i dati (come verrà fatto prima della lezione sull'[app Web](../../../3-Web-App/translations/README.it.md) ). Infine, si potrebbe anche aver bisogno di renderli casuali e mescolarli, a seconda delle proprie tecniche di addestramento.
-
-✅ Dopo aver raccolto ed elaborato i propri dati, si prenda un momento per vedere se la loro forma consentirà di rispondere alla domanda prevista. Potrebbe essere che i dati non funzionino bene nello svolgere il compito assegnato, come si scopre nelle lezioni di [Clustering](../../../5-Clustering/1-Visualize/translations/README.it.md)!
-
-### Selezione della variabile caratteristica
-
-Una [caratteristica](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) è una proprietà misurabile dei propri dati. In molti insiemi di dati è espressa come un'intestazione di colonna come "data", "dimensione" o "colore". La propria variabile caratteristica, solitamente rappresentata come `y` nel codice, rappresenta la risposta alla domanda che si sta cercando di porre ai propri dati: a dicembre, di che **colore** saranno le zucche più economiche? A San Francisco, quali quartieri avranno il miglior **prezzo** immobiliare?
-
-🎓 **Selezione ed estrazione della caratteristica** Come si fa a sapere quale variabile scegliere quando si costruisce un modello? Probabilmente si dovrà passare attraverso un processo di selezione o estrazione delle caratteristiche per scegliere le variabili giuste per il modello più efficace. Tuttavia, non è la stessa cosa: "L'estrazione delle caratteristiche crea nuove caratteristiche dalle funzioni delle caratteristiche originali, mentre la selezione delle caratteristiche restituisce un sottoinsieme delle caratteristiche". ([fonte](https://it.wikipedia.org/wiki/Selezione_delle_caratteristiche))
-
-### Visualizzare i dati
-
-Un aspetto importante del bagaglio del data scientist è la capacità di visualizzare i dati utilizzando diverse eccellenti librerie come Seaborn o MatPlotLib. Rappresentare visivamente i propri dati potrebbe consentire di scoprire correlazioni nascoste che si possono sfruttare. Le visualizzazioni potrebbero anche aiutare a scoprire pregiudizi o dati sbilanciati (come si scopre in [Classificazione](../../../4-Classification/2-Classifiers-1/translations/README.it.md)).
-
-### Dividere l'insieme di dati
-
-Prima dell'addestramento, è necessario dividere l'insieme di dati in due o più parti di dimensioni diverse che rappresentano comunque bene i dati.
-
-- **Addestramento**. Questa parte dell'insieme di dati è adatta al proprio modello per addestrarlo. Questo insieme costituisce la maggior parte dell'insieme di dati originale.
-- **Test**. Un insieme di dati di test è un gruppo indipendente di dati, spesso raccolti dai dati originali, che si utilizzano per confermare le prestazioni del modello creato.
-- **Convalida**. Un insieme di convalida è un gruppo indipendente più piccolo di esempi da usare per ottimizzare gli iperparametri, o architettura, del modello per migliorarlo. A seconda delle dimensioni dei propri dati e della domanda che si sta ponendo, si potrebbe non aver bisogno di creare questo terzo insieme (come si nota in [Previsione delle Serie Temporali](../../../7-TimeSeries/1-Introduction/translations/README.it.md)).
-
-## Costruire un modello
-
-Utilizzando i dati di addestramento, l'obiettivo è costruire un modello o una rappresentazione statistica dei propri dati, utilizzando vari algoritmi per **addestrarlo** . L'addestramento di un modello lo espone ai dati e consente di formulare ipotesi sui modelli percepiti che scopre, convalida e accetta o rifiuta.
-
-### Decidere un metodo di addestramento
-
-A seconda della domanda e della natura dei dati, si sceglierà un metodo per addestrarlo. Passando attraverso [la documentazione di Scikit-learn](https://scikit-learn.org/stable/user_guide.html), che si usa in questo corso, si possono esplorare molti modi per addestrare un modello. A seconda della propria esperienza, si potrebbe dover provare diversi metodi per creare il modello migliore. È probabile che si attraversi un processo in cui i data scientist valutano le prestazioni di un modello fornendogli dati non visti, verificandone l'accuratezza, i pregiudizi e altri problemi che degradano la qualità e selezionando il metodo di addestramento più appropriato per l'attività da svolgere.
-
-### Allenare un modello
-
-Occorre armarsi dei propri dati di allenamento, per essere pronti per "adattarli" per creare un modello. Si noterà che in molte librerie ML si trova il codice "model.fit" - è in questo momento che si inviano i propri dati come un vettore di valori (di solito "X") e una variabile di caratteristica (di solito "y" ).
-
-### Valutare il modello
-
-Una volta completato il processo di addestramento (potrebbero essere necessarie molte iterazioni, o "epoche", per addestrare un modello di grandi dimensioni), si sarà in grado di valutare la qualità del modello utilizzando i dati di test per valutarne le prestazioni. Questi dati sono un sottoinsieme dei dati originali che il modello non ha analizzato in precedenza. Si può stampare una tabella di metriche sulla qualità del proprio modello.
-
-**Adattamento del modello**
-
-Nel contesto di machine learning, l'adattamento del modello si riferisce all'accuratezza della funzione sottostante del modello mentre tenta di analizzare dati con cui non ha familiarità.
-
-? **Inadeguatezza** o **sovraadattamento** sono problemi comuni che degradano la qualità del modello, poiché il modello non si adatta abbastanza bene o troppo bene. Ciò fa sì che il modello esegua previsioni troppo allineate o troppo poco allineate con i suoi dati di addestramento. Un modello overfit (sovraaddestrato) prevede troppo bene i dati di addestramento perché ha appreso troppo bene i dettagli e il rumore dei dati. Un modello underfit (inadeguato) non è accurato in quanto non può né analizzare accuratamente i suoi dati di allenamento né i dati che non ha ancora "visto".
-
-![modello sovraaddestrato](../images/overfitting.png)
-> Infografica di [Jen Looper](https://twitter.com/jenlooper)
-
-## Sintonia dei parametri
-
-Una volta completato l'addestramento iniziale, si osservi la qualità del modello e si valuti di migliorarlo modificando i suoi "iperparametri". Maggiori informazioni sul processo [nella documentazione](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
-
-## Previsione
-
-Questo è il momento in cui si possono utilizzare dati completamente nuovi per testare l'accuratezza del proprio modello. In un'impostazione ML "applicata", in cui si creano risorse Web per utilizzare il modello in produzione, questo processo potrebbe comportare la raccolta dell'input dell'utente (ad esempio, la pressione di un pulsante) per impostare una variabile e inviarla al modello per l'inferenza, oppure valutazione.
-
-In queste lezioni si scoprirà come utilizzare questi passaggi per preparare, costruire, testare, valutare e prevedere - tutti gesti di un data scientist e altro ancora, mentre si avanza nel proprio viaggio per diventare un ingegnere ML "full stack".
-
----
-
-## 🚀 Sfida
-
-Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficoltà? Cosa sembra facile?
-
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
-
-## Revisione e Auto Apprendimento
-
-Cercare online le interviste con i data scientist che discutono del loro lavoro quotidiano. Eccone [una](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
-
-## Compito
-
-[Intervista a un data scientist](assignment.it.md)
+# Tecniche di Machine Learning
+
+Il processo di creazione, utilizzo e mantenimento dei modelli di machine learning e dei dati che utilizzano è un processo molto diverso da molti altri flussi di lavoro di sviluppo. In questa lezione si demistifica il processo, e si delineano le principali tecniche che occorre conoscere. Si dovrà:
+
+- Comprendere i processi ad alto livello alla base di machine learning.
+- Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento".
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+
+## Introduzione
+
+Ad alto livello, il mestiere di creare processi di apprendimento automatico (ML) comprende una serie di passaggi:
+
+1. **Decidere circa la domanda**. La maggior parte dei processi ML inizia ponendo una domanda alla quale non è possibile ottenere risposta da un semplice programma condizionale o da un motore basato su regole. Queste domande spesso ruotano attorno a previsioni basate su una raccolta di dati.
+2. **Raccogliere e preparare i dati**. Per poter rispondere alla domanda, servono dati. La qualità e, a volte, la quantità dei dati determineranno quanto bene sarà possibile rispondere alla domanda iniziale. La visualizzazione dei dati è un aspetto importante di questa fase. Questa fase include anche la suddivisione dei dati in un gruppo di addestramento (training) e test per costruire un modello.
+3. **Scegliere un metodo di addestramento**. A seconda della domanda e della natura dei dati, è necessario scegliere come si desidera addestrare un modello per riflettere al meglio i dati e fare previsioni accurate su di essi. Questa è la parte del processo di ML che richiede competenze specifiche e, spesso, una notevole quantità di sperimentazione.
+4. **Addestrare il modello**. Usando i dati di addestramento, si utilizzeranno vari algoritmi per addestrare un modello a riconoscere modelli nei dati. Il modello potrebbe sfruttare pesi interni che possono essere regolati per privilegiare alcune parti dei dati rispetto ad altre per costruire un modello migliore.
+5. **Valutare il modello**. Si utilizzano dati mai visti prima (i dati di test) da quelli raccolti per osservare le prestazioni del modello.
+6. **Regolazione dei parametri**. In base alle prestazioni del modello, si può ripetere il processo utilizzando parametri differenti, o variabili, che controllano il comportamento degli algoritmi utilizzati per addestrare il modello.
+7. **Prevedere**. Usare nuovi input per testare la precisione del modello.
+
+## Che domanda fare
+
+I computer sono particolarmente abili nello scoprire modelli nascosti nei dati. Questa caratteristica è molto utile per i ricercatori che hanno domande su un determinato campo a cui non è possibile rispondere facilmente creando un motore di regole basato su condizioni. Dato un compito attuariale, ad esempio, un data scientist potrebbe essere in grado di costruire manualmente regole sulla mortalità dei fumatori rispetto ai non fumatori.
+
+Quando molte altre variabili vengono introdotte nell'equazione, tuttavia, un modello ML potrebbe rivelarsi più efficiente per prevedere i tassi di mortalità futuri in base alla storia sanitaria passata. Un esempio più allegro potrebbe essere fare previsioni meteorologiche per il mese di aprile in una determinata località sulla base di dati che includono latitudine, longitudine, cambiamento climatico, vicinanza all'oceano, modelli della corrente a getto e altro ancora.
+
+✅ Questa [presentazione](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) sui modelli meteorologici offre una prospettiva storica per l'utilizzo di ML nell'analisi meteorologica.
+
+## Attività di pre-costruzione
+
+Prima di iniziare a costruire il proprio modello, ci sono diverse attività da completare. Per testare la domanda e formare un'ipotesi basata sulle previsioni di un modello, occorre identificare e configurare diversi elementi.
+
+### Dati
+
+Per poter rispondere con sicurezza alla domanda, serve una buona quantità di dati del tipo giusto. Ci sono due cose da fare a questo punto:
+
+- **Raccogliere dati**. Tenendo presente la lezione precedente sull'equità nell'analisi dei dati, si raccolgano i dati con cura. Ci sia consapevolezza delle fonti di questi dati, di eventuali pregiudizi intrinseci che potrebbero avere e si documenti la loro origine.
+- **Preparare i dati**. Ci sono diversi passaggi nel processo di preparazione dei dati. Potrebbe essere necessario raccogliere i dati e normalizzarli se provengono da fonti diverse. Si può migliorare la qualità e la quantità dei dati attraverso vari metodi come la conversione di stringhe in numeri (come si fa in [Clustering](../../../5-Clustering/1-Visualize/transaltions/README.it.md)). Si potrebbero anche generare nuovi dati, basati sull'originale (come si fa in [Classificazione](../../../4-Classification/1-Introduction/translations/README.it.md)). Si possono pulire e modificare i dati (come verrà fatto prima della lezione sull'[app Web](../../../3-Web-App/translations/README.it.md) ). Infine, si potrebbe anche aver bisogno di renderli casuali e mescolarli, a seconda delle proprie tecniche di addestramento.
+
+✅ Dopo aver raccolto ed elaborato i propri dati, si prenda un momento per vedere se la loro forma consentirà di rispondere alla domanda prevista. Potrebbe essere che i dati non funzionino bene nello svolgere il compito assegnato, come si scopre nelle lezioni di [Clustering](../../../5-Clustering/1-Visualize/translations/README.it.md)!
+
+### Selezione della variabile caratteristica
+
+Una [caratteristica](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) è una proprietà misurabile dei propri dati. In molti insiemi di dati è espressa come un'intestazione di colonna come "data", "dimensione" o "colore". La propria variabile caratteristica, solitamente rappresentata come `y` nel codice, rappresenta la risposta alla domanda che si sta cercando di porre ai propri dati: a dicembre, di che **colore** saranno le zucche più economiche? A San Francisco, quali quartieri avranno il miglior **prezzo** immobiliare?
+
+🎓 **Selezione ed estrazione della caratteristica** Come si fa a sapere quale variabile scegliere quando si costruisce un modello? Probabilmente si dovrà passare attraverso un processo di selezione o estrazione delle caratteristiche per scegliere le variabili giuste per il modello più efficace. Tuttavia, non è la stessa cosa: "L'estrazione delle caratteristiche crea nuove caratteristiche dalle funzioni delle caratteristiche originali, mentre la selezione delle caratteristiche restituisce un sottoinsieme delle caratteristiche". ([fonte](https://it.wikipedia.org/wiki/Selezione_delle_caratteristiche))
+
+### Visualizzare i dati
+
+Un aspetto importante del bagaglio del data scientist è la capacità di visualizzare i dati utilizzando diverse eccellenti librerie come Seaborn o MatPlotLib. Rappresentare visivamente i propri dati potrebbe consentire di scoprire correlazioni nascoste che si possono sfruttare. Le visualizzazioni potrebbero anche aiutare a scoprire pregiudizi o dati sbilanciati (come si scopre in [Classificazione](../../../4-Classification/2-Classifiers-1/translations/README.it.md)).
+
+### Dividere l'insieme di dati
+
+Prima dell'addestramento, è necessario dividere l'insieme di dati in due o più parti di dimensioni diverse che rappresentano comunque bene i dati.
+
+- **Addestramento**. Questa parte dell'insieme di dati è adatta al proprio modello per addestrarlo. Questo insieme costituisce la maggior parte dell'insieme di dati originale.
+- **Test**. Un insieme di dati di test è un gruppo indipendente di dati, spesso raccolti dai dati originali, che si utilizzano per confermare le prestazioni del modello creato.
+- **Convalida**. Un insieme di convalida è un gruppo indipendente più piccolo di esempi da usare per ottimizzare gli iperparametri, o architettura, del modello per migliorarlo. A seconda delle dimensioni dei propri dati e della domanda che si sta ponendo, si potrebbe non aver bisogno di creare questo terzo insieme (come si nota in [Previsione delle Serie Temporali](../../../7-TimeSeries/1-Introduction/translations/README.it.md)).
+
+## Costruire un modello
+
+Utilizzando i dati di addestramento, l'obiettivo è costruire un modello o una rappresentazione statistica dei propri dati, utilizzando vari algoritmi per **addestrarlo** . L'addestramento di un modello lo espone ai dati e consente di formulare ipotesi sui modelli percepiti che scopre, convalida e accetta o rifiuta.
+
+### Decidere un metodo di addestramento
+
+A seconda della domanda e della natura dei dati, si sceglierà un metodo per addestrarlo. Passando attraverso [la documentazione di Scikit-learn](https://scikit-learn.org/stable/user_guide.html), che si usa in questo corso, si possono esplorare molti modi per addestrare un modello. A seconda della propria esperienza, si potrebbe dover provare diversi metodi per creare il modello migliore. È probabile che si attraversi un processo in cui i data scientist valutano le prestazioni di un modello fornendogli dati non visti, verificandone l'accuratezza, i pregiudizi e altri problemi che degradano la qualità e selezionando il metodo di addestramento più appropriato per l'attività da svolgere.
+
+### Allenare un modello
+
+Occorre armarsi dei propri dati di allenamento, per essere pronti per "adattarli" per creare un modello. Si noterà che in molte librerie ML si trova il codice "model.fit" - è in questo momento che si inviano i propri dati come un vettore di valori (di solito "X") e una variabile di caratteristica (di solito "y" ).
+
+### Valutare il modello
+
+Una volta completato il processo di addestramento (potrebbero essere necessarie molte iterazioni, o "epoche", per addestrare un modello di grandi dimensioni), si sarà in grado di valutare la qualità del modello utilizzando i dati di test per valutarne le prestazioni. Questi dati sono un sottoinsieme dei dati originali che il modello non ha analizzato in precedenza. Si può stampare una tabella di metriche sulla qualità del proprio modello.
+
+🎓 **Adattamento del modello**
+
+Nel contesto di machine learning, l'adattamento del modello si riferisce all'accuratezza della funzione sottostante del modello mentre tenta di analizzare dati con cui non ha familiarità.
+
+🎓 **Inadeguatezza** o **sovraadattamento** sono problemi comuni che degradano la qualità del modello, poiché il modello non si adatta abbastanza bene o troppo bene. Ciò fa sì che il modello esegua previsioni troppo allineate o troppo poco allineate con i suoi dati di addestramento. Un modello overfit (sovraaddestrato) prevede troppo bene i dati di addestramento perché ha appreso troppo bene i dettagli e il rumore dei dati. Un modello underfit (inadeguato) non è accurato in quanto non può né analizzare accuratamente i suoi dati di allenamento né i dati che non ha ancora "visto".
+
+![modello sovraaddestrato](../images/overfitting.png)
+> Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+## Sintonia dei parametri
+
+Una volta completato l'addestramento iniziale, si osservi la qualità del modello e si valuti di migliorarlo modificando i suoi "iperparametri". Maggiori informazioni sul processo [nella documentazione](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+
+## Previsione
+
+Questo è il momento in cui si possono utilizzare dati completamente nuovi per testare l'accuratezza del proprio modello. In un'impostazione ML "applicata", in cui si creano risorse Web per utilizzare il modello in produzione, questo processo potrebbe comportare la raccolta dell'input dell'utente (ad esempio, la pressione di un pulsante) per impostare una variabile e inviarla al modello per l'inferenza, oppure valutazione.
+
+In queste lezioni si scoprirà come utilizzare questi passaggi per preparare, costruire, testare, valutare e prevedere - tutti gesti di un data scientist e altro ancora, mentre si avanza nel proprio viaggio per diventare un ingegnere ML "full stack".
+
+---
+
+## 🚀 Sfida
+
+Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficoltà? Cosa sembra facile?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+
+## Revisione e Auto Apprendimento
+
+Cercare online le interviste con i data scientist che discutono del loro lavoro quotidiano. Eccone [una](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+
+## Compito
+
+[Intervista a un data scientist](assignment.it.md)
diff --git a/1-Introduction/translations/README.it.md b/1-Introduction/translations/README.it.md
index ea29d1d3d1..a9460c2659 100644
--- a/1-Introduction/translations/README.it.md
+++ b/1-Introduction/translations/README.it.md
@@ -1,22 +1,22 @@
-# Introduzione a machine learning
-
-In questa sezione del curriculum, verranno presentati i concetti di base sottostanti machine learning, di cosa si tratta, e si imparerà la sua storia e le tecniche utilizzate dai ricercatori per lavorarci. Si esplorerà insieme questo nuovo mondo di ML!
-
-![globo](../images/globe.jpg)
-> Foto di <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> su <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
-
-### Lezioni
-
-1. [Introduzione a machine learning](../1-intro-to-ML/translations/README.it.md)
-1. [La storia di machine learning e dell'AI](../2-history-of-ML/translations/README.it.md)
-1. [Equità e machine learning](../3-fairness/translations/README.it.md)
-1. [Tecniche di machine learning](../4-techniques-of-ML/translations/README.it.md)
-### Crediti
-
-"Introduzione a Machine Learning" è stato scritto con ♥� da un team di persone tra cui [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) e [Jen Looper](https://twitter.com/jenlooper)
-
-"La Storia di Machine Learning" è stato scritto con ♥� da [Jen Looper](https://twitter.com/jenlooper) e [Amy Boyd](https://twitter.com/AmyKateNicho)
-
-"Equità e Machine Learning" è stato scritto con ♥� da [Tomomi Imura](https://twitter.com/girliemac)
-
-"Tecniche di Machine Learning" è stato scritto con ♥� da [Jen Looper](https://twitter.com/jenlooper) e [Chris Noring](https://twitter.com/softchris)
\ No newline at end of file
+# Introduzione a machine learning
+
+In questa sezione del programma di studi, verranno presentati i concetti di base sottostanti machine learning, di cosa si tratta, e si imparerà la sua storia e le tecniche utilizzate dai ricercatori per lavorarci. Si esplorerà insieme questo nuovo mondo di ML!
+
+![globo](../images/globe.jpg)
+> Foto di <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> su <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+### Lezioni
+
+1. [Introduzione a machine learning](../1-intro-to-ML/translations/README.it.md)
+1. [La storia di machine learning e dell'AI](../2-history-of-ML/translations/README.it.md)
+1. [Equità e machine learning](../3-fairness/translations/README.it.md)
+1. [Tecniche di machine learning](../4-techniques-of-ML/translations/README.it.md)
+### Crediti
+
+"Introduzione a Machine Learning" scritto con ♥� da un team di persone tra cui [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) e [Jen Looper](https://twitter.com/jenlooper)
+
+"La Storia di Machine Learning" scritto con ♥� da [Jen Looper](https://twitter.com/jenlooper) e [Amy Boyd](https://twitter.com/AmyKateNicho)
+
+"Equità e Machine Learning" scritto con ♥� da [Tomomi Imura](https://twitter.com/girliemac)
+
+"Tecniche di Machine Learning" scritto con ♥� da [Jen Looper](https://twitter.com/jenlooper) e [Chris Noring](https://twitter.com/softchris)
\ No newline at end of file

From b028149817384030eb0427bc1e3d4890a719f79e Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 22:02:11 +0800
Subject: [PATCH 069/368] [WIP]translate 2.1 to Simplified Chinese

---
 .../1-Tools/translations/README.zh-cn.md      | 206 ++++++++++++++++++
 1 file changed, 206 insertions(+)
 create mode 100644 2-Regression/1-Tools/translations/README.zh-cn.md

diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
new file mode 100644
index 0000000000..e36c34fe68
--- /dev/null
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -0,0 +1,206 @@
+# Get started with Python and Scikit-learn for regression models
+
+![Summary of regressions in a sketchnote](../../sketchnotes/ml-regression.png)
+
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## Introduction
+
+In these four lessons, you will discover how to build regression models. We will discuss what these are for shortly. But before you do anything, make sure you have the right tools in place to start the process!
+
+In this lesson, you will learn how to:
+
+- Configure your computer for local machine learning tasks.
+- Work with Jupyter notebooks.
+- Use Scikit-learn, including installation.
+- Explore linear regression with a hands-on exercise.
+
+## Installations and configurations
+
+[![Using Python with Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
+
+> 🎥 Click the image above for a video: using Python within VS Code.
+
+1. **Install Python**. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) available as well, to ease the setup for some users.
+
+   Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a [virtual environment](https://docs.python.org/3/library/venv.html).
+
+2. **Install Visual Studio Code**. Make sure you have Visual Studio Code installed on your computer. Follow these instructions to [install Visual Studio Code](https://code.visualstudio.com/) for the basic installation. You are going to use Python in Visual Studio Code in this course, so you might want to brush up on how to [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) for Python development.
+
+   > Get comfortable with Python by working through this collection of [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
+
+3. **Install Scikit-learn**, by following [these instructions](https://scikit-learn.org/stable/install.html). Since you need to ensure that you use Python 3, it's recommended that you use a virtual environment. Note, if you are installing this library on a M1 Mac, there are special instructions on the page linked above.
+
+1. **Install Jupyter Notebook**. You will need to [install the Jupyter package](https://pypi.org/project/jupyter/). 
+
+## Your ML authoring environment
+
+You are going to use **notebooks** to develop your Python code and create machine learning models. This type of file is a common tool for data scientists, and they can be identified by their suffix or extension `.ipynb`.
+
+Notebooks are an interactive environment that allow the developer to both code and add notes and write documentation around the code which is quite helpful for experimental or research-oriented projects.
+
+### Exercise - work with a notebook
+
+In this folder, you will find the file _notebook.ipynb_. 
+
+1. Open _notebook.ipynb_ in Visual Studio Code.
+
+   A Jupyter server will start with Python 3+ started. You will find areas of the notebook that can be `run`, pieces of code. You can run a code block, by selecting the icon that looks like a play button.
+
+1. Select the `md` icon and add a bit of markdown, and the following text **# Welcome to your notebook**.
+
+   Next, add some Python code. 
+
+1. Type **print('hello notebook')** in the code block.
+1. Select the arrow to run the code.
+
+   You should see the printed statement:
+
+    ```output
+    hello notebook
+    ```
+
+![VS Code with a notebook open](images/notebook.png)
+
+You can interleaf your code with comments to self-document the notebook.
+
+✅ Think for a minute how different a web developer's working environment is versus that of a data scientist.
+
+## Up and running with Scikit-learn
+
+Now that Python is set up in your local environment, and you are comfortable with Jupyter notebooks, let's get equally comfortable with Scikit-learn (pronounce it `sci` as in `science`). Scikit-learn provides an [extensive API](https://scikit-learn.org/stable/modules/classes.html#api-ref) to help you perform ML tasks.
+
+According to their [website](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities."
+
+In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. 
+
+Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let's explore the process of loading prepackaged data and using a built in estimator  first ML model with Scikit-learn with some basic data.
+
+## Exercise - your first Scikit-learn notebook
+
+> This tutorial was inspired by the [linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) on Scikit-learn's web site.
+
+In the _notebook.ipynb_ file associated to this lesson, clear out all the cells by pressing the 'trash can' icon.
+
+In this section, you will work with a small dataset about diabetes that is built into Scikit-learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
+
+✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use linear regression, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use logistic regression. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
+
+Let's get started on this task.
+
+### Import libraries
+
+For this task we will import some libraries:
+
+- **matplotlib**. It's a useful [graphing tool](https://matplotlib.org/) and we will use it to create a line plot.
+- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) is a useful library for handling numeric data in Python.
+- **sklearn**. This is the Scikit-learn library.
+
+Import some libraries to help with your tasks.
+
+1. Add imports by typing the following code:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   from sklearn import datasets, linear_model, model_selection
+   ```
+
+   Above you are importing `matplottlib`, `numpy` and you are importing `datasets`, `linear_model` and `model_selection` from `sklearn`. `model_selection` is used for splitting data into training and test sets.
+
+### The diabetes dataset
+
+The built-in [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) includes 442 samples of data around diabetes, with 10 feature variables, some of which include:
+
+age: age in years
+bmi: body mass index
+bp: average blood pressure
+s1 tc: T-Cells (a type of white blood cells)
+
+✅ This dataset includes the concept of 'sex' as a feature variable important to research around diabetes. Many medical datasets include this type of binary classification. Think a bit about how categorizations such as this might exclude certain parts of a population from treatments.
+
+Now, load up the X and y data.
+
+> 🎓 Remember, this is supervised learning, and we need a named 'y' target.
+
+In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The input `return_X_y=True` signals that `X` will be a data matrix, and `y` will be the regression target. 
+
+1. Add some print commands to show the shape of the data matrix and its first element:
+
+    ```python
+    X, y = datasets.load_diabetes(return_X_y=True)
+    print(X.shape)
+    print(X[0])
+    ```
+
+    What you are getting back as a response, is a tuple. What you are doing is to assign the two first values of the tuple to `X` and `y` respectively. Learn more [about tuples](https://wikipedia.org/wiki/Tuple).
+
+    You can see that this data has 442 items shaped in arrays of 10 elements:
+
+    ```text
+    (442, 10)
+    [ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076
+    -0.04340085 -0.00259226  0.01990842 -0.01764613]
+    ```
+
+    ✅ Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target?
+
+2. Next, select a portion of this dataset to plot by arranging it into a new array using numpy's `newaxis` function. We are going to use linear regression to generate a line between values in this data, according to a pattern it determines.
+
+   ```python
+   X = X[:, np.newaxis, 2]
+   ```
+
+   ✅ At any time, print out the data to check its shape.
+
+3. Now that you have data ready to be plotted, you can see if a machine can help determine a logical split between the numbers in this dataset. To do this, you need to split both the data (X) and the target (y) into test and training sets. Scikit-learn has a straightforward way to do this; you can split your test data at a given point.
+
+   ```python
+   X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
+   ```
+
+4. Now you are ready to train your model! Load up the linear regression model and train it with your X and y training sets using `model.fit()`:
+
+    ```python
+    model = linear_model.LinearRegression()
+    model.fit(X_train, y_train)
+    ```
+
+    ✅ `model.fit()` is a function you'll see in many ML libraries such as TensorFlow
+
+5. Then, create a prediction using test data, using the function `predict()`. This will be used to draw the line between data groups
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+6. Now it's time to show the data in a plot. Matplotlib is a very useful tool for this task. Create a scatterplot of all the X and y test data, and use the prediction to draw a line in the most appropriate place, between the model's data groupings.
+
+    ```python
+    plt.scatter(X_test, y_test,  color='black')
+    plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.show()
+    ```
+
+   ![a scatterplot showing datapoints around diabetes](./images/scatterplot.png)
+
+   ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.
+
+Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!
+
+---
+## 🚀Challenge
+
+Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+
+## Review & Self Study
+
+In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+
+Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) to deepen your understanding.
+
+## Assignment 
+
+[A different dataset](assignment.md)

From 24442a9c3b8c61eeec639364e1740211b91eb973 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 22:04:34 +0800
Subject: [PATCH 070/368] [WIP]translate 2.2 to Simplified Chinese

---
 .../2-Data/translations/README.zh-cn.md       | 200 ++++++++++++++++++
 1 file changed, 200 insertions(+)
 create mode 100644 2-Regression/2-Data/translations/README.zh-cn.md

diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md
new file mode 100644
index 0000000000..2c7f23adcd
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+++ b/2-Regression/2-Data/translations/README.zh-cn.md
@@ -0,0 +1,200 @@
+# Build a regression model using Scikit-learn: prepare and visualize data
+
+> ![Data visualization infographic](./images/data-visualization.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+
+## Introduction
+
+Now that you are set up with the tools you need to start tackling machine learning model building with Scikit-learn, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.
+
+In this lesson, you will learn:
+
+- How to prepare your data for model-building.
+- How to use Matplotlib for data visualization.
+
+## Asking the right question of your data
+
+The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.
+
+Take a look at the [data](../data/US-pumpkins.csv) provided for this lesson. You can open this .csv file in VS Code. A quick skim immediately shows that there are blanks and a mix of strings and numeric data. There's also a strange column called 'Package' where the data is a mix between 'sacks', 'bins' and other values. The data, in fact, is a bit of a mess.
+
+In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. In this lesson, you will learn how to prepare a raw dataset using standard Python libraries. You will also learn various techniques to visualize the data.
+
+## Case study: 'the pumpkin market'
+
+In this folder you will find a .csv file in the root `data` folder called [US-pumpkins.csv](../data/US-pumpkins.csv) which includes 1757 lines of data about the market for pumpkins, sorted into groupings by city. This is raw data extracted from the [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distributed by the United States Department of Agriculture.
+
+### Preparing data
+
+This data is in the public domain. It can be downloaded in many separate files, per city, from the USDA web site. To avoid too many separate files, we have concatenated all the city data into one spreadsheet, thus we have already _prepared_ the data a bit. Next, let's take a closer look at the data.
+
+### The pumpkin data - early conclusions
+
+What do you notice about this data? You already saw that there is a mix of strings, numbers, blanks and strange values that you need to make sense of.
+
+What question can you ask of this data, using a Regression technique? What about "Predict the price of a pumpkin for sale during a given month". Looking again at the data, there are some changes you need to make to create the data structure necessary for the task. 
+## Exercise - analyze the pumpkin data
+
+Let's use [Pandas](https://pandas.pydata.org/), (the name stands for `Python Data Analysis`) a tool very useful for shaping data, to analyze and prepare this pumpkin data.
+
+### First, check for missing dates
+
+You will first need to take steps to check for missing dates:
+
+1. Convert the dates to a month format (these are US dates, so the format is `MM/DD/YYYY`).
+2. Extract the month to a new column.
+
+Open the _notebook.ipynb_ file in Visual Studio Code and import the spreadsheet in to a new Pandas dataframe.
+
+1. Use the `head()` function to view the first five rows.
+
+    ```python
+    import pandas as pd
+    pumpkins = pd.read_csv('../data/US-pumpkins.csv')
+    pumpkins.head()
+    ```
+
+    ✅ What function would you use to view the last five rows?
+
+1. Check if there is missing data in the current dataframe:
+
+    ```python
+    pumpkins.isnull().sum()
+    ```
+
+    There is missing data, but maybe it won't matter for the task at hand.
+
+1. To make your dataframe easier to work with, drop several of its columns, using `drop()`, keeping only the columns you need: 
+
+    ```python
+    new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
+    pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    ```
+
+### Second, determine average price of pumpkin
+
+Think about how to determine the average price of a pumpkin in a given month. What columns would you pick for this task? Hint: you'll need 3 columns.
+
+Solution: take the average of the `Low Price` and `High Price` columns to populate the new Price column, and convert the Date column to only show the month. Fortunately, according to the check above, there is no missing data for dates or prices.
+
+1. To calculate the average, add the following code:
+
+    ```python
+    price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
+    
+    month = pd.DatetimeIndex(pumpkins['Date']).month
+    
+    ```
+
+   ✅ Feel free to print any data you'd like to check using `print(month)`.
+
+2. Now, copy your converted data into a fresh Pandas dataframe:
+
+    ```python
+    new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
+    ```
+
+    Printing out your dataframe will show you a clean, tidy dataset on which you can build your new regression model.
+
+### But wait! There's something odd here
+
+If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in '1 1/9 bushel' measures, and some in '1/2 bushel' measures, some per pumpkin, some per pound, and some in big boxes with varying widths.
+
+> Pumpkins seem very hard to weigh consistently
+
+Digging into the original data, it's interesting that anything with `Unit of Sale` equalling 'EACH' or 'PER BIN' also have the `Package` type per inch, per bin, or 'each'. Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string 'bushel' in their `Package` column.
+
+1. Add a filter at the top of the file, under the initial .csv import:
+
+    ```python
+    pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
+    ```
+
+    If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel.
+
+### But wait! There's one more thing to do
+
+Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, so do some math to standardize it.
+
+1. Add these lines after the block creating the new_pumpkins dataframe:
+
+    ```python
+    new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
+    
+    new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
+    ```
+
+✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!
+
+Now, you can analyze the pricing per unit based on their bushel measurement. If you print out the data one more time, you can see how it's standardized.
+
+✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.
+
+## Visualization Strategies
+
+Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover. 
+
+Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.
+
+One data visualization libary that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson).
+
+> Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
+
+## Exercise - experiment with Matplotlib
+
+Try to create some basic plots to display the new dataframe you just created. What would a basic line plot show?
+
+1. Import Matplotlib at the top of the file, under the Pandas import:
+
+    ```python
+    import matplotlib.pyplot as plt
+    ```
+
+1. Rerun the entire notebook to refresh.
+1. At the bottom of the notebook, add a cell to plot the data as a box:
+
+    ```python
+    price = new_pumpkins.Price
+    month = new_pumpkins.Month
+    plt.scatter(price, month)
+    plt.show()
+    ```
+
+    ![A scatterplot showing price to month relationship](./images/scatterplot.png)
+
+    Is this a useful plot? Does anything about it surprise you?
+
+    It's not particularly useful as all it does is display in your data as a spread of points in a given month.
+
+### Make it useful
+
+To get charts to display useful data, you usually need to group the data somehow. Let's try creating a plot where the y axis shows the months and the data demonstrates the distribution of data.
+
+1. Add a cell to create a grouped bar chart:
+
+    ```python
+    new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
+    plt.ylabel("Pumpkin Price")
+    ```
+
+    ![A bar chart showing price to month relationship](./images/barchart.png)
+
+    This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?
+
+---
+
+## 🚀Challenge
+
+Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+
+## Review & Self Study
+
+Take a look at the many ways to visualize data. Make a list of the various libraries available and note which are best for given types of tasks, for example 2D visualizations vs. 3D visualizations. What do you discover?
+
+## Assignment
+
+[Exploring visualization](assignment.md)

From bf6fd47c2d9804cd301b607f82bf8295828ae4ca Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 22:06:23 +0800
Subject: [PATCH 071/368] [WIP]translate 2.3 to Simplified Chinese

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+# Build a regression model using Scikit-learn: regression two ways
+
+![Linear vs polynomial regression infographic](./images/linear-polynomial.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+### Introduction 
+
+So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using Matplotlib. 
+
+Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: _basic linear regression_ and _polynomial regression_, along with some of the math underlying these techniques.
+
+> Throughout this curriculum, we assume minimal knowledge of math, and seek to make it accessible for students coming from other fields, so watch for notes, 🧮 callouts, diagrams, and other learning tools to aid in comprehension.
+
+### Prerequisite
+
+You should be familiar by now with the structure of the pumpkin data that we are examining. You can find it preloaded and pre-cleaned in this lesson's _notebook.ipynb_ file. In the file, the pumpkin price is displayed per bushel in a new dataframe.  Make sure you can run these notebooks in kernels in Visual Studio Code.
+
+### Preparation
+
+As a reminder, you are loading this data so as to ask questions of it. 
+
+- When is the best time to buy pumpkins? 
+- What price can I expect of a case of miniature pumpkins?
+- Should I buy them in half-bushel baskets or by the 1 1/9 bushel box?
+Let's keep digging into this data.
+
+In the previous lesson, you created a Pandas dataframe and populated it with part of the original dataset, standardizing the pricing by the bushel. By doing that, however, you were only able to gather about 400 datapoints and only for the fall months. 
+
+Take a look at the data that we preloaded in this lesson's accompanying notebook. The data is preloaded and an initial scatterplot is charted to show month data. Maybe we can get a little more detail about the nature of the data by cleaning it more.
+
+## A linear regression line
+
+As you learned in Lesson 1, the goal of a linear regression exercise is to be able to plot a line to:
+
+- **Show variable relationships**. Show the relationship between variables
+- **Make predictions**. Make accurate predictions on where a new datapoint would fall in relationship to that line. 
+ 
+It is typical of **Least-Squares Regression** to draw this type of line. The term 'least-squares' means that all the datapoints surrounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. 
+
+We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.
+
+> **🧮 Show me the math** 
+> 
+> This line, called the _line of best fit_ can be expressed by [an equation](https://en.wikipedia.org/wiki/Simple_linear_regression): 
+> 
+> ```
+> Y = a + bX
+> ```
+>
+> `X` is the 'explanatory variable'. `Y` is the 'dependent variable'. The slope of the line is `b` and `a` is the y-intercept, which refers to the value of `Y` when `X = 0`. 
+>
+>![calculate the slope](images/slope.png)
+>
+> First, calculate the slope `b`. Infographic by [Jen Looper](https://twitter.com/jenlooper)
+>
+> In other words, and referring to our pumpkin data's original question: "predict the price of a pumpkin per bushel by month", `X` would refer to the price and `Y` would refer to the month of sale. 
+>
+>![complete the equation](images/calculation.png)
+>
+> Calculate the value of Y. If you're paying around $4, it must be April! Infographic by [Jen Looper](https://twitter.com/jenlooper)
+>
+> The math that calculates the line must demonstrate the slope of the line, which is also dependent on the intercept, or where `Y` is situated when `X = 0`.
+>
+> You can observe the method of calculation for these values on the [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web site. Also visit [this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html) to watch how the numbers' values impact the line.
+
+## Correlation
+
+One more term to understand is the **Correlation Coefficient** between given X and Y variables. Using a scatterplot, you can quickly visualize this coefficient. A plot with datapoints scattered in a neat line have high correlation, but a plot with datapoints scattered everywhere between X and Y have a low correlation.
+
+A good linear regression model will be one that has a high (nearer to 1 than 0) Correlation Coefficient using the Least-Squares Regression method with a line of regression.
+
+✅ Run the notebook accompanying this lesson and look at the City to Price scatterplot. Does the data associating City to Price for pumpkin sales seem to have high or low correlation, according to your visual interpretation of the scatterplot? 
+
+
+## Prepare your data for regression
+
+Now that you have an understanding of the math behind this exercise, create a Regression model to see if you can predict which package of pumpkins will have the best pumpkin prices. Someone buying pumpkins for a holiday pumpkin patch might want this information to be able to optimize their purchases of pumpkin packages for the patch.
+
+Since you'll use Scikit-learn, there's no reason to do this by hand (although you could!). In the main data-processing block of your lesson notebook, add a library from Scikit-learn to automatically convert all string data to numbers:
+
+```python
+from sklearn.preprocessing import LabelEncoder
+
+new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
+```
+
+If you look at the new_pumpkins dataframe now, you see that all the strings are now numeric. This makes it harder for you to read but much more intelligible for Scikit-learn!
+Now you can make more educated decisions (not just based on eyeballing a scatterplot) about the data that is best suited to regression.
+
+Try to find a good correlation between two points of your data to potentially build a good predictive model. As it turns out, there's only weak correlation between the City and Price:
+
+```python
+print(new_pumpkins['City'].corr(new_pumpkins['Price']))
+0.32363971816089226
+```
+
+However there's a bit better correlation between the Package and its Price. That makes sense, right? Normally, the bigger the produce box, the higher the price.
+
+```python
+print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
+0.6061712937226021
+```
+
+A good question to ask of this data will be: 'What price can I expect of a given pumpkin package?'
+
+Let's build this regression model
+
+## Building a linear model
+
+Before building your model, do one more tidy-up of your data. Drop any null data and check once more what the data looks like.
+
+```python
+new_pumpkins.dropna(inplace=True)
+new_pumpkins.info()
+```
+
+Then, create a new dataframe from this minimal set and print it out:
+
+```python
+new_columns = ['Package', 'Price']
+lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+lin_pumpkins
+```
+
+```output
+	Package	Price
+70	0	13.636364
+71	0	16.363636
+72	0	16.363636
+73	0	15.454545
+74	0	13.636364
+...	...	...
+1738	2	30.000000
+1739	2	28.750000
+1740	2	25.750000
+1741	2	24.000000
+1742	2	24.000000
+415 rows × 2 columns
+```
+
+1. Now you can assign your X and y coordinate data:
+
+   ```python
+   X = lin_pumpkins.values[:, :1]
+   y = lin_pumpkins.values[:, 1:2]
+   ```
+✅ What's going on here? You're using [Python slice notation](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295) to create arrays to populate `X` and `y`.
+
+2. Next, start the regression model-building routines:
+
+   ```python
+   from sklearn.linear_model import LinearRegression
+   from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+   from sklearn.model_selection import train_test_split
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   lin_reg = LinearRegression()
+   lin_reg.fit(X_train,y_train)
+
+   pred = lin_reg.predict(X_test)
+
+   accuracy_score = lin_reg.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   Because the correlation isn't particularly good, the model produced isn't terribly accurate.
+
+   ```output
+   Model Accuracy:  0.3315342327998987
+   ```
+
+3. You can visualize the line that's drawn in the process:
+
+   ```python
+   plt.scatter(X_test, y_test,  color='black')
+   plt.plot(X_test, pred, color='blue', linewidth=3)
+
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+
+   plt.show()
+   ```
+   ![A scatterplot showing package to price relationship](./images/linear.png)
+
+4. Test the model against a hypothetical variety:
+
+   ```python
+   lin_reg.predict( np.array([ [2.75] ]) )
+   ```
+   
+   The returned price for this mythological Variety is:
+
+   ```output
+   array([[33.15655975]])
+   ```
+
+That number makes sense, if the logic of the regression line holds true.
+
+🎃 Congratulations, you just created a model that can help predict the price of a few varieties of pumpkins. Your holiday pumpkin patch will be beautiful. But you can probably create a better model!
+## Polynomial regression
+
+Another type of linear regression is polynomial regression. While sometimes there's a linear relationship between variables - the bigger the pumpkin in volume, the higher the price - sometimes these relationships can't be plotted as a plane or straight line. 
+
+✅ Here are [some more examples](https://online.stat.psu.edu/stat501/lesson/9/9.8) of data that could use polynomial regression
+
+Take another look at the relationship between Variety to Price in the previous plot. Does this scatterplot seem like it should necessarily be analyzed by a straight line? Perhaps not. In this case, you can try polynomial regression.
+
+✅ Polynomials are mathematical expressions that might consist of one or more variables and coefficients
+
+Polynomial regression creates a curved line to better fit nonlinear data. 
+
+1. Let's recreate a dataframe populated with a segment of the original pumpkin data:
+
+   ```python
+   new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
+   poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+   poly_pumpkins
+   ```
+
+A good way to visualize the correlations between data in dataframes is to display it in a 'coolwarm' chart:
+
+2. Use the `Background_gradient()` method with `coolwarm` as its argument value:
+
+   ```python
+   corr = poly_pumpkins.corr()
+   corr.style.background_gradient(cmap='coolwarm')
+   ```
+   This code creates a heatmap:
+   ![A heatmap showing data correlation](./images/heatmap.png)
+
+Looking at this chart, you can visualize the good correlation between Package and Price. So you should be able to create a somewhat better model than the last one.
+### Create a pipeline
+
+Scikit-learn includes a helpful API for building polynomial regression models - the `make_pipeline` [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline). A 'pipeline' is created which is a chain of estimators. In this case, the pipeline includes polynomial features, or predictions that form a nonlinear path.
+
+1. Build out the X and y columns:
+
+   ```python
+   X=poly_pumpkins.iloc[:,3:4].values
+   y=poly_pumpkins.iloc[:,4:5].values
+   ```
+
+2. Create the pipeline by calling the `make_pipeline()` method:
+
+   ```python
+   from sklearn.preprocessing import PolynomialFeatures
+   from sklearn.pipeline import make_pipeline
+
+   pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+   pipeline.fit(np.array(X_train), y_train)
+
+   y_pred=pipeline.predict(X_test)
+   ```
+
+### Create a sequence
+
+At this point, you need to create a new dataframe with _sorted_ data so that the pipeline can create a sequence.
+
+Add the following code:
+
+   ```python
+   df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})
+   df.sort_values(by='x',inplace = True)
+   points = pd.DataFrame(df).to_numpy()
+
+   plt.plot(points[:, 0], points[:, 1],color="blue", linewidth=3)
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+   plt.scatter(X,y, color="black")
+   plt.show()
+   ```
+
+You created a new dataframe by calling `pd.DataFrame`. Then you sorted the values by calling `sort_values()`. Finally you created a polynomial plot:
+
+![A polynomial plot showing package to price relationship](./images/polynomial.png)
+
+You can see a curved line that fits your data better. 
+
+Let's check the model's accuracy:
+
+   ```python
+   accuracy_score = pipeline.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   And voila!
+
+   ```output
+   Model Accuracy:  0.8537946517073784
+   ```
+
+That's better! Try to predict a price:
+
+### Do a prediction
+
+Can we input a new value and get a prediction?
+
+Call `predict()` to make a prediction:
+ 
+   ```python
+   pipeline.predict( np.array([ [2.75] ]) )
+   ```
+   You are given this prediction:
+
+   ```output
+   array([[46.34509342]])
+   ```
+
+It does make sense, given the plot! And, if this is a better model than the previous one, looking at the same data, you need to budget for these more expensive pumpkins!
+
+� Well done! You created two regression models in one lesson. In the final section on regression, you will learn about logistic regression to determine categories. 
+
+---
+## 🚀Challenge
+
+Test several different variables in this notebook to see how correlation corresponds to model accuracy.
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+
+## Review & Self Study
+
+In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
+
+## Assignment 
+
+[Build a Model](assignment.md)

From e859c7484c3066cf0d6e990ffe5732fe9268b50e Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 22:07:26 +0800
Subject: [PATCH 072/368] [WIP]translate 2.4 to Simplified Chinese

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+# Logistic regression to predict categories
+
+![Logistic vs. linear regression infographic](./images/logistic-linear.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+
+## Introduction
+
+In this final lesson on Regression, one of the basic _classic_ ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict binary categories. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not? 
+
+In this lesson, you will learn:
+
+- A new library for data visualization
+- Techniques for logistic regression
+
+✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
+## Prerequisite
+
+Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one binary category that we can work with: `Color`.
+
+Let's build a logistic regression model to predict that, given some variables, _what color a given pumpkin is likely to be_ (orange 🎃 or white 👻).
+
+> Why are we talking about binary classification in a lesson grouping about regression? Only for linguistic convenience, as logistic regression is [really a classification method](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), albeit a linear-based one. Learn about other ways to classify data in the next lesson group.
+
+## Define the question
+
+For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.
+
+> 🎃 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking!
+
+## About logistic regression
+
+Logistic regression differs from linear regression, which you learned about previously, in a few important ways.
+
+### Binary classification
+
+Logistic regression does not offer the same features as linear regression. The former offers a prediction about a binary category ("orange or not orange") whereas the latter is capable of predicting continual values, for example given the origin of a pumpkin and the time of harvest, _how much its price will rise_.
+
+![Pumpkin classification Model](./images/pumpkin-classifier.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+### Other classifications
+
+There are other types of logistic regression, including multinomial and ordinal:
+
+- **Multinomial**, which involves having more than one category - "Orange, White, and Striped".
+- **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
+
+![Multinomial vs ordinal regression](./images/multinomial-ordinal.png)
+> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+### It's still linear
+
+Even though this type of Regression is all about 'category predictions', it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not.
+
+### Variables DO NOT have to correlate
+
+Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations.
+
+### You need a lot of clean data
+
+Logistic regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind.
+
+✅ Think about the types of data that would lend themselves well to logistic regression
+
+## Exercise - tidy the data
+
+First, clean the data a bit, dropping null values and selecting only some of the columns:
+
+1. Add the following code:
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']
+    
+    new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    
+    new_pumpkins.dropna(inplace=True)
+    
+    new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
+    ```
+
+    You can always take a peek at your new dataframe:
+
+    ```python
+    new_pumpkins.info
+    ```
+
+### Visualization - side-by-side grid
+
+By now you have loaded up the [starter notebook](./notebook.ipynb) with pumpkin data once again and cleaned it so as to preserve a dataset containing a few variables, including `Color`. Let's visualize the dataframe in the notebook using a different library: [Seaborn](https://seaborn.pydata.org/index.html), which is built on Matplotlib which we used earlier. 
+
+Seaborn offers some neat ways to visualize your data. For example, you can compare distributions of the data for each point in a side-by-side grid.
+
+1. Create such a grid by instantiating a `PairGrid`, using our pumpkin data `new_pumpkins`, followed by calling `map()`:
+
+    ```python
+    import seaborn as sns
+    
+    g = sns.PairGrid(new_pumpkins)
+    g.map(sns.scatterplot)
+    ```
+
+    ![A grid of visualized data](images/grid.png)
+
+    By observing data side-by-side, you can see how the Color data relates to the other columns.
+
+    ✅ Given this scatterplot grid, what are some interesting explorations you can envision?
+
+### Use a swarm plot
+
+Since Color is a binary category (Orange or Not), it's called 'categorical data' and needs 'a more [specialized approach](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) to visualization'. There are other ways to visualize the relationship of this category with other variables. 
+
+You can visualize variables side-by-side with Seaborn plots.
+
+1. Try a 'swarm' plot to show the distribution of values:
+
+    ```python
+    sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
+    ```
+
+    ![A swarm of visualized data](images/swarm.png)
+
+### Violin plot
+
+A 'violin' type plot is useful as you can easily visualize the way that data in the two categories is distributed. Violin plots don't work so well with smaller datasets as the distribution is displayed more 'smoothly'.
+
+1. As parameters `x=Color`, `kind="violin"` and call `catplot()`:
+
+    ```python
+    sns.catplot(x="Color", y="Item Size",
+                kind="violin", data=new_pumpkins)
+    ```
+
+    ![a violin type chart](images/violin.png)
+
+    ✅ Try creating this plot, and other Seaborn plots, using other variables.
+
+Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color.
+
+> **🧮 Show Me The Math**
+>
+> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus:
+>
+> ![logistic function](images/sigmoid.png)
+>
+> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class '1' of the binary choice. If not, it will be classified as '0'.
+
+## Build your model
+
+Building a model to find these binary classification is surprisingly straightforward in Scikit-learn.
+
+1. Select the variables you want to use in your classification model and split the training and test sets calling `train_test_split()`:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Origin','Item Size','Variety','City Name','Package']
+    
+    X = new_pumpkins[Selected_features]
+    y = new_pumpkins['Color']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    
+    ```
+
+1. Now you can train your model, by calling `fit()` with your training data, and print out its result:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.linear_model import LogisticRegression
+    
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+    Take a look at your model's scoreboard. It's not too bad, considering you have only about 1000 rows of data:
+
+    ```output
+                       precision    recall  f1-score   support
+    
+               0       0.85      0.95      0.90       166
+               1       0.38      0.15      0.22        33
+    
+        accuracy                           0.82       199
+       macro avg       0.62      0.55      0.56       199
+    weighted avg       0.77      0.82      0.78       199
+    
+    Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+     0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 1 0 1 0 0 1 0 0 0 1 0]
+    ```
+
+## Better comprehension via a confusion matrix
+
+While you can get a scoreboard report [terms](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) by printing out the items above, you might be able to understand your model more easily by using a [confusion matrix](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix) to help us understand how the model is performing.
+
+> 🎓 A '[confusion matrix](https://wikipedia.org/wiki/Confusion_matrix)' (or 'error matrix') is a table that expresses your model's true vs. false positives and negatives, thus gauging the accuracy of predictions.
+
+1. To use a confusion metrics, call `confusin_matrix()`:
+
+    ```python
+    from sklearn.metrics import confusion_matrix
+    confusion_matrix(y_test, predictions)
+    ```
+
+    Take a look at your model's confusion matrix:
+
+    ```output
+    array([[162,   4],
+           [ 33,   0]])
+    ```
+
+What's going on here? Let's say our model is asked to classify items between two binary categories, category 'pumpkin' and category 'not-a-pumpkin'.
+
+- If your model predicts something as a pumpkin and it belongs to category 'pumpkin' in reality we call it a true positive, shown by the top left number. 
+- If your model predicts something as not a pumpkin and it belongs to category 'pumpkin' in reality we call it a false positive, shown by the top right number. 
+- If your model predicts something as a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a false negative, shown by the bottom left number. 
+- If your model predicts something as not a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a true negative, shown by the bottom right number.
+
+![Confusion Matrix](images/confusion-matrix.png)
+
+> Infographic by [Jen Looper](https://twitter.com/jenlooper)
+
+As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.
+
+✅ Q: According to the confusion matrix, how did the model do? A: Not too bad; there are a good number of true positives but also several false negatives. 
+
+Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN:
+
+🎓 Precision: TP/(TP + FN) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
+
+🎓 Recall: TP/(TP + FP) The fraction of relevant instances that were retrieved, whether well-labeled or not
+
+🎓 f1-score: (2 * precision * recall)/(precision + recall) A weighted average of the precision and recall, with best being 1 and worst being 0
+
+🎓 Support: The number of occurrences of each label retrieved
+
+🎓 Accuracy: (TP + TN)/(TP + TN + FP + FN) The percentage of labels predicted accurately for a sample.
+
+🎓 Macro Avg: The calculation of the unweighted mean metrics for each label, not taking label imbalance into account.
+
+🎓 Weighted Avg: The calculation of the mean metrics for each label, taking label imbalance into account by weighting them by their support (the number of true instances for each label).
+
+✅ Can you think which metric you should watch if you want your model to reduce the number of false negatives?
+## Visualize the ROC curve of this model
+
+This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables.
+
+Let's do one more visualization to see the so-called 'ROC' score:
+
+```python
+from sklearn.metrics import roc_curve, roc_auc_score
+
+y_scores = model.predict_proba(X_test)
+# calculate ROC curve
+fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
+sns.lineplot([0, 1], [0, 1])
+sns.lineplot(fpr, tpr)
+```
+Using Seaborn again, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly:
+
+![ROC](./images/ROC.png)
+
+Finally, use Scikit-learn's [`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) to compute the actual 'Area Under the Curve' (AUC):
+
+```python
+auc = roc_auc_score(y_test,y_scores[:,1])
+print(auc)
+```
+The result is `0.6976998904709748`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is _pretty good_. 
+
+In future lessons on classifications, you will learn how to iterate to improve your model's scores. But for now, congratulations! You've completed these regression lessons!
+
+---
+## 🚀Challenge
+
+There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://kaggle.com) for interesting datasets.
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+
+## Review & Self Study
+
+Read the first few pages of [this paper from Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) on some practical uses for logistic regression. Think about tasks that are better suited for one or the other type of regression tasks that we have studied up to this point. What would work best?
+
+## Assignment 
+
+[Retrying this regression](assignment.md)

From 717594285ef576327ca596606929cbbbbda60520 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 10:16:25 -0400
Subject: [PATCH 073/368] edit for paths for Indonesian translations

---
 2-Regression/translations/README.id.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/2-Regression/translations/README.id.md b/2-Regression/translations/README.id.md
index 6b228c1474..da1fa19399 100644
--- a/2-Regression/translations/README.id.md
+++ b/2-Regression/translations/README.id.md
@@ -3,7 +3,7 @@
 
 Di Amerika Utara, labu seringkali diukir menjadi muka-muka seram untuk Halloween. Mari mencari tahu lebih banyak tentang sayur menarik ini!
 
-![jack-o-lantern](./images/jack-o-lanterns.jpg)
+![jack-o-lantern](../images/jack-o-lanterns.jpg)
 > Foto oleh <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> di <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Apa yang kamu akan pelajari
@@ -18,10 +18,10 @@ Selain itu, kamu akan disiapkan untuk mulai mengerjakan tugas *machine learning*
 
 ### Pelajaran
 
-1. [Alat-alat seorang *data scientist*](1-Tools/README.md)
-2. [Mengelola data](2-Data/README.md)
-3. [Regresi linear dan polinomial](3-Linear/README.md)
-4. [Regresi logistik](4-Logistic/README.md)
+1. [Alat-alat seorang *data scientist*](1-Tools/translations/README.id.md)
+2. [Mengelola data](2-Data/translations/README.id.md)
+3. [Regresi linear dan polinomial](3-Linear/translations/README.id.md)
+4. [Regresi logistik](4-Logistic/translations/README.id.md)
 
 ---
 ### Kredit

From 6c46e9d882c19bf213425ef3c16504ee5dd43b32 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 10:17:27 -0400
Subject: [PATCH 074/368] editing Russian translation image path

---
 2-Regression/translations/README.ru.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/translations/README.ru.md b/2-Regression/translations/README.ru.md
index 58270106de..074f0858ac 100644
--- a/2-Regression/translations/README.ru.md
+++ b/2-Regression/translations/README.ru.md
@@ -3,7 +3,7 @@
 
 В Северной �мерике на Х�ллоуин из тыкв ча�то вырезают �трашные лица. Давайте узнаем больше об �тих во�хитительных овощах!
 
-! [jack-o-lanterns](./images/jack-o-lanterns.jpg)
+![jack-o-lanterns](../images/jack-o-lanterns.jpg)
 > Фото <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Бет Тойчманн </a> на <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText"> Unsplash </a>
   
 ## Что вы узнаете
@@ -30,4 +30,4 @@
 
 ♥ � Среди уча�тников викторины: [Мухаммад Сакиб Хан Инан](https://twitter.com/Sakibinan) и [Орнелла �лтун�н](https://twitter.com/ornelladotcom)
 
-�абор данных по тыкве предлагает�� [�тот проект на Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices), а его данные вз�ты из [Стандартных отчетов по рынкам �пециальных культур на терминалах](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) ра�про�тран�ет�� Мини�тер�твом �ель�кого хоз�й�тва СШ�. Мы добавили не�колько точек вокруг цвета на о�нове разнообрази�, чтобы нормализовать ра�пределение. Эти данные наход�т�� в открытом до�тупе.
\ No newline at end of file
+�абор данных по тыкве предлагает�� [�тот проект на Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices), а его данные вз�ты из [Стандартных отчетов по рынкам �пециальных культур на терминалах](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) ра�про�тран�ет�� Мини�тер�твом �ель�кого хоз�й�тва СШ�. Мы добавили не�колько точек вокруг цвета на о�нове разнообрази�, чтобы нормализовать ра�пределение. Эти данные наход�т�� в открытом до�тупе.

From 884ba41b520abd382e7c1db0e02516e31036f8d4 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 10:18:45 -0400
Subject: [PATCH 075/368] editing translation sketchnote path

---
 2-Regression/1-Tools/translations/README.id.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
index 4345460ab3..b7ab4bf8c1 100644
--- a/2-Regression/1-Tools/translations/README.id.md
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -1,6 +1,6 @@
 # Memulai dengan Python dan Scikit-learn untuk model regresi
 
-![Ringkisan regresi dalam sebuah catatan sketsa](../../sketchnotes/ml-regression.png)
+![Ringkisan regresi dalam sebuah catatan sketsa](../../../sketchnotes/ml-regression.png)
 
 > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
 

From 163f301675b75c216c2af90031b8b9cea7a3bd10 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 10:21:19 -0400
Subject: [PATCH 076/368] image path edits

---
 2-Regression/2-Data/translations/README.id.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md
index 4e932101d7..b9889ae8fb 100644
--- a/2-Regression/2-Data/translations/README.id.md
+++ b/2-Regression/2-Data/translations/README.id.md
@@ -1,6 +1,6 @@
 # Membangun sebuah model regresi dengan Scikit-learn: siapkan dan visualisasikan data
 
-> ![Infografik visualisasi data](./images/data-visualization.png)
+![Infografik visualisasi data](../images/data-visualization.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
@@ -164,7 +164,7 @@ Coba membuat beberapa grafik sederhana untuk menunjukkan *dataframe* baru yang b
     plt.show()
     ```
 
-    ![Sebuah petak sebar yang menunjukkan hubungan antara harga dan bulan](./images/scatterplot.png)
+    ![Sebuah petak sebar yang menunjukkan hubungan antara harga dan bulan](../images/scatterplot.png)
 
     Apakah grafik ini berguna? Apa ada yang mengejutkanmu?
 
@@ -181,7 +181,7 @@ Untuk menjadikan sebuah grafik menjadi berguna, biasanya datanya harus dikelompo
     plt.ylabel("Pumpkin Price")
     ```
 
-    ![Sebuah diagram batang yang menunjukkan hubungan antara harga dan bulan](./images/barchart.png)
+    ![Sebuah diagram batang yang menunjukkan hubungan antara harga dan bulan](../images/barchart.png)
 
     Nah, ini lebih berguna! Kelihatannya visualisasi ini mengindikasi bahwa labu itu paling mahal pada bulan September dan Oktober. Apa itu sesuai ekspektasimu? Mengapa?
 
@@ -199,4 +199,4 @@ Lihatlah beragam cara memvisualisasi data. Buatlah sebuah daftar dari aneka *lib
 
 ## Tugas
 
-[Menjelajahi visualisasi](assignment.md)
+[Menjelajahi visualisasi](../assignment.md)

From b7936697a4741b833b9278cd2f2628ff5980fcdd Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 10:23:32 -0400
Subject: [PATCH 077/368] editing for paths

---
 2-Regression/3-Linear/translations/README.id.md | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md
index 548d2030a3..f2ae6b7cfd 100644
--- a/2-Regression/3-Linear/translations/README.id.md
+++ b/2-Regression/3-Linear/translations/README.id.md
@@ -1,6 +1,6 @@
 # Bangun sebuah model regresi dengan Scikit-learn: regresi dua arah
 
-![Infografik regresi linear vs polinomial](./images/linear-polynomial.png)
+![Infografik regresi linear vs polinomial](../images/linear-polynomial.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
 ## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
 ### Pembukaan
@@ -49,13 +49,13 @@ Kita melakukan itu sebab kita ingin memodelkan sebuah garis yang jarak kumulatif
 >
 > `X` adalah 'variabel penerang'. `Y` adalah 'variabel dependen'. Gradien garisnya adalah `b`, dan `a` adalah titik potong sumbu y yaitu nilai `Y` saat `X = 0`. 
 >
->![hitunglah gradiennya](images/slope.png)
+>![hitunglah gradiennya](../images/slope.png)
 >
 > Pertama, hitunglah gradien `b`. Infografik oleh [Jen Looper](https://twitter.com/jenlooper)
 >
 > Dalam kata lain, dan berhubungan pula dengan pertanyaan awal data labu kita "prediksikan harga satu bushel labu setiap bulan", `X` merujuk pada harganya, sedangkan `Y` akan merujuk pada bulan penjualan.
 >
->![lengkapilah persamaan ini](images/calculation.png)
+>![lengkapilah persamaan ini](../images/calculation.png)
 >
 > Hitunglah nilai Y. Kalau harganya $4, artinya pasti April! Infografik oleh [Jen Looper](https://twitter.com/jenlooper)
 >
@@ -182,7 +182,7 @@ lin_pumpkins
 
    plt.show()
    ```
-   ![Sebuah petak sebar yang menunjukkan hubungan antara paket dan harga](./images/linear.png)
+   ![Sebuah petak sebar yang menunjukkan hubungan antara paket dan harga](../images/linear.png)
 
 3. Ujilah modelnya dengan sebuah jenis labu hipotetis:
 
@@ -231,7 +231,7 @@ Sebuah cara bagus untuk memvisualisasi korelasi-korelasi antara data dalam *data
    ```
    This code creates a heatmap:
    Kode ini membuat sebuah peta panas
-   ![Sebuah peta panas yang menunjukkan korelasi data](./images/heatmap.png)
+   ![Sebuah peta panas yang menunjukkan korelasi data](../images/heatmap.png)
 
 Melihat peta ini, kamu bisa memvisualisasikan korelasi yang baik antara Package dan Price. Jadi kamu seharusnya bisa membuat sebuah model yang lebih baik daripada yang sebelumnya.
 
@@ -281,7 +281,7 @@ Tambahlah kode ini:
 
 Kamu membuat sebuah *dataframe* baru dengan fungsi `pd.DataFrame`. Lalu kamu mengurutkan isinya dengan fungsi `sort_values()`. Akhirnya kamu membuat sebuah bagan polinomial:
 
-![Sebuah bagan polinomial yang menunjukkan hubungan antara paket dan harga](./images/polynomial.png)
+![Sebuah bagan polinomial yang menunjukkan hubungan antara paket dan harga](../images/polynomial.png)
 
 Kamu bisa melihat garis lengkungnya yang lebih cocok terhadap datamu.
 
@@ -332,4 +332,4 @@ Dalam pelajaran ini kita belajar tentang regresi linear. Ada banyak jenis regres
 
 ## Tugas 
 
-[Buatlah sebuah model](assignment.md)
\ No newline at end of file
+[Buatlah sebuah model](../assignment.md)

From a4dfccbd0f22238429354b5e66cb6691dca90b8b Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 10:25:05 -0400
Subject: [PATCH 078/368] editing for paths for Indonesian translation

---
 .../4-Logistic/translations/README.id.md      | 23 +++++++++++--------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
index 6158e6d97e..ac5a3a98a5 100644
--- a/2-Regression/4-Logistic/translations/README.id.md
+++ b/2-Regression/4-Logistic/translations/README.id.md
@@ -1,7 +1,8 @@
 # Regresi logistik untuk memprediksi kategori-kategori
 
-![Infografik regresi logistik vs. linear](./images/logistic-linear.png)
+![Infografik regresi logistik vs. linear](../images/logistic-linear.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
 ## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
 
 ## Pembukaan
@@ -14,6 +15,7 @@ Dalam pelajaran ini, kamu akan belajar:
 - Teknik-teknik untuk regresi logistik
 
 ✅ Perdalamkan pemahamanmu dalam bekerja dengan regresi jenis ini dalam [modul pembelajaran ini](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
+
 ## Prasyarat
 
 Setelah bekerja dengan data labu, kita sekarang sudah terbiasa dengannya untuk menyadari bahwa adapula sebuah kategori binari yang kita dapat menggunakan: `Color` (warna).
@@ -36,8 +38,9 @@ Regresi logistik berbeda dari regresi linear, jenis regresi yang kamu pelajari s
 
 Regresi logistik tidak mempunyai beberapa fitur regresi linear. Regresi logistik menyediakan sebuah prediksi tentang sebuah kategori binari (seperti "oranye atau bukan oranye"), sedangkan yang lainnya dapat memprediksi nilai-nilai kontinu. Contohnya, dengan mengetahui dari mana labu ini dan kapan dipanennya, regresi linear dapat memprediksi _berapa harganya akan naik_, namun regresi logistik tidak bisa.
 
-![Model klasifikasi labu](./images/pumpkin-classifier.png)
+![Model klasifikasi labu](../images/pumpkin-classifier.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
 ### Klasifikasi lain
 
 Ditambah itu, ada banyak jenis regresi logistik, termasuk jenis multinomial dan ordinal:
@@ -101,7 +104,7 @@ Seaborn menyediakan beberapa cara keren untuk memvisualisasi datamu. Contohnya,
     g.map(sns.scatterplot)
     ```
 
-    ![Sebuah visualisasi *grid* data](images/grid.png)
+    ![Sebuah visualisasi *grid* data](../images/grid.png)
 
     Dengan mengobservasi datanya secara berdampingan, kamu bisa lihat bagaimana data warnanya berhubungan dengan kolom-kolom lainnya.
 
@@ -119,7 +122,7 @@ Kamu bisa memvisualisasikan variabel-variabel secara berdampingan dengan bagan-b
     sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
     ```
 
-    ![Sekawanan data yang divisualisasi](images/swarm.png)
+    ![Sekawanan data yang divisualisasi](../images/swarm.png)
 
 ### Bagan biola
 
@@ -132,7 +135,7 @@ Sebuah bagan 'biola' itu berguna sebab kamu bisa memvisualisasi bagaimana data d
                 kind="violin", data=new_pumpkins)
     ```
 
-    ![sebuah bagan biola](images/violin.png)
+    ![sebuah bagan biola](../images/violin.png)
 
     ✅ Cobalah membuat bagan ini dan jenis-jenis bagan Seaborn lainnya dengan variabel-variabel lainnya.
 
@@ -142,7 +145,7 @@ Sekarang kita sudah dapat bayangan hubungan antara kedua kategori binary warna d
 >
 > Ingat bagaiaman regresi linear seringkali menggunakan metode kuadrat terkecil untuk tiba pada sebuah nilai? Regresi logistik tergantung pada konsep 'kemungkinan terbesar' menggunakan [fungsi sigmoid](https://wikipedia.org/wiki/Sigmoid_function). Sebuah 'fungsi Sigmoid' terlihat seperti huruf 'S' dalam sistem koordinat Kartesius. Fungsi ini mengambil sebuah nilai dan 'mencorongkannya' menjadi sebuah nomor antara 0 dan 1. Kurva ini juga dipanggil sebuah 'kurva logistik'. Formulanya seperti ini:
 >
-> ![Fungsi logistic](images/sigmoid.png)
+> ![Fungsi logistic](../images/sigmoid.png)
 >
 > Titik tengah sigmoidnya terletak di sumbu X. L adalah nilai maksimum kurvanya. k adalah terjalnya kurvanya. Jika hasil fungsinya lebih dari 0.5, nilai yang diberikan kepada fungsi tersebut akan diklasifikasikan sebagai '1'. Kalau tidak, nilai itu akan diklasifikasikan sebagai '0'.
 
@@ -227,7 +230,7 @@ Apa yang sedang terjadi di sini? Mari kita asumsi dulu bahwa model kita ditanyak
 - Kalau modelmu memprediksi sesuati sebagai sebuah labu tetapi sebenarnya bukan sebuah labu, itu disebut negatif palsu yang diindikasi angka di pojok kiri bawah.
 - Kalau modelmu memprediksi sesuati sebagai bukan sebuah labu dan memang benar sesuatu itu bukan sebuah labu, itu disebut negatif benar yang diindikasi angka di pojok kanan bawah.
 
-![Matriks Kebingungan](images/confusion-matrix.png)
+![Matriks Kebingungan](../images/confusion-matrix.png)
 
 > Infografik oleh [Jen Looper](https://twitter.com/jenlooper)
 
@@ -257,6 +260,7 @@ Mari kita lihat kembali istilah-istilah yang kita lihat tadi dengan bantuan matr
 🎓 Rata-rata Tertimbang: Hitungan rata-rata metrik setiap label dengan mempertimbangkan ketidakseimbangan label. Rata-ratanya tertimbang nilai Dukungan (jumlah kejadian dalam realita) setiap label.
 
 ✅ Apa kamu bisa tebak metrik apa yang harus dipantau untuk mengurangi jumlah negatif palsu modelmu?
+
 ## Visualisasikan kurva ROC model ini
 
 Ini bukanlah sebuah model buruk. Akurasinya sekitar 80%, jadi sebenarnya bisa digunakan untuk memprediksi warna sebuah labu berdasarkan beberapa variabel.
@@ -274,7 +278,7 @@ sns.lineplot(fpr, tpr)
 ```
 Menggunakan Seaborn lagi, gambarlah [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) (ROC) model ini. Kurva ROC seringkali digunakan untuk menunjukkan output sebuah pembuat klasifikasi berdasarkan jumlah positif benar dan positif palsunya. "Kurva ROC biasanya menetapkan persentase positif benar di sumbu Y dan positif palsunya di sumbu X" (diterjemahkan). Maka, terjalnya kurva ini dan ruang antara garis titik tengah dan kurvanya penting: kamu mau sebuah kurva yang naik ke atas garisnya secepat mungkin. Dalam kasus ini, ada positif palsu di awal, terus kurvanya naik di atas garisnya dengan benar:
 
-![ROC](./images/ROC.png)
+![ROC](../images/ROC.png)
 
 Akhirnya, gunakanlah [API `roc_auc_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) Scikit-learn untuk menghitung 'Area Di Bawah Kurva'-nya (ADBK) secara persis:
 
@@ -290,6 +294,7 @@ Nanti dalam pelajaran lebih lanjut tentang klasifikasi, kamu akan belajar bagaim
 ## 🚀 Tantangan
 
 Masih ada banyak tentang regresi logistik! Tetapi cara paling baik adalah untuk bereksperimen. Carilah sebuah *dataset* yang bisa diteliti seperti ini dan bangunlah sebuah model darinya. Apa yang kamu pelajari? Petunjuk: Coba [Kaggle](https://kaggle.com) untuk *dataset-dataset* menarik.
+
 ## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
 
 ## Review & Pembelajaran mandiri
@@ -298,4 +303,4 @@ Bacalah beberapa halaman pertama [makalah ini dari Stanford](https://web.stanfor
 
 ## Tugas 
 
-[Coba lagi regresi ini](assignment.md)
+[Coba lagi regresi ini](../assignment.md)

From 72be28e50e889de5d918c52b2b696cb74169aad8 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 10:42:35 -0400
Subject: [PATCH 079/368] fixing a bug in classification - save of a fresh
 dataframe for export

---
 4-Classification/1-Introduction/README.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index 38516901af..4490131c71 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -264,6 +264,12 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
 
     The data is nice and clean, balanced, and very delicious! 
 
+1. The last step is to save your balanced data, including labels and features, into a new dataframe that can be exported into a file:
+
+    ```python
+    transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
+    ```
+
 1. You can take one more look at the data using `transformed_df.head()` and `transformed_df.info()`. Save a copy of this data for use in future lessons:
 
     ```python

From 9bfc6a9c9eaec1be0f6b735dbd239411b8131340 Mon Sep 17 00:00:00 2001
From: Alex Faria <alex.faria@me.com>
Date: Fri, 9 Jul 2021 16:09:55 +0100
Subject: [PATCH 080/368] Added readme in portuguese

---
 README.pt.md | 121 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 121 insertions(+)
 create mode 100644 README.pt.md

diff --git a/README.pt.md b/README.pt.md
new file mode 100644
index 0000000000..460d8f05a4
--- /dev/null
+++ b/README.pt.md
@@ -0,0 +1,121 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# Machine Learning para Principiantes - Um Curso
+
+> � Viaja pelo mundo enquanto exploramos o Machine Learning através das culturas mundiais �
+
+Os Azure Cloud Advocates da Microsoft têm o gosto de oferecer um curso de 24 lições com a duração 12 semanas sobre **Machine Learning**. Neste curso, irás aprender o que algumas vezes chamamos de **machine learning clássico**, usando principalmente Scikit-learn como biblioteca e evitando deep learning, que será coberto no próximo curso 'IA para Iniciantes'. Combina também estas lições com o nosso próximo curso de 'Data Science para Iniciantes'.
+
+Viaja connosco ao redor do mundo enquanto aplicamos estas técnicas clássicas a dados de muitas áreas do mundo. Cada aula inclui questionários pré- e pós-aula, instruções escritas para completar a aula, uma solução, uma tarefa e muito mais. A nossa pedagogia baseada em projetos permite que aprendas enquanto constróis, uma forma comprovada de "colar"" as novas habilidades.
+
+**�� Muito obrigado aos nossos autores** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan, e Amy Boyd
+
+**🎨 Obrigado também aos nossos ilustradores** Tomomi Imura, Dasani Madipalli, e Jen Looper
+
+**� Agradecimento especial � aos nossos autores Microsoft Student Ambassador, revisores e contribuidores**, notavelmente Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, e Snigdha Agarwal
+
+---
+
+# Começando
+
+**Estudantes**, para usar este curso, faz fork no nosso repositório para a tua conta GitHub e completa os exercícios individualmente ou em grupo:
+
+- Começa com o teste pré-aula
+- Lê a aula e completa as atividades, fazendo uma pausa e refletindo a cada verificação de conhecimento.
+- Tenta criar os projetos compreendedo as aulas ao invés de executar o código da solução. No entanto, esse código está disponível nas pastas `/solution` em cada aula que contenha um projeto.
+- Faz o teste pós-aula
+- Completa um desafio
+- Conclui a tarefa
+- Depois de concluir uma aula em grupo, visita o [Quadro de discussão](https://github.com/microsoft/ML-For-Beginners/discussions) e "aprende em voz alta" preenchendo a rúbrica PAT apropriada. Um 'PAT' é uma ferramenta de avaliação de progresso que é uma rúbrica que preenches para promover a tua aprendizagem. Também podes reagir a outros PATs para que possamos aprender juntos.
+
+> Para um estudo mais aprofundado, recomendamos que sigas estes [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) módulos e percursos de aprendizagem.
+
+**Professores**, [incluímos algumas sugestões](for-teachers.md) em como usar este curso.
+
+---
+
+## Conhece a equipa
+
+[![Promo video](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU 'Promo video')
+
+> 🎥 Clica na imagem acima para um vídeo sobre o projeto e as pessoas que o criaram!
+
+---
+
+## Pedagogia
+
+Escolhemos dois princípios pedagógicos ao construir este curso: garantindo que seja um trabalho prático **baseado em projeto** e isso inclui **testes frequentes**. Adicionalmente, este currículo tem um **tema** comum para dar-lhe coesão.
+
+Ao garantir que o conteúdo esteja alinhado com os projetos, o processo torna-se mais envolvente para os estudantes e a retenção dos conceitos é aumentada. Além disso, um teste de baixo risco antes da aula define a intenção do estudante de aprender um tópico, enquanto que um segundo teste após a aula garante maior retenção. Este curso foi projetado para ser flexível e divertido e pode ser realizado no seu todo ou parcialmente. Os projetos começam pequenos e tornam-se cada vez mais complexos no final do ciclo de 12 semanas. Este currículo também inclui um pós-escrito sobre aplicações reais de ML, que pode ser usado como crédito extra ou como base para discussão.
+
+> Encontra o nosso [Código de Conduta](CODE_OF_CONDUCT.md), diretrizes de [Contribuição](CONTRIBUTING.md), e [Tradução](TRANSLATIONS.md). Agradecemos o teu feedback construtivo!
+
+## Cada aula inclui:
+
+- esboço opcional
+- vídeo suplementar opcional
+- teste de aquecimento pré-aula
+- aula escrita
+- para aulas baseadas em projetos, guias passo a passo sobre como contruir o projeto
+- verificações de conhecimento
+- um desafio
+- leitura suplementar
+- tarefa
+- teste pós-aula
+
+> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://jolly-sea-0a877260f.azurestaticapps.net), para um total de 50 testes de 3 perguntas cada. Eles estão vinculados às aulas, mas a aplicação do teste pode ser executada localmente; segue as intruções na pasta `quiz-app`.
+
+| Número de aula |                                Tópico                                 |                 Agrupamento de Aulas                  | Objetivos de aprendizagem                                                                                                            |                   Aula vinculada                    |    Autor     |
+| :------------: | :-------------------------------------------------------------------: | :---------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------: | :----------: |
+|       01       |                    Introdução ao machine learning                     |        [Introdução](1-Introduction/README.md)         | Aprender os conceitos básicos atrás do machine learning                                                                              |   [aula](1-Introduction/1-intro-to-ML/README.md)    |   Muhammad   |
+|       02       |                    A História de machine learning                     |        [Introdução](1-Introduction/README.md)         | Aprender a história subjacente nesta área                                                                                            |  [aula](1-Introduction/2-history-of-ML/README.md)   |  Jen e Amy   |
+|       03       |                      Justiça e machine learning                       |        [Introdução](1-Introduction/README.md)         | Quais são as questões filosóficas importantes sobre justiça que os estudantes devem considerar ao construir e aplicar modelos de ML? |     [aula](1-Introduction/3-fairness/README.md)     |    Tomomi    |
+|       04       |                    Técnicas para machine learning                     |        [Introdução](1-Introduction/README.md)         | Que técnicas os investigadores de ML usam para construir modelos de ML?                                                              | [aula](1-Introduction/4-techniques-of-ML/README.md) | Chris e Jen  |
+|       05       |                        Introdução à regressão                         |          [Regressão](2-Regression/README.md)          | Começando a usar Python e Scikit-learn para modelos de regressão                                                                     |       [aula](2-Regression/1-Tools/README.md)        |     Jen      |
+|       06       |                Preços das abóboras norte americanas 🎃                |          [Regressão](2-Regression/README.md)          | Visualizar e limpar os dados em preparação para o ML                                                                                 |        [aula](2-Regression/2-Data/README.md)        |     Jen      |
+|       07       |                Preços das abóboras norte americanas 🎃                |          [Regressão](2-Regression/README.md)          | Construir modelos de regressão linear e polinomial                                                                                   |       [aula](2-Regression/3-Linear/README.md)       |     Jen      |
+|       08       |                Preços das abóboras norte americanas 🎃                |          [Regressão](2-Regression/README.md)          | Construir um modelo de regressão logística                                                                                           |      [aula](2-Regression/4-Logistic/README.md)      |     Jen      |
+|       09       |                            Uma Web App 🔌                             |            [Web App](3-Web-App/README.md)             | Criar uma web app para usar o teu modelo treinado                                                                                    |        [aula](3-Web-App/1-Web-App/README.md)        |     Jen      |
+|       10       |                      Introdução à classificação                       |      [Classificação](4-Classification/README.md)      | Limpar, preparar e visualizar os dados; introdução à classificação                                                                   |  [aula](4-Classification/1-Introduction/README.md)  | Jen e Cassie |
+|       11       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](4-Classification/README.md)      | Introdução aos classificadores                                                                                                       | [aula](4-Classification/2-Classifiers-1/README.md)  | Jen e Cassie |
+|       12       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](4-Classification/README.md)      | Mais classificadores                                                                                                                 | [aula](4-Classification/3-Classifiers-2/README.md)  | Jen e Cassie |
+|       13       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](4-Classification/README.md)      | Criar uma web app de recomendação usando o teu modelo                                                                                |    [aula](4-Classification/4-Applied/README.md)     |     Jen      |
+|       14       |                       Introdução ao clustering                        |         [Clustering](5-Clustering/README.md)          | Limpar, preparar e visualizar os dados; Introdução ao clustering                                                                     |     [aula](5-Clustering/1-Visualize/README.md)      |     Jen      |
+|       15       |               Explorando Gostos Musicais Nigerianos 🎧                |         [Clustering](5-Clustering/README.md)          | Explorar o método de agrupamento K-Means                                                                                             |      [aula](5-Clustering/2-K-Means/README.md)       |     Jen      |
+|       16       |           Introdução processamento de linguagem natural ☕�           | [Processamento de linguagem natural](6-NLP/README.md) | Aprender o básico sobre NLP construindo um bot simples                                                                               |    [aula](6-NLP/1-Introduction-to-NLP/README.md)    |   Stephen    |
+|       17       |                        Tarefas NLP comuns ☕�                         | [Processamento de linguagem natural](6-NLP/README.md) | Aprofundar o conhecimento de NLP percebendo tarefas comuns exigidas ao lidar com estruturas de linguagem                             |           [aula](6-NLP/2-Tasks/README.md)           |   Stephen    |
+|       18       |                  Tradução e análise de sentimento ♥�                  | [Processamento de linguagem natural](6-NLP/README.md) | Tradução e análise de sentimento com Jane Austen                                                                                     |   [aula](6-NLP/3-Translation-Sentiment/README.md)   |   Stephen    |
+|       19       |                    Hotéis romanticos da Europa ♥�                     | [Processamento de linguagem natural](6-NLP/README.md) | Análise de sentimento com avaliações de hotéis, 1                                                                                    |      [aula](6-NLP/4-Hotel-Reviews-1/README.md)      |   Stephen    |
+|       20       |                    Hotéis romanticos da Europa ♥�                     | [Processamento de linguagem natural](6-NLP/README.md) | Análise de sentimento com avaliações de hotéis, 2                                                                                    |      [aula](6-NLP/5-Hotel-Reviews-2/README.md)      |   Stephen    |
+|       21       |                Introdução previsão de séries temporais                |      [Séries temporais](7-TimeSeries/README.md)       | Introdução a previsão de séries temporais                                                                                            |    [aula](7-TimeSeries/1-Introduction/README.md)    |  Francesca   |
+|       22       | ⚡� Uso de Energia Mundial ⚡� - previsão de série temporal com ARIMA |      [Séries temporais](7-TimeSeries/README.md)       | Previsão de séries temporais com ARIMA                                                                                               |       [aula](7-TimeSeries/2-ARIMA/README.md)        |  Francesca   |
+|       23       |                 Introdução à aprendizagem por reforço                 | [Aprendizagem por reforço](8-Reinforcement/README.md) | Introdução à aprendizagem por reforço com Q-Learning                                                                                 |    [aula](8-Reinforcement/1-QLearning/README.md)    |    Dmitry    |
+|       24       |                  Ajuda o Pedro a escapar do lobo! �                  | [Aprendizagem por reforço](8-Reinforcement/README.md) | Ginásio de aprendizagem por reforço                                                                                                  |       [aula](8-Reinforcement/2-Gym/README.md)       |    Dmitry    |
+|   Postscript   |                   Cenários reais de ML e aplicações                   |       [ML na vida real](9-Real-World/README.md)       | Aplicações interessantes e reveladoras do mundo real do ML clássico                                                                  |    [aula](9-Real-World/1-Applications/README.md)    |    Equipa    |
+
+## Acesso Offline
+
+Podes usar este documento offline usando o [Docsify](https://docsify.js.org/#/). Dá fork neste repositório, [instala o Docsify](https://docsify.js.org/#/quickstart) no teu computador, e depois na pasta raiz deste repositório, escreve `docsify serve`. O site será executado na porta 3000 do teu localhost: `localhost:3000`.
+
+## PDFs
+
+Encontras um pdf do curso com links [aqui](pdf/readme.pdf)
+
+## Help Wanted!
+
+Gostarias de contribuir para uma tradução? Por favor lê as nossas [diretrizes de tradução](TRANSLATIONS.md) e adiciona o teu input [aqui](https://github.com/microsoft/ML-For-Beginners/issues/71)
+
+## Outros Cursos
+
+A nossa equipa produziu outros cursos! Confere:
+
+- [Web Dev para Iniciantes](https://aka.ms/webdev-beginners)
+- [IoT para Iniciantes](https://aka.ms/iot-beginners)

From 033659b7ed94bf0372b0714d6c43bbbc66a88bcf Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 23:26:21 +0800
Subject: [PATCH 081/368] Update README.zh-cn.md

---
 .../4-Logistic/translations/README.zh-cn.md   | 211 +++++++++---------
 1 file changed, 106 insertions(+), 105 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index a4488c11e4..ce78aa06b7 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -1,72 +1,73 @@
-# Logistic regression to predict categories
+# 逻辑回归预测分类
 
-![Logistic vs. linear regression infographic](./images/logistic-linear.png)
-> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+![逻辑与线性回归信�图](./images/logistic-linear.png)
+> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
 
-## Introduction
+## 介�
 
-In this final lesson on Regression, one of the basic _classic_ ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict binary categories. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not? 
+在关于回归的最�一课中，我们将学习逻辑回归，这是�典的基本技术之一。您�以使用此技术��现预测二元分类的模�。这是�是巧克力糖？这�病会传染�？这个顾客会选择这个产��？
 
-In this lesson, you will learn:
+在本课中，您将学习：
 
-- A new library for data visualization
-- Techniques for logistic regression
+- 用于数��视化的新库
+- 逻辑回归技术
 
-✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
-## Prerequisite
+✅ 在此[学习模�](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) 中加深您对使用此类回归的�解
 
-Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one binary category that we can work with: `Color`.
+## ��
 
-Let's build a logistic regression model to predict that, given some variables, _what color a given pumpkin is likely to be_ (orange 🎃 or white 👻).
+使用�瓜数��，我们现在对它已�足够熟悉了，�以�识到我们�以使用一个二元类别：`Color`。
 
-> Why are we talking about binary classification in a lesson grouping about regression? Only for linguistic convenience, as logistic regression is [really a classification method](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), albeit a linear-based one. Learn about other ways to classify data in the next lesson group.
+让我们建立一个逻辑回归模型�预测，给定一些��，_给定的�瓜�能是什么颜色_（橙色🎃或白色👻）。
 
-## Define the question
+> 为什么我们在关于回归的课程分组中谈论二元分类？ �是为了语言上的方便，因为逻辑回归[真的是一�分类方法](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)，尽管是基于线性的。我们将在在下一课组中了解对数�进行分类的其他方法。
 
-For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.
+## 定义问题
 
-> 🎃 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking!
+出于我们的目的，我们将其表示为二进制：“橙色�或“�橙色�。我们的数�集中还有一个“�纹�类别，但它的实例很少，所以我们�会使用它。无论如何，一旦我们从数�集中删除空值，它就会消失。
 
-## About logistic regression
+> 🎃 有趣的是，我们有时称白�瓜为鬼�瓜。他们�是很容易雕刻，所以它们��橙色的那么�欢迎，但它们看起�很酷�
 
-Logistic regression differs from linear regression, which you learned about previously, in a few important ways.
+## 关于逻辑回归
 
-### Binary classification
+逻辑回归在一些��方�与您之�了解的线性回归��。
 
-Logistic regression does not offer the same features as linear regression. The former offers a prediction about a binary category ("orange or not orange") whereas the latter is capable of predicting continual values, for example given the origin of a pumpkin and the time of harvest, _how much its price will rise_.
+### 二元分类
 
-![Pumpkin classification Model](./images/pumpkin-classifier.png)
-> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-### Other classifications
+逻辑回归��供与线性回归相�的功能。�者�供关于二元类别（“橙色或�橙色�）的预测，而�者能够预测连续值，例如，给定�瓜的起�和收获时间，_其价格将上涨多少_。
 
-There are other types of logistic regression, including multinomial and ordinal:
+![�瓜分类模型](./images/pumpkin-classifier.png)
+> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
+### 其他分类
 
-- **Multinomial**, which involves having more than one category - "Orange, White, and Striped".
-- **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
+还有其他类型的逻辑回归，包括多项和有�：
 
-![Multinomial vs ordinal regression](./images/multinomial-ordinal.png)
-> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+- **多项**，涉�多个类别 - “橙色�白色和�纹�。
+- **有�**，涉�有�类别，如果我们想对我们的结果进行逻辑排��常有用，例如我们的�瓜按有�数�的大�（mini�sm�med�lg�xl�xxl）排�。
 
-### It's still linear
+![多项�与有�回归](./images/multinomial-ordinal.png)
+> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-Even though this type of Regression is all about 'category predictions', it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not.
+### �然是线性的
 
-### Variables DO NOT have to correlate
+尽管这�类型的回归都是关于“类别预测�的，但当因��（颜色）和其他自��（数�集的其余部分，如城市�称和大�）之间存在明显的线性关系时，它�然效果最好。最好了解一下这些��是�存在线性划分。
 
-Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations.
+### ���必相关
 
-### You need a lot of clean data
+还记得线性回归如何更好地处�更多相关���？逻辑回归是相�的——���必对�。这适用于相关性较弱的数�。
 
-Logistic regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind.
+### 你需�大�干净的数�
 
-✅ Think about the types of data that would lend themselves well to logistic regression
+如果使用更多数�，逻辑回归将给出更准确的结果；我们的�数�集对于这项任务�是最佳的，请记�这一点。
 
-## Exercise - tidy the data
+✅ 考虑适�逻辑回归的数�类型
 
-First, clean the data a bit, dropping null values and selecting only some of the columns:
+## 练习 - 整�数�
 
-1. Add the following code:
+首先，�微清�一下数�，删除空值并仅选择其中一些列：
+
+1. 添加以下代�：
 
     ```python
     from sklearn.preprocessing import LabelEncoder
@@ -80,19 +81,19 @@ First, clean the data a bit, dropping null values and selecting only some of the
     new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
     ```
 
-    You can always take a peek at your new dataframe:
+    您�以�时查看新的数�帧：
 
     ```python
     new_pumpkins.info
     ```
 
-### Visualization - side-by-side grid
+### �视化 - 并列网格
 
-By now you have loaded up the [starter notebook](./notebook.ipynb) with pumpkin data once again and cleaned it so as to preserve a dataset containing a few variables, including `Color`. Let's visualize the dataframe in the notebook using a different library: [Seaborn](https://seaborn.pydata.org/index.html), which is built on Matplotlib which we used earlier. 
+到现在为止，您已��次使用�瓜数�加载了[starter notebook](./notebook.ipynb)并对其进行了清�，以�留包�一些��（包括`Color`）的数�集。让我们使用��的库��视化notebook中的数�帧：[Seaborn](https://seaborn.pydata.org/index.html)，它是基于我们之�使用的Matplotlib构建的。
 
-Seaborn offers some neat ways to visualize your data. For example, you can compare distributions of the data for each point in a side-by-side grid.
+Seaborn�供了一些巧妙的方法��视化您的数�。例如，您�以比较并列网格中�个点的数�分布。
 
-1. Create such a grid by instantiating a `PairGrid`, using our pumpkin data `new_pumpkins`, followed by calling `map()`:
+1. 通过实例化一个`PairGrid`，使用我们的�瓜数�`new_pumpkins`，然�调用`map()`�创建这样一个网格：
 
     ```python
     import seaborn as sns
@@ -101,56 +102,56 @@ Seaborn offers some neat ways to visualize your data. For example, you can compa
     g.map(sns.scatterplot)
     ```
 
-    ![A grid of visualized data](images/grid.png)
+    ![�视化数�网格](images/grid.png)
 
-    By observing data side-by-side, you can see how the Color data relates to the other columns.
+    通过并列观察数�，您�以看到颜色数�与其他列的关系。
 
-    ✅ Given this scatterplot grid, what are some interesting explorations you can envision?
+    ✅ 鉴于此散点图网格，您�以设想哪些有趣的探索？
 
-### Use a swarm plot
+### 使用分类散点图
 
-Since Color is a binary category (Orange or Not), it's called 'categorical data' and needs 'a more [specialized approach](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) to visualization'. There are other ways to visualize the relationship of this category with other variables. 
+由于颜色是一个二元类别（橙色或�橙色），它被称为“分类数��，需�一�更[专业的方法](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar)��视化。还有其他方法�以�视化此类别与其他��的关系。
 
-You can visualize variables side-by-side with Seaborn plots.
+您�以使用Seaborn图并列�视化��。
 
-1. Try a 'swarm' plot to show the distribution of values:
+1. �试使用“分类散点�图�显示值的分布：
 
     ```python
     sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
     ```
 
-    ![A swarm of visualized data](images/swarm.png)
+    ![分类散点图�视化数�](images/swarm.png)
 
-### Violin plot
+### ���图
 
-A 'violin' type plot is useful as you can easily visualize the way that data in the two categories is distributed. Violin plots don't work so well with smaller datasets as the distribution is displayed more 'smoothly'.
+“����类型的图很有用，因为您�以轻�地�视化两个类别中数�的分布方�。���图�适用于较�的数�集，因为分布显示得更“平滑�。
 
-1. As parameters `x=Color`, `kind="violin"` and call `catplot()`:
+1. 作为�数`x=Color`�`kind="violin"`并调用`catplot()`：
 
     ```python
     sns.catplot(x="Color", y="Item Size",
                 kind="violin", data=new_pumpkins)
     ```
 
-    ![a violin type chart](images/violin.png)
+    ![���图](images/violin.png)
 
-    ✅ Try creating this plot, and other Seaborn plots, using other variables.
+    ✅ �试使用其他��创建此图和其他Seaborn图。
 
-Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color.
+现在我们已�了解了颜色的二元类别与更大的尺寸组之间的关系，让我们探索逻辑回归�确定给定�瓜的�能颜色。
 
-> **🧮 Show Me The Math**
+> **🧮 给我看看数学**
 >
-> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus:
+> 还记得线性回归如何�常使用普通最�二乘法�得出一个值�？逻辑回归�赖于使用[sigmoid 函数](https://wikipedia.org/wiki/Sigmoid_function) 的“最大似然�概念。绘图上的“Sigmoid 函数�看起��“S�形。它接�一个值并将其映射到0和1之间的�个�置。它的曲线也称为“逻辑曲线�。它的公�如下所示：
 >
-> ![logistic function](images/sigmoid.png)
+> ![逻辑函数](images/sigmoid.png)
 >
-> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class '1' of the binary choice. If not, it will be classified as '0'.
+> 其中sigmoid的中点�于x的0点，L是曲线的最大值，k是曲线的陡度。如果函数的结果大于0.5，则所讨论的标签将被赋予二进制选择的类“1�。�则，它将被分类为“0�。
 
-## Build your model
+## 建立你的模型
 
-Building a model to find these binary classification is surprisingly straightforward in Scikit-learn.
+在Scikit-learn中构建模型�查找这些二元分类�常简�。
 
-1. Select the variables you want to use in your classification model and split the training and test sets calling `train_test_split()`:
+1. 选择�在分类模型中使用的��，并调用`train_test_split()`拆分训练集和测试集：
 
     ```python
     from sklearn.model_selection import train_test_split
@@ -164,7 +165,7 @@ Building a model to find these binary classification is surprisingly straightfor
     
     ```
 
-1. Now you can train your model, by calling `fit()` with your training data, and print out its result:
+2. 现在你�以训练你的模型，用你的训练数�调用`fit()`，并打�出它的结果：
 
     ```python
     from sklearn.model_selection import train_test_split
@@ -180,7 +181,7 @@ Building a model to find these binary classification is surprisingly straightfor
     print('Accuracy: ', accuracy_score(y_test, predictions))
     ```
 
-    Take a look at your model's scoreboard. It's not too bad, considering you have only about 1000 rows of data:
+    看看你的模型的记分�。考虑到您�有大约1000行数�，这还�错：
 
     ```output
                        precision    recall  f1-score   support
@@ -200,63 +201,63 @@ Building a model to find these binary classification is surprisingly straightfor
      0 0 0 1 0 1 0 0 1 0 0 0 1 0]
     ```
 
-## Better comprehension via a confusion matrix
+## 通过混淆矩阵更好地�解
 
-While you can get a scoreboard report [terms](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) by printing out the items above, you might be able to understand your model more easily by using a [confusion matrix](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix) to help us understand how the model is performing.
+虽然您�以通过获得记分�报告[�目](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report)把上�的项目打�出�，通过使用[混淆矩阵](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix)�以更容易地�解您的模型，帮助我们了解模型的性能。
 
-> 🎓 A '[confusion matrix](https://wikipedia.org/wiki/Confusion_matrix)' (or 'error matrix') is a table that expresses your model's true vs. false positives and negatives, thus gauging the accuracy of predictions.
+> 🎓 “[混淆矩阵](https://wikipedia.org/wiki/Confusion_matrix)�（或“误差矩阵�）是一个表格，用于表示模型的真�阳性和�阴性，从而衡�预测的准确性。
 
-1. To use a confusion metrics, call `confusin_matrix()`:
+1. �使用混淆指标，请调用 `confusin_matrix()`：
 
     ```python
     from sklearn.metrics import confusion_matrix
     confusion_matrix(y_test, predictions)
     ```
 
-    Take a look at your model's confusion matrix:
+    看看你的模型的混淆矩阵：
 
     ```output
     array([[162,   4],
            [ 33,   0]])
     ```
 
-What's going on here? Let's say our model is asked to classify items between two binary categories, category 'pumpkin' and category 'not-a-pumpkin'.
+这里�生了什么？�设我们的模型被�求对两个二元类别之间的项目进行分类，�类别“�瓜�和类别“��瓜�。
 
-- If your model predicts something as a pumpkin and it belongs to category 'pumpkin' in reality we call it a true positive, shown by the top left number. 
-- If your model predicts something as not a pumpkin and it belongs to category 'pumpkin' in reality we call it a false positive, shown by the top right number. 
-- If your model predicts something as a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a false negative, shown by the bottom left number. 
-- If your model predicts something as not a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a true negative, shown by the bottom right number.
+- 如果您的模型将�物预测为�瓜并且它实际上属于“�瓜�类别，我们将其称为真阳性，由左上角的数字显示。
+- 如果您的模型预测�物�是�瓜，并且它实际上属于“�瓜�类别，我们将其称为�阳性，如�上角的数字所示。
+- 如果您的模型将�物预测为�瓜并且它实际上属于“��瓜�类别，我们将其称为�阴性，由左下角的数字显示。
+- 如果您的模型预测�物�是�瓜，并且它实际上属于“��瓜�类别，我们将其称为真阴性，如�下角的数字所示。
 
-![Confusion Matrix](images/confusion-matrix.png)
+![混淆矩阵](images/confusion-matrix.png)
 
-> Infographic by [Jen Looper](https://twitter.com/jenlooper)
+> 作者[Jen Looper](https://twitter.com/jenlooper)
 
-As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.
+正如您�能已�猜到的那样，最好有更多的真阳性和真阴性以�较少的�阳性和�阴性，这�味�模型性能更好。
 
-✅ Q: According to the confusion matrix, how did the model do? A: Not too bad; there are a good number of true positives but also several false negatives. 
+✅ Q：根�混淆矩阵，模型怎么样？ A：还�错；有很多真阳性，但也有一些�阴性。
 
-Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN:
+让我们借助混淆矩阵对TP/TN和FP/FN的映射，�新审视一下我们之�看到的术语：
 
-🎓 Precision: TP/(TP + FN) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
+🎓 准确率：TP/（TP+FN）检索实例中相关实例的分数（例如，哪些标签标记得很好）
 
-🎓 Recall: TP/(TP + FP) The fraction of relevant instances that were retrieved, whether well-labeled or not
+🎓 �回率: TP/(TP + FP) 检索到的相关实例的比例，无论是�标记良好
 
-🎓 f1-score: (2 * precision * recall)/(precision + recall) A weighted average of the precision and recall, with best being 1 and worst being 0
+🎓 F1分数: (2 * 准确率 * �回率)/(准确率 + �回率) 准确率和�回率的加�平�值，最好为1，最差为0
 
-🎓 Support: The number of occurrences of each label retrieved
+🎓 Support：检索到的�个标签的出现次数
 
-🎓 Accuracy: (TP + TN)/(TP + TN + FP + FN) The percentage of labels predicted accurately for a sample.
+🎓 准确度：(TP + TN)/(TP + TN + FP + FN) 为样本准确预测的标签百分比。
 
-🎓 Macro Avg: The calculation of the unweighted mean metrics for each label, not taking label imbalance into account.
+🎓 �平�值: 计算�个标签的未加�平�指标，�考虑标签�平衡。
 
-🎓 Weighted Avg: The calculation of the mean metrics for each label, taking label imbalance into account by weighting them by their support (the number of true instances for each label).
+🎓 加�平�值：计算�个标签的平�指标，通过按支�度（�个标签的真实实例数）加��考虑标签�平衡。
 
-✅ Can you think which metric you should watch if you want your model to reduce the number of false negatives?
-## Visualize the ROC curve of this model
+✅ 如果你想让你的模型�少�阴性的数�，你能想出应该关注哪个指标�？
+## �视化该模型的ROC曲线
 
-This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables.
+这�是一个糟糕的模型；它的准确率在80%范围内，因此�想情况下，您�以使用它�预测给定一组��的�瓜颜色。
 
-Let's do one more visualization to see the so-called 'ROC' score:
+让我们��一个�视化�查看所谓的“ROC�分数
 
 ```python
 from sklearn.metrics import roc_curve, roc_auc_score
@@ -267,30 +268,30 @@ fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
 sns.lineplot([0, 1], [0, 1])
 sns.lineplot(fpr, tpr)
 ```
-Using Seaborn again, plot the model's [Receiving Operating Characteristic](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) or ROC. ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. "ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis." Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly:
+�次使用Seaborn，绘制模型的[接收�作特性](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc)或ROC。 ROC曲线通常用于根�分类器的真�阳性�了解分类器的输出。“ROC曲线通常具有Y轴上的真阳性率和X轴上的�阳性率。� 因此，曲线的陡度以�中点线与曲线之间的空间很��：您需�一�快速�上并越过直线的曲线。在我们的例�中，一开始就有误报，然�这�线正确地�上和��：
 
 ![ROC](./images/ROC.png)
 
-Finally, use Scikit-learn's [`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) to compute the actual 'Area Under the Curve' (AUC):
+最�，使用Scikit-learn的[`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score)�计算实际“曲线下�积�（AUC）：
 
 ```python
 auc = roc_auc_score(y_test,y_scores[:,1])
 print(auc)
 ```
-The result is `0.6976998904709748`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is _pretty good_. 
-
-In future lessons on classifications, you will learn how to iterate to improve your model's scores. But for now, congratulations! You've completed these regression lessons!
+结果是`0.6976998904709748`。 鉴于AUC的范围从0到1，您需�一个高分，因为预测100%正确的模型的AUC为1；在这�情况下，模型_相当�错_。
 
+在以�的分类课程中，您将学习如何迭代以�高模型的分数。但是现在，�喜�您已�完�了这些回归课程�
 ---
-## 🚀Challenge
+## 🚀挑战
+
+关于逻辑回归，还有很多东西需�解开�但最好的学习方法是实验。找到适�此类分�的数�集并用它构建模型。你学到了什么？�贴士：�试[Kaggle](https://kaggle.com)获�有趣的数�集。
 
-There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://kaggle.com) for interesting datasets.
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
 
-## Review & Self Study
+## �习与自学
 
-Read the first few pages of [this paper from Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) on some practical uses for logistic regression. Think about tasks that are better suited for one or the other type of regression tasks that we have studied up to this point. What would work best?
+阅读[斯��大学的这篇论文](https://web.stanford.edu/~jurafsky/slp3/5.pdf)的�几页关于逻辑回归的一些实际应用。想想那些更适�于我们目�所研究的一�或�一�类型的回归任务的任务。什么最有效？
 
-## Assignment 
+## 任务 
 
-[Retrying this regression](assignment.md)
+[�试此回归](assignment.md)

From a05577934bba29fb454ea4fec862a586c019b3fd Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 23:38:17 +0800
Subject: [PATCH 082/368] Update README.zh-cn.md

---
 .../4-Logistic/translations/README.zh-cn.md   | 44 +++++++++----------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index ce78aa06b7..1b1e8524aa 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -6,14 +6,14 @@
 
 ## 介�
 
-在关于回归的最�一课中，我们将学习逻辑回归，这是�典的基本技术之一。您�以使用此技术��现预测二元分类的模�。这是�是巧克力糖？这�病会传染�？这个顾客会选择这个产��？
+在关于回归的最�一课中，我们将学习逻辑回归，这是�典的基本技术之一。你�以使用此技术��现预测二元分类的模�。这是�是巧克力糖？这�病会传染�？这个顾客会选择这个产��？
 
-在本课中，您将学习：
+在本课中，你将学习：
 
 - 用于数��视化的新库
 - 逻辑回归技术
 
-✅ 在此[学习模�](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) 中加深您对使用此类回归的�解
+✅ 在此[学习模�](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) 中加深你对使用此类回归的�解
 
 ## ��
 
@@ -31,7 +31,7 @@
 
 ## 关于逻辑回归
 
-逻辑回归在一些��方�与您之�了解的线性回归��。
+逻辑回归在一些��方�与你之�了解的线性回归��。
 
 ### 二元分类
 
@@ -81,7 +81,7 @@
     new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
     ```
 
-    您�以�时查看新的数�帧：
+    你�以�时查看新的数�帧：
 
     ```python
     new_pumpkins.info
@@ -89,9 +89,9 @@
 
 ### �视化 - 并列网格
 
-到现在为止，您已��次使用�瓜数�加载了[starter notebook](./notebook.ipynb)并对其进行了清�，以�留包�一些��（包括`Color`）的数�集。让我们使用��的库��视化notebook中的数�帧：[Seaborn](https://seaborn.pydata.org/index.html)，它是基于我们之�使用的Matplotlib构建的。
+到现在为止，你已��次使用�瓜数�加载了[starter notebook](./notebook.ipynb)并对其进行了清�，以�留包�一些��（包括`Color`）的数�集。让我们使用��的库��视化notebook中的数�帧：[Seaborn](https://seaborn.pydata.org/index.html)，它是基于我们之�使用的Matplotlib构建的。
 
-Seaborn�供了一些巧妙的方法��视化您的数�。例如，您�以比较并列网格中�个点的数�分布。
+Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�以比较并列网格中�个点的数�分布。
 
 1. 通过实例化一个`PairGrid`，使用我们的�瓜数�`new_pumpkins`，然�调用`map()`�创建这样一个网格：
 
@@ -104,15 +104,15 @@ Seaborn�供了一些巧妙的方法��视化您的数�。例如，您�
 
     ![�视化数�网格](images/grid.png)
 
-    通过并列观察数�，您�以看到颜色数�与其他列的关系。
+    通过并列观察数�，你�以看到颜色数�与其他列的关系。
 
-    ✅ 鉴于此散点图网格，您�以设想哪些有趣的探索？
+    ✅ 鉴于此散点图网格，你�以设想哪些有趣的探索？
 
 ### 使用分类散点图
 
 由于颜色是一个二元类别（橙色或�橙色），它被称为“分类数��，需�一�更[专业的方法](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar)��视化。还有其他方法�以�视化此类别与其他��的关系。
 
-您�以使用Seaborn图并列�视化��。
+你�以使用Seaborn图并列�视化��。
 
 1. �试使用“分类散点�图�显示值的分布：
 
@@ -124,7 +124,7 @@ Seaborn�供了一些巧妙的方法��视化您的数�。例如，您�
 
 ### ���图
 
-“����类型的图很有用，因为您�以轻�地�视化两个类别中数�的分布方�。���图�适用于较�的数�集，因为分布显示得更“平滑�。
+“����类型的图很有用，因为你�以轻�地�视化两个类别中数�的分布方�。���图�适用于较�的数�集，因为分布显示得更“平滑�。
 
 1. 作为�数`x=Color`�`kind="violin"`并调用`catplot()`：
 
@@ -181,7 +181,7 @@ Seaborn�供了一些巧妙的方法��视化您的数�。例如，您�
     print('Accuracy: ', accuracy_score(y_test, predictions))
     ```
 
-    看看你的模型的记分�。考虑到您�有大约1000行数�，这还�错：
+    看看你的模型的记分�。考虑到你�有大约1000行数�，这还�错：
 
     ```output
                        precision    recall  f1-score   support
@@ -203,7 +203,7 @@ Seaborn�供了一些巧妙的方法��视化您的数�。例如，您�
 
 ## 通过混淆矩阵更好地�解
 
-虽然您�以通过获得记分�报告[�目](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report)把上�的项目打�出�，通过使用[混淆矩阵](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix)�以更容易地�解您的模型，帮助我们了解模型的性能。
+虽然你�以通过获得记分�报告[�目](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report)把上�的项目打�出�，通过使用[混淆矩阵](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix)�以更容易地�解你的模型，帮助我们了解模型的性能。
 
 > 🎓 “[混淆矩阵](https://wikipedia.org/wiki/Confusion_matrix)�（或“误差矩阵�）是一个表格，用于表示模型的真�阳性和�阴性，从而衡�预测的准确性。
 
@@ -223,16 +223,16 @@ Seaborn�供了一些巧妙的方法��视化您的数�。例如，您�
 
 这里�生了什么？�设我们的模型被�求对两个二元类别之间的项目进行分类，�类别“�瓜�和类别“��瓜�。
 
-- 如果您的模型将�物预测为�瓜并且它实际上属于“�瓜�类别，我们将其称为真阳性，由左上角的数字显示。
-- 如果您的模型预测�物�是�瓜，并且它实际上属于“�瓜�类别，我们将其称为�阳性，如�上角的数字所示。
-- 如果您的模型将�物预测为�瓜并且它实际上属于“��瓜�类别，我们将其称为�阴性，由左下角的数字显示。
-- 如果您的模型预测�物�是�瓜，并且它实际上属于“��瓜�类别，我们将其称为真阴性，如�下角的数字所示。
+- 如果你的模型将�物预测为�瓜并且它实际上属于“�瓜�类别，我们将其称为真阳性，由左上角的数字显示。
+- 如果你的模型预测�物�是�瓜，并且它实际上属于“�瓜�类别，我们将其称为�阳性，如�上角的数字所示。
+- 如果你的模型将�物预测为�瓜并且它实际上属于“��瓜�类别，我们将其称为�阴性，由左下角的数字显示。
+- 如果你的模型预测�物�是�瓜，并且它实际上属于“��瓜�类别，我们将其称为真阴性，如�下角的数字所示。
 
 ![混淆矩阵](images/confusion-matrix.png)
 
 > 作者[Jen Looper](https://twitter.com/jenlooper)
 
-正如您�能已�猜到的那样，最好有更多的真阳性和真阴性以�较少的�阳性和�阴性，这�味�模型性能更好。
+正如你�能已�猜到的那样，最好有更多的真阳性和真阴性以�较少的�阳性和�阴性，这�味�模型性能更好。
 
 ✅ Q：根�混淆矩阵，模型怎么样？ A：还�错；有很多真阳性，但也有一些�阴性。
 
@@ -255,7 +255,7 @@ Seaborn�供了一些巧妙的方法��视化您的数�。例如，您�
 ✅ 如果你想让你的模型�少�阴性的数�，你能想出应该关注哪个指标�？
 ## �视化该模型的ROC曲线
 
-这�是一个糟糕的模型；它的准确率在80%范围内，因此�想情况下，您�以使用它�预测给定一组��的�瓜颜色。
+这�是一个糟糕的模型；它的准确率在80%范围内，因此�想情况下，你�以使用它�预测给定一组��的�瓜颜色。
 
 让我们��一个�视化�查看所谓的“ROC�分数
 
@@ -268,7 +268,7 @@ fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
 sns.lineplot([0, 1], [0, 1])
 sns.lineplot(fpr, tpr)
 ```
-�次使用Seaborn，绘制模型的[接收�作特性](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc)或ROC。 ROC曲线通常用于根�分类器的真�阳性�了解分类器的输出。“ROC曲线通常具有Y轴上的真阳性率和X轴上的�阳性率。� 因此，曲线的陡度以�中点线与曲线之间的空间很��：您需�一�快速�上并越过直线的曲线。在我们的例�中，一开始就有误报，然�这�线正确地�上和��：
+�次使用Seaborn，绘制模型的[接收�作特性](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc)或ROC。 ROC曲线通常用于根�分类器的真�阳性�了解分类器的输出。“ROC曲线通常具有Y轴上的真阳性率和X轴上的�阳性率。� 因此，曲线的陡度以�中点线与曲线之间的空间很��：你需�一�快速�上并越过直线的曲线。在我们的例�中，一开始就有误报，然�这�线正确地�上和��：
 
 ![ROC](./images/ROC.png)
 
@@ -278,9 +278,9 @@ sns.lineplot(fpr, tpr)
 auc = roc_auc_score(y_test,y_scores[:,1])
 print(auc)
 ```
-结果是`0.6976998904709748`。 鉴于AUC的范围从0到1，您需�一个高分，因为预测100%正确的模型的AUC为1；在这�情况下，模型_相当�错_。
+结果是`0.6976998904709748`。 鉴于AUC的范围从0到1，你需�一个高分，因为预测100%正确的模型的AUC为1；在这�情况下，模型_相当�错_。
 
-在以�的分类课程中，您将学习如何迭代以�高模型的分数。但是现在，�喜�您已�完�了这些回归课程�
+在以�的分类课程中，你将学习如何迭代以�高模型的分数。但是现在，�喜�你已�完�了这些回归课程�
 ---
 ## 🚀挑战
 

From 4c0395572cafaa7edf34360e71c42cceb6468243 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 23:39:41 +0800
Subject: [PATCH 083/368] Update README.zh-cn.md

---
 .../translations/README.zh-cn.md              | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
index f14d54aef7..12e5aae086 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
@@ -8,24 +8,24 @@
 
 ### 介�
 
-欢迎�到这个�典机器学习的�学者课程�无论您是这个主题的新手，还是一个有�验的ML从业者，我们都很高兴您能加入我们�我们希望为您的ML研究创建一个好的开始，并很��评估�回应和接�您的[�馈](https://github.com/microsoft/ML-For-Beginners/discussions)。
+欢迎�到这个�典机器学习的�学者课程�无论你是这个主题的新手，还是一个有�验的ML从业者，我们都很高兴你能加入我们�我们希望为你的ML研究创建一个好的开始，并很��评估�回应和接�你的[�馈](https://github.com/microsoft/ML-For-Beginners/discussions)。
 
 [![机器学习简介](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
 
 > 🎥 �击上图观看视频：麻��工学院的 John Guttag 介�机器学习
 ### 机器学习入门
 
-在开始本课程之�，您需�设置计算机能在本地�行Jupyter Notebooks。
+在开始本课程之�，你需�设置计算机能在本地�行Jupyter Notebooks。
 
-- **按照这些视频里的讲解�置您的计算机**。了解有关如何在此[视频集](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)中设置计算机的更多信�。
-- **学习Python**. 还建议您对[Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)，我们在本课程中使用的一�对数�科学家有用的编程语言，有一个基本的了解。
-- **学习Node.js和JavaScript**。在本课程中，我们在构建web应用程�时也使用过几次JavaScript，因此您需�有[node](https://nodejs.org)和[npm](https://www.npmjs.com/) 以�[Visual Studio Code](https://code.visualstudio.com/)用于Python和JavaScript开�。
-- **创建GitHub�户**。既然你在[GitHub](https://github.com)上找到我们，您�能已�有了一个�户，但如果没有，请创建一个�户，然�fork此课程自己使用(也给我们一颗星星�😊) 
+- **按照这些视频里的讲解�置你的计算机**。了解有关如何在此[视频集](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)中设置计算机的更多信�。
+- **学习Python**. 还建议你对[Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)，我们在本课程中使用的一�对数�科学家有用的编程语言，有一个基本的了解。
+- **学习Node.js和JavaScript**。在本课程中，我们在构建web应用程�时也使用过几次JavaScript，因此你需�有[node](https://nodejs.org)和[npm](https://www.npmjs.com/) 以�[Visual Studio Code](https://code.visualstudio.com/)用于Python和JavaScript开�。
+- **创建GitHub�户**。既然你在[GitHub](https://github.com)上找到我们，你�能已�有了一个�户，但如果没有，请创建一个�户，然�fork此课程自己使用(也给我们一颗星星�😊) 
 - **探索Scikit-learn**. 熟悉[Scikit-learn]([https://scikit-learn.org/stable/user_guide.html)，我们在这些课程中引用的一组ML库。
 
 ### 什么是机器学习？
 
-术语“机器学习�是当今最�行和最常用的术语之一。 如果您对科技有��程度的熟悉，那么很�能您至少�说过这个术语一次，无论您在哪个领域工作。然而，机器学习的机制对大多数人�说是一个谜。 对于机器学习�学者�说，这个主题有时会让人感到�知所措。 因此，了解机器学习的实质是什么，并通过实例一步一步地了解机器学习是很��的。
+术语“机器学习�是当今最�行和最常用的术语之一。 如果你对科技有��程度的熟悉，那么很�能你至少�说过这个术语一次，无论你在哪个领域工作。然而，机器学习的机制对大多数人�说是一个谜。 对于机器学习�学者�说，这个主题有时会让人感到�知所措。 因此，了解机器学习的实质是什么，并通过实例一步一步地了解机器学习是很��的。
 
 ![机器学习趋势曲线](images/hype.png)
 
@@ -43,11 +43,11 @@
 
 > 显示AI�ML�深度学习和数�科学之间关系的图表。图片作者[Jen Looper](https://twitter.com/jenlooper)，�感�自[这张图](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 
-## 您将在本课程中学到什么
+## 你将在本课程中学到什么
 
 在本课程中，我们将仅涵盖�学者必须了解的机器学习的核心概念。 我们主�使用Scikit-learn�介�我们所谓的“�典机器学习�，这是一个许多学生用�学习基础知识的优秀库。��解更广泛的人工智能或深度学习的概念，机器学习的基础知识是必��少的，所以我们想在这里�供它。
 
-在本课程中，您将学习：
+在本课程中，你将学习：
 
 - 机器学习的核心概念
 - 机器学习的历�
@@ -94,7 +94,7 @@
 ---
 ## 🚀 挑战
 
-在纸上或使用[Excalidraw](https://excalidraw.com/)等在线应用程�绘制�图，了解您对AI�ML�深度学习和数�科学之间差异的�解。添加一些关于这些技术擅长解决的问题的想法。
+在纸上或使用[Excalidraw](https://excalidraw.com/)等在线应用程�绘制�图，了解你对AI�ML�深度学习和数�科学之间差异的�解。添加一些关于这些技术擅长解决的问题的想法。
 
 ## [阅读�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
 

From 439ffc2e367e4bf8511d3ac657336078432ec5a8 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 23:40:46 +0800
Subject: [PATCH 084/368] Update README.zh-cn.md

---
 1-Introduction/2-history-of-ML/translations/README.zh-cn.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index 0491f865a8..c8f74e6994 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -12,7 +12,7 @@
 ## 主��现
 
 - 1763, 1812 [��斯定�](https://wikipedia.org/wiki/Bayes%27_theorem) �其�身。该定��其应用是推�的基础，�述了基于先验知识的事件�生的概率。
-- 1805 [最�二乘�论](https://wikipedia.org/wiki/Least_squares)由法国数学家Adrien-Marie Legendre�出。 您将在我们的回归�元中了解这一�论，它有助于数�拟�。
+- 1805 [最�二乘�论](https://wikipedia.org/wiki/Least_squares)由法国数学家Adrien-Marie Legendre�出。 你将在我们的回归�元中了解这一�论，它有助于数�拟�。
 - 1913 [马尔�夫链](https://wikipedia.org/wiki/Markov_chain)以俄罗斯数学家Andrey Markov的�字命�，用于�述基于先�状�的一系列�能事件。
 - 1957 [感知器](https://wikipedia.org/wiki/Perceptron)是美国心�学家Frank Rosenblatt�明的一�线性分类器，是深度学习�展的基础。
 - 1967 [最近邻](https://wikipedia.org/wiki/Nearest_neighbor)是一�最�设计用于映射路线的算法。 在ML中，它用于检测模�。
@@ -22,7 +22,7 @@
 ✅ �点调查。在ML和AI的历�上，还有哪些日期是��的？
 ## 1950: 会�考的机器 
 
-Alan Turing，一个真正�出的人，[在2019年被公众投票选出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作为20世纪最伟大的科学家，他认为有助于为“会�考的机器�的概念打下基础。他通过创建 [图�测试](https://www.bbc.com/news/technology-18475646)�解决�对者和他自己对这一概念的�验��的需求，您将在我们的 NLP 课程中进行探索。
+Alan Turing，一个真正�出的人，[在2019年被公众投票选出](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 作为20世纪最伟大的科学家，他认为有助于为“会�考的机器�的概念打下基础。他通过创建 [图�测试](https://www.bbc.com/news/technology-18475646)�解决�对者和他自己对这一概念的�验��的需求，你将在我们的 NLP 课程中进行探索。
 
 ## 1956: 达特茅斯�季研究项目
 
@@ -47,7 +47,7 @@ Alan Turing，一个真正�出的人，[在2019年被公众投票选出](https
     ![Shakey, 智能机器人](images/shakey.jpg)
     > 1972 å¹´çš„Shakey
 
-* Eliza，一个早期的“�天机器人�，�以与人交谈并充当原始的“治疗师�。 您将在NLP课程中了解有关Eliza的更多信�。 
+* Eliza，一个早期的“�天机器人�，�以与人交谈并充当原始的“治疗师�。 你将在NLP课程中了解有关Eliza的更多信�。 
 
     ![Eliza, 机器人](images/eliza.png)
     > Eliza的一个版本，一个�天机器人 

From 05bd6631d3fb00833d1f1157114574eafa481146 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 23:41:26 +0800
Subject: [PATCH 085/368] Update README.zh-cn.md

---
 .../3-fairness/translations/README.zh-cn.md   | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index bb43a027fa..55f0d21a31 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -7,11 +7,11 @@
  
 ## 介�
 
-在本课程中，您将开始了解机器学习如何影�我们的日常生活。截至目�，系统和模型已��与到日常决策任务中，例如医疗诊断或�现欺诈。因此，这些模型�行良好，并为�个人�供公平的结果�常��。
+在本课程中，你将开始了解机器学习如何影�我们的日常生活。截至目�，系统和模型已��与到日常决策任务中，例如医疗诊断或�现欺诈。因此，这些模型�行良好，并为�个人�供公平的结果�常��。
 
-想象一下，当您用于构建这些模型的数�缺少�些人�统计信�时会�生什么情况，例如���性别�政治观点�宗教，或者��比例地代表了这些人�统计信�。当模型的输出被解释为有利于�些人�统计学的时候呢？申请结果如何？
+想象一下，当你用于构建这些模型的数�缺少�些人�统计信�时会�生什么情况，例如���性别�政治观点�宗教，或者��比例地代表了这些人�统计信�。当模型的输出被解释为有利于�些人�统计学的时候呢？申请结果如何？
 
-在本课中，您将：
+在本课中，你将：
 
 - �高你对机器学习中公平的��性的认识。
 - 了解公平相关的�害。
@@ -42,7 +42,7 @@
 与公平相关的主��害�分为：
 
 - **分�**，如果一个性别或��比�一个更���。
-- **�务质�**。 如果您针对一�特定场景训练数�，但实际情况���得多，则会导致�务性能�佳。
+- **�务质�**。 如果你针对一�特定场景训练数�，但实际情况���得多，则会导致�务性能�佳。
 - **刻��象**。 将给定的组与预先分�的属性相关�。
 - **诋�**。 �公平地批评和标记�事或�人。
 - **代表性过高或过低**。这�想法是，�个群体在�个行业中�被看到，而这个行业一直在��，这是造�伤害的原因。
@@ -104,11 +104,11 @@
 
 开�过程中�出的错误�设也会导致�公平。例如，旨在根�人脸图�预测�将犯罪的�部分�系统�能会导致破�性�设。这�能会对错误分类的人造��大伤害。
 
-## 了解您的模型并建立公平性
+## 了解你的模型并建立公平性
  
-尽管公平性的许多方�都没有包�在�化公平性指标中，并且��能从系统中完全消除��以��公平性，但您�然有责任尽�能多地检测和缓解公平性问题。
+尽管公平性的许多方�都没有包�在�化公平性指标中，并且��能从系统中完全消除��以��公平性，但你�然有责任尽�能多地检测和缓解公平性问题。
 
-当您使用机器学习模型时，通过确�模型的�解释性以�评估和�轻�公平性��解模型�常��。
+当你使用机器学习模型时，通过确�模型的�解释性以�评估和�轻�公平性��解模型�常��。
 
 让我们使用贷款选择示例�作为分�案例，以确定�个因素对预测的影�程度。
 
@@ -136,7 +136,7 @@
  
 你已�确定了伤害和�影�的群体，在本例中，是按性别划分的。现在，使用�化因��分解它们的度�。例如，使用下�的数�，你�以看到女性的�阳性率最大，男性的�阳性率最�，而对于�阴性则相�。
 
-✅ 在以�关于�类的课程中，您将看到如何在代�中构建这个“混淆矩阵�
+✅ 在以�关于�类的课程中，你将看到如何在代�中构建这个“混淆矩阵�
 
 |            | �阳性率 | �阴性率 | 数� |
 | ---------- | ------------------- | ------------------- | ----- |
@@ -147,7 +147,7 @@
  
 这张桌�告诉我们几件事。首先，我们注�到数�中的未列出性别的人相对较少。数�是有�差的，所以你需��心解释这些数字。
 
-在本例中，我们有3个组和2个度�。当我们考虑我们的系统如何影�贷款申请人的客户群时，这�能就足够了，但是当您想�定义更多的组时，您�能需�将其��到更�的摘�集。为此，您�以添加更多的度�，例如�个�阴性和�阳性的最大差异或最�比率。
+在本例中，我们有3个组和2个度�。当我们考虑我们的系统如何影�贷款申请人的客户群时，这�能就足够了，但是当你想�定义更多的组时，你�能需�将其��到更�的摘�集。为此，你�以添加更多的度�，例如�个�阴性和�阳性的最大差异或最�比率。
  
 ✅ �下�想一想：还有哪些群体�能会�到贷款申请的影�？
  
@@ -159,9 +159,9 @@
 
 ### Fairlearn 
  
-[Fairlearn](https://fairlearn.github.io/) 是一个开�Python包，�让您评估系统的公平性并�轻�公平性。
+[Fairlearn](https://fairlearn.github.io/) 是一个开�Python包，�让你评估系统的公平性并�轻�公平性。
 
-该工具�帮助您评估模型的预测如何影���的组，使您能够通过使用公平性和性能指标�比较多个模型，并�供一组算法��轻二元分类和回归中的�公平性。
+该工具�帮助你评估模型的预测如何影���的组，使你能够通过使用公平性和性能指标�比较多个模型，并�供一组算法��轻二元分类和回归中的�公平性。
 
 - 通过查看Fairlearn的[GitHub](https://github.com/fairlearn/fairlearn/)了解如何使用��的组件
 
@@ -189,7 +189,7 @@
 ## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
 ## �习与自学
  
-在本课中，您学习了机器学习中公平和�公平概念的一些基础知识。
+在本课中，你学习了机器学习中公平和�公平概念的一些基础知识。
 
 观看本次研讨会，深入探讨以下主题：
 

From a7bc4300905b24c36f7b6bbafed490e3e0a2c4d7 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 9 Jul 2021 23:42:17 +0800
Subject: [PATCH 086/368] Update README.zh-cn.md

---
 .../translations/README.zh-cn.md              | 153 +++++++++++++++---
 1 file changed, 130 insertions(+), 23 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index 156639c3f9..519ab68353 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -1,6 +1,6 @@
 # 机器学习技术
 
-构建�使用和维护机器学习模型�其使用的数�的过程与许多其他开�工作�程截然��。 在本课中，我们将�开该过程的神秘�纱，并概述您需�了解的主�技术。 您会： 
+构建�使用和维护机器学习模型�其使用的数�的过程与许多其他开�工作�程截然��。 在本课中，我们将�开该过程的神秘�纱，并概述你需�了解的主�技术。 你会： 
 
 - 在高层次上�解支�机器学习的过程。 
 - 探索基本概念，例如“模型��“预测�和“训练数��。 
@@ -11,11 +11,11 @@
 在较高的层次上，创建机器学习（ML）过程的工艺包括许多步骤：
 
 1. **决定问题**。 大多数机器学习过程都是从�出一个简�的�件程�或基于规则的引擎无法回答的问题开始的。 这些问题通常围绕基于数�集�的预测展开。 
-2. **收集和准备数�**。为了能够回答您的问题，您需�数�。数�的质�（有时是数�）将决定您回答最�问题的能力。�视化数�是这个阶段的一个��方�。此阶段还包括将数�拆分为训练和测试组以构建模型。 
-3. **选择一�训练方法**。根�您的问题和数�的性质，您需�选择如何训练模型以最好地�映您的数�并对其进行准确预测。这是您的ML过程的一部分，需�特定的专业知识，并且通常需�大�的实验。 
-4. **训练模型**。使用您的训练数�，您将使用��算法�训练模型以识别数�中的模�。该模型�能会利用�以调整的内部���使数�的�些部分优于其他部分，从而构建更好的模型。 
-5. **评估模型**。您使用收集到的集�中从未�过的数�（您的测试数�）�查看模型的性能。 
-6. **�数调整**。根�模型的性能，您�以使用��的�数或����该过程，这些�数或��控制用于训练模型的算法的行为。 
+2. **收集和准备数�**。为了能够回答你的问题，你需�数�。数�的质�（有时是数�）将决定你回答最�问题的能力。�视化数�是这个阶段的一个��方�。此阶段还包括将数�拆分为训练和测试组以构建模型。 
+3. **选择一�训练方法**。根�你的问题和数�的性质，你需�选择如何训练模型以最好地�映你的数�并对其进行准确预测。这是你的ML过程的一部分，需�特定的专业知识，并且通常需�大�的实验。 
+4. **训练模型**。使用你的训练数�，你将使用��算法�训练模型以识别数�中的模�。该模型�能会利用�以调整的内部���使数�的�些部分优于其他部分，从而构建更好的模型。 
+5. **评估模型**。你使用收集到的集�中从未�过的数�（你的测试数�）�查看模型的性能。 
+6. **�数调整**。根�模型的性能，你�以使用��的�数或����该过程，这些�数或��控制用于训练模型的算法的行为。 
 7. **预测**。使用新输入�测试模型的准确性。 
 
 ## �问什么问题 
@@ -28,48 +28,48 @@
 
 ## 预构建任务
 
-在开始构建模型之�，您需�完�多项任务。�测试您的问题并根�模型的预测形��设，您需�识别和�置多个元素。 
+在开始构建模型之�，你需�完�多项任务。�测试你的问题并根�模型的预测形��设，你需�识别和�置多个元素。 
 
 ### Data
 
-为了能够确定地回答您的问题，您需�大�正确类型的数�。 此时您需��两件事： 
+为了能够确定地回答你的问题，你需�大�正确类型的数�。 此时你需��两件事： 
 
 - **收集数�**。记�之�关于数�分�公平性的课程，�心收集数�。请注�此数�的���它�能具有的任何固有��，并记录其��。 
-- **准备数�**。数�准备过程有几个步骤。如果数��自��的��，您�能需�整�数�并对其进行标准化。您�以通过��方法�高数�的质�和数�，例如将字符串转�为数字（就�我们在[�类](../../5-Clustering/1-Visualize/README.md)中所�的那样）。您还�以根�原始数�生�新数�（正如我们在[分类](../../4-Classification/1-Introduction/README.md)中所�的那样）。您�以清�和编辑数�（就�我们在 [Web App](../3-Web-App/README.md)课程之�所�的那样）。最�，您�能还需�对其进行�机化和打乱，具体�决于您的训练技术。
+- **准备数�**。数�准备过程有几个步骤。如果数��自��的��，你�能需�整�数�并对其进行标准化。你�以通过��方法�高数�的质�和数�，例如将字符串转�为数字（就�我们在[�类](../../5-Clustering/1-Visualize/README.md)中所�的那样）。你还�以根�原始数�生�新数�（正如我们在[分类](../../4-Classification/1-Introduction/README.md)中所�的那样）。你�以清�和编辑数�（就�我们在 [Web App](../3-Web-App/README.md)课程之�所�的那样）。最�，你�能还需�对其进行�机化和打乱，具体�决于你的训练技术。
 
-✅ 在收集和处�您的数��，花点时间看看它的形状是�能让您解决您的预期问题。正如我们在[�类](../../5-Clustering/1-Visualize/README.md)课程中�现的那样，数��能在您的给定任务中表现�佳�
+✅ 在收集和处�你的数��，花点时间看看它的形状是�能让你解决你的预期问题。正如我们在[�类](../../5-Clustering/1-Visualize/README.md)课程中�现的那样，数��能在你的给定任务中表现�佳�
 
 ### 选择特���
 
-[特�](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection)是数�的�衡�属性。在许多数�集中，它表示为列标题，如“日期��“大��或“颜色�。您的特���（通常在代�中表示为`y`）代表您试图对数��出的问题的答案：在12月，哪�**颜色**的�瓜最便宜？在旧金山，哪些街区的房地产**价格**最好？
+[特�](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection)是数�的�衡�属性。在许多数�集中，它表示为列标题，如“日期��“大��或“颜色�。你的特���（通常在代�中表示为`y`）代表你试图对数��出的问题的答案：在12月，哪�**颜色**的�瓜最便宜？在旧金山，哪些街区的房地产**价格**最好？
 
-🎓 **特�选择和特���** 构建模型时如何知�选择哪个��？您�能会�历一个特�选择或特���的过程，以便为性能最好的模型选择正确的��。然而，它们�是一回事：“特���是从基于原始特�的函数中创建新特�，而特�选择返回特�的一个�集。�（[��](https://wikipedia.org/wiki/Feature_selection)）
+🎓 **特�选择和特���** 构建模型时如何知�选择哪个��？你�能会�历一个特�选择或特���的过程，以便为性能最好的模型选择正确的��。然而，它们�是一回事：“特���是从基于原始特�的函数中创建新特�，而特�选择返回特�的一个�集。�（[��](https://wikipedia.org/wiki/Feature_selection)）
 ### �视化数�
 
-数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示您的数��能会让您�现�以利用的��关�。 您的�视化还�以帮助您�现��或�平衡的数�（正如我们在 [分类](../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
+数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示你的数��能会让你�现�以利用的��关�。 你的�视化还�以帮助你�现��或�平衡的数�（正如我们在 [分类](../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
 ### 拆分数�集
 
-在训练之�，您需�将数�集拆分为两个或多个大��等但�能很好地代表数�的部分。
+在训练之�，你需�将数�集拆分为两个或多个大��等但�能很好地代表数�的部分。
 
-- **训练**。这部分数�集适�您的模型进行训练。这个集�构�了原始数�集的大部分。
+- **训练**。这部分数�集适�你的模型进行训练。这个集�构�了原始数�集的大部分。
 - **测试**。测试数�集是一组独立的数�，通常从原始数�中收集，用于确认构建模型的性能。
-- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�您的数�大�和您�出的问题，您�能�需�构建第三组（正如我们在[时间�列预测](../7-TimeSeries/1-Introduction/README.md)中所述）。 
+- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../7-TimeSeries/1-Introduction/README.md)中所述）。 
 
 ## 建立模型 
 
-使用您的训练数�，您的目标是构建模型或数�的统计表示，并使用��算法对其进行**训练**。训练模型将其暴露给数�，并�许它对其�现�验�和接�或拒�的感知模��出�设。 
+使用你的训练数�，你的目标是构建模型或数�的统计表示，并使用��算法对其进行**训练**。训练模型将其暴露给数�，并�许它对其�现�验�和接�或拒�的感知模��出�设。 
 
 ### 决定一�训练方法
 
-根�您的问题和数�的性质，您将选择一�方法�训练它。�步完� [Scikit-learn的文档](https://scikit-learn.org/stable/user_guide.html) - 我们在本课程中使用 - 您�以探索多�训练模型的方法。 根�您的�验，您�能需��试多���的方法�构建最佳模型。您�能会�历一个过程，在该过程中，数�科学家通过�供未�过的数��评估模型的性能，检查准确性��差和其他�低质�的问题，并为手头的任务选择最�适的训练方法。
+根�你的问题和数�的性质，你将选择一�方法�训练它。�步完� [Scikit-learn的文档](https://scikit-learn.org/stable/user_guide.html) - 我们在本课程中使用 - 你�以探索多�训练模型的方法。 根�你的�验，你�能需��试多���的方法�构建最佳模型。你�能会�历一个过程，在该过程中，数�科学家通过�供未�过的数��评估模型的性能，检查准确性��差和其他�低质�的问题，并为手头的任务选择最�适的训练方法。
 
 ### 训练模型
 
-有了您的训练数�，您就�以“拟��它以创建模型。您会注�到，在许多ML库中，您会找到代�'model.fit'——此时您将数�作为值数组（通常为'X'）和特���（通常为'y'）��）。 
+有了你的训练数�，你就�以“拟��它以创建模型。你会注�到，在许多ML库中，你会找到代�'model.fit'——此时你将数�作为值数组（通常为'X'）和特���（通常为'y'）��）。 
 
 ### 评估模型 
 
-训练过程完��（训练大型模型�能需�多次迭代或“时期�），您将能够通过使用测试数��衡�模型的性能�评估模型的质�。此数�是模型先�未分�的原始数�的�集。 您�以打�出有关模型质�的指标表。 
+训练过程完��（训练大型模型�能需�多次迭代或“时期�），你将能够通过使用测试数��衡�模型的性能�评估模型的质�。此数�是模型先�未分�的原始数�的�集。 你�以打�出有关模型质�的指标表。 
 
 🎓 **模型拟� **
 
@@ -86,15 +86,122 @@
 
 ## 预测
 
-这是您�以使用全新数��测试模型准确性的时刻。在“应用�ML设置中，您正在构建Web资�以在生产中使用模型，此过程�能涉�收集用户输入（例如按下按钮）以设置��并将其��到模型进行推�，或者评估。
+这是你�以使用全新数��测试模型准确性的时刻。在“应用�ML设置中，你正在构建Web资�以在生产中使用模型，此过程�能涉�收集用户输入（例如按下按钮）以设置��并将其��到模型进行推�，或者评估。
 
-在这些课程中，您将了解如何使用这些步骤�准备�构建�测试�评估和预测—所有这些都是数�科学家的姿�，而且��您在�为一�“全栈�ML工程师的旅程中�得进展，您将了解更多。 
+在这些课程中，你将了解如何使用这些步骤�准备�构建�测试�评估和预测—所有这些都是数�科学家的姿�，而且��你在�为一�“全栈�ML工程师的旅程中�得进展，你将了解更多。 
 
 ---
 
 ## 🚀挑战
 
-画一个�程图，�映ML的步骤。在这个过程中，您认为自己现在在哪里？您预测您在哪里会�到困难？什么对您�说很容易？ 
+画一个�程图，�映ML的步骤。在这个过程中，你认为自己现在在哪里？你预测你在哪里会�到困难？什么对你�说很容易？ 
+
+## [阅读�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+
+## �习与自学 
+
+在线�索对讨论日常工作的数�科学家的采访。 这是[其中之一](https://www.youtube.com/watch?v=Z3IjgbbCEfs)。
+
+## 任务
+
+[采访一�数�科学家](assignment.md)
+# 机器学习技术
+
+构建�使用和维护机器学习模型�其使用的数�的过程与许多其他开�工作�程截然��。 在本课中，我们将�开该过程的神秘�纱，并概述你需�了解的主�技术。 你会： 
+
+- 在高层次上�解支�机器学习的过程。 
+- 探索基本概念，例如“模型��“预测�和“训练数��。 
+  
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## 介�
+
+在较高的层次上，创建机器学习（ML）过程的工艺包括许多步骤：
+
+1. **决定问题**。 大多数机器学习过程都是从�出一个简�的�件程�或基于规则的引擎无法回答的问题开始的。 这些问题通常围绕基于数�集�的预测展开。 
+2. **收集和准备数�**。为了能够回答你的问题，你需�数�。数�的质�（有时是数�）将决定你回答最�问题的能力。�视化数�是这个阶段的一个��方�。此阶段还包括将数�拆分为训练和测试组以构建模型。 
+3. **选择一�训练方法**。根�你的问题和数�的性质，你需�选择如何训练模型以最好地�映你的数�并对其进行准确预测。这是你的ML过程的一部分，需�特定的专业知识，并且通常需�大�的实验。 
+4. **训练模型**。使用你的训练数�，你将使用��算法�训练模型以识别数�中的模�。该模型�能会利用�以调整的内部���使数�的�些部分优于其他部分，从而构建更好的模型。 
+5. **评估模型**。你使用收集到的集�中从未�过的数�（你的测试数�）�查看模型的性能。 
+6. **�数调整**。根�模型的性能，你�以使用��的�数或����该过程，这些�数或��控制用于训练模型的算法的行为。 
+7. **预测**。使用新输入�测试模型的准确性。 
+
+## �问什么问题 
+
+计算机特别擅长�现数�中的��模�。此实用程�对于对给定领域有疑问的研究人员�常有帮助，这些问题无法通过创建基于�件的规则引擎�轻�回答。例如，给定一项精算任务，数�科学家�能能够围绕�烟者与��烟者的死亡率构建手工规则。 
+
+然而，当将许多其他��纳入等�时，ML模型�能会更有效地根�过去的�康�预测未�的死亡率。一个更令人愉快的例��能是根�包括纬度��度�气候�化�与海洋的接近程度�急�模�等在内的数�对给定�置的4月份进行天气预报。 
+
+✅ 这个关于天气模型的[幻�片](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf)为在天气分�中使用机器学习�供了一个历�视角。
+
+## 预构建任务
+
+在开始构建模型之�，你需�完�多项任务。�测试你的问题并根�模型的预测形��设，你需�识别和�置多个元素。 
+
+### Data
+
+为了能够确定地回答你的问题，你需�大�正确类型的数�。 此时你需��两件事： 
+
+- **收集数�**。记�之�关于数�分�公平性的课程，�心收集数�。请注�此数�的���它�能具有的任何固有��，并记录其��。 
+- **准备数�**。数�准备过程有几个步骤。如果数��自��的��，你�能需�整�数�并对其进行标准化。你�以通过��方法�高数�的质�和数�，例如将字符串转�为数字（就�我们在[�类](../../5-Clustering/1-Visualize/README.md)中所�的那样）。你还�以根�原始数�生�新数�（正如我们在[分类](../../4-Classification/1-Introduction/README.md)中所�的那样）。你�以清�和编辑数�（就�我们在 [Web App](../3-Web-App/README.md)课程之�所�的那样）。最�，你�能还需�对其进行�机化和打乱，具体�决于你的训练技术。
+
+✅ 在收集和处�你的数��，花点时间看看它的形状是�能让你解决你的预期问题。正如我们在[�类](../../5-Clustering/1-Visualize/README.md)课程中�现的那样，数��能在你的给定任务中表现�佳�
+
+### 选择特���
+
+[特�](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection)是数�的�衡�属性。在许多数�集中，它表示为列标题，如“日期��“大��或“颜色�。你的特���（通常在代�中表示为`y`）代表你试图对数��出的问题的答案：在12月，哪�**颜色**的�瓜最便宜？在旧金山，哪些街区的房地产**价格**最好？
+
+🎓 **特�选择和特���** 构建模型时如何知�选择哪个��？你�能会�历一个特�选择或特���的过程，以便为性能最好的模型选择正确的��。然而，它们�是一回事：“特���是从基于原始特�的函数中创建新特�，而特�选择返回特�的一个�集。�（[��](https://wikipedia.org/wiki/Feature_selection)）
+### �视化数�
+
+数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示你的数��能会让你�现�以利用的��关�。 你的�视化还�以帮助你�现��或�平衡的数�（正如我们在 [分类](../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
+### 拆分数�集
+
+在训练之�，你需�将数�集拆分为两个或多个大��等但�能很好地代表数�的部分。
+
+- **训练**。这部分数�集适�你的模型进行训练。这个集�构�了原始数�集的大部分。
+- **测试**。测试数�集是一组独立的数�，通常从原始数�中收集，用于确认构建模型的性能。
+- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../7-TimeSeries/1-Introduction/README.md)中所述）。 
+
+## 建立模型 
+
+使用你的训练数�，你的目标是构建模型或数�的统计表示，并使用��算法对其进行**训练**。训练模型将其暴露给数�，并�许它对其�现�验�和接�或拒�的感知模��出�设。 
+
+### 决定一�训练方法
+
+根�你的问题和数�的性质，你将选择一�方法�训练它。�步完� [Scikit-learn的文档](https://scikit-learn.org/stable/user_guide.html) - 我们在本课程中使用 - 你�以探索多�训练模型的方法。 根�你的�验，你�能需��试多���的方法�构建最佳模型。你�能会�历一个过程，在该过程中，数�科学家通过�供未�过的数��评估模型的性能，检查准确性��差和其他�低质�的问题，并为手头的任务选择最�适的训练方法。
+
+### 训练模型
+
+有了你的训练数�，你就�以“拟��它以创建模型。你会注�到，在许多ML库中，你会找到代�'model.fit'——此时你将数�作为值数组（通常为'X'）和特���（通常为'y'）��）。 
+
+### 评估模型 
+
+训练过程完��（训练大型模型�能需�多次迭代或“时期�），你将能够通过使用测试数��衡�模型的性能�评估模型的质�。此数�是模型先�未分�的原始数�的�集。 你�以打�出有关模型质�的指标表。 
+
+🎓 **模型拟� **
+
+在机器学习的背景下，模型拟�是指模型在�试分��熟悉的数�时其底层功能的准确性。 
+
+🎓 **欠拟�**和**过拟�**是�低模型质�的常�问题，因为模型拟�得�够好或太好。这会导致模型�出与其训练数�过于紧密对�或过于�散对�的预测。 过拟�模型对训练数�的预测太好，因为它已�很好地了解了数�的细节和噪声。欠拟�模型并�准确，因为它既�能准确分�其训练数�，也�能准确分�尚未“看到�的数�。
+
+![过拟�模型 ](images/overfitting.png)
+> 作者[Jen Looper](https://twitter.com/jenlooper)
+
+## �数调优 
+
+�始训练完��，观察模型的质�并考虑通过调整其“超�数��改进它。[在此文档中](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa)阅读有关该过程的更多信�。
+
+## 预测
+
+这是你�以使用全新数��测试模型准确性的时刻。在“应用�ML设置中，你正在构建Web资�以在生产中使用模型，此过程�能涉�收集用户输入（例如按下按钮）以设置��并将其��到模型进行推�，或者评估。
+
+在这些课程中，你将了解如何使用这些步骤�准备�构建�测试�评估和预测—所有这些都是数�科学家的姿�，而且��你在�为一�“全栈�ML工程师的旅程中�得进展，你将了解更多。 
+
+---
+
+## 🚀挑战
+
+画一个�程图，�映ML的步骤。在这个过程中，你认为自己现在在哪里？你预测你在哪里会�到困难？什么对你�说很容易？ 
 
 ## [阅读�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
 

From 96a1ea345015c47827430ba94392ce59dee7ee5e Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Sat, 10 Jul 2021 01:19:27 +0900
Subject: [PATCH 087/368] translate README of 2-Regression/1-Tools

---
 .../1-Tools/translations/README.ja.md         | 218 ++++++++++++++++++
 1 file changed, 218 insertions(+)
 create mode 100644 2-Regression/1-Tools/translations/README.ja.md

diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
new file mode 100644
index 0000000000..ce4cde1520
--- /dev/null
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -0,0 +1,218 @@
+# 回帰モデル����Python�Scikit-learn�ら始���ょ�
+
+![回帰��約�����スケッ�ノート](../../../sketchnotes/ml-regression.png)
+
+> [Tomomi Imura](https://www.twitter.com/girlie_mac) �よ��制作�れ�スケッ�ノート
+
+## [講義�クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+
+## イントロダクション
+
+�れら�4��レッスン���回帰モデルを構築�る方法����学���。回帰モデル�何を�る���も������後��説明���。����何�を始�る��プロセスを開始�る����切�ツール�用��れ��る��を確��������
+
+��レッスン���以下���を学���。
+
+- ローカル環境�機械学習タスクを実行�る���設定
+- Jupyter Notebook�使�方
+- Scikit-learn�インストール�使�方
+- 線形回帰�関�る�ンズオン
+
+## インストール�設定
+
+[![VisualStudioCode��Python�使用方法](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "VisualStudioCode��Python�使用方法")
+
+> 🎥 上�画�をクリック�る�ビデオ��生�れ��: VisualStudioCode��Python�使用方法
+
+1. **Python�インストール**: [Python](https://www.python.org/downloads/) �コンピュータ�インストール�れ��る��を確�������。Python�多��データサイエンス�機械学習�タスク�使用���。��ん��コンピュータシステム��Python����インストール�れ����。一部�ユーザ�セットアップを簡���る��� [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) を利用�る��も����。
+
+   ����Pythonを使���る�時�異�る�ージョンを必���る場���り��。�����[仮想環境](https://docs.python.org/3/library/venv.html) を利用�る�便利��。   
+
+
+2. **Visual Studio Code�インストール**: Visual Studio Code�コンピュータ�インストール�れ��る��を確�������。[��ら�手順](https://code.visualstudio.com/) �Visual Studio Codeをインストール������。��コース���Visual Studio Code�Pythonを使用����� [Visual Studio Code�設定](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) をブラッシュアップ���������。
+
+   > �� [学習モジュール](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa) を利用���Python�使�方�慣れ�����。
+
+
+3. **Scikit-learn�インストール**: [��ら�手順](https://scikit-learn.org/stable/install.html) �従��インストール������。Python3�環境�実行�る必���る���仮想環境を使用�る��を�������。�����ライブラリをM1�Mac�インストール�る場���上記リンク先�ページ�特別�説明��り��。
+
+
+4. **Jupyter Notebook�インストール**: [Jupyter package](https://pypi.org/project/jupyter/) をインストール�る必���り��。
+
+## ML�オーサリング環境
+
+**ノートブック**を利用���Pythonコード��開発や機械学習モデル�作�を行���。��よ��種類�ファイル��データサイエンティスト����一般的�ツール��り�接尾辞���拡張�� `.ipynb` ��る���識別����。
+
+ノートブック��開発者�コードを書���時����コード�メモを加��り�文書を書��り�る�����るインタラクティブ�環境��。�����実験や研究を目的���プロジェクト��常�役立���。
+
+### エクササイズ - ノートブック��作業
+
+フォルダ�中� _notebook.ipynb_ ���ファイル�入�����。 
+
+1. Visual Studio Code� _notebook.ipynb_ を開������。
+
+   Jupyterサー�ー�Python3+�起動��状態�スタート���。ノートブック�中���コードを「実行���る部分��り��。�生ボタン�よ��アイコンを�択�る��コードブロックを実行�る�������。
+
+
+2. `md`アイコンを�択���**# Welcome to your notebook** ���テキスト�簡��マークダウンを追加�����ょ�.
+
+   次��Python�コードを追加���。
+
+
+3. コードブロック� **print('hello notebook')** �入力������。
+   
+
+4. 矢�を�択�る�コード�実行�れ��。
+
+   以下�よ���果�出力�れ��:
+
+    ```output
+    hello notebook
+    ```
+
+![VS Code�開��ノートブック](../images/notebook.png)
+
+コード�コメントを追加�る����ノートブックをセルフドキュメント化�る�������。
+
+✅ ウェブ開発者�データサイエンティスト�仕事環境��れ�������ょ��考��������。
+
+## Scikit-learnを使�����ょ�
+
+���ローカル環境�Python�セットアップ�れ�Jupyter notebook�慣れ��������Scikit-learn（「science��よ��「sci��発音������）�も�様�慣れ�����ょ�。Scikit-learn��MLタスクを実行�る��� [広範�API](https://scikit-learn.org/stable/modules/classes.html#api-ref) を�供�����。
+
+�社� [Webサイト](https://scikit-learn.org/stable/getting_started.html) ��次�よ��説明��り��。"Scikit-learn��教師�り�教師���学習をサ�ート�るオープンソース�機械学習ライブラリ��。���モデル�フィッティング�データ��処��モデル��択�評価���他多��ユーティリティー����様々�ツールを�供�����。"
+
+��コース���Scikit-learnや��他�ツールを使��機械学習モデルを構築������「�統的�機械学習��呼�タスクを実行���。ニューラル�ットワークやディープラーニング������近日公開予定�「AI for Beginners�カリキュラム�詳��解説�������図的�������。
+
+Scikit-learn��モデルを構築��評価を行��実際�利用�る������簡������。主�数値データ�使用�焦点を当���り�学習ツール���使用�る���既製�データセット�������れ����。���事��構築済��モデルも������れ����。���Scikit-learn��ら���用��れ��るデータを使���最��MLモデルを構築�るプロセスを体験���ょ�。
+
+## エクササイズ - ����Scikit-learnノートブック
+
+> ���ュートリアル�Scikit-learn�Webサイト�掲載�れ��る [linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) を�考������。
+
+��レッスン�関連�る _notebook.ipynb_ ファイル��「ゴミ箱�アイコンを押��全��セルを消去���。
+
+��セクション���学習用�Scikit-learn�組�込�れ��糖尿病�関�る���データセットを扱���。糖尿病患者�治療法をテスト���場�を想���������。機械学習モデル��変数�組�����基������患者���治療法�よ��応�る�を判断�る��役立��も�れ��ん。�常�基本的�回帰モデル����も�視覚化�る�����論的�臨床試験を整��る��役立�変数�関�る情報�得られる�も�れ��ん。
+
+✅ 回帰��様々�種類��り�求�る答��よ����手法を����異�り��。例����る人�年齢�与�られ�身長を予測���場����**数値**を求�����線形回帰を利用���。�る種類�料��ビーガン����れる������を発見�る���興味��る場���**カテゴリー�割り当�**を求���る���ロジスティック回帰を使用�る��ょ�。ロジスティック回帰�����後��詳��説明���。�����データ�������よ��手法�より�切��る�を少�考��������。
+
+���今回�タスク��り掛�り��ょ�。
+
+### ライブラリ�イン�ート
+
+��タスク��������ライブラリをイン�ート���。
+
+- **matplotlib**: 便利� [グラフ作�ツール](https://matplotlib.org/) ��。今回��れを使��折れ線グラフを作����。
+- **numpy**: [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) �Python�数値データを扱����便利�ライブラリ��。
+- **sklearn**: Scikit-learn�ライブラリ��。
+
+作業�役立�ライブラリを����紹介���。
+
+1. 以下�コードを入力��イン�ートを追加���。
+
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   from sklearn import datasets, linear_model, model_selection
+   ```
+
+   上記���`matplottlib`�`numpy`をイン�ート��`sklearn`�ら`datasets`�`linear_model`�`model_selection`をイン�ート�����。`model_selection`�データをトレーニングセット�テストセット�分割�る際�使用���。
+
+### 糖尿病�データセット
+
+組�込�� [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) ���糖尿病�関�る442サンプル�データ���れ��り�10個�変数���れ����。
+
+age: å¹´é½¢
+bmi: ボディマス指数
+bp: 平�血圧
+s1 tc: T細胞（白血��一種）
+
+✅ ��データセット���糖尿病�関�る研究����変数���「性別��概念���れ����。多��医療データセット�����よ���イナリ分類���れ����。��よ��分類��人���る部分を治療�ら排除�る�能性��る�������少�考�����ょ�。
+
+���X�y�データを読�込���。
+
+> 🎓 今回�教師�り学習����「y��表�れるターゲット�必����を覚���������。
+
+新��コードセル��`load_diabetes()` を呼�出��糖尿病データセットを読�込���。入力� `return_X_y=True` ��`X` �データ行列��り�`y` �回帰�対象��る��を示�����。
+
+1. データ行列�形���最���素を表示�る���������printコマンドを追加���。
+
+    ```python
+    X, y = datasets.load_diabetes(return_X_y=True)
+    print(X.shape)
+    print(X[0])
+    ```
+
+   `load_diabetes()` �レス�ンス���タプル�返�����。タプル�2��値を�れ�れ `X`�` y`�割り当���。詳���� [タプル����](https://wikipedia.org/wiki/Tuple) を確�������。  
+
+    ��データ��442個�アイテム�構��れ��り�一��アイテム�10個��素を���列��る�����り��。
+
+    ```text
+    (442, 10)
+    [ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076
+    -0.04340085 -0.00259226  0.01990842 -0.01764613]
+    ```
+
+    ✅ データ�回帰対象�関係����少�考�����ょ�。線形回帰���特徴�X�ターゲット変数y�関係を予測���。ドキュメント��糖尿病データセット� [ターゲット](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) を見��る��������？��ターゲットを�������データセット�何を示������？
+   
+
+2. 次��numpy�`newaxis`関数を使��新���列を作��る����プロット�る���データセット�一部を�択���。最終的��線形回帰を使用�る����決���パターン�従�����データ�値�間�線を生����。
+
+   ```python
+   X = X[:, np.newaxis, 2]
+   ```
+
+   ✅ ���も�データをprint�����形を確��る�������。
+
+
+3. データをプロット�る準備���������データセット�数字�論�的�分割を機械�判断��る����を確������ょ�。�������データ(X)�ターゲット(y)�両方をトレーニングセット�テストセット�分�る必���り��。Scikit-learn����れを行����簡��方法�用��れ����。
+   
+   ```python
+   X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
+   ```
+
+
+4. �れ��モデルをトレーニング�る準備�整�����線形回帰モデルを読�込��X�y�トレーニングセット� `model.fit()` を�用���モデル�学習を行���。
+   
+    ```python
+    model = linear_model.LinearRegression()
+    model.fit(X_train, y_train)
+    ```
+
+    ✅ `model.fit()` ��TensorFlow���MLライブラリ�よ�見��る関数��。
+
+
+5. 次��`predict()` 関数を使���テストデータ�対�る予測を行���。�れ��データグループ間�線を引����使用�れ��。
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+6. ����よ�よデータをプロット��表示�����ょ�。Matplotlib���作業���も便利�ツール��。テストデータ�全��X�y�関�る散布図を作���モデル�予測を使用�る���データグループ間�最も�切�場所�線を引���。
+
+    ```python
+    plt.scatter(X_test, y_test,  color='black')
+    plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.show()
+    ```
+
+   ![糖尿病周辺�データ�イントを示�散布図](../images/scatterplot.png)
+
+   ✅ ���何�起����る���少�考�����ょ�。直線����ん����データ�点を通�������正確��何を���る���ょ��？��直線を使���見������新��データ�イント�プロット�y軸��関係����当���る�を予測�る�����る�����������り���？��モデル�実用的�使�方を言葉���������。
+
+������������������線形回帰モデルを構築���れを使��予測を行���れをプロット�表示�����
+
+---
+## 🚀�ャレンジ
+
+��データセット�ら別�変数をプロット������。ヒント： `X = X[:, np.newaxis, 2]` �行を編集�る。今回�データセット�ターゲット��る�糖尿病���病気�進行�������よ��発見��る���ょ��？
+## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+
+## レビュー & 自主学習
+
+���ュートリアル����変�回帰や�回帰������純�線形回帰を扱����。�れら�手法�������少�調���る���� [ビデオ](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) を見����ょ�。
+
+回帰�概念����詳��調����手法���よ��質��答�られる�を考�����ょ�。�� [�ュートリアル](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) ��解を深�る��も����。
+
+## 課題
+
+[異�るデータセット](assignment.md)

From eba28539614343b13be21f3eb3783f3cbcb96e46 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Sat, 10 Jul 2021 01:39:23 +0900
Subject: [PATCH 088/368] README of 2-Regression/1-Tools translated to JP

---
 2-Regression/1-Tools/translations/README.ja.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index ce4cde1520..0bebf16d0a 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -8,7 +8,7 @@
 
 ## イントロダクション
 
-�れら�4��レッスン���回帰モデルを構築�る方法����学���。回帰モデル�何を�る���も������後��説明���。����何�を始�る��プロセスを開始�る����切�ツール�用��れ��る��を確��������
+��章�用��れ��る4��レッスン���回帰モデルを構築�る方法����学���。回帰モデル�何を�る���も������後��説明���。����何�を始�る��プロセスを開始�る����切�ツール�用��れ��る��を確��������
 
 ��レッスン���以下���を学���。
 
@@ -73,7 +73,7 @@
 
 コード�コメントを追加�る����ノートブックをセルフドキュメント化�る�������。
 
-✅ ウェブ開発者�データサイエンティスト�仕事環境��れ�������ょ��考��������。
+✅ ウェブ開発者�データサイエンティスト�開発環境��れ�������ょ��考��������。
 
 ## Scikit-learnを使�����ょ�
 
@@ -199,17 +199,17 @@ s1 tc: T細胞（白血��一種）
 
    ✅ ���何�起����る���少�考�����ょ�。直線����ん����データ�点を通�������正確��何を���る���ょ��？��直線を使���見������新��データ�イント�プロット�y軸��関係����当���る�を予測�る�����る�����������り���？��モデル�実用的�使�方を言葉���������。
 
-������������������線形回帰モデルを構築���れを使��予測を行���れをプロット�表示�����
+��������������線形回帰モデルを構築���れを使��予測を行���果をプロット�表示�����
 
 ---
 ## 🚀�ャレンジ
 
-��データセット�ら別�変数をプロット������。ヒント： `X = X[:, np.newaxis, 2]` �行を編集�る。今回�データセット�ターゲット��る�糖尿病���病気�進行�������よ��発見��る���ょ��？
+��データセット�ら別�変数を�択��プロット������。ヒント： `X = X[:, np.newaxis, 2]` �行を編集�る。今回�データセット�ターゲット��る�糖尿病���病気�進行�������よ��発見��る���ょ��？
 ## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
 
 ## レビュー & 自主学習
 
-���ュートリアル����変�回帰や�回帰������純�線形回帰を扱����。�れら�手法�������少�調���る���� [ビデオ](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) を見����ょ�。
+���ュートリアル����変�線形回帰や多変�線形回帰������純線形回帰を扱����。�れら�手法�������少�調���る���� [ビデオ](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) を見����ょ�。
 
 回帰�概念����詳��調����手法���よ��質��答�られる�を考�����ょ�。�� [�ュートリアル](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) ��解を深�る��も����。
 

From 791e61d1ecda4663714dd2e346b18fa83d5cfe55 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Sat, 10 Jul 2021 01:45:16 +0900
Subject: [PATCH 089/368] remove draft

---
 2-Regression/translations/README.ja.md | 32 --------------------------
 1 file changed, 32 deletions(-)
 delete mode 100644 2-Regression/translations/README.ja.md

diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md
deleted file mode 100644
index dd58ff9a48..0000000000
--- a/2-Regression/translations/README.ja.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# 機械学習����回帰モデル
-## トピック: 北米�カボ�ャ価格�関�る回帰モデル 🎃
-
-北米����ロウィン����カボ�ャ�よ�怖�顔�彫られ����。�ん�魅力的�野�����も��知り��ょ��
-
-![jack-o-lanterns](../images/jack-o-lanterns.jpg)
-> <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a>�よ��<a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>�投稿�れ�写真
-
-## 今回学���
-��章�レッスン���機械学習�文脈���る回帰�種類����説明���。回帰モデル�変数間�"関係"を決定�る��役立���。��タイプ�モデル��長��温度�年齢���値を予測��データ�イント�分�を�る���変数間�関係性を明ら�����。
-
-今回�レッスン���線形回帰�ロジスティック回帰���や��よ��使�分�る�を説明���。
-
-データサイエンティスト�共通開発環境��るノートブックを管��る���Visual Studio Code�構�や機械学習�タスクを開始�る���準備を行���。���機械学習用�ライブラリ��るScikit-learnを利用�最��モデルを構築���。��章��回帰モデル�焦点を当���。
-
-> 回帰モデルを学習�る��役立�ローコードツール��り��。��[Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)を使��������。
-
-### レッスン
-
-1. [商売�具](../1-Tools/README.md)
-2. [データ管�](../2-Data/README.md)
-3. [線形回帰�多項�回帰](../3-Linear/README.md)
-4. [ロジスティック回帰](../4-Logistic/README.md)
-
----
-### クレジット
-
-"機械学習�回帰"��[Jen Looper](https://twitter.com/jenlooper)�よ��制作�れ���。
-
-クイズ�貢献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan)�[Ornella Altunyan](https://twitter.com/ornelladotcom)
-
-pumpkin dataset��[��ら�Kaggleプロジェクト](https://www.kaggle.com/usda/a-year-of-pumpkin-prices)��案�れ����。��データ��アメリカ�衆国農務���布���る[Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)�元�������。�����分布を正�化�る���多様性を元�色��������追加を行�����。��データ�パブリックドメイン��。

From 5f6db59a11949f4bc568e64e838a3de09ca4c2b7 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 13:13:10 -0400
Subject: [PATCH 090/368] editing image paths

---
 1-Introduction/1-intro-to-ML/translations/README.zh-cn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
index 12e5aae086..ff8d07008a 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
@@ -27,7 +27,7 @@
 
 术语“机器学习�是当今最�行和最常用的术语之一。 如果你对科技有��程度的熟悉，那么很�能你至少�说过这个术语一次，无论你在哪个领域工作。然而，机器学习的机制对大多数人�说是一个谜。 对于机器学习�学者�说，这个主题有时会让人感到�知所措。 因此，了解机器学习的实质是什么，并通过实例一步一步地了解机器学习是很��的。
 
-![机器学习趋势曲线](images/hype.png)
+![机器学习趋势曲线](../images/hype.png)
 
 > 谷歌趋势显示了“机器学习�一�最近的“趋势曲线�
 
@@ -39,7 +39,7 @@
 
 尽管这些术语�能会混淆，但机器学习 (ML) 是人工智能的一个���集。 **机器学习关注使用专门的算法��现有�义的信�，并从感知数�中找到��的模�，以�实�性的决策过程**。
 
-![人工智能�机器学习�深度学习�数�科学](images/ai-ml-ds.png)
+![人工智能�机器学习�深度学习�数�科学](../images/ai-ml-ds.png)
 
 > 显示AI�ML�深度学习和数�科学之间关系的图表。图片作者[Jen Looper](https://twitter.com/jenlooper)，�感�自[这张图](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 

From 1ca33b8b1b519e4b05c06dcc90b00547128eed4f Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 13:15:25 -0400
Subject: [PATCH 091/368] updating image paths

---
 .../2-history-of-ML/translations/README.zh-cn.md          | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index c8f74e6994..51e66ecd2b 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -1,6 +1,6 @@
 # 机器学习的历� 
 
-![机器学习历�概述](../../sketchnotes/ml-history.png)
+![机器学习历�概述](../../../sketchnotes/ml-history.png)
 > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
@@ -44,12 +44,12 @@ Alan Turing，一个真正�出的人，[在2019年被公众投票选出](https
 
 * [机器人Shakey](https://wikipedia.org/wiki/Shakey_the_robot)，他们�以“�明地��纵和决定如何执行任务。
 
-    ![Shakey, 智能机器人](images/shakey.jpg)
+    ![Shakey, 智能机器人](../images/shakey.jpg)
     > 1972 å¹´çš„Shakey
 
 * Eliza，一个早期的“�天机器人�，�以与人交谈并充当原始的“治疗师�。 你将在NLP课程中了解有关Eliza的更多信�。 
 
-    ![Eliza, 机器人](images/eliza.png)
+    ![Eliza, 机器人](../images/eliza.png)
     > Eliza的一个版本，一个�天机器人 
 
 * “积木世界�是一个微观世界的例�，在那里积木�以堆�和分类，并且�以测试教机器�出决策的实验。 使用[SHRDLU](https://wikipedia.org/wiki/SHRDLU)等库构建的高级功能有助于推动语言处����展。
@@ -113,4 +113,4 @@ Alan Turing，一个真正�出的人，[在2019年被公众投票选出](https
 
 ## 任务
 
-[创建时间线](assignment.md)
+[创建时间线](../assignment.md)

From 67ebfed74fa4a090b98b0c8b2ad3a2d17129323d Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 13:16:25 -0400
Subject: [PATCH 092/368] updating image paths

---
 1-Introduction/3-fairness/translations/README.zh-cn.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index 55f0d21a31..02f4177756 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -1,6 +1,6 @@
 # 机器学习中的公平性
  
-![机器学习中的公平性概述](../../sketchnotes/ml-fairness.png)
+![机器学习中的公平性概述](../../../sketchnotes/ml-fairness.png)
 > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
@@ -67,9 +67,9 @@
 
 机器翻译中存在�刻�的性别观。在将“他是护士，她是医生�翻译�土耳其语时，�到了一些问题。土耳其语是一�无性别的语言，它有一个代�“o��表示�数第三人称，但把这个��从土耳其语翻译�英语，会产生“她是护士，他是医生�这样的刻��象和错误。
 
-![翻译�土耳其语](images/gender-bias-translate-en-tr.png)
+![翻译�土耳其语](../images/gender-bias-translate-en-tr.png)
 
-![翻译�英语](images/gender-bias-translate-tr-en.png)
+![翻译�英语](../images/gender-bias-translate-tr-en.png)
 
 ### 诋�
 
@@ -82,7 +82,7 @@
  
 有倾�性的图��索结果就是一个很好的例�。在�索男性比例等于或高于女性的�业的图片时，比如工程或首席执行官，�注�那些更倾�于特定性别的结果。
 
-![必应CEO�索](images/ceos.png)
+![必应CEO�索](../images/ceos.png)
 > 在Bing上�索“CEO�会得到�常全�的结果
 
 这五�主�类型的�害�是相互排斥的，一个�一的系统�以表现出一�以上的�害。此外，�个案例的严�程度��相�。例如，�公平地给�人贴上罪犯的标签比给形象贴上错误的标签�严�得多。然而，��的是�记�，�使是相对�严�的伤害也会让人感到�远或被孤立，累积的影��能会�常压抑。
@@ -211,4 +211,4 @@
 
 ## 任务
 
-[探索Fairlearn](assignment.md) 
+[探索Fairlearn](../assignment.md) 

From db3cf5c3a688d1e73d0c879eb006deddb54d974b Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 9 Jul 2021 13:18:05 -0400
Subject: [PATCH 093/368] fixing path issues

---
 .../translations/README.zh-cn.md              | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index 519ab68353..d54494ef6c 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -46,14 +46,14 @@
 🎓 **特�选择和特���** 构建模型时如何知�选择哪个��？你�能会�历一个特�选择或特���的过程，以便为性能最好的模型选择正确的��。然而，它们�是一回事：“特���是从基于原始特�的函数中创建新特�，而特�选择返回特�的一个�集。�（[��](https://wikipedia.org/wiki/Feature_selection)）
 ### �视化数�
 
-数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示你的数��能会让你�现�以利用的��关�。 你的�视化还�以帮助你�现��或�平衡的数�（正如我们在 [分类](../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
+数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示你的数��能会让你�现�以利用的��关�。 你的�视化还�以帮助你�现��或�平衡的数�（正如我们在 [分类](../../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
 ### 拆分数�集
 
 在训练之�，你需�将数�集拆分为两个或多个大��等但�能很好地代表数�的部分。
 
 - **训练**。这部分数�集适�你的模型进行训练。这个集�构�了原始数�集的大部分。
 - **测试**。测试数�集是一组独立的数�，通常从原始数�中收集，用于确认构建模型的性能。
-- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../7-TimeSeries/1-Introduction/README.md)中所述）。 
+- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../../7-TimeSeries/1-Introduction/README.md)中所述）。 
 
 ## 建立模型 
 
@@ -77,7 +77,7 @@
 
 🎓 **欠拟�**和**过拟�**是�低模型质�的常�问题，因为模型拟�得�够好或太好。这会导致模型�出与其训练数�过于紧密对�或过于�散对�的预测。 过拟�模型对训练数�的预测太好，因为它已�很好地了解了数�的细节和噪声。欠拟�模型并�准确，因为它既�能准确分�其训练数�，也�能准确分�尚未“看到�的数�。
 
-![过拟�模型 ](images/overfitting.png)
+![过拟�模型 ](../images/overfitting.png)
 > 作者[Jen Looper](https://twitter.com/jenlooper)
 
 ## �数调优 
@@ -104,7 +104,7 @@
 
 ## 任务
 
-[采访一�数�科学家](assignment.md)
+[采访一�数�科学家](../assignment.md)
 # 机器学习技术
 
 构建�使用和维护机器学习模型�其使用的数�的过程与许多其他开�工作�程截然��。 在本课中，我们将�开该过程的神秘�纱，并概述你需�了解的主�技术。 你会： 
@@ -142,9 +142,9 @@
 为了能够确定地回答你的问题，你需�大�正确类型的数�。 此时你需��两件事： 
 
 - **收集数�**。记�之�关于数�分�公平性的课程，�心收集数�。请注�此数�的���它�能具有的任何固有��，并记录其��。 
-- **准备数�**。数�准备过程有几个步骤。如果数��自��的��，你�能需�整�数�并对其进行标准化。你�以通过��方法�高数�的质�和数�，例如将字符串转�为数字（就�我们在[�类](../../5-Clustering/1-Visualize/README.md)中所�的那样）。你还�以根�原始数�生�新数�（正如我们在[分类](../../4-Classification/1-Introduction/README.md)中所�的那样）。你�以清�和编辑数�（就�我们在 [Web App](../3-Web-App/README.md)课程之�所�的那样）。最�，你�能还需�对其进行�机化和打乱，具体�决于你的训练技术。
+- **准备数�**。数�准备过程有几个步骤。如果数��自��的��，你�能需�整�数�并对其进行标准化。你�以通过��方法�高数�的质�和数�，例如将字符串转�为数字（就�我们在[�类](../../../5-Clustering/1-Visualize/README.md)中所�的那样）。你还�以根�原始数�生�新数�（正如我们在[分类](../../../4-Classification/1-Introduction/README.md)中所�的那样）。你�以清�和编辑数�（就�我们在 [Web App](../../3-Web-App/README.md)课程之�所�的那样）。最�，你�能还需�对其进行�机化和打乱，具体�决于你的训练技术。
 
-✅ 在收集和处�你的数��，花点时间看看它的形状是�能让你解决你的预期问题。正如我们在[�类](../../5-Clustering/1-Visualize/README.md)课程中�现的那样，数��能在你的给定任务中表现�佳�
+✅ 在收集和处�你的数��，花点时间看看它的形状是�能让你解决你的预期问题。正如我们在[�类](../../../5-Clustering/1-Visualize/README.md)课程中�现的那样，数��能在你的给定任务中表现�佳�
 
 ### 选择特���
 
@@ -153,14 +153,14 @@
 🎓 **特�选择和特���** 构建模型时如何知�选择哪个��？你�能会�历一个特�选择或特���的过程，以便为性能最好的模型选择正确的��。然而，它们�是一回事：“特���是从基于原始特�的函数中创建新特�，而特�选择返回特�的一个�集。�（[��](https://wikipedia.org/wiki/Feature_selection)）
 ### �视化数�
 
-数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示你的数��能会让你�现�以利用的��关�。 你的�视化还�以帮助你�现��或�平衡的数�（正如我们在 [分类](../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
+数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示你的数��能会让你�现�以利用的��关�。 你的�视化还�以帮助你�现��或�平衡的数�（正如我们在 [分类](../../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
 ### 拆分数�集
 
 在训练之�，你需�将数�集拆分为两个或多个大��等但�能很好地代表数�的部分。
 
 - **训练**。这部分数�集适�你的模型进行训练。这个集�构�了原始数�集的大部分。
 - **测试**。测试数�集是一组独立的数�，通常从原始数�中收集，用于确认构建模型的性能。
-- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../7-TimeSeries/1-Introduction/README.md)中所述）。 
+- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../../7-TimeSeries/1-Introduction/README.md)中所述）。 
 
 ## 建立模型 
 
@@ -184,7 +184,7 @@
 
 🎓 **欠拟�**和**过拟�**是�低模型质�的常�问题，因为模型拟�得�够好或太好。这会导致模型�出与其训练数�过于紧密对�或过于�散对�的预测。 过拟�模型对训练数�的预测太好，因为它已�很好地了解了数�的细节和噪声。欠拟�模型并�准确，因为它既�能准确分�其训练数�，也�能准确分�尚未“看到�的数�。
 
-![过拟�模型 ](images/overfitting.png)
+![过拟�模型 ](../images/overfitting.png)
 > 作者[Jen Looper](https://twitter.com/jenlooper)
 
 ## �数调优 
@@ -211,4 +211,4 @@
 
 ## 任务
 
-[采访一�数�科学家](assignment.md)
+[采访一�数�科学家](../assignment.md)

From 83a83b54f0e78555b38d57a2b1dcb63299cad278 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 10 Jul 2021 11:22:32 +0800
Subject: [PATCH 094/368] Update README.zh-cn.md

---
 .../4-Logistic/translations/README.zh-cn.md        | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 1b1e8524aa..6a421ca1eb 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -1,6 +1,6 @@
 # 逻辑回归预测分类
 
-![逻辑与线性回归信�图](./images/logistic-linear.png)
+![逻辑与线性回归信�图](../images/logistic-linear.png)
 > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
 ## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
 
@@ -37,7 +37,7 @@
 
 逻辑回归��供与线性回归相�的功能。�者�供关于二元类别（“橙色或�橙色�）的预测，而�者能够预测连续值，例如，给定�瓜的起�和收获时间，_其价格将上涨多少_。
 
-![�瓜分类模型](./images/pumpkin-classifier.png)
+![�瓜分类模型](../images/pumpkin-classifier.png)
 > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
 ### 其他分类
 
@@ -46,7 +46,7 @@
 - **多项**，涉�多个类别 - “橙色�白色和�纹�。
 - **有�**，涉�有�类别，如果我们想对我们的结果进行逻辑排��常有用，例如我们的�瓜按有�数�的大�（mini�sm�med�lg�xl�xxl）排�。
 
-![多项�与有�回归](./images/multinomial-ordinal.png)
+![多项�与有�回归](../images/multinomial-ordinal.png)
 > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ### �然是线性的
@@ -102,7 +102,7 @@ Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�
     g.map(sns.scatterplot)
     ```
 
-    ![�视化数�网格](images/grid.png)
+    ![�视化数�网格](../images/grid.png)
 
     通过并列观察数�，你�以看到颜色数�与其他列的关系。
 
@@ -143,7 +143,7 @@ Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�
 >
 > 还记得线性回归如何�常使用普通最�二乘法�得出一个值�？逻辑回归�赖于使用[sigmoid 函数](https://wikipedia.org/wiki/Sigmoid_function) 的“最大似然�概念。绘图上的“Sigmoid 函数�看起��“S�形。它接�一个值并将其映射到0和1之间的�个�置。它的曲线也称为“逻辑曲线�。它的公�如下所示：
 >
-> ![逻辑函数](images/sigmoid.png)
+> ![逻辑函数](../images/sigmoid.png)
 >
 > 其中sigmoid的中点�于x的0点，L是曲线的最大值，k是曲线的陡度。如果函数的结果大于0.5，则所讨论的标签将被赋予二进制选择的类“1�。�则，它将被分类为“0�。
 
@@ -228,7 +228,7 @@ Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�
 - 如果你的模型将�物预测为�瓜并且它实际上属于“��瓜�类别，我们将其称为�阴性，由左下角的数字显示。
 - 如果你的模型预测�物�是�瓜，并且它实际上属于“��瓜�类别，我们将其称为真阴性，如�下角的数字所示。
 
-![混淆矩阵](images/confusion-matrix.png)
+![混淆矩阵](../images/confusion-matrix.png)
 
 > 作者[Jen Looper](https://twitter.com/jenlooper)
 
@@ -294,4 +294,4 @@ print(auc)
 
 ## 任务 
 
-[�试此回归](assignment.md)
+[�试此回归](../assignment.md)

From c1e9bd3aa93813a4b600456b7051daf8d08032a7 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 10 Jul 2021 11:25:50 +0800
Subject: [PATCH 095/368] Update README.zh-cn.md

---
 1-Introduction/1-intro-to-ML/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
index ff8d07008a..8693ff20c0 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
@@ -104,4 +104,4 @@
 
 ## 任务
 
-[�动并�行](assignment.md)
+[�动并�行](../assignment.md)

From 0b0857076165a7bd3c9f1241d8a7bf6a6eaef5b0 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 10 Jul 2021 11:28:14 +0800
Subject: [PATCH 096/368] Update README.zh-cn.md

---
 .../translations/README.zh-cn.md              | 106 ------------------
 1 file changed, 106 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index d54494ef6c..d01d5bbfe2 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -1,110 +1,4 @@
-# 机器学习技术
-
-构建�使用和维护机器学习模型�其使用的数�的过程与许多其他开�工作�程截然��。 在本课中，我们将�开该过程的神秘�纱，并概述你需�了解的主�技术。 你会： 
-
-- 在高层次上�解支�机器学习的过程。 
-- 探索基本概念，例如“模型��“预测�和“训练数��。 
-  
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
-## 介�
-
-在较高的层次上，创建机器学习（ML）过程的工艺包括许多步骤：
-
-1. **决定问题**。 大多数机器学习过程都是从�出一个简�的�件程�或基于规则的引擎无法回答的问题开始的。 这些问题通常围绕基于数�集�的预测展开。 
-2. **收集和准备数�**。为了能够回答你的问题，你需�数�。数�的质�（有时是数�）将决定你回答最�问题的能力。�视化数�是这个阶段的一个��方�。此阶段还包括将数�拆分为训练和测试组以构建模型。 
-3. **选择一�训练方法**。根�你的问题和数�的性质，你需�选择如何训练模型以最好地�映你的数�并对其进行准确预测。这是你的ML过程的一部分，需�特定的专业知识，并且通常需�大�的实验。 
-4. **训练模型**。使用你的训练数�，你将使用��算法�训练模型以识别数�中的模�。该模型�能会利用�以调整的内部���使数�的�些部分优于其他部分，从而构建更好的模型。 
-5. **评估模型**。你使用收集到的集�中从未�过的数�（你的测试数�）�查看模型的性能。 
-6. **�数调整**。根�模型的性能，你�以使用��的�数或����该过程，这些�数或��控制用于训练模型的算法的行为。 
-7. **预测**。使用新输入�测试模型的准确性。 
-
-## �问什么问题 
-
-计算机特别擅长�现数�中的��模�。此实用程�对于对给定领域有疑问的研究人员�常有帮助，这些问题无法通过创建基于�件的规则引擎�轻�回答。例如，给定一项精算任务，数�科学家�能能够围绕�烟者与��烟者的死亡率构建手工规则。 
-
-然而，当将许多其他��纳入等�时，ML模型�能会更有效地根�过去的�康�预测未�的死亡率。一个更令人愉快的例��能是根�包括纬度��度�气候�化�与海洋的接近程度�急�模�等在内的数�对给定�置的4月份进行天气预报。 
-
-✅ 这个关于天气模型的[幻�片](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf)为在天气分�中使用机器学习�供了一个历�视角。
-
-## 预构建任务
-
-在开始构建模型之�，你需�完�多项任务。�测试你的问题并根�模型的预测形��设，你需�识别和�置多个元素。 
-
-### Data
-
-为了能够确定地回答你的问题，你需�大�正确类型的数�。 此时你需��两件事： 
-
-- **收集数�**。记�之�关于数�分�公平性的课程，�心收集数�。请注�此数�的���它�能具有的任何固有��，并记录其��。 
-- **准备数�**。数�准备过程有几个步骤。如果数��自��的��，你�能需�整�数�并对其进行标准化。你�以通过��方法�高数�的质�和数�，例如将字符串转�为数字（就�我们在[�类](../../5-Clustering/1-Visualize/README.md)中所�的那样）。你还�以根�原始数�生�新数�（正如我们在[分类](../../4-Classification/1-Introduction/README.md)中所�的那样）。你�以清�和编辑数�（就�我们在 [Web App](../3-Web-App/README.md)课程之�所�的那样）。最�，你�能还需�对其进行�机化和打乱，具体�决于你的训练技术。
-
-✅ 在收集和处�你的数��，花点时间看看它的形状是�能让你解决你的预期问题。正如我们在[�类](../../5-Clustering/1-Visualize/README.md)课程中�现的那样，数��能在你的给定任务中表现�佳�
-
-### 选择特���
-
-[特�](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection)是数�的�衡�属性。在许多数�集中，它表示为列标题，如“日期��“大��或“颜色�。你的特���（通常在代�中表示为`y`）代表你试图对数��出的问题的答案：在12月，哪�**颜色**的�瓜最便宜？在旧金山，哪些街区的房地产**价格**最好？
-
-🎓 **特�选择和特���** 构建模型时如何知�选择哪个��？你�能会�历一个特�选择或特���的过程，以便为性能最好的模型选择正确的��。然而，它们�是一回事：“特���是从基于原始特�的函数中创建新特�，而特�选择返回特�的一个�集。�（[��](https://wikipedia.org/wiki/Feature_selection)）
-### �视化数�
-
-数�科学家工具包的一个��方�是能够使用多个优秀的库（例如 Seaborn 或 MatPlotLib）将数��视化。直观地表示你的数��能会让你�现�以利用的��关�。 你的�视化还�以帮助你�现��或�平衡的数�（正如我们在 [分类](../../../4-Classification/2-Classifiers-1/README.md)中�现的那样）。
-### 拆分数�集
-
-在训练之�，你需�将数�集拆分为两个或多个大��等但�能很好地代表数�的部分。
-
-- **训练**。这部分数�集适�你的模型进行训练。这个集�构�了原始数�集的大部分。
-- **测试**。测试数�集是一组独立的数�，通常从原始数�中收集，用于确认构建模型的性能。
-- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../../7-TimeSeries/1-Introduction/README.md)中所述）。 
-
-## 建立模型 
-
-使用你的训练数�，你的目标是构建模型或数�的统计表示，并使用��算法对其进行**训练**。训练模型将其暴露给数�，并�许它对其�现�验�和接�或拒�的感知模��出�设。 
-
-### 决定一�训练方法
-
-根�你的问题和数�的性质，你将选择一�方法�训练它。�步完� [Scikit-learn的文档](https://scikit-learn.org/stable/user_guide.html) - 我们在本课程中使用 - 你�以探索多�训练模型的方法。 根�你的�验，你�能需��试多���的方法�构建最佳模型。你�能会�历一个过程，在该过程中，数�科学家通过�供未�过的数��评估模型的性能，检查准确性��差和其他�低质�的问题，并为手头的任务选择最�适的训练方法。
-
-### 训练模型
-
-有了你的训练数�，你就�以“拟��它以创建模型。你会注�到，在许多ML库中，你会找到代�'model.fit'——此时你将数�作为值数组（通常为'X'）和特���（通常为'y'）��）。 
-
-### 评估模型 
-
-训练过程完��（训练大型模型�能需�多次迭代或“时期�），你将能够通过使用测试数��衡�模型的性能�评估模型的质�。此数�是模型先�未分�的原始数�的�集。 你�以打�出有关模型质�的指标表。 
-
-🎓 **模型拟� **
-
-在机器学习的背景下，模型拟�是指模型在�试分��熟悉的数�时其底层功能的准确性。 
-
-🎓 **欠拟�**和**过拟�**是�低模型质�的常�问题，因为模型拟�得�够好或太好。这会导致模型�出与其训练数�过于紧密对�或过于�散对�的预测。 过拟�模型对训练数�的预测太好，因为它已�很好地了解了数�的细节和噪声。欠拟�模型并�准确，因为它既�能准确分�其训练数�，也�能准确分�尚未“看到�的数�。
-
-![过拟�模型 ](../images/overfitting.png)
-> 作者[Jen Looper](https://twitter.com/jenlooper)
-
-## �数调优 
-
-�始训练完��，观察模型的质�并考虑通过调整其“超�数��改进它。[在此文档中](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa)阅读有关该过程的更多信�。
-
-## 预测
-
-这是你�以使用全新数��测试模型准确性的时刻。在“应用�ML设置中，你正在构建Web资�以在生产中使用模型，此过程�能涉�收集用户输入（例如按下按钮）以设置��并将其��到模型进行推�，或者评估。
-
-在这些课程中，你将了解如何使用这些步骤�准备�构建�测试�评估和预测—所有这些都是数�科学家的姿�，而且��你在�为一�“全栈�ML工程师的旅程中�得进展，你将了解更多。 
-
----
 
-## 🚀挑战
-
-画一个�程图，�映ML的步骤。在这个过程中，你认为自己现在在哪里？你预测你在哪里会�到困难？什么对你�说很容易？ 
-
-## [阅读�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
-
-## �习与自学 
-
-在线�索对讨论日常工作的数�科学家的采访。 这是[其中之一](https://www.youtube.com/watch?v=Z3IjgbbCEfs)。
-
-## 任务
-
-[采访一�数�科学家](../assignment.md)
 # 机器学习技术
 
 构建�使用和维护机器学习模型�其使用的数�的过程与许多其他开�工作�程截然��。 在本课中，我们将�开该过程的神秘�纱，并概述你需�了解的主�技术。 你会： 

From e0cc55e0cdfd48ce79e88fac5a6a7957d5e4d7a9 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 10 Jul 2021 16:13:09 +0800
Subject: [PATCH 097/368] Update README.zh-cn.md

---
 2-Regression/4-Logistic/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 6a421ca1eb..2bd4d4cb52 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -139,7 +139,7 @@ Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�
 
 现在我们已�了解了颜色的二元类别与更大的尺寸组之间的关系，让我们探索逻辑回归�确定给定�瓜的�能颜色。
 
-> **🧮 给我看看数学**
+> **🧮 数学知识**
 >
 > 还记得线性回归如何�常使用普通最�二乘法�得出一个值�？逻辑回归�赖于使用[sigmoid 函数](https://wikipedia.org/wiki/Sigmoid_function) 的“最大似然�概念。绘图上的“Sigmoid 函数�看起��“S�形。它接�一个值并将其映射到0和1之间的�个�置。它的曲线也称为“逻辑曲线�。它的公�如下所示：
 >

From d690b9e65d7c1f48c1761d5284e833d510903e76 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 10 Jul 2021 18:16:07 +0800
Subject: [PATCH 098/368] Update README.zh-cn.md

---
 2-Regression/4-Logistic/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 2bd4d4cb52..52453de5ab 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -270,7 +270,7 @@ sns.lineplot(fpr, tpr)
 ```
 �次使用Seaborn，绘制模型的[接收�作特性](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc)或ROC。 ROC曲线通常用于根�分类器的真�阳性�了解分类器的输出。“ROC曲线通常具有Y轴上的真阳性率和X轴上的�阳性率。� 因此，曲线的陡度以�中点线与曲线之间的空间很��：你需�一�快速�上并越过直线的曲线。在我们的例�中，一开始就有误报，然�这�线正确地�上和��：
 
-![ROC](./images/ROC.png)
+![ROC](../images/ROC.png)
 
 最�，使用Scikit-learn的[`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score)�计算实际“曲线下�积�（AUC）：
 

From 01c8a7cc94efb7b3908609b22fd46ea577843ad2 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 10 Jul 2021 18:17:05 +0800
Subject: [PATCH 099/368] done

---
 .../3-Linear/translations/README.zh-cn.md     | 199 +++++++++---------
 1 file changed, 100 insertions(+), 99 deletions(-)

diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md
index 2a60658b2e..7bb46c4540 100644
--- a/2-Regression/3-Linear/translations/README.zh-cn.md
+++ b/2-Regression/3-Linear/translations/README.zh-cn.md
@@ -1,82 +1,81 @@
-# Build a regression model using Scikit-learn: regression two ways
+# 使用Scikit-learn构建回归模型：两�方�的回归
 
-![Linear vs polynomial regression infographic](./images/linear-polynomial.png)
-> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
-### Introduction 
+![线性与多项�回归信�图](../images/linear-polynomial.png)
+> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+### 介� 
 
-So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using Matplotlib. 
+到目�为止，您已�通过从我们将在本课程中使用的�瓜定价数�集收集的样本数�探索了什么是回归。您还使用Matplotlib对其进行了�视化。
 
-Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: _basic linear regression_ and _polynomial regression_, along with some of the math underlying these techniques.
+现在您已准备好深入研究ML的回归。 在本课中，您将详细了解两�类型的回归：_基本线性回归_和_多项�回归_，以�这些技术背�的一些数学知识。 
 
-> Throughout this curriculum, we assume minimal knowledge of math, and seek to make it accessible for students coming from other fields, so watch for notes, 🧮 callouts, diagrams, and other learning tools to aid in comprehension.
+> 在整个课程中，我们�设数学知识最少，并试图让�自其他领域的学生也能接触到它，因此请使用笔记�🧮标注�图表和其他学习工具以帮助�解。
 
-### Prerequisite
+### ��
 
-You should be familiar by now with the structure of the pumpkin data that we are examining. You can find it preloaded and pre-cleaned in this lesson's _notebook.ipynb_ file. In the file, the pumpkin price is displayed per bushel in a new dataframe.  Make sure you can run these notebooks in kernels in Visual Studio Code.
+您现在应该熟悉我们正在检查的�瓜数�的结构。您�以在本课的_notebook.ipynb_文件中找到它。 在这个文件中，�瓜的价格显示在一个新的dataframe 中。确��以在Visual Studio Code代�的内核中�行这些notebooks。
 
-### Preparation
+### 准备
 
-As a reminder, you are loading this data so as to ask questions of it. 
+�醒一下，您正在加载此数�以�出问题。
 
-- When is the best time to buy pumpkins? 
-- What price can I expect of a case of miniature pumpkins?
-- Should I buy them in half-bushel baskets or by the 1 1/9 bushel box?
-Let's keep digging into this data.
+- 什么时候买�瓜最好？
+- 一箱微型�瓜的价格是多少？
+- 我应该买�蒲�耳还是1 1/9蒲�耳？
+让我们继续深入研究这些数�。
 
-In the previous lesson, you created a Pandas dataframe and populated it with part of the original dataset, standardizing the pricing by the bushel. By doing that, however, you were only able to gather about 400 datapoints and only for the fall months. 
+在上一课中，您创建了一个Pandas dataframe并用原始数�集的一部分填充它，按蒲�耳标准化定价。但是，通过这样�，您�能收集大约400个数�点，而且�能收集秋季月份的数�。
 
-Take a look at the data that we preloaded in this lesson's accompanying notebook. The data is preloaded and an initial scatterplot is charted to show month data. Maybe we can get a little more detail about the nature of the data by cleaning it more.
+看看我们在本课�附的notebook中预加载的数�。数�已预加载，并绘制了�始散点图以显示月份数�。也许我们�以通过更多地清�数��获得更多关于数�性质的细节。 
 
-## A linear regression line
+## 线性回归线
 
-As you learned in Lesson 1, the goal of a linear regression exercise is to be able to plot a line to:
+正如您在第1课中学到的，线性回归练习的目标是能够绘制一�线以便：
 
-- **Show variable relationships**. Show the relationship between variables
-- **Make predictions**. Make accurate predictions on where a new datapoint would fall in relationship to that line. 
+- **显示��关系**。 显示��之间的关系
+- **作出预测**。 准确预测新数�点与该线的关系。
  
-It is typical of **Least-Squares Regression** to draw this type of line. The term 'least-squares' means that all the datapoints surrounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. 
+绘制这�类型的线是**最�二乘回归**的典型�法。术语“最�二乘法��味�将回归线周围的所有数�点平方，然�相加。�想情况下，最终和尽�能�，因为我们希望错误数�较少，或“最�二乘法�。
 
-We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.
+我们这样�是因为我们想�对一�与所有数�点的累积�离最�的线进行建模。我们还在添加它们之�对这些项进行平方，因为我们关心的是它的大�而�是它的方�。
 
-> **🧮 Show me the math** 
+> **🧮 数学知识** 
 > 
-> This line, called the _line of best fit_ can be expressed by [an equation](https://en.wikipedia.org/wiki/Simple_linear_regression): 
+> 这�线称为_最佳拟�线_，�以用[一个等�](https://en.wikipedia.org/wiki/Simple_linear_regression)表示：
 > 
 > ```
 > Y = a + bX
 > ```
 >
-> `X` is the 'explanatory variable'. `Y` is the 'dependent variable'. The slope of the line is `b` and `a` is the y-intercept, which refers to the value of `Y` when `X = 0`. 
+> `X`是“解释���。`Y`是“因���。直线的斜率是`b`，`a`是y轴截�，指的是`X = 0`时`Y`的值。
 >
->![calculate the slope](images/slope.png)
+>![计算斜率](../images/slope.png)
 >
-> First, calculate the slope `b`. Infographic by [Jen Looper](https://twitter.com/jenlooper)
+> 首先，计算斜率`b`。作者[Jen Looper](https://twitter.com/jenlooper)
 >
-> In other words, and referring to our pumpkin data's original question: "predict the price of a pumpkin per bushel by month", `X` would refer to the price and `Y` would refer to the month of sale. 
+> ���说，�考我们的�瓜数�的原始问题：“按月预测�蒲�耳�瓜的价格�，`X`指的是价格，`Y`指的是销售月份。
 >
->![complete the equation](images/calculation.png)
+>![完�等�](../images/calculation.png)
 >
-> Calculate the value of Y. If you're paying around $4, it must be April! Infographic by [Jen Looper](https://twitter.com/jenlooper)
+> 计算Y的值。如果你支付大约4美元，那一定是四月�作者[Jen Looper](https://twitter.com/jenlooper)
 >
-> The math that calculates the line must demonstrate the slope of the line, which is also dependent on the intercept, or where `Y` is situated when `X = 0`.
+> 计算直线的数学必须�明直线的斜率，这也�决于截�，或者当`X = 0`时`Y`所在的�置。
 >
-> You can observe the method of calculation for these values on the [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web site. Also visit [this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html) to watch how the numbers' values impact the line.
+> 您�以在[Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html)网站上观察这些值的计算方法。�请访问[这个最�二乘计算器](https://www.mathsisfun.com/data/least-squares-calculator.html)以观察数字的值如何影�直线。
 
-## Correlation
+## 相关性
 
-One more term to understand is the **Correlation Coefficient** between given X and Y variables. Using a scatterplot, you can quickly visualize this coefficient. A plot with datapoints scattered in a neat line have high correlation, but a plot with datapoints scattered everywhere between X and Y have a low correlation.
+�一个需��解的术语是给定X和Y��之间的**相关系数**。使用散点图，您�以快速�视化该系数。数�点散布在一�直线上的图具有高相关性，但数�点散布在X和Y之间的图具有低相关性。
 
-A good linear regression model will be one that has a high (nearer to 1 than 0) Correlation Coefficient using the Least-Squares Regression method with a line of regression.
+一个好的线性回归模型将是一个用最�二乘回归法与直线回归得到的高（更接近于1）相关系数的模型。
 
-✅ Run the notebook accompanying this lesson and look at the City to Price scatterplot. Does the data associating City to Price for pumpkin sales seem to have high or low correlation, according to your visual interpretation of the scatterplot? 
+✅ �行本课�附的notebook并查看City to Price散点图。根�您对散点图的视觉解释，将�瓜销售的城市与价格相关�的数�似乎具有高相关性或低相关性？
 
+## 为回归准备数�
 
-## Prepare your data for regression
+现在您已�了解了本练习背�的数学原�，�以创建一个回归模型，看看您是��以预测哪个�瓜包装的�瓜价格最优惠。为节日购买�瓜的人�能希望此信�能够优化他们如何购买�瓜包装。
 
-Now that you have an understanding of the math behind this exercise, create a Regression model to see if you can predict which package of pumpkins will have the best pumpkin prices. Someone buying pumpkins for a holiday pumpkin patch might want this information to be able to optimize their purchases of pumpkin packages for the patch.
-
-Since you'll use Scikit-learn, there's no reason to do this by hand (although you could!). In the main data-processing block of your lesson notebook, add a library from Scikit-learn to automatically convert all string data to numbers:
+由于您将使用Scikit-learn，因此没有�由手动执行此�作（尽管您�以�）。在课程notebook的主�数�处��中，从Scikit-learn添加一个库以自动将所有字符串数�转�为数字：
 
 ```python
 from sklearn.preprocessing import LabelEncoder
@@ -84,37 +83,38 @@ from sklearn.preprocessing import LabelEncoder
 new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
 ```
 
-If you look at the new_pumpkins dataframe now, you see that all the strings are now numeric. This makes it harder for you to read but much more intelligible for Scikit-learn!
-Now you can make more educated decisions (not just based on eyeballing a scatterplot) about the data that is best suited to regression.
+如果您现在查看new_pumpkins dataframe，您会看到所有字符串现在都是数字。这让你更难阅读，但对Scikit-learn�说更容易�解�
+
+现在，您�以对最适�回归的数��出更有根�的决策（�仅仅是基于观察散点图）。
 
-Try to find a good correlation between two points of your data to potentially build a good predictive model. As it turns out, there's only weak correlation between the City and Price:
+�试在数�的两点之间找到良好的相关性，以构建良好的预测模型。事实�明，城市和价格之间�有微弱的相关性：
 
 ```python
 print(new_pumpkins['City'].corr(new_pumpkins['Price']))
 0.32363971816089226
 ```
 
-However there's a bit better correlation between the Package and its Price. That makes sense, right? Normally, the bigger the produce box, the higher the price.
+然而，包装和它的价格之间有更好的相关性。这是有��的，对�？通常，农产�箱越大，价格越高。
 
 ```python
 print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
 0.6061712937226021
 ```
 
-A good question to ask of this data will be: 'What price can I expect of a given pumpkin package?'
+对这些数��出的一个很好的问题是：“我�以期望给定的�瓜包装的价格是多少？�
 
-Let's build this regression model
+让我们建立这个回归模型 
 
-## Building a linear model
+## 建立线性模型
 
-Before building your model, do one more tidy-up of your data. Drop any null data and check once more what the data looks like.
+在构建模型之�，�对数�进行一次整�。删除任何空数�并�次检查数�的样�。
 
 ```python
 new_pumpkins.dropna(inplace=True)
 new_pumpkins.info()
 ```
 
-Then, create a new dataframe from this minimal set and print it out:
+然�，从这个最�集�创建一个新的dataframe并将其打�出�：
 
 ```python
 new_columns = ['Package', 'Price']
@@ -139,15 +139,15 @@ lin_pumpkins
 415 rows × 2 columns
 ```
 
-1. Now you can assign your X and y coordinate data:
+1. 现在您�以分�X和y�标数�：
 
    ```python
    X = lin_pumpkins.values[:, :1]
    y = lin_pumpkins.values[:, 1:2]
    ```
-✅ What's going on here? You're using [Python slice notation](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295) to create arrays to populate `X` and `y`.
+✅ 这里�生了什么？您正在使用[Python slice notation](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295)�创建数组�填充`X`和`y`。
 
-2. Next, start the regression model-building routines:
+2. 接下�，开始回归模型构建例程：
 
    ```python
    from sklearn.linear_model import LinearRegression
@@ -164,13 +164,13 @@ lin_pumpkins
    print('Model Accuracy: ', accuracy_score)
    ```
 
-   Because the correlation isn't particularly good, the model produced isn't terribly accurate.
+   因为相关性�是特别好，所以生�的模型�是�常准确。
 
    ```output
    Model Accuracy:  0.3315342327998987
    ```
 
-3. You can visualize the line that's drawn in the process:
+3. 您�以将过程中绘制的线��视化：
 
    ```python
    plt.scatter(X_test, y_test,  color='black')
@@ -181,37 +181,37 @@ lin_pumpkins
 
    plt.show()
    ```
-   ![A scatterplot showing package to price relationship](./images/linear.png)
+   ![散点图显示包装与价格的关系](../images/linear.png)
 
-4. Test the model against a hypothetical variety:
+4. 针对�设的��测试模型：
 
    ```python
    lin_reg.predict( np.array([ [2.75] ]) )
    ```
    
-   The returned price for this mythological Variety is:
+   这个神�般的��的价格是：
 
    ```output
    array([[33.15655975]])
    ```
 
-That number makes sense, if the logic of the regression line holds true.
+如果回归线的逻辑�立，这个数字是有�义的。
 
-🎃 Congratulations, you just created a model that can help predict the price of a few varieties of pumpkins. Your holiday pumpkin patch will be beautiful. But you can probably create a better model!
-## Polynomial regression
+🎃 �喜你，你刚刚创建了一个模型，�以帮助预测几个�瓜��的价格。你的节日�瓜地会很漂亮的。但是你�以创造一个更好的模型�
 
-Another type of linear regression is polynomial regression. While sometimes there's a linear relationship between variables - the bigger the pumpkin in volume, the higher the price - sometimes these relationships can't be plotted as a plane or straight line. 
+## 多项�回归
 
-✅ Here are [some more examples](https://online.stat.psu.edu/stat501/lesson/9/9.8) of data that could use polynomial regression
+�一�线性回归是多项�回归。虽然有时��之间存在线性关系——�瓜的体积越大，价格就越高——但有时这些关系�能绘制�平�或直线。
 
-Take another look at the relationship between Variety to Price in the previous plot. Does this scatterplot seem like it should necessarily be analyzed by a straight line? Perhaps not. In this case, you can try polynomial regression.
+✅ 这里有�以使用多项�回归数�的[更多示例](https://online.stat.psu.edu/stat501/lesson/9/9.8)
 
-✅ Polynomials are mathematical expressions that might consist of one or more variables and coefficients
+�看一下上图中��与价格之间的关系。这个散点图看起�是�应该用一�直线�分�？也许�是。在这�情况下，您�以�试多项�回归。
 
-Polynomial regression creates a curved line to better fit nonlinear data. 
+✅ 多项�是�能由一个或多个��和系数组�的数学表达�
 
-1. Let's recreate a dataframe populated with a segment of the original pumpkin data:
+多项�回归创建一�曲线以更好地拟��线性数�。
 
+1. 让我们�新创建一个填充了原始�瓜数�片段的dataframe：
    ```python
    new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
    poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
@@ -219,30 +219,31 @@ Polynomial regression creates a curved line to better fit nonlinear data.
    poly_pumpkins
    ```
 
-A good way to visualize the correlations between data in dataframes is to display it in a 'coolwarm' chart:
+�视化dataframe中数�之间相关性的一�好方法是将其显示在“coolwarm�图表中：
 
-2. Use the `Background_gradient()` method with `coolwarm` as its argument value:
+2. 使用`Background_gradient()`方法和`coolwarm`作为其�数值：
 
    ```python
    corr = poly_pumpkins.corr()
    corr.style.background_gradient(cmap='coolwarm')
    ```
-   This code creates a heatmap:
-   ![A heatmap showing data correlation](./images/heatmap.png)
+   这段代�创建了一个热图:
+   ![显示数�相关性的热图](../images/heatmap.png)
+
+查看此图表，您�以直观地看到Package和Price之间的良好相关性。所以你应该能够创建一个比上一个更好的模型。
 
-Looking at this chart, you can visualize the good correlation between Package and Price. So you should be able to create a somewhat better model than the last one.
-### Create a pipeline
+### 创建管�
 
-Scikit-learn includes a helpful API for building polynomial regression models - the `make_pipeline` [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline). A 'pipeline' is created which is a chain of estimators. In this case, the pipeline includes polynomial features, or predictions that form a nonlinear path.
+Scikit-learn包�一个用于构建多项�回归模型的有用API - `make_pipeline` [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline)。 创建了一个“管��，它是一个估计器链。 在这�情况下，管�包括多项�特�或形��线性路径的预测。
 
-1. Build out the X and y columns:
+1. 构建X和y列：
 
    ```python
    X=poly_pumpkins.iloc[:,3:4].values
    y=poly_pumpkins.iloc[:,4:5].values
    ```
 
-2. Create the pipeline by calling the `make_pipeline()` method:
+2. 通过调用`make_pipeline()`方法创建管�：
 
    ```python
    from sklearn.preprocessing import PolynomialFeatures
@@ -257,11 +258,11 @@ Scikit-learn includes a helpful API for building polynomial regression models -
    y_pred=pipeline.predict(X_test)
    ```
 
-### Create a sequence
+### 创建�列
 
-At this point, you need to create a new dataframe with _sorted_ data so that the pipeline can create a sequence.
+此时，您需�使用_排�好的_数�创建一个新的dataframe ，以便管��以创建�列。
 
-Add the following code:
+添加以下代�：
 
    ```python
    df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})
@@ -275,57 +276,57 @@ Add the following code:
    plt.show()
    ```
 
-You created a new dataframe by calling `pd.DataFrame`. Then you sorted the values by calling `sort_values()`. Finally you created a polynomial plot:
+您通过调用`pd.DataFrame`创建了一个新的dataframe。然�通过调用`sort_values()`对值进行排�。最�你创建了一个多项�图：
 
-![A polynomial plot showing package to price relationship](./images/polynomial.png)
+![显示包装与价格关系的多项�图](../images/polynomial.png)
 
-You can see a curved line that fits your data better. 
+您�以看到更适�您的数�的曲线。
 
-Let's check the model's accuracy:
+让我们检查模型的准确性：
 
    ```python
    accuracy_score = pipeline.score(X_train,y_train)
    print('Model Accuracy: ', accuracy_score)
    ```
 
-   And voila!
+   瞧�
 
    ```output
    Model Accuracy:  0.8537946517073784
    ```
 
-That's better! Try to predict a price:
+这样好多了�试�预测一个价格：
 
-### Do a prediction
+### �个预测
 
-Can we input a new value and get a prediction?
+我们�以输入一个新值并得到一个预测�？
 
-Call `predict()` to make a prediction:
+调用`predict()`进行预测：
  
    ```python
    pipeline.predict( np.array([ [2.75] ]) )
    ```
-   You are given this prediction:
+   你会得到这样的预测：
 
    ```output
    array([[46.34509342]])
    ```
 
-It does make sense, given the plot! And, if this is a better model than the previous one, looking at the same data, you need to budget for these more expensive pumpkins!
+�照图�，这确实有���而且，如果这是一个比�一个更好的模型，看�样的数�，你需�为这些更昂贵的�瓜�好预算�
 
-� Well done! You created two regression models in one lesson. In the final section on regression, you will learn about logistic regression to determine categories. 
+� 干得�错�您在一节课中创建了两个回归模型。在回归的最�一节中，您将了解逻辑回归以确定类别。 
 
 ---
-## 🚀Challenge
+## 🚀挑战
 
-Test several different variables in this notebook to see how correlation corresponds to model accuracy.
+在此notebook中测试几个��的��，以查看相关性与模型准确性的对应关系。
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
 
-## Review & Self Study
+## �习与自学
 
-In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
+在本课中，我们学习了线性回归。还有其他��的回归类型。了解Stepwise�Ridge�Lasso和Elasticnet技术。学习更多信�的好课程是[斯��统计学习课程](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
 
-## Assignment 
+## 任务 
 
-[Build a Model](assignment.md)
+[构建模型](../assignment.md)

From 03d7069e4b16c12fac8bfab00e53338abe8df7ae Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Sat, 10 Jul 2021 19:48:50 +0800
Subject: [PATCH 100/368] Update README.zh-cn.md

---
 .../3-Linear/translations/README.zh-cn.md     | 44 +++++++++----------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md
index 7bb46c4540..55c4d29395 100644
--- a/2-Regression/3-Linear/translations/README.zh-cn.md
+++ b/2-Regression/3-Linear/translations/README.zh-cn.md
@@ -5,32 +5,32 @@
 ## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
 ### 介� 
 
-到目�为止，您已�通过从我们将在本课程中使用的�瓜定价数�集收集的样本数�探索了什么是回归。您还使用Matplotlib对其进行了�视化。
+到目�为止，你已�通过从我们将在本课程中使用的�瓜定价数�集收集的样本数�探索了什么是回归。你还使用Matplotlib对其进行了�视化。
 
-现在您已准备好深入研究ML的回归。 在本课中，您将详细了解两�类型的回归：_基本线性回归_和_多项�回归_，以�这些技术背�的一些数学知识。 
+现在你已准备好深入研究ML的回归。 在本课中，你将详细了解两�类型的回归：_基本线性回归_和_多项�回归_，以�这些技术背�的一些数学知识。 
 
 > 在整个课程中，我们�设数学知识最少，并试图让�自其他领域的学生也能接触到它，因此请使用笔记�🧮标注�图表和其他学习工具以帮助�解。
 
 ### ��
 
-您现在应该熟悉我们正在检查的�瓜数�的结构。您�以在本课的_notebook.ipynb_文件中找到它。 在这个文件中，�瓜的价格显示在一个新的dataframe 中。确��以在Visual Studio Code代�的内核中�行这些notebooks。
+你现在应该熟悉我们正在检查的�瓜数�的结构。你�以在本课的_notebook.ipynb_文件中找到它。 在这个文件中，�瓜的价格显示在一个新的dataframe 中。确��以在Visual Studio Code代�的内核中�行这些notebooks。
 
 ### 准备
 
-�醒一下，您正在加载此数�以�出问题。
+�醒一下，你正在加载此数�以�出问题。
 
 - 什么时候买�瓜最好？
 - 一箱微型�瓜的价格是多少？
 - 我应该买�蒲�耳还是1 1/9蒲�耳？
 让我们继续深入研究这些数�。
 
-在上一课中，您创建了一个Pandas dataframe并用原始数�集的一部分填充它，按蒲�耳标准化定价。但是，通过这样�，您�能收集大约400个数�点，而且�能收集秋季月份的数�。
+在上一课中，你创建了一个Pandas dataframe并用原始数�集的一部分填充它，按蒲�耳标准化定价。但是，通过这样�，你�能收集大约400个数�点，而且�能收集秋季月份的数�。
 
 看看我们在本课�附的notebook中预加载的数�。数�已预加载，并绘制了�始散点图以显示月份数�。也许我们�以通过更多地清�数��获得更多关于数�性质的细节。 
 
 ## 线性回归线
 
-正如您在第1课中学到的，线性回归练习的目标是能够绘制一�线以便：
+正如你在第1课中学到的，线性回归练习的目标是能够绘制一�线以便：
 
 - **显示��关系**。 显示��之间的关系
 - **作出预测**。 准确预测新数�点与该线的关系。
@@ -61,21 +61,21 @@
 >
 > 计算直线的数学必须�明直线的斜率，这也�决于截�，或者当`X = 0`时`Y`所在的�置。
 >
-> 您�以在[Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html)网站上观察这些值的计算方法。�请访问[这个最�二乘计算器](https://www.mathsisfun.com/data/least-squares-calculator.html)以观察数字的值如何影�直线。
+> 你�以在[Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html)网站上观察这些值的计算方法。�请访问[这个最�二乘计算器](https://www.mathsisfun.com/data/least-squares-calculator.html)以观察数字的值如何影�直线。
 
 ## 相关性
 
-�一个需��解的术语是给定X和Y��之间的**相关系数**。使用散点图，您�以快速�视化该系数。数�点散布在一�直线上的图具有高相关性，但数�点散布在X和Y之间的图具有低相关性。
+�一个需��解的术语是给定X和Y��之间的**相关系数**。使用散点图，你�以快速�视化该系数。数�点散布在一�直线上的图具有高相关性，但数�点散布在X和Y之间的图具有低相关性。
 
 一个好的线性回归模型将是一个用最�二乘回归法与直线回归得到的高（更接近于1）相关系数的模型。
 
-✅ �行本课�附的notebook并查看City to Price散点图。根�您对散点图的视觉解释，将�瓜销售的城市与价格相关�的数�似乎具有高相关性或低相关性？
+✅ �行本课�附的notebook并查看City to Price散点图。根�你对散点图的视觉解释，将�瓜销售的城市与价格相关�的数�似乎具有高相关性或低相关性？
 
 ## 为回归准备数�
 
-现在您已�了解了本练习背�的数学原�，�以创建一个回归模型，看看您是��以预测哪个�瓜包装的�瓜价格最优惠。为节日购买�瓜的人�能希望此信�能够优化他们如何购买�瓜包装。
+现在你已�了解了本练习背�的数学原�，�以创建一个回归模型，看看你是��以预测哪个�瓜包装的�瓜价格最优惠。为节日购买�瓜的人�能希望此信�能够优化他们如何购买�瓜包装。
 
-由于您将使用Scikit-learn，因此没有�由手动执行此�作（尽管您�以�）。在课程notebook的主�数�处��中，从Scikit-learn添加一个库以自动将所有字符串数�转�为数字：
+由于你将使用Scikit-learn，因此没有�由手动执行此�作（尽管你�以�）。在课程notebook的主�数�处��中，从Scikit-learn添加一个库以自动将所有字符串数�转�为数字：
 
 ```python
 from sklearn.preprocessing import LabelEncoder
@@ -83,9 +83,9 @@ from sklearn.preprocessing import LabelEncoder
 new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
 ```
 
-如果您现在查看new_pumpkins dataframe，您会看到所有字符串现在都是数字。这让你更难阅读，但对Scikit-learn�说更容易�解�
+如果你现在查看new_pumpkins dataframe，你会看到所有字符串现在都是数字。这让你更难阅读，但对Scikit-learn�说更容易�解�
 
-现在，您�以对最适�回归的数��出更有根�的决策（�仅仅是基于观察散点图）。
+现在，你�以对最适�回归的数��出更有根�的决策（�仅仅是基于观察散点图）。
 
 �试在数�的两点之间找到良好的相关性，以构建良好的预测模型。事实�明，城市和价格之间�有微弱的相关性：
 
@@ -139,13 +139,13 @@ lin_pumpkins
 415 rows × 2 columns
 ```
 
-1. 现在您�以分�X和y�标数�：
+1. 现在你�以分�X和y�标数�：
 
    ```python
    X = lin_pumpkins.values[:, :1]
    y = lin_pumpkins.values[:, 1:2]
    ```
-✅ 这里�生了什么？您正在使用[Python slice notation](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295)�创建数组�填充`X`和`y`。
+✅ 这里�生了什么？你正在使用[Python slice notation](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295)�创建数组�填充`X`和`y`。
 
 2. 接下�，开始回归模型构建例程：
 
@@ -170,7 +170,7 @@ lin_pumpkins
    Model Accuracy:  0.3315342327998987
    ```
 
-3. 您�以将过程中绘制的线��视化：
+3. 你�以将过程中绘制的线��视化：
 
    ```python
    plt.scatter(X_test, y_test,  color='black')
@@ -205,7 +205,7 @@ lin_pumpkins
 
 ✅ 这里有�以使用多项�回归数�的[更多示例](https://online.stat.psu.edu/stat501/lesson/9/9.8)
 
-�看一下上图中��与价格之间的关系。这个散点图看起�是�应该用一�直线�分�？也许�是。在这�情况下，您�以�试多项�回归。
+�看一下上图中��与价格之间的关系。这个散点图看起�是�应该用一�直线�分�？也许�是。在这�情况下，你�以�试多项�回归。
 
 ✅ 多项�是�能由一个或多个��和系数组�的数学表达�
 
@@ -230,7 +230,7 @@ lin_pumpkins
    这段代�创建了一个热图:
    ![显示数�相关性的热图](../images/heatmap.png)
 
-查看此图表，您�以直观地看到Package和Price之间的良好相关性。所以你应该能够创建一个比上一个更好的模型。
+查看此图表，你�以直观地看到Package和Price之间的良好相关性。所以你应该能够创建一个比上一个更好的模型。
 
 ### 创建管�
 
@@ -260,7 +260,7 @@ Scikit-learn包�一个用于构建多项�回归模型的有用API - `make_pi
 
 ### 创建�列
 
-此时，您需�使用_排�好的_数�创建一个新的dataframe ，以便管��以创建�列。
+此时，你需�使用_排�好的_数�创建一个新的dataframe ，以便管��以创建�列。
 
 添加以下代�：
 
@@ -276,11 +276,11 @@ Scikit-learn包�一个用于构建多项�回归模型的有用API - `make_pi
    plt.show()
    ```
 
-您通过调用`pd.DataFrame`创建了一个新的dataframe。然�通过调用`sort_values()`对值进行排�。最�你创建了一个多项�图：
+你通过调用`pd.DataFrame`创建了一个新的dataframe。然�通过调用`sort_values()`对值进行排�。最�你创建了一个多项�图：
 
 ![显示包装与价格关系的多项�图](../images/polynomial.png)
 
-您�以看到更适�您的数�的曲线。
+你�以看到更适�你的数�的曲线。
 
 让我们检查模型的准确性：
 
@@ -314,7 +314,7 @@ Scikit-learn包�一个用于构建多项�回归模型的有用API - `make_pi
 
 �照图�，这确实有���而且，如果这是一个比�一个更好的模型，看�样的数�，你需�为这些更昂贵的�瓜�好预算�
 
-� 干得�错�您在一节课中创建了两个回归模型。在回归的最�一节中，您将了解逻辑回归以确定类别。 
+� 干得�错�你在一节课中创建了两个回归模型。在回归的最�一节中，你将了解逻辑回归以确定类别。 
 
 ---
 ## 🚀挑战

From 0a7758e7021c52127eb26ae244b603b02174aa30 Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Sat, 10 Jul 2021 19:53:08 +0700
Subject: [PATCH 101/368] Add Indonesian translation for 1-3 assignment

---
 .../3-fairness/translations/assignment.id.md          | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/3-fairness/translations/assignment.id.md

diff --git a/1-Introduction/3-fairness/translations/assignment.id.md b/1-Introduction/3-fairness/translations/assignment.id.md
new file mode 100644
index 0000000000..90389a14de
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.id.md
@@ -0,0 +1,11 @@
+# Jelajahi Fairlearn
+
+## Instruksi
+
+Dalam pelajaran ini kamu telah belajar mengenai Fairlearn, sebuah "proyek *open-source* berbasis komunitas untuk membantu para *data scientist* meningkatkan keadilan dari sistem AI." Untuk penugasan kali ini, jelajahi salah satu dari [notebook](https://fairlearn.org/v0.6.2/auto_examples/index.html) yang disediakan Fairlearn dan laporkan penemuanmu dalam sebuah paper atau presentasi.
+
+## Rubrik
+
+| Kriteria | Sangat Bagus | Cukup | Perlu Peningkatan |
+| -------- | --------- | -------- | ----------------- |
+|          |  Sebuah *paper* atau presentasi powerpoint yang membahas sistem Fairlearn, *notebook* yang dijalankan, dan kesimpulan yang diambil dari hasil menjalankannya         |   Sebuah paper yang dipresentasikan tanpa kesimpulan       |  Tidak ada paper yang dipresentasikan                 |

From 28b1b7534cc79fcd973d7e89fe41d067c21f41ab Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 10 Jul 2021 13:12:11 -0400
Subject: [PATCH 102/368] Delete lesson-card.md

---
 .github/ISSUE_TEMPLATE/lesson-card.md | 15 ---------------
 1 file changed, 15 deletions(-)
 delete mode 100644 .github/ISSUE_TEMPLATE/lesson-card.md

diff --git a/.github/ISSUE_TEMPLATE/lesson-card.md b/.github/ISSUE_TEMPLATE/lesson-card.md
deleted file mode 100644
index 71ebeba606..0000000000
--- a/.github/ISSUE_TEMPLATE/lesson-card.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-name: Lesson Card
-about: Add a Lesson Card
-title: "[LESSON]"
-labels: ''
-assignees: ''
-
----
-
-- [ ] quiz 1
-- [ ] written content
-- [ ] quiz 2
-- [ ] challenge
-- [ ] extra reading
-- [ ] assignment

From a5f032abc0e7c92ffc5cbc78a47c7db8ed1a4a98 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 10 Jul 2021 13:12:18 -0400
Subject: [PATCH 103/368] Delete lesson_elements.md

---
 .github/ISSUE_TEMPLATE/lesson_elements.md | 6 ------
 1 file changed, 6 deletions(-)
 delete mode 100644 .github/ISSUE_TEMPLATE/lesson_elements.md

diff --git a/.github/ISSUE_TEMPLATE/lesson_elements.md b/.github/ISSUE_TEMPLATE/lesson_elements.md
deleted file mode 100644
index eef1892228..0000000000
--- a/.github/ISSUE_TEMPLATE/lesson_elements.md
+++ /dev/null
@@ -1,6 +0,0 @@
-- [ ] quiz 1
-- [ ] written content
-- [ ] quiz 2
-- [ ] challenge
-- [ ] extra reading
-- [ ] assignment

From 7c3f8002a1173f0d0f87da12285781aa97bc2cc2 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 10 Jul 2021 21:58:16 -0400
Subject: [PATCH 104/368] moving Portuguese base translation to proper folder

---
 README.pt.md => translations/README.pt.md | 58 +++++++++++------------
 1 file changed, 29 insertions(+), 29 deletions(-)
 rename README.pt.md => translations/README.pt.md (55%)

diff --git a/README.pt.md b/translations/README.pt.md
similarity index 55%
rename from README.pt.md
rename to translations/README.pt.md
index 460d8f05a4..fe759ef2fb 100644
--- a/README.pt.md
+++ b/translations/README.pt.md
@@ -38,7 +38,7 @@ Viaja connosco ao redor do mundo enquanto aplicamos estas técnicas clássicas a
 
 > Para um estudo mais aprofundado, recomendamos que sigas estes [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) módulos e percursos de aprendizagem.
 
-**Professores**, [incluímos algumas sugestões](for-teachers.md) em como usar este curso.
+**Professores**, [incluímos algumas sugestões](../for-teachers.md) em como usar este curso.
 
 ---
 
@@ -56,7 +56,7 @@ Escolhemos dois princípios pedagógicos ao construir este curso: garantindo que
 
 Ao garantir que o conteúdo esteja alinhado com os projetos, o processo torna-se mais envolvente para os estudantes e a retenção dos conceitos é aumentada. Além disso, um teste de baixo risco antes da aula define a intenção do estudante de aprender um tópico, enquanto que um segundo teste após a aula garante maior retenção. Este curso foi projetado para ser flexível e divertido e pode ser realizado no seu todo ou parcialmente. Os projetos começam pequenos e tornam-se cada vez mais complexos no final do ciclo de 12 semanas. Este currículo também inclui um pós-escrito sobre aplicações reais de ML, que pode ser usado como crédito extra ou como base para discussão.
 
-> Encontra o nosso [Código de Conduta](CODE_OF_CONDUCT.md), diretrizes de [Contribuição](CONTRIBUTING.md), e [Tradução](TRANSLATIONS.md). Agradecemos o teu feedback construtivo!
+> Encontra o nosso [Código de Conduta](../CODE_OF_CONDUCT.md), diretrizes de [Contribuição](../CONTRIBUTING.md), e [Tradução](../TRANSLATIONS.md). Agradecemos o teu feedback construtivo!
 
 ## Cada aula inclui:
 
@@ -75,31 +75,31 @@ Ao garantir que o conteúdo esteja alinhado com os projetos, o processo torna-se
 
 | Número de aula |                                Tópico                                 |                 Agrupamento de Aulas                  | Objetivos de aprendizagem                                                                                                            |                   Aula vinculada                    |    Autor     |
 | :------------: | :-------------------------------------------------------------------: | :---------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------: | :----------: |
-|       01       |                    Introdução ao machine learning                     |        [Introdução](1-Introduction/README.md)         | Aprender os conceitos básicos atrás do machine learning                                                                              |   [aula](1-Introduction/1-intro-to-ML/README.md)    |   Muhammad   |
-|       02       |                    A História de machine learning                     |        [Introdução](1-Introduction/README.md)         | Aprender a história subjacente nesta área                                                                                            |  [aula](1-Introduction/2-history-of-ML/README.md)   |  Jen e Amy   |
-|       03       |                      Justiça e machine learning                       |        [Introdução](1-Introduction/README.md)         | Quais são as questões filosóficas importantes sobre justiça que os estudantes devem considerar ao construir e aplicar modelos de ML? |     [aula](1-Introduction/3-fairness/README.md)     |    Tomomi    |
-|       04       |                    Técnicas para machine learning                     |        [Introdução](1-Introduction/README.md)         | Que técnicas os investigadores de ML usam para construir modelos de ML?                                                              | [aula](1-Introduction/4-techniques-of-ML/README.md) | Chris e Jen  |
-|       05       |                        Introdução à regressão                         |          [Regressão](2-Regression/README.md)          | Começando a usar Python e Scikit-learn para modelos de regressão                                                                     |       [aula](2-Regression/1-Tools/README.md)        |     Jen      |
-|       06       |                Preços das abóboras norte americanas 🎃                |          [Regressão](2-Regression/README.md)          | Visualizar e limpar os dados em preparação para o ML                                                                                 |        [aula](2-Regression/2-Data/README.md)        |     Jen      |
-|       07       |                Preços das abóboras norte americanas 🎃                |          [Regressão](2-Regression/README.md)          | Construir modelos de regressão linear e polinomial                                                                                   |       [aula](2-Regression/3-Linear/README.md)       |     Jen      |
-|       08       |                Preços das abóboras norte americanas 🎃                |          [Regressão](2-Regression/README.md)          | Construir um modelo de regressão logística                                                                                           |      [aula](2-Regression/4-Logistic/README.md)      |     Jen      |
-|       09       |                            Uma Web App 🔌                             |            [Web App](3-Web-App/README.md)             | Criar uma web app para usar o teu modelo treinado                                                                                    |        [aula](3-Web-App/1-Web-App/README.md)        |     Jen      |
-|       10       |                      Introdução à classificação                       |      [Classificação](4-Classification/README.md)      | Limpar, preparar e visualizar os dados; introdução à classificação                                                                   |  [aula](4-Classification/1-Introduction/README.md)  | Jen e Cassie |
-|       11       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](4-Classification/README.md)      | Introdução aos classificadores                                                                                                       | [aula](4-Classification/2-Classifiers-1/README.md)  | Jen e Cassie |
-|       12       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](4-Classification/README.md)      | Mais classificadores                                                                                                                 | [aula](4-Classification/3-Classifiers-2/README.md)  | Jen e Cassie |
-|       13       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](4-Classification/README.md)      | Criar uma web app de recomendação usando o teu modelo                                                                                |    [aula](4-Classification/4-Applied/README.md)     |     Jen      |
-|       14       |                       Introdução ao clustering                        |         [Clustering](5-Clustering/README.md)          | Limpar, preparar e visualizar os dados; Introdução ao clustering                                                                     |     [aula](5-Clustering/1-Visualize/README.md)      |     Jen      |
-|       15       |               Explorando Gostos Musicais Nigerianos 🎧                |         [Clustering](5-Clustering/README.md)          | Explorar o método de agrupamento K-Means                                                                                             |      [aula](5-Clustering/2-K-Means/README.md)       |     Jen      |
-|       16       |           Introdução processamento de linguagem natural ☕�           | [Processamento de linguagem natural](6-NLP/README.md) | Aprender o básico sobre NLP construindo um bot simples                                                                               |    [aula](6-NLP/1-Introduction-to-NLP/README.md)    |   Stephen    |
-|       17       |                        Tarefas NLP comuns ☕�                         | [Processamento de linguagem natural](6-NLP/README.md) | Aprofundar o conhecimento de NLP percebendo tarefas comuns exigidas ao lidar com estruturas de linguagem                             |           [aula](6-NLP/2-Tasks/README.md)           |   Stephen    |
-|       18       |                  Tradução e análise de sentimento ♥�                  | [Processamento de linguagem natural](6-NLP/README.md) | Tradução e análise de sentimento com Jane Austen                                                                                     |   [aula](6-NLP/3-Translation-Sentiment/README.md)   |   Stephen    |
-|       19       |                    Hotéis romanticos da Europa ♥�                     | [Processamento de linguagem natural](6-NLP/README.md) | Análise de sentimento com avaliações de hotéis, 1                                                                                    |      [aula](6-NLP/4-Hotel-Reviews-1/README.md)      |   Stephen    |
-|       20       |                    Hotéis romanticos da Europa ♥�                     | [Processamento de linguagem natural](6-NLP/README.md) | Análise de sentimento com avaliações de hotéis, 2                                                                                    |      [aula](6-NLP/5-Hotel-Reviews-2/README.md)      |   Stephen    |
-|       21       |                Introdução previsão de séries temporais                |      [Séries temporais](7-TimeSeries/README.md)       | Introdução a previsão de séries temporais                                                                                            |    [aula](7-TimeSeries/1-Introduction/README.md)    |  Francesca   |
-|       22       | ⚡� Uso de Energia Mundial ⚡� - previsão de série temporal com ARIMA |      [Séries temporais](7-TimeSeries/README.md)       | Previsão de séries temporais com ARIMA                                                                                               |       [aula](7-TimeSeries/2-ARIMA/README.md)        |  Francesca   |
-|       23       |                 Introdução à aprendizagem por reforço                 | [Aprendizagem por reforço](8-Reinforcement/README.md) | Introdução à aprendizagem por reforço com Q-Learning                                                                                 |    [aula](8-Reinforcement/1-QLearning/README.md)    |    Dmitry    |
-|       24       |                  Ajuda o Pedro a escapar do lobo! �                  | [Aprendizagem por reforço](8-Reinforcement/README.md) | Ginásio de aprendizagem por reforço                                                                                                  |       [aula](8-Reinforcement/2-Gym/README.md)       |    Dmitry    |
-|   Postscript   |                   Cenários reais de ML e aplicações                   |       [ML na vida real](9-Real-World/README.md)       | Aplicações interessantes e reveladoras do mundo real do ML clássico                                                                  |    [aula](9-Real-World/1-Applications/README.md)    |    Equipa    |
+|       01       |                    Introdução ao machine learning                     |        [Introdução](../1-Introduction/README.md)         | Aprender os conceitos básicos atrás do machine learning                                                                              |   [aula](../1-Introduction/1-intro-to-ML/README.md)    |   Muhammad   |
+|       02       |                    A História de machine learning                     |        [Introdução](../1-Introduction/README.md)         | Aprender a história subjacente nesta área                                                                                            |  [aula](../1-Introduction/2-history-of-ML/README.md)   |  Jen e Amy   |
+|       03       |                      Justiça e machine learning                       |        [Introdução](../1-Introduction/README.md)         | Quais são as questões filosóficas importantes sobre justiça que os estudantes devem considerar ao construir e aplicar modelos de ML? |     [aula](../1-Introduction/3-fairness/README.md)     |    Tomomi    |
+|       04       |                    Técnicas para machine learning                     |        [Introdução](../1-Introduction/README.md)         | Que técnicas os investigadores de ML usam para construir modelos de ML?                                                              | [aula](../1-Introduction/4-techniques-of-ML/README.md) | Chris e Jen  |
+|       05       |                        Introdução à regressão                         |          [Regressão](../2-Regression/README.md)          | Começando a usar Python e Scikit-learn para modelos de regressão                                                                     |       [aula](../2-Regression/1-Tools/README.md)        |     Jen      |
+|       06       |                Preços das abóboras norte americanas 🎃                |          [Regressão](../2-Regression/README.md)          | Visualizar e limpar os dados em preparação para o ML                                                                                 |        [aula](../2-Regression/2-Data/README.md)        |     Jen      |
+|       07       |                Preços das abóboras norte americanas 🎃                |          [Regressão](../2-Regression/README.md)          | Construir modelos de regressão linear e polinomial                                                                                   |       [aula](../2-Regression/3-Linear/README.md)       |     Jen      |
+|       08       |                Preços das abóboras norte americanas 🎃                |          [Regressão](../2-Regression/README.md)          | Construir um modelo de regressão logística                                                                                           |      [aula](../2-Regression/4-Logistic/README.md)      |     Jen      |
+|       09       |                            Uma Web App 🔌                             |            [Web App](../3-Web-App/README.md)             | Criar uma web app para usar o teu modelo treinado                                                                                    |        [aula](../3-Web-App/1-Web-App/README.md)        |     Jen      |
+|       10       |                      Introdução à classificação                       |      [Classificação](../4-Classification/README.md)      | Limpar, preparar e visualizar os dados; introdução à classificação                                                                   |  [aula](../4-Classification/1-Introduction/README.md)  | Jen e Cassie |
+|       11       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](../4-Classification/README.md)      | Introdução aos classificadores                                                                                                       | [aula](../4-Classification/2-Classifiers-1/README.md)  | Jen e Cassie |
+|       12       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](../4-Classification/README.md)      | Mais classificadores                                                                                                                 | [aula](../4-Classification/3-Classifiers-2/README.md)  | Jen e Cassie |
+|       13       |              Deliciosas cozinhas asiáticas e indianas �              |      [Classificação](../4-Classification/README.md)      | Criar uma web app de recomendação usando o teu modelo                                                                                |    [aula](../4-Classification/4-Applied/README.md)     |     Jen      |
+|       14       |                       Introdução ao clustering                        |         [Clustering](../5-Clustering/README.md)          | Limpar, preparar e visualizar os dados; Introdução ao clustering                                                                     |     [aula](../5-Clustering/1-Visualize/README.md)      |     Jen      |
+|       15       |               Explorando Gostos Musicais Nigerianos 🎧                |         [Clustering](../5-Clustering/README.md)          | Explorar o método de agrupamento K-Means                                                                                             |      [aula](../5-Clustering/2-K-Means/README.md)       |     Jen      |
+|       16       |           Introdução processamento de linguagem natural ☕�           | [Processamento de linguagem natural](../6-NLP/README.md) | Aprender o básico sobre NLP construindo um bot simples                                                                               |    [aula](../6-NLP/1-Introduction-to-NLP/README.md)    |   Stephen    |
+|       17       |                        Tarefas NLP comuns ☕�                         | [Processamento de linguagem natural](../6-NLP/README.md) | Aprofundar o conhecimento de NLP percebendo tarefas comuns exigidas ao lidar com estruturas de linguagem                             |           [aula](../6-NLP/2-Tasks/README.md)           |   Stephen    |
+|       18       |                  Tradução e análise de sentimento ♥�                  | [Processamento de linguagem natural](../6-NLP/README.md) | Tradução e análise de sentimento com Jane Austen                                                                                     |   [aula](../6-NLP/3-Translation-Sentiment/README.md)   |   Stephen    |
+|       19       |                    Hotéis romanticos da Europa ♥�                     | [Processamento de linguagem natural](../6-NLP/README.md) | Análise de sentimento com avaliações de hotéis, 1                                                                                    |      [aula](../6-NLP/4-Hotel-Reviews-1/README.md)      |   Stephen    |
+|       20       |                    Hotéis romanticos da Europa ♥�                     | [Processamento de linguagem natural](../6-NLP/README.md) | Análise de sentimento com avaliações de hotéis, 2                                                                                    |      [aula](../6-NLP/5-Hotel-Reviews-2/README.md)      |   Stephen    |
+|       21       |                Introdução previsão de séries temporais                |      [Séries temporais](../7-TimeSeries/README.md)       | Introdução a previsão de séries temporais                                                                                            |    [aula](../7-TimeSeries/1-Introduction/README.md)    |  Francesca   |
+|       22       | ⚡� Uso de Energia Mundial ⚡� - previsão de série temporal com ARIMA |      [Séries temporais](../7-TimeSeries/README.md)       | Previsão de séries temporais com ARIMA                                                                                               |       [aula](../7-TimeSeries/2-ARIMA/README.md)        |  Francesca   |
+|       23       |                 Introdução à aprendizagem por reforço                 | [Aprendizagem por reforço](../8-Reinforcement/README.md) | Introdução à aprendizagem por reforço com Q-Learning                                                                                 |    [aula](../8-Reinforcement/1-QLearning/README.md)    |    Dmitry    |
+|       24       |                  Ajuda o Pedro a escapar do lobo! �                  | [Aprendizagem por reforço](../8-Reinforcement/README.md) | Ginásio de aprendizagem por reforço                                                                                                  |       [aula](../8-Reinforcement/2-Gym/README.md)       |    Dmitry    |
+|   Postscript   |                   Cenários reais de ML e aplicações                   |       [ML na vida real](../9-Real-World/README.md)       | Aplicações interessantes e reveladoras do mundo real do ML clássico                                                                  |    [aula](../9-Real-World/1-Applications/README.md)    |    Equipa    |
 
 ## Acesso Offline
 
@@ -107,11 +107,11 @@ Podes usar este documento offline usando o [Docsify](https://docsify.js.org/#/).
 
 ## PDFs
 
-Encontras um pdf do curso com links [aqui](pdf/readme.pdf)
+Encontras um pdf do curso com links [aqui](../pdf/readme.pdf)
 
 ## Help Wanted!
 
-Gostarias de contribuir para uma tradução? Por favor lê as nossas [diretrizes de tradução](TRANSLATIONS.md) e adiciona o teu input [aqui](https://github.com/microsoft/ML-For-Beginners/issues/71)
+Gostarias de contribuir para uma tradução? Por favor lê as nossas [diretrizes de tradução](../TRANSLATIONS.md) e adiciona o teu input [aqui](https://github.com/microsoft/ML-For-Beginners/issues/71)
 
 ## Outros Cursos
 

From cc77e973a6378e3f00926d065bd6ac92ca737176 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 10 Jul 2021 22:00:24 -0400
Subject: [PATCH 105/368] promo video image fix

---
 translations/README.pt.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/translations/README.pt.md b/translations/README.pt.md
index fe759ef2fb..6985cbf430 100644
--- a/translations/README.pt.md
+++ b/translations/README.pt.md
@@ -44,7 +44,7 @@ Viaja connosco ao redor do mundo enquanto aplicamos estas técnicas clássicas a
 
 ## Conhece a equipa
 
-[![Promo video](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU 'Promo video')
+[![Promo video](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
 
 > 🎥 Clica na imagem acima para um vídeo sobre o projeto e as pessoas que o criaram!
 

From 85b2d840ac2485e7fdf8466649b7322f6f42963c Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 10 Jul 2021 22:02:26 -0400
Subject: [PATCH 106/368] Create translation_tasks.md

---
 .github/ISSUE_TEMPLATE/translation_tasks.md | 61 +++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/translation_tasks.md

diff --git a/.github/ISSUE_TEMPLATE/translation_tasks.md b/.github/ISSUE_TEMPLATE/translation_tasks.md
new file mode 100644
index 0000000000..e349d8b4f5
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/translation_tasks.md
@@ -0,0 +1,61 @@
+- [ ] Base README.md
+- [ ] Quizzes
+- [ ] Introduction base README
+   - [ ] Intro to ML README
+   - [ ] Intro to ML assignment
+   - [ ] History of ML README
+   - [ ] History of ML assignment
+   - [ ] Fairness README
+   - [ ] Fairness assignment
+   - [ ] Techniques of ML README
+   - [ ] Techniques of ML assignment
+- [ ] Regression base README
+   - [ ] Tools README
+   - [ ] Tools assignment
+   - [ ] Data README
+   - [ ] Data assignment
+   - [ ] Linear README
+   - [ ] Linear assignment
+   - [ ] Logistic README
+   - [ ] Logistic assignment
+- [ ] Web app base README
+   - [ ] Web app README
+   - [ ] Web app assignment
+- [ ] Classification base README
+   - [ ] Intro README
+   - [ ] Intro assignment
+   - [ ] Classifiers 1 README
+   - [ ] Classifiers 1  assignment
+   - [ ] Classifiers 2  README
+   - [ ] Classifiers 2  assignment
+   - [ ] Applied README
+   - [ ] Applied assignment
+- [ ] Clustering base README
+   - [ ] Visualize README
+   - [ ] Visualize assignment
+   - [ ] K-means README
+   - [ ] K-means assignment
+- [ ] NLP base README
+   - [ ] Intro README
+   - [ ] Intro assignment
+   - [ ] Tasks README
+   - [ ] Tasks  assignment
+   - [ ] Translation README
+   - [ ] Translation  assignment
+   - [ ] Reviews 1 README
+   - [ ] Reviews 1 assignment
+   - [ ] Reviews 2 README
+   - [ ] Reviews 2 assignment
+- [ ] Time Series base README
+   - [ ] Intro README
+   - [ ] Intro assignment
+   - [ ] ARIMA README
+   - [ ] ARIMA  assignment
+- [ ] Reinforcement base README
+   - [ ] QLearning README
+   - [ ] QLearning assignment
+   - [ ] gym README
+   - [ ] gym  assignment
+- [ ] Real World base README
+   - [ ] Real World README
+   - [ ] Real World assignment

From 7dce88fb2d41859fd5e2d3b2b34ca24f9c05217b Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 10 Jul 2021 22:09:23 -0400
Subject: [PATCH 107/368] Delete translation_tasks.md

---
 .github/ISSUE_TEMPLATE/translation_tasks.md | 61 ---------------------
 1 file changed, 61 deletions(-)
 delete mode 100644 .github/ISSUE_TEMPLATE/translation_tasks.md

diff --git a/.github/ISSUE_TEMPLATE/translation_tasks.md b/.github/ISSUE_TEMPLATE/translation_tasks.md
deleted file mode 100644
index e349d8b4f5..0000000000
--- a/.github/ISSUE_TEMPLATE/translation_tasks.md
+++ /dev/null
@@ -1,61 +0,0 @@
-- [ ] Base README.md
-- [ ] Quizzes
-- [ ] Introduction base README
-   - [ ] Intro to ML README
-   - [ ] Intro to ML assignment
-   - [ ] History of ML README
-   - [ ] History of ML assignment
-   - [ ] Fairness README
-   - [ ] Fairness assignment
-   - [ ] Techniques of ML README
-   - [ ] Techniques of ML assignment
-- [ ] Regression base README
-   - [ ] Tools README
-   - [ ] Tools assignment
-   - [ ] Data README
-   - [ ] Data assignment
-   - [ ] Linear README
-   - [ ] Linear assignment
-   - [ ] Logistic README
-   - [ ] Logistic assignment
-- [ ] Web app base README
-   - [ ] Web app README
-   - [ ] Web app assignment
-- [ ] Classification base README
-   - [ ] Intro README
-   - [ ] Intro assignment
-   - [ ] Classifiers 1 README
-   - [ ] Classifiers 1  assignment
-   - [ ] Classifiers 2  README
-   - [ ] Classifiers 2  assignment
-   - [ ] Applied README
-   - [ ] Applied assignment
-- [ ] Clustering base README
-   - [ ] Visualize README
-   - [ ] Visualize assignment
-   - [ ] K-means README
-   - [ ] K-means assignment
-- [ ] NLP base README
-   - [ ] Intro README
-   - [ ] Intro assignment
-   - [ ] Tasks README
-   - [ ] Tasks  assignment
-   - [ ] Translation README
-   - [ ] Translation  assignment
-   - [ ] Reviews 1 README
-   - [ ] Reviews 1 assignment
-   - [ ] Reviews 2 README
-   - [ ] Reviews 2 assignment
-- [ ] Time Series base README
-   - [ ] Intro README
-   - [ ] Intro assignment
-   - [ ] ARIMA README
-   - [ ] ARIMA  assignment
-- [ ] Reinforcement base README
-   - [ ] QLearning README
-   - [ ] QLearning assignment
-   - [ ] gym README
-   - [ ] gym  assignment
-- [ ] Real World base README
-   - [ ] Real World README
-   - [ ] Real World assignment

From 3a448bbb498a89140b90e8f812890fe9cd5be01e Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 10 Jul 2021 22:11:09 -0400
Subject: [PATCH 108/368] Update issue templates

---
 .../ISSUE_TEMPLATE/translations-checklist.md  | 70 +++++++++++++++++++
 1 file changed, 70 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/translations-checklist.md

diff --git a/.github/ISSUE_TEMPLATE/translations-checklist.md b/.github/ISSUE_TEMPLATE/translations-checklist.md
new file mode 100644
index 0000000000..4cbf577418
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/translations-checklist.md
@@ -0,0 +1,70 @@
+---
+name: Translations Checklist
+about: These are all the files that need to be translated
+title: "[TRANSLATIONS]"
+labels: translations
+assignees: ''
+
+---
+
+- [ ] Base README.md
+- [ ] Quizzes
+- [ ] Introduction base README
+   - [ ] Intro to ML README
+   - [ ] Intro to ML assignment
+   - [ ] History of ML README
+   - [ ] History of ML assignment
+   - [ ] Fairness README
+   - [ ] Fairness assignment
+   - [ ] Techniques of ML README
+   - [ ] Techniques of ML assignment
+- [ ] Regression base README
+   - [ ] Tools README
+   - [ ] Tools assignment
+   - [ ] Data README
+   - [ ] Data assignment
+   - [ ] Linear README
+   - [ ] Linear assignment
+   - [ ] Logistic README
+   - [ ] Logistic assignment
+- [ ] Web app base README
+   - [ ] Web app README
+   - [ ] Web app assignment
+- [ ] Classification base README
+   - [ ] Intro README
+   - [ ] Intro assignment
+   - [ ] Classifiers 1 README
+   - [ ] Classifiers 1  assignment
+   - [ ] Classifiers 2  README
+   - [ ] Classifiers 2  assignment
+   - [ ] Applied README
+   - [ ] Applied assignment
+- [ ] Clustering base README
+   - [ ] Visualize README
+   - [ ] Visualize assignment
+   - [ ] K-means README
+   - [ ] K-means assignment
+- [ ] NLP base README
+   - [ ] Intro README
+   - [ ] Intro assignment
+   - [ ] Tasks README
+   - [ ] Tasks  assignment
+   - [ ] Translation README
+   - [ ] Translation  assignment
+   - [ ] Reviews 1 README
+   - [ ] Reviews 1 assignment
+   - [ ] Reviews 2 README
+   - [ ] Reviews 2 assignment
+- [ ] Time Series base README
+   - [ ] Intro README
+   - [ ] Intro assignment
+   - [ ] ARIMA README
+   - [ ] ARIMA  assignment
+- [ ] Reinforcement base README
+   - [ ] QLearning README
+   - [ ] QLearning assignment
+   - [ ] gym README
+   - [ ] gym  assignment
+- [ ] Real World base README
+   - [ ] Real World README
+   - [ ] Real World assignment

From 96ce28c03a5d8304a841c1c3b29df02e412ed9e6 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 10 Jul 2021 22:28:09 -0400
Subject: [PATCH 109/368] Update README.ru.md

path edit for Russian translation images
---
 8-Reinforcement/translations/README.ru.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/8-Reinforcement/translations/README.ru.md b/8-Reinforcement/translations/README.ru.md
index 6d6217500b..939554b445 100644
--- a/8-Reinforcement/translations/README.ru.md
+++ b/8-Reinforcement/translations/README.ru.md
@@ -3,7 +3,7 @@ Reinforcement learning (обучение � подкреплением), RL, р
 
 Пред�тавьте, что у ва� е�ть �моделированна� �реда, така� как фондовый рынок. Что произойдет, е�ли вы введете определенное правило. Имеет ли �то положительный или отрицательный �ффект? Е�ли прои�ходит что-то негативное, вам нужно прин�ть �то _негативное подкрепление_, извлечь из него урок и изменить кур�. Е�ли �то положительный результат, вам нужно и�пользовать �то _положительное подкрепление_.
 
-![peter and the wolf](images/peter.png)
+![peter and the wolf](../images/peter.png)
 
 > Петьке и его друзь�м нужно �па�ти�ь от голодного волка! �втор изображени� [Jen Looper](https://twitter.com/jenlooper)
 
@@ -22,8 +22,8 @@ Reinforcement learning (обучение � подкреплением), RL, р
 
 В предыдущих разделах вы видели два примера проблем машинного обучени�:
 
-- **Supervised**, где у на� е�ть наборы данных, которые предлагают примеры решений проблемы, которую мы хотим решить. [Кла��ификаци�](../4-Classification/README.md) и [регре��и�] (../ 2-Регре��и� / README.md) �вл�ют�� контролируемыми учебными задачами.
-- **Unsupervised**, в котором у на� нет помеченных данных обучени�. О�новным примером unsupervised learning �вл�ет�� [Кла�теризаци�](../5-Clustering/README.md).
+- **Supervised**, где у на� е�ть наборы данных, которые предлагают примеры решений проблемы, которую мы хотим решить. [Кла��ификаци�](../../4-Classification/README.md) и [регре��и�] (../ 2-Регре��и� / README.md) �вл�ют�� контролируемыми учебными задачами.
+- **Unsupervised**, в котором у на� нет помеченных данных обучени�. О�новным примером unsupervised learning �вл�ет�� [Кла�теризаци�](../../5-Clustering/README.md).
 
 В �том разделе мы познакомим ва� � новым типом задач обучени�, которые не требуют маркированных данных обучени�. Е�ть не�колько типов таких проблем:
 
@@ -44,9 +44,9 @@ Reinforcement learning (обучение � подкреплением), RL, р
 
 ## Уроки
 
-1. [Введение в обучение � подкреплением и Q-Learning](1-QLearning/README.md)
-2. [И�пользование тренажерного зала](2-Gym/README.md)
+1. [Введение в обучение � подкреплением и Q-Learning](../1-QLearning/README.md)
+2. [И�пользование тренажерного зала](../2-Gym/README.md)
 
 ## Благодарно�ти
 
-«Введение в обучение � подкреплением» напи�ано � ♥ �[Дмитрием Сошниковым](http://soshnikov.com)
\ No newline at end of file
+«Введение в обучение � подкреплением» напи�ано � ♥ �[Дмитрием Сошниковым](http://soshnikov.com)

From 371cbdc10f3524572b702ebb6bcc4cbf9e02fdc1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Emre=20=C3=87a=C4=9Fatay=20K=C3=B6se?=
 <emrecgty@hotmail.com>
Date: Sun, 11 Jul 2021 21:15:39 +0300
Subject: [PATCH 110/368] add: Turkish quiz translations

---
 quiz-app/src/App.vue                      |    1 +
 quiz-app/src/assets/translations/index.js |    2 +
 quiz-app/src/assets/translations/tr.json  | 2815 +++++++++++++++++++++
 3 files changed, 2818 insertions(+)
 create mode 100644 quiz-app/src/assets/translations/tr.json

diff --git a/quiz-app/src/App.vue b/quiz-app/src/App.vue
index ea282b440d..78482d496f 100644
--- a/quiz-app/src/App.vue
+++ b/quiz-app/src/App.vue
@@ -5,6 +5,7 @@
       <label for="locale">locale</label>
       <select v-model="locale">
         <option>en</option>
+        <option>tr</option>
       </select>
     </nav>
     <div id="app">
diff --git a/quiz-app/src/assets/translations/index.js b/quiz-app/src/assets/translations/index.js
index ffcbad948d..e4abf6eb96 100644
--- a/quiz-app/src/assets/translations/index.js
+++ b/quiz-app/src/assets/translations/index.js
@@ -1,10 +1,12 @@
 // index.js
 import en from './en.json';
+import tr from './tr.json';
 
 //export const defaultLocale = 'en';
 
 const messages = {
 	en: en[0],
+	tr: tr[0],
 };
 
 export default messages;
diff --git a/quiz-app/src/assets/translations/tr.json b/quiz-app/src/assets/translations/tr.json
new file mode 100644
index 0000000000..c07894a0d9
--- /dev/null
+++ b/quiz-app/src/assets/translations/tr.json
@@ -0,0 +1,2815 @@
+[
+	{
+		"title": "Yeni Başlayanlar için Makine Öğrenimi: Kısa Sınavlar",
+		"complete": "Tebrikler, testi tamamladınız!",
+		"error": "Üzgünüz, tekrar deneyin",
+		"quizzes": [
+			{
+				"id": 1,
+				"title": "Makine Öğrenmesine Giriş: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Makine öğrenimi uygulamaları her yerdedir",
+						"answerOptions": [
+							{
+								"answerText": "DoÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Yanlış",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Klasik ML(Makine Öğrenimi) ile derin öğrenme arasındaki teknik fark nedir?",
+						"answerOptions": [
+							{
+								"answerText": "ilk olarak klasik makine öğrenimi icat edildi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "sinir ağlarının kullanımı",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "robotlarda derin öğrenme kullanılır",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bir işletme neden makine öğrenimi stratejilerini kullanmak isteyebilir?",
+						"answerOptions": [
+							{
+								"answerText": "çok boyutlu problemlerin çözümünü otomatikleştirmek için",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "müşteri türüne göre bir alışveriş deneyimini özelleştirmek için",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 2,
+				"title": "Makine Öğrenmesine Giriş: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Makine öğrenimi algoritmaları aşağıdakilerin hangisini simüle etmek içindir?",
+						"answerOptions": [
+							{
+								"answerText": "akıllı makineler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "insan beyni",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "orangutanlar",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Klasik ML tekniğine bir örnek nedir?",
+						"answerOptions": [
+							{
+								"answerText": "doÄŸal dil iÅŸleme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "derin öğrenme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Sinir Ağları",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Neden herkes makine öğreniminin temellerini öğrenmeli?",
+						"answerOptions": [
+							{
+								"answerText": "ML öğrenmek eğlencelidir ve herkes için erişilebilirdir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "ML stratejileri birçok endüstri ve alanda kullanılıyor",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 3,
+				"title": "Makine Öğreniminin Tarihçesi: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "'Yapay zeka' terimi yaklaşık olarak ne zaman ortaya çıktı?",
+						"answerOptions": [
+							{
+								"answerText": "1980'ler",
+								"isCorrect": "false"
+							},
+							{
+								"cevapText": "1950'ler",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "1930'lar",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Hangisi makine öğreniminin ilk öncülerinden biridir?",
+						"answerOptions": [
+							{
+								"answerText": "Alan Turing",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Bill Gates",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Robot Shakey",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "1970'lerde yapay zekâdaki ilerlemenin yavaşlamasının nedenlerinden biri nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Sınırlı işlem gücü",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Yeterli kalifiye mühendis olmaması",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Ülkeler arası çatışmalar",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 4,
+				"title": "Makine Öğreniminin Tarihçesi: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "'Düzensiz' bir yapay zeka sistemi örneği nedir?",
+						"answerOptions": [
+							{
+								"cevapText": "ELIZA",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "HACKML",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "SSYSTEM",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "'Altın Yıllar'da geliştirilen bir teknoloji örneği nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Dünya Blokları",
+								"isCorrect": "true"
+							},
+							{
+								"cevapText": "Jibo",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Robot köpekler",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Yapay zeka alanının yaratılması ve genişletilmesinde hangi olay temel oluşturmuştur?",
+						"answerOptions": [
+							{
+								"answerText": "Turing Testi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Dartmouth Yaz Araştırma Projesi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "AI Kışı",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 5,
+				"title": "Adillik ve Makine Öğrenimi: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Makine Öğreniminde adaletsizlik ne şekilde olabilir?",
+						"answerOptions": [
+							{
+								"answerText": "kasıtlı olarak",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "istemeden",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "ML'deki 'haksızlık' terimi şu anlama gelir:",
+						"answerOptions": [
+							{
+								"answerText": "bir grup insana zarar verilmesi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir kiÅŸiye zarar verilmesi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "insanların çoğuna zarar verilmesi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Hangisi beş ana zarar türüdür?",
+						"answerOptions": [
+							{
+								"answerText": "tahsis, hizmet kalitesi, klişeleştirme, aşağılama ve gereğinden fazla veya eksik temsil",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yerleştirme, hizmet kalitesi, klişeleştirme, aşağılama ve gereğinden fazla veya eksik temsil",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "tahsis, hizmet kalitesi, stereofoni, aşağılama ve fazla veya eksik temsil",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 6,
+				"title": "Adillik ve Makine Öğrenimi: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Bir modeldeki adaletsizlik ÅŸundan kaynaklanabilir",
+						"answerOptions": [
+							{
+								"answerText": "geçmiş verilere aşırı güven",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "geçmiş verilere yetersiz güven",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "geçmiş verilere çok yakın durmak",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Haksızlığı azaltmak için ne yapabilirsiniz?",
+						"answerOptions": [
+							{
+								"answerText": "zararları ve etkilenen grupları tanımlarım",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "adalet ölçütlerini tanımlarım",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin her ikisi de",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Fairlearn paketi hangisini yapabilir?",
+						"answerOptions": [
+							{
+								"answerText": "adalet ve performans ölçütlerini kullanarak birden çok modeli karşılaştırır",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "ihtiyaçlarınıza en uygun modeli seçer",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Neyin adil olup neyin olmadığına karar vermenize yardımcı olur",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 7,
+				"title": "Araçlar ve Teknikler: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Bir model oluştururken şunları yapmalısınız:",
+						"answerOptions": [
+							{
+								"answerText": "verilerinizi hazırlamalı, ardından modelinizi eğitmelisiniz",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir eğitim yöntemi seçip, ardından verilerinizi hazırlamalısınız",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "parametreleri ayarlamalı, ardından modelinizi eğitmelisiniz",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Verilerinizin ___ makine öğrenimi modelinizin kalitesini etkiler",
+						"answerOptions": [
+							{
+								"answerText": "miktarı",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "ÅŸekli",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Bir özellik değişkeni:",
+						"answerOptions": [
+							{
+								"answerText": "verilerinizin kalitesini gösteren bir değişkendir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "verilerinizin ölçülebilir bir özelliğini gösteren bir değişkendir",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "verilerinizin bir satırıdır",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 8,
+				"title": "Araçlar ve Teknikler: Ders Sonrası Test",
+				"quiz": [
+					{
+						"questionText": "Verilerinizi görselleştirmelisiniz çünkü",
+						"answerOptions": [
+							{
+								"answerText": "aykırı değerleri keşfedebilirsiniz",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "önyargı için potansiyel bir sebebi keşfedebilirsiniz",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bunların her ikisi",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Verilerinizi şu şekilde bölün:",
+						"answerOptions": [
+							{
+								"answerText": "eÄŸitim ve turing setleri",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "eÄŸitim ve test setleri",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "doÄŸrulama ve deÄŸerlendirme setleri",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Çeşitli ML kütüphanelerinde modelin eğitim sürecini başlatmak için yaygın bir komut şudur:",
+						"answerOptions": [
+							{
+								"answerText": "model.travel",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "model.train",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "model.fit",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 9,
+				"title": "Regresyona Giriş: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Bu değişkenlerden hangisi sayısal bir değişkendir?",
+						"answerOptions": [
+							{
+								"answerText": "Yükseklik",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Cinsiyet",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Saç Rengi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bu deÄŸiÅŸkenlerden hangisi kategorik bir deÄŸiÅŸkendir?",
+						"answerOptions": [
+							{
+								"answerText": "Kalp Atış Hızı",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Kan Tipi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Ağırlık",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bu problemlerden hangisi Regresyon analizine dayalı bir problemdir?",
+						"answerOptions": [
+							{
+								"answerText": "Öğrencinin final sınav notlarını tahmin etme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "KiÅŸinin kan grubunu tahmin etme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bir e-postanın spam (gereksiz) olup olmadığını tahmin etme",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 10,
+				"title": "Regresyona Giriş: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Makine Öğrenimi modelinizin eğitim doğruluğu %95 ve test doğruluğu %30 ise buna ne denir?",
+						"answerOptions": [
+							{
+								"answerText": "Aşırı öğrenme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Eksik öğrenme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Çift öğrenme",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bir dizi özellikten önemli özellikleri belirleme sürecine ne denir?",
+						"answerOptions": [
+							{
+								"answerText": "Özellik Çıkarma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Özellik Boyutu Azaltma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Özellik Seçimi",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Scikit Learn kütüphanesinin 'train_test_split()' metodunu/fonksiyonunu kullanarak bir veri setini belirli bir oranda eğitim ve test veri setine bölme işlemine ne ad verilir?",
+						"answerOptions": [
+							{
+								"answerText": "Çapraz doğrulama",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Dışarıda bırakma (Hold-out) doğrulaması",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Birini dışarıda bırakma (Leave one out) doğrulaması",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 11,
+				"title": "Regresyon için Verileri Hazırlayın ve Görselleştirin: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Verilerin görselleştirilmesi için bu Python modüllerinden hangisi kullanılır?",
+						"answerOptions": [
+							{
+								"answerText": "Numpy",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Scikit-learn",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Matplotlib",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Veri setinizdeki veri noktalarının yayılımını veya diğer özelliklerini anlamak istiyorsanız yapmanız gereken nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Veri Görselleştirme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Veri Ön İşlemesi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Eğitim Test Bölümü",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bunlardan hangisi bir Makine Öğrenimi projesinde Veri Görselleştirme adımının bir parçasıdır?",
+						"answerOptions": [
+							{
+								"answerText": "Belirli bir Makine Öğrenimi algoritması kullanma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Farklı çizim yöntemlerini kullanarak verilerin görsel bir temsilini oluşturma",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Bir veri kümesinin değerlerini normalleştirme",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 12,
+				"title": "Regresyon için Verileri Hazırlayın ve Görselleştirin: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Verisetinizde kayıp verilerin varlığını kontrol etmek istiyorsanız, derste gördüklerinize göre aşağıdaki kod parçacıklarından hangisi doğrudur? Verisetinizin 'dataset' adlı bir değişkende Pandas Dataframe objesi olarak tutulduğunu varsayın.",
+						"answerOptions": [
+							{
+								"answerText": "dataset.isnull().sum()",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "findMissing(dataset)",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "sum(null(dataset))",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Veri kümenizden farklı veri noktası gruplarının yayılımını anlamak istediğinizde bu çizim yöntemlerinden hangisi yararlıdır?",
+						"answerOptions": [
+							{
+								"answerText": "Dağılım Grafiği",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Çizgi Grafiği",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Çubuk Grafiği",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Veri Görselleştirme size ne söyleyemez?",
+						"answerOptions": [
+							{
+								"answerText": "Veri noktaları arasındaki ilişkiler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Veri kümesinin toplandığı kaynak",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Veri kümesinde aykırı değerlerin varlığı",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 13,
+				"title": "Doğrusal ve Polinom Regresyon: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Matplotlib bir ",
+						"answerOptions": [
+							{
+								"answerText": "çizim kütüphanesidir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "veri görselleştirme kütüphanesidir",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "ödünç kitap kütüphanesidir",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Doğrusal Regresyon, değişkenler arasındaki ilişkileri çizmek için aşağıdakileri kullanır",
+						"answerOptions": [
+							{
+								"answerText": "düz bir çizgi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir daire",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "bir eÄŸri",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "İyi bir Doğrusal Regresyon modelinin ___ Korelasyon Katsayısı vardır",
+						"answerOptions": [
+							{
+								"answerText": "düşük",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yüksek",
+								"isCorrect": "true"
+							},
+							{
+								"cevapText": "düz",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 14,
+				"title": "Doğrusal ve Polinom Regresyon: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Verileriniz doÄŸrusal deÄŸilse, ___ tipi bir Regresyon deneyin",
+						"answerOptions": [
+							{
+								"answerText": "doÄŸrusal",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "küresel",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "polinom",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Bunların tümü Regresyon yöntemleridir",
+						"answerOptions": [
+							{
+								"answerText": "Falsestep, Ridge, Lasso ve Elasticnet",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Kademeli, Ridge, Lasso ve Elasticnet",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Kademeli, Ridge, Lariat ve Elasticnet",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "En Küçük Kareler Regresyonunda, regresyon çizgisini çevreleyen tüm veri noktalarının regresyon çizgisine olan mesafelerinin",
+						"answerOptions": [
+							{
+								"answerText": "kareleri alınır ve sonra çıkarılır",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "çarpımı kullanılır",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "kareleri alınır ve sonra toplanır",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 15,
+				"title": "Lojistik Regresyon: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Hangisini tahmin etmek için Lojistik Regresyon kullanılır",
+						"answerOptions": [
+							{
+								"answerText": "bir elmanın olgun olup olmadığı",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Bir ayda kaç bilet satılabileceği",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yarın 18.00'de gökyüzünün ne renge döneceği",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Lojistik Regresyon Türleri şunları içerir",
+						"answerOptions": [
+							{
+								"answerText": "çok terimli ve kardinal",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "çok terimli ve sıralı",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "asıl ve sıralı",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Verilerinizin korelasyonları zayıf ise kullanılacak en iyi Regresyon türü:",
+						"answerOptions": [
+							{
+								"answerText": "Lojistik",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "DoÄŸrusal",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Kardinal",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 16,
+				"title": "Lojistik Regresyon: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Seaborn ne tür bir kütüphanedir?",
+						"answerOptions": [
+							{
+								"answerText": "veri görselleştirme kütüphanesi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "haritalama kütüphanesi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "matematiksel kütüphanesi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bir karışıklık matrisi şu şekilde de bilinir:",
+						"answerOptions": [
+							{
+								"answerText": "hata matrisi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "doÄŸruluk matrisi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "isabet matrisi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Ä°yi bir modelde ÅŸunlar bulunur:",
+						"answerOptions": [
+							{
+								"answerText": "karışıklık matrisinde çok sayıda yanlış pozitif ve gerçek negatif",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "karışıklık matrisinde çok sayıda gerçek pozitif ve gerçek negatif",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "karışıklık matrisinde çok sayıda doğru pozitif ve yanlış negatif",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 17,
+				"title": "Web Uygulaması Oluşturun: Ders Öncesi Test",
+				"quiz": [
+					{
+						"questionText": "ONNX ne anlama gelir?",
+						"answerOptions": [
+							{
+								"answerText": "Sinir Ağı Değişimi üzerinden",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Açık Sinir Ağı Değişimi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Çıkış Sinir Ağı Değişimi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Flask, yaratıcıları tarafından nasıl tanımlanır?",
+						"answerOptions": [
+							{
+								"answerText": "mini framework",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "büyük framework",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "mikro framework",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Python'un Pickle modülü ne işe yarar",
+						"answerOptions": [
+							{
+								"answerText": "Bir Python nesnesini serileÅŸtirir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bir Python nesnesini serileÅŸtirilmiÅŸ halden geri getirir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bir Python nesnesini serileÅŸtirir ve serileÅŸtirilmiÅŸ halden geri getirir",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 18,
+				"title": "Web Uygulaması Oluşturun: Ders Sonrası Test",
+				"quiz": [
+					{
+						"questionText": "Python kullanarak web üzerinde önceden eğitilmiş bir modeli barındırmak için kullanabileceğimiz araçlar nelerdir?",
+						"answerOptions": [
+							{
+								"answerText": "Flask",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "TensorFlow.js",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "onnx.js",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "SaaS ne anlama gelir?",
+						"answerOptions": [
+							{
+								"answerText": "Hizmet Olarak Sistem",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Hizmet Olarak Yazılım",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Hizmet Olarak Güvenlik",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Scikit-learn'ün LabelEncoder kitaplığı ne işe yarar?",
+						"answerOptions": [
+							{
+								"answerText": "Verileri alfabetik olarak kodlar",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Verileri sayısal olarak kodlar",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Verileri seri olarak kodlar",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 19,
+				"title": "Sınıflandırma 1: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Sınıflandırma, şununla birçok ortak noktası olan bir denetimli öğrenme şeklidir",
+						"answerOptions": [
+							{
+								"answerText": "Zaman Serileri",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Regresyon teknikleri",
+								"isCorrect": "true"
+							},
+							{
+								"cevapText": "NLP",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Sınıflandırma hangi soruyu yanıtlamaya yardımcı olabilir?",
+						"answerOptions": [
+							{
+								"answerText": "Bu e-posta spam mı değil mi?",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Domuzlar uçabilir mi?",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Hayatın anlamı nedir?",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Sınıflandırma tekniklerini kullanmanın ilk adımı nedir?",
+						"answerOptions": [
+							{
+								"answerText": "bir veri kümesinin sınıflarını oluşturma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "verilerinizi temizleme ve dengeleme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir gruba veya sonuca veri noktası atama",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 20,
+				"title": "Sınıflandırma 1: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Çok sınıflı soru nedir?",
+						"answerOptions": [
+							{
+								"answerText": "veri noktalarını birden çok sınıfa sınıflandırma görevi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "veri noktalarını birkaç sınıftan birine sınıflandırma görevi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "veri noktalarını birden çok yolla temizleme görevi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Sınıflandırıcılarınızın probleminizi çözmesine yardımcı olmak için tekrarlayan veya yardımcı olmayan verileri temizlemek önemlidir.",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Verilerinizi dengelemek için en iyi neden nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Dengesiz veriler görselleştirmelerde kötü görünüyor",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bir ML modeli bir sınıfa doğru eğilmeyeceğinden, verilerinizi dengelemek daha iyi sonuçlar verir",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Verilerinizi dengelemek size daha fazla veri noktası sağlar",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 21,
+				"title": "Sınıflandırma 2: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Dengeli, temiz veriler en iyi sınıflandırma sonuçlarını verir",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Doğru sınıflandırıcıyı nasıl seçersiniz?",
+						"answerOptions": [
+							{
+								"answerText": "Hangi senaryolar için hangi sınıflandırıcıların en iyi çalıştığını öğrenirsiniz",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "EÄŸitimli tahmin ve kontrol",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Yukarıdakilerin ikisi de",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Sınıflandırma bir:",
+						"answerOptions": [
+							{
+								"cevapText": "NLP türüdür",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Denetimli Öğrenme türüdür",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Programlama dilidir",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 22,
+				"title": "Sınıflandırma 2: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "'Çözücü' nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Çalışmanızı kontrol eden kişi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "optimizasyon probleminde kullanılacak algoritma",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir makine öğrenimi tekniği",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bu derste hangi sınıflandırıcıyı kullandık?",
+						"answerOptions": [
+							{
+								"answerText": "Lojistik Regresyon",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Karar Ağaçları",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bire-Tüm Çok Sınıflı Sınıflandırıcı",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Sınıflandırma algoritmasının beklendiği gibi çalışıp çalışmadığını nasıl anlarsınız?",
+						"answerOptions": [
+							{
+								"answerText": "Tahminlerinin doÄŸruluÄŸunu kontrol ederek",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Diğer algoritmalara karşı kontrol ederek",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bu algoritmanın benzer sorunları çözmede ne kadar iyi olduğuna dair geçmiş verilere bakarak",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 23,
+				"title": "Sınıflandırma 3: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Denemek için iyi bir ilk sınıflandırıcı:",
+						"answerOptions": [
+							{
+								"answerText": "DoÄŸrusal SVC",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "K-Means",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Mantıksal SVC",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Düzenlileştirme şunu kontrol eder:",
+						"answerOptions": [
+							{
+								"answerText": "parametrelerin etkisi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "eğitim hızının etkisi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "aykırı değerlerin etkisi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "K-Neighbors (K-Komşu) sınıflandırıcısı şunlar ne için kullanılabilir?",
+						"answerOptions": [
+							{
+								"answerText": "denetimli öğrenme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "denetimsiz öğrenme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "bunların her ikisi",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 24,
+				"title": "Sınıflandırma 3: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Destek-Vektör sınıflandırıcıları ne ne için kullanılabilir?",
+						"answerOptions": [
+							{
+								"answerText": "sınıflandırma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "regresyon",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "bunların her ikisi",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Random Forest, ___ bir sınıflandırıcıdır",
+						"answerOptions": [
+							{
+								"answerText": "topluluk türü",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "dağınık türden",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "birleştirici türden",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Adaboost şunlarla tanınır:",
+						"answerOptions": [
+							{
+								"answerText": "yanlış sınıflandırılmış öğelerin ağırlıklarına odaklanma",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "aykırı değerlere odaklanma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yanlış verilere odaklanma",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 25,
+				"title": "Sınıflandırma 4: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Öneri sistemleri ne için kullanılabilir?",
+						"answerOptions": [
+							{
+								"answerText": "İyi bir restoran önermek",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Denenecek moda önerileri",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bunların her ikisi",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Bir web uygulamasına bir model gömmek, onun çevrimdışı özellikli olmasına yardımcı olur",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Onnx Runtime ne için kullanılabilir?",
+						"answerOptions": [
+							{
+								"answerText": "Modelleri bir web uygulamasında çalıştırmak",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Model eÄŸitmek",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Hiperparametre ayarlamak",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 26,
+				"title": "Sınıflandırma 4: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Netron uygulaması size ne konuda yardımcı olur?",
+						"answerOptions": [
+							{
+								"answerText": "Verileri görselleştirir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Modelinizin yapısını görselleştirir",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Web uygulamanızı test eder",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Scikit-learn modelinizi, Onnx ile kullanacak şekilde dönüştürmek için aşağıdakilerin hangisini kullanabilirsiniz?",
+						"answerOptions": [
+							{
+								"answerText": "sklearn-app",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "sklearn-web",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "sklearn-onnx",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Modelinizi bir web uygulamasında kullanmaya ne denir?",
+						"answerOptions": [
+							{
+								"answerText": "çıkarım",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "parazit",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "sigorta",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 27,
+				"title": "Kümelemeye Giriş: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Kümelemenin gerçek hayattan bir örneği şu olabilir",
+						"answerOptions": [
+							{
+								"answerText": "Yemek sofrasını kurmak",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Çamaşırları ayırmak",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Bakkal alışverişi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bu sektörlerde kümeleme teknikleri kullanılabilir",
+						"answerOptions": [
+							{
+								"answerText": "bankacılık",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "e-ticaret",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "bunların her ikisi",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Kümeleme hangi eğitim türündendir?",
+						"answerOptions": [
+							{
+								"answerText": "denetimli öğrenme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "denetimsiz öğrenme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "pekiştirmeli öğrenme",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 28,
+				"title": "Kümelemeye Giriş: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Öklid geometrisi, şunların düzenine dayanır:",
+						"answerOptions": [
+							{
+								"answerText": "eksenler",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "eÄŸriler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "küreler",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Kümeleme verilerinizin yoğunluğu ne ile ilgilidir?",
+						"answerOptions": [
+							{
+								"answerText": "gürültü",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "derinlik",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "geçerlilik",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "En iyi bilinen kümeleme algoritması nedir?",
+						"answerOptions": [
+							{
+								"answerText": "k-means",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "k-middle",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "k-mart",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 29,
+				"title": "K-Means Kümelemesi: Pre-Lecture Quiz",
+				"quiz": [
+					{
+						"questionText": "K-Means şu alandan türetilmiştir:",
+						"answerOptions": [
+							{
+								"answerText": "elektrik mühendisliği",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "sinyal iÅŸleme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "hesaplamalı dilbilim",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "İyi bir Silhouette puanı şu anlama gelir:",
+						"answerOptions": [
+							{
+								"answerText": "kümeler iyi ayrılmış ve iyi tanımlanmış",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "birkaç küme var",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "birçok küme var",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Varyans:",
+						"answerOptions": [
+							{
+								"answerText": "ortalamadan farkların karelerinin ortalaması",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "çok yüksek olursa kümeleme açısından sorun oluşturur",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "bunların her ikisi",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 30,
+				"title": "K-Means Kümelemesi: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Bir Voronoi diyagramı şunları gösterir:",
+						"answerOptions": [
+							{
+								"answerText": "bir kümenin varyansı",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "bir kümenin çekirdeği ve bölgesi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir kümenin ataleti",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Atalet:",
+						"answerOptions": [
+							{
+								"answerText": "kümelerin kendi içinde ne kadar tutarlı olduğunun bir ölçüsü",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "kümelerin ne kadar hareket ettiğinin bir ölçüsü",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "küme kalitesinin bir ölçüsü",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "K-Means'i kullanmak için önce 'k' değerini belirlemelisiniz",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 31,
+				"title": "NLP'ye Giriş: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "NLP bu derslerde ne anlama geliyor?",
+						"answerOptions": [
+							{
+								"answerText": "Sinirsel Dil Ä°ÅŸleme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "DoÄŸal dil iÅŸleme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "DoÄŸal Dilbilimsel Ä°ÅŸleme",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Eliza ne gibi davranan eski bir bottur?",
+						"answerOptions": [
+							{
+								"answerText": "terapist",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "doktor",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "hemÅŸire",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Alan Turing'in 'Turing Testi' bir bilgisayarın hangi özelliklerini belirlemeye çalışır?",
+						"answerOptions": [
+							{
+								"answerText": "insandan ayırt edilememe",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "düşünme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 32,
+				"title": "NLP'ye Giriş: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Joseph Weizenbaum hangi botu icat etti?",
+						"answerOptions": [
+							{
+								"answerText": "Elisha",
+								"isCorrect": "false"
+							},
+							{
+								"cevapText": "Eliza",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Eloise",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bir konuşma botu şuna dayalı olarak çıktı verir",
+						"answerOptions": [
+							{
+								"answerText": "Önceden tanımlanmış seçenekleri rastgele seçme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Girdileri analiz etme ve makine zekasını kullanma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bunların her ikisi",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Botu nasıl daha etkili hale getirirsiniz?",
+						"answerOptions": [
+							{
+								"answerText": "Daha fazla soru sorarak",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Daha fazla veri besleyerek ve buna göre eğiterek",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Bot aptaldır, öğrenemez :(",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 33,
+				"title": "NLP Görevleri: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Jetonlaştırma",
+						"answerOptions": [
+							{
+								"answerText": "Metni noktalama işaretleri ile böler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Metni ayrı jetonlara (kelimelere) böler",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Metni cümlelere böler",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "YerleÅŸtirmeler",
+						"answerOptions": [
+							{
+								"answerText": "kelimelerin kümelenebilmesi için metin verilerini sayılara dönüştürür",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "kelimeleri tümcelere yerleştirir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "cümleleri paragraflara yerleştirir",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Konuşma Bölümleri Etiketleme",
+						"answerOptions": [
+							{
+								"answerText": "cümleleri konuşma bölümlerine böler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "jetonlaştırılmış sözcükleri alır ve bunları konuşmanın bölümlerine göre etiketler",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "cümleleri diyagram haline getirir",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 34,
+				"title": "NLP Görevleri: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Kelimelerin ne sıklıkta tekrarlandığına dair bir sözlük oluşturun:",
+						"answerOptions": [
+							{
+								"answerText": "Kelime ve Cümle Sözlüğü",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Kelime ve Cümle Sıklıkları",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Kelime ve Cümle Kitaplığı",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "N-gram terimi ne anlama gelir?",
+						"answerOptions": [
+							{
+								"answerText": "Bir metin, belirli bir uzunluktaki sözcük dizilerine bölünebilir",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Bir kelime, belirli bir uzunluktaki karakter dizilerine bölünebilir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bir metin, belirli bir uzunlukta paragraflara bölünebilir",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Duygu analizi",
+						"answerOptions": [
+							{
+								"answerText": "olumluluk veya olumsuzluk için bir ifadeyi analiz eder",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "duygusallık için bir ifadeyi analiz eder",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "üzüntü için bir ifadeyi analiz eder",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 35,
+				"title": "NLP ve Çeviri: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Naif çeviri",
+						"answerOptions": [
+							{
+								"answerText": "yalnızca sözcükleri çevirir",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "cümle yapısını çevirir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "duyguları çevirir",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bir metin *korpusu* ne anlama gelir?",
+						"answerOptions": [
+							{
+								"answerText": "Az sayıda metin",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Çok sayıda metin",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Bir standart metin",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Bir ML modelinde model oluşturmak için yeterli insan çevirisi varsa, aşağıdakilerden hangisi yapılabilir?",
+						"answerOptions": [
+							{
+								"answerText": "çevirileri kısaltılabilir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "çeviriler standartlaştırılabilir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "çevirilerin doğruluğu iyileştirilebilir",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 36,
+				"title": "NLP ve Çeviri: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "TextBlob'un çeviri kitaplığının altında yatan şey nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Google Çeviri",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Bing",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Özel bir makine öğrenimi modeli",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "`blob.translate`i kullanmak için neye ihtiyacınız vardır?",
+						"answerOptions": [
+							{
+								"answerText": "internet bağlantısı",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir sözlük",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "JavaScript",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Duygu analizi yapmak için nasıl bir ML yaklaşımı izlenmelidir?",
+						"answerOptions": [
+							{
+								"answerText": "Manuel olarak oluşturulan görüşlere ve puanlara Regresyon teknikleri uygulanır ve kalıplar aranır",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "NLP teknikleri manuel olarak oluşturulan görüşlere ve puanlara uygulanır ve kalıplar aranır",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Manuel olarak oluşturulan görüşlere ve puanlara Kümeleme teknikleri uygulanır ve kalıplar aranır",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 37,
+				"title": "NLP 4: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Bir insan tarafından yazılan veya konuşulan metinlerden hangi bilgileri alabiliriz?",
+						"answerOptions": [
+							{
+								"answerText": "kalıplar ve frekanslar",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "duygu ve anlam",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Duygu analizi nedir?",
+						"answerOptions": [
+							{
+								"answerText": "bir aile yadigarının manevi değeri olup olmadığına dair bir araştırma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "duygusal durumları ve öznel bilgileri sistematik olarak tanımlama, çıkarma, ölçme ve inceleme yöntemi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Birinin üzgün mü mutlu mu olduğunu anlama yeteneği",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Otel incelemeleri, Python ve duygu analizi veri kümesi kullanılarak hangi soru yanıtlanabilir?",
+						"answerOptions": [
+							{
+								"answerText": "İncelemelerde en sık kullanılan kelime ve deyimler nelerdir?",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "En iyi havuz hangi tesiste bulunur?",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Bu otelde vale park hizmeti var mı?",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 38,
+				"title": "NLP 4: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "NLP'nin özü nedir?",
+						"answerOptions": [
+							{
+								"answerText": "insan dilini mutlu veya üzgün olarak sınıflandırmak",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "insanın yapmasına gerek kalmadan anlamı veya duyguyu yorumlamak",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "duygudaki aykırı değerleri bulma ve inceleme",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Verileri temizlerken arayabileceğiniz bazı şeyler nelerdir?",
+						"answerOptions": [
+							{
+								"answerText": "diÄŸer dillerdeki karakterler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "boş satırlar veya sütunlar",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Üzerinde işlem yapmadan önce verilerinizi ve zaaflarını anlamak önemlidir.",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 39,
+				"title": "NLP 5: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Verileri analiz etmeden önce temizlemek neden önemlidir?",
+						"answerOptions": [
+							{
+								"answerText": "Bazı sütunlarda eksik veya yanlış veriler olabilir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Dağınık veriler, veri kümesi hakkında yanlış sonuçlara yol açabilir",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Yukarıdakilerin ikisi de",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Verileri temizlemek için bir strateji örneği nedir?",
+						"answerOptions": [
+							{
+								"answerText": "belirli bir soruyu yanıtlamak için yararlı olmayan sütunları/satırları kaldırma",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "hipotezinize uymayan doğrulanmış değerlerden kurtulma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "aykırı değerleri ayrı bir tabloya taşımak ve eşleşip eşleşmediklerini görmek için o tablo için hesaplamaları çalıştırmak",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Verileri bir Etiket sütunu kullanarak kategorilere ayırmak faydalı olabilir.",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 40,
+				"title": "NLP 5: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Kullandığımız veri setinin amacı nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Dünyadaki oteller için kaç tane olumlu ve olumsuz yorum olduğunu görmek",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "En iyi oteli seçmemize yardımcı olacak duyguları ve sütunları eklemek",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "İnsanların belirli yorumları neden bıraktığını analiz etmek",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Duruş sözcükleri nelerdir?",
+						"answerOptions": [
+							{
+								"answerText": "Bir cümlenin duygusunu değiştirmeyen yaygın İngilizce kelimeler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "duygu analizini hızlandırmak için kaldırabileceğiniz kelimeler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Duygu analizini test etmek için, aynı inceleme için gözden geçirenin puanıyla eşleştiğinden emin olun.",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 41,
+				"title": "Zaman Serisine Giriş: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Zaman Serisi Tahmini şu durumlarda yararlıdır",
+						"answerOptions": [
+							{
+								"answerText": "gelecekteki maliyetlerin belirlenmesi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "gelecekteki fiyatlandırmayı tahmin etme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin her ikisi de",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Zaman serisi:",
+						"answerOptions": [
+							{
+								"answerText": "uzayda ardışık eşit aralıklı noktalardır",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "zamanda ardışık eşit aralıklı noktalardır",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "uzay ve zamanda ardışık eşit aralıklı noktalardır",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Zaman serileri şurada kullanılabilir:",
+						"answerOptions": [
+							{
+								"answerText": "deprem tahmini",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bilgisayarlı görü",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "renk analizi",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 42,
+				"title": "Zaman Serisine Giriş: Ders Sonrası Test",
+				"quiz": [
+					{
+						"questionText": "Zaman serisi trendleri nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Zaman içinde ölçülebilir artış ve azalışlar",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Zamanla gerçekleşen azalmanın ölçümü",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Zaman içinde artışlar ve azalışlar arasındaki boşluklar",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Aykırı değerler",
+						"answerOptions": [
+							{
+								"answerText": "standart veri varyansına yakın noktalardır",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "standart veri varyansından çok uzak noktalardır",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "standart veri varyansının dahilindeki noktalardır",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Zaman Serisi Tahmini en çok şunlar için yararlıdır",
+						"answerOptions": [
+							{
+								"answerText": "Ekonometri",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Tarih",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Kütüphaneler",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 43,
+				"title": "Zaman Serisi ARIMA: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "ARIMA'nın açılımı",
+						"answerOptions": [
+							{
+								"answerText": "Otomatik Regresif Ä°ntegral Hareketli Ortalama",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Otomatik Regresif Entegre Hareketli Eylem",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Otomatik Regresif Entegre Hareketli Ortalama",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Durağanlık neyi ifade eder?",
+						"answerOptions": [
+							{
+								"answerText": "Zaman içinde kaydırıldığında özellikleri değişmeyen veriler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Zaman içinde kaydırıldığında dağılımı değişmeyen veriler",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Zaman içinde kaydırıldığında dağılımı değişen veriler",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Farklandırma",
+						"answerOptions": [
+							{
+								"answerText": "trendi ve mevsimselliÄŸi dengeler",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "trendi ve mevsimselliği artırır",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "trendi ve mevsimselliği ortadan kaldırır",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 44,
+				"title": "Zaman Serisi ARIMA: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "ARIMA, bir modeli zaman serisi verilerinin özel formuna şu şekilde uydurmak için kullanılır:",
+						"answerOptions": [
+							{
+								"answerText": "mümkün olduğunca düz",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "mümkün olduğunca yakından",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "dağılım grafikleri aracılığıyla",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "SARIMAX ne için kullanılır?",
+						"answerOptions": [
+							{
+								"answerText": "mevsimsel ARIMA modellerini yönetmek",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "özel ARIMA modellerini yönetmek",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "istatistiksel ARIMA modellerini yönetmek",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "'Walk-Forward' doğrulama şunları içerir",
+						"answerOptions": [
+							{
+								"answerText": "bir modelin doğrulanırken aşamalı olarak yeniden değerlendirilmesi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "bir modelin doğrulanırken aşamalı olarak yeniden eğitilmesi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir modelin doğrulanırken aşamalı olarak yeniden yapılandırılması",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 45,
+				"title": "Pekiştirmeli Öğrenme 1: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Pekiştirmeli öğrenme nedir?",
+						"answerOptions": [
+							{
+								"answerText": "birine bir şeyi anlayana kadar tekrar tekrar öğretmek",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "bir aracının bazı ortamlardaki en uygun davranışını birçok deney çalıştırarak deşifre eden bir öğrenme tekniği",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "birden çok deneyin aynı anda nasıl çalıştırılacağını anlamak",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Eylem politikası nedir?",
+						"answerOptions": [
+							{
+								"answerText": "herhangi bir durumda eylemi döndüren bir fonksiyon",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "bir eşyayı iade edip edemeyeceğinizi söyleyen bir belge",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "rastgele bir amaç için kullanılan bir fonksiyon",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Ödül fonksiyonu, ortamın her durumu için bir puan döndürür.",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 46,
+				"title": "Pekiştirmeli Öğrenme 1: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Q-öğrenme nedir?",
+						"answerOptions": [
+							{
+								"answerText": "her durumun 'iyiliğini' kaydetmek için bir mekanizma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "eylem politikasının bir Q-Tablosu tarafından tanımlandığı bir algoritma",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yukarıdakilerin tümü",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Q-Tablosu hangi değerler için rastgele yürüyüş ilkesine karşılık gelir?",
+						"answerOptions": [
+							{
+								"answerText": "hepsi eÅŸit olan deÄŸerler",
+								"isCorrect": "true"
+							},
+							{
+								"cevapText": "-0.25",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "tüm farklı değerler",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Dersimizde öğrenme süreci boyunca keşif kullanmak, sömürü kullanmaktan daha iyiydi.",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "true"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 47,
+				"title": "Pekiştirmeli Öğrenme 2: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Satranç ve Go, süreğen durumları olan oyunlardır.",
+						"answerOptions": [
+							{
+								"cevapText": "doÄŸru",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "yanlış",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "CartPole sorunu nedir?",
+						"answerOptions": [
+							{
+								"answerText": "aykırı değerleri ortadan kaldırmak için bir işlem",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "alışveriş sepetinizi optimize etmek için bir yöntem",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "dengelemenin basitleÅŸtirilmiÅŸ bir versiyonu",
+								"isCorrect": "true"
+							}
+						]
+					},
+					{
+						"questionText": "Bir oyunda potansiyel durumların farklı senaryolarını oynamak için hangi aracı kullanabiliriz?",
+						"answerOptions": [
+							{
+								"answerText": "tahmin ve kontrol",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "simülasyon ortamları",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "durum geçiş testi",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 48,
+				"title": "Pekiştirmeli Öğrenme 2: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Bir ortamda tüm olası eylemleri nerede tanımlarız?",
+						"answerOptions": [
+							{
+								"answerText": "metodlar",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "eylem uzayı",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "eylem listesi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Sözlükte anahtar/değer çifti olarak hangi çifti kullandık?",
+						"answerOptions": [
+							{
+								"answerText": "anahtar olarak (durum, eylem), deÄŸer olarak Q-Table girdisi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "anahtar olarak durum, deÄŸer olarak eylem",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "anahtar olarak qvalues fonksiyonunun ​​değeri, değer olarak eylem",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Q-öğrenme sırasında kullandığımız hiperparametreler nelerdir?",
+						"answerOptions": [
+							{
+								"answerText": "q-tablo değeri, mevcut ödül, rastgele eylem",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "öğrenme hızı, indirim faktörü, keşif/sömürü faktörü",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "kümülatif ödüller, öğrenme hızı, keşif faktörü",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 49,
+				"title": "Gerçek Dünya Uygulamaları: Ders Öncesi Sınav",
+				"quiz": [
+					{
+						"questionText": "Finans sektöründeki bir makine öğrenimi uygulaması örneği nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Müşteri deneyimini NLP kullanarak kişiselleştirme",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Doğrusal regresyon kullanarak servet yönetimi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Zaman Serilerini kullanarak enerji yönetimi",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Hastaneler, yeniden hastaneye yatacak hastaların yönetimi için hangi ML tekniğini kullanabilir?",
+						"answerOptions": [
+							{
+								"answerText": "Kümeleme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Zaman Serileri",
+								"isCorrect": "false"
+							},
+							{
+								"cevapText": "NLP",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Enerji yönetimi için Zaman Serilerini kullanma örneği nedir?",
+						"answerOptions": [
+							{
+								"answerText": "Hareket algılayan hayvanlar",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Akıllı parkmetreler",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Orman yangınlarının takibi",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			},
+			{
+				"id": 50,
+				"title": "Gerçek Dünya Uygulamaları: Ders Sonrası Sınav",
+				"quiz": [
+					{
+						"questionText": "Kredi kartı dolandırıcılığını tespit etmek için hangi ML tekniği kullanılabilir?",
+						"answerOptions": [
+							{
+								"answerText": "Regresyon",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Kümeleme",
+								"isCorrect": "true"
+							},
+							{
+								"cevapText": "NLP",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Orman yönetiminde hangi ML tekniği örneklendirilmiştir?",
+						"answerOptions": [
+							{
+								"answerText": "Pekiştirilmiş Öğrenme",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Zaman Serileri",
+								"isCorrect": "false"
+							},
+							{
+								"cevapText": "NLP",
+								"isCorrect": "false"
+							}
+						]
+					},
+					{
+						"questionText": "Hangisi Sağlık Hizmetleri sektöründe bir makine öğrenimi uygulaması örneğidir?",
+						"answerOptions": [
+							{
+								"answerText": "Regresyon kullanarak öğrenci davranışının tahmin edilmesi",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "Sınıflandırıcılar kullanarak klinik araştırmaların yönetimi",
+								"isCorrect": "true"
+							},
+							{
+								"answerText": "Sınıflandırıcılar kullanarak hayvanların hareketlerinin algılaması",
+								"isCorrect": "false"
+							}
+						]
+					}
+				]
+			}
+		]
+	}
+]
\ No newline at end of file

From 6b37df0491615f5285314d42f39971a64bbe251c Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Sun, 11 Jul 2021 20:25:54 +0200
Subject: [PATCH 111/368] Ch2-1 readme completed

---
 .../1-Tools/translations/README.it.md         | 210 ++++++++++++++++++
 2-Regression/translations/README.it.md        |  34 +++
 2 files changed, 244 insertions(+)
 create mode 100644 2-Regression/1-Tools/translations/README.it.md
 create mode 100644 2-Regression/translations/README.it.md

diff --git a/2-Regression/1-Tools/translations/README.it.md b/2-Regression/1-Tools/translations/README.it.md
new file mode 100644
index 0000000000..94c0a5c6ef
--- /dev/null
+++ b/2-Regression/1-Tools/translations/README.it.md
@@ -0,0 +1,210 @@
+# Iniziare con Python e Scikit-learn per i modelli di regressione
+
+![Sommario delle regressioni in uno sketchnote](../../../sketchnotes/ml-regression.png)
+
+> Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Qui Pre-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+
+## Introduzione
+
+In queste quattro lezioni, si scoprirà come costruire modelli di regressione. Si discuterà di cosa siano fra breve.
+Prima di tutto, ci si deve assicurare di avere a disposizione gli strumenti adatti per far partire il processo!
+
+In questa lezione, si imparerà come:
+
+- Configurare il proprio computer per attività locali di machine learning.
+- Lavorare con i Jupyter notebook.
+- Usare Scikit-learn, compresa l'installazione.
+- Esplorare la regressione lineare con un esercizio pratico.
+
+## Installazioni e configurazioni
+
+[![Usare Python con Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
+
+> 🎥 Fare click sull'immagine qui sopra per un video: usare Python all'interno di VS Code.
+
+1. **Installare Python**. Assicurarsi che [Python](https://www.python.org/downloads/) sia installato nel proprio computer. Si userà Python for per molte attività di data science e machine learning. La maggior parte dei sistemi già include una installazione di Python. Ci sono anche utili [Pacchetti di Codice Python](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) disponbili, per facilitare l'installazione per alcuni utenti.
+
+   Alcuni utilizzi di Python, tuttavia, richiedono una versione del software, laddove altri ne richiedono un'altra differente. Per questa ragione, è utile lavorare con un [ambiente virtuale](https://docs.python.org/3/library/venv.html).
+
+2. **Installare Visual Studio Code**. Assicurarsi di avere installato Visual Studio Code sul proprio computer. Si seguano queste istruzioni per [installare Visual Studio Code](https://code.visualstudio.com/) per l'installazione basica. Si userà Python in Visual Studio Code in questo corso, quindi meglio rinfrescarsi le idee su come [configurare Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) per lo sviluppo in Python.
+
+   > Si prenda confidenza con Python tramite questa collezione di [moduli di apprendimento](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
+
+3. **Installare Scikit-learn**, Seguendo [quest istruczionsi](https://scikit-learn.org/stable/install.html). Visto che ci si deve assicurare di usare Python 3, ci si raccomanda di usare un ambiente virtuale. Si noti che se si installa questa libreria in un M1 Mac, ci sono istruzioni speciali nella pagina di cui al riferimento qui sotto.
+
+1. **Installare Jupyter Notebook**. Servirà [installare il pacchetto Jupyter](https://pypi.org/project/jupyter/). 
+
+## Ambiente di crezione ML
+
+Si useranno **notebook** per sviluppare il codice Python e creare modelli di machine learning. Questo tipo di file è uno strumento comune per i data scientist, e viene identificato dal suffisso o estensione `.ipynb`.
+
+I notebook sono un ambiente interattivo che consente allo sviluppatore di scrivere codie, aggiungere note e scrivere documentazione attorno al codice il che è particolarmente utile per progetti sperimentali o orientati alla ricerca.
+
+### Esercizio - lavorare con un notebook
+
+In questa cartella, si troverà il file _notebook.ipynb_. 
+
+1. Aprure _notebook.ipynb_ in Visual Studio Code.
+
+   Un server Jupyter verrà lanciato con Python 3+. Si troveranno aree del notebook che possono essere `eseguite`, pezzi di codice. Si può eseguire un blocco di codice selezionando l'icona che assomiglia a un bottone di riproduzione.
+
+1. Selezionare l'icona `md` e aggiungere un  poco di markdown, e il seguente testo **# Benvenuto nel tuo notebook**.
+
+   Poi, aggiungere un blocco di codice Python. 
+
+1. Digitare **print('hello notebook')** nell'area riservata al codice.
+1. Selezionare la freccia per eseguire il codice.
+
+   Si dovrebbe vedere stampata la seguente frase:
+
+    ```output
+    hello notebook
+    ```
+
+![VS Code con un notebook aperto](images/notebook.png)
+
+Si può inframezzare il codice con commenti per auto documentare il notebook.
+
+✅ Si pensi per un minuto all'ambiente di lavoro di uno sviluppatore web rispetto a quello di un data scientist.
+
+## Scikit-learn installato e funzionante
+
+Adesso che Python è impostato nel proprio ambiente locale, e si è familiari con i notebook Jupyter, si acquisterà ora confidenza con Scikit-learn (si pronuncia con la `si` di `science`). Scikit-learn fornisce una [API estensiva](https://scikit-learn.org/stable/modules/classes.html#api-ref) che aiuta a eseguire attività ML.
+
+Stando al loro [sito web](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn è una libreria di machine learning open source che supporta l'apprendimento assistito (supervised learning) e non assistito (unsuperivised learnin). Fornisce anche strumenti vari per l'adattamento del modello, la pre-elaborazione dei dati, la selezione e la valutazione dei modelli e molte altre utilità."
+
+In questo corso, si userà Scikit-learn e altri strumenti per costruire modelli di machine learning per eseguire quelle che vengono chiamate attività di 'machine learning tradizionale'. Si sono deliberamente evitate le reti neurali e il deep learning visto che saranno meglio trattati nel prossimo programma di studi 'AI per Principianti'.
+
+Scikit-Learn rende semplice costruire modelli e valutarli per l'uso. Si concentra principalmente sull'utilizzo di dati numerici e contiene diversi insiemi di dati già pronti per l'uso come strumenti di apprendimento. Include anche modelli pre-costruiti per gli studenti da provare. Si esplora ora il processo di caricamento dei dati preconfezionati, e utilizzando un modello di stimatore incorporato prima il modello ML con Scikit-Learn con alcuni dati di base.
+
+# Esercizio - Il Primo notebook Scikit-learn
+
+> Questo tutorial è stato ispirato dall'[esempio di regressione lineare](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) nel sito web di Scikit-learn.
+
+Nel file _notebook.ipynb_ associato a questa lezione, svuotare tutte le celle usando l'icona cestino ('trash can').
+
+In questa sezione, di lavorerà con un piccolo insieme di dati sul diabete che è incorporato in Scikit-learn per scopi di apprendimento. Si immagini di voler testare un trattamento per i pazienti diabetici. I modelli di machine learning potrebbero essere di aiuto nel determinare quali pazienti risponderebbero meglio al trattamento, in base a combinazioni di variabili. Anche un modello di regressione molto semplice, quando visualizzato, potrebbe mostrare informazioni sulle variabili che aiuteranno a organizzare le proprie sperimentazioni cliniche teoriche.
+
+✅ Esistono molti tipi di metodi di regressione e quale scegliere dipende dalla risposta che si sta cercando. Se si vuole prevedere l'altezza probabile per una persona di una data età, si dovrebbe usare la regressione lineare, visto che si sta cercando un **valore numerico**. Se si è interessati a scoprire se un tipo di cucina dovrebbe essere considerato vegano o no, si sta cercando un' **assegnazione di categoria** quindi si dovrebbe usare la regressione logistica. Si imparerà di più sulla regressione logistica in seguito. Si pensi ad alcune domande che si possono chiedere ai dati e quale di questi metodi sarebbe più appropriato.
+
+Si inizia su questo compito.
+
+### Importare le librerie
+
+Per questo compito verranno importate alcune librerie:
+
+- **matplotlib**. E' un utile [strumento grafico](https://matplotlib.org/) e verrà usato per creare una trama a linee.
+- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) è una libreira utile per gestire i dati numerici in Python.
+- **sklearn**. Questa è la libreria Scikit-learn.
+
+Importare alcune librerie che saranno di aiuto per le proprie attività.
+
+1. Con il seguente codice si aggiungono le importazioni:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import numpy as np
+   from sklearn import datasets, linear_model, model_selection
+   ```
+
+   Qui sopra vengono importati `matplottlib`, `numpy` da `sklearn` si importa `datasets`, `linear_model` e `model_selection`. `model_selection` viene usato per dividere i dati negli insiemi di addestramento e test.
+
+### L'insieme di dati riguardante il diabete
+
+L'[insieme dei dati sul diabetes](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) include 442 campioni di dati sul diabete, con 10 variabili caratteristiche, alcune delle quali includono:
+
+age (età): età in anni
+bmi: indice di massa corporea (body mass index)
+bp: media pressione sanguinea
+s1 tc: Cellule T (un tipo di leucocito)
+
+✅ Questo insieme di dati include il concetto di "sesso" come caratteristica variabile importante per la ricerca sul diabete. Molti insiemi di dati medici includono questo tipo di classificazione binaria. Si rifletta su come categorizzazioni come questa potrebbe escludere alcune parti di una popolazione dai trattamenti.
+
+Ora si caricano i dati di x e y.
+
+> 🎓 Si ricordi, questo è apprendimento supervisionato (supervised learning), e serve dare un nome all'obiettivo 'y'.
+
+In una nuova cella di codice, caricare l'insieme di dati sul diabete chiamando `load_diabetes()`. Il parametro `return_X_y=True` segnala che `X` sarà una matrice di dati e `y` sarà l'obiettivo della regressione. 
+
+1. Si aggiungono alcuni comandi di stampa per msotrare la forma della matrice di dati e suoi primi elementi:
+
+    ```python
+    X, y = datasets.load_diabetes(return_X_y=True)
+    print(X.shape)
+    print(X[0])
+    ```
+
+    Quella che viene ritornata è una tuple. Quello che si sta facento è assegnare i primi due valori della tupla a  `X` e `y` rispettivamente. Per saperne di più sulle [tuples](https://wikipedia.org/wiki/Tuple).
+
+    Si può vedere che questi dati hanno 442 elementi divisi in array di 10 elementi:
+
+    ```text
+    (442, 10)
+    [ 0.03807591  0.05068012  0.06169621  0.02187235 -0.0442235  -0.03482076
+    -0.04340085 -0.00259226  0.01990842 -0.01764613]
+    ```
+
+    ✅ Si rifletta sulla relazione tra i dati e l'obiettivo di regressione. La regressione lineare prevede le relazioni tra la caratteristica X e la variabile di destinazione Y. Si può trovare l'[obiettivo](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) per l'insieme di dati sul diabete nella documentazione? Cosa dimostra questo insieme di dati, dato questo obiettivo?
+
+2. Successivamente, selezionare una porzione di questo insieme di dati da tracciare sistemandola in un nuovo array usando la funzione di numpy's `newaxis`. Verrà usata la regressione lineare per generare una linea tra i quali in questi dati secondo il modello che determina.
+
+   ```python
+   X = X[:, np.newaxis, 2]
+   ```
+
+   ✅ A picere, stampare i dati per verificarne la forma.
+
+3. Ora che si hanno dei dati pronti per essere tracciati, è possibile vedere se una macchina può aiutare a determinare una divisione logica tra i numeri in questo insieme di dati. Per fare ciò, è necessario dividere sia i dati (x) che l'obiettivo (Y) in insiemi di test e addestamento. Scikit-Learn ha un modo semplice per farlo; si possono dividere i  dati di prova in un determinato punto.
+
+   ```python
+   X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
+   ```
+
+4. Ora si è pronti ad addestare il modello! Caricare il modello di regressione lineare e addestrarlo con i propri insiemi di addestramento X e y usando `model.fit()`:
+
+    ```python
+    model = linear_model.LinearRegression()
+    model.fit(X_train, y_train)
+    ```
+
+    ✅ `model.fit()` è una funzione che si vedrà in molte librerie ML tipo TensorFlow
+
+5. Successivamente creare una previsione usando i dati di test, con la funzione `predict()`. Questo servirà per tracciare la linea tra i gruppi di dati
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+6. Ora è il momento di mostrare i dati in un tracciato. Matplotlib è uno strumento molto utile per questo compito.Si crei un grafico a dispersione (scatterplot) di tutti i dati del test X e Y e si utilizzi la previsione per disegnare una linea nel luogo più appropriato, tra i raggruppamenti dei dati del modello.
+
+    ```python
+    plt.scatter(X_test, y_test,  color='black')
+    plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.show()
+    ```
+
+   ![un grafico a dispersione che mostra i punti dati sul diabete](../images/scatterplot.png)
+
+   ✅ Si pensi a cosa sta succedendo qui. Una linea retta scorre attraverso molti piccoli punti dati, ma cosa sta facendo esattamente? Si può capire come si dovrebbe utilizzare questa linea per prevedere dove un nuovo punto di dati non noto dovrebbe adattarsi alla relazione con l'asse y del tracciato? Si cerchi di mettere in parole l'uso pratico di questo modello.
+
+Congratulazioni, si è costruito il primo modello di regressione lineare, creato una previsione con esso, e visualizzata in una tracciato!
+
+---
+
+## 🚀Sfida
+
+Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modificare questa riga: `X = X[:, np.newaxis, 2]`. Dato l'obiettivo di questo insieme di dati, cosa si potrebbe riuscire a scoprire circa la progressione del diabete come matattia?
+
+## [Qui post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+
+## Revisione e Auto Apprendimento
+
+In questo tutorial, si è lavorato con una semplice regressione lineare, piuttosto che un regressione univariata o multipla. Si legga circa le differenze tra questi metodi oppure si dia uno sguardo a [questo video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+
+Si legga di più sul concetto di regressione e si pensi a quale tipo di domande potrebbero trovare risposta con questa tecnica. Seguire questo [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) per approfondire la propria conoscenza.
+
+## Compito
+
+[Un insieme di dati diverso](assignment.md)
diff --git a/2-Regression/translations/README.it.md b/2-Regression/translations/README.it.md
new file mode 100644
index 0000000000..19e0100bd5
--- /dev/null
+++ b/2-Regression/translations/README.it.md
@@ -0,0 +1,34 @@
+# Modelli di regressione per machine learning
+
+## Argomento regionale: modelli di Regressione per i prezzi della zucca in Nord America 🎃
+
+In Nord America, le zucche sono spesso intagliate in facce spaventose per Halloween. Si scoprirà di più su queste affascinanti verdure!
+
+![jack-o-lantern](../images/jack-o-lanterns.jpg)
+> Foto di <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> su <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Cosa si imparerà
+
+Le lezioni in questa sezione riguardano i tipi di regressione nel contesto di machine learning. I modelli di regressione possono aiutare a determinare la _relazione_ tra le variabili. Questo tipo di modello può prevedere valori come lunghezza, temperatura o età, scoprendo così le relazioni tra le variabili mentre analizza i punti dati.
+
+In questa serie di lezioni si scoprirà la differenza tra regressione lineare e regressione logistica e quando si dovrebbe usare l'una o l'altra.
+
+In questo gruppo di lezioni si imposterà una configurazione per iniziare le attività di machine learning, inclusa la configurazione di Visual Studio Code per gestire i notebook, l'ambiente comune per i data scientist. Si scoprirà Scikit-learn, una libreria per machine learning, e si creeranno i primi modelli, concentrandosi in questo capitolo sui modelli di Regressione.
+
+> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di regressione. Si provi [Azure Machine Learning per questa attività](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+### Lezioni
+
+1. [Gli Attrezzi Necessari](1-Tools/README.md)
+2. [Gestione dati](2-Data/README.md)
+3. [Regressione lineare e polinomiale](3-Linear/README.md)
+4. [Regressione logistica](4-Logistic/README.md)
+
+---
+### Crediti
+
+"ML con regressione" scritto con ♥� da [Jen Looper](https://twitter.com/jenlooper)
+
+♥� I collaboratori del quiz includono: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) e [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+L'insieme di dati relativi alla zucca è suggerito da [questo progetto su](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) Kaggle e i suoi dati provengono dai [Rapporti Standard sui Mercati Terminali delle Colture Speciali](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)  distribuiti dal Dipartimento dell'Agricoltura degli Stati Uniti. Sono stati aggiunti alcuni punti intorno al colore in base alla varietà per normalizzare la distribuzione. Questi dati sono di pubblico dominio.

From ccd8331e4fee255e73068f1f3329ac03fd2c297a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Emre=20=C3=87a=C4=9Fatay=20K=C3=B6se?=
 <emrecgty@hotmail.com>
Date: Mon, 12 Jul 2021 05:11:13 +0300
Subject: [PATCH 112/368] Translate 1.1 and 1.2 to Turkish

---
 .../1-intro-to-ML/translations/README.tr.md   | 114 +++++++++++++++++
 .../translations/assignment.tr.md             |   9 ++
 .../2-history-of-ML/translations/README.tr.md | 117 ++++++++++++++++++
 .../translations/assignment.tr.md             |  11 ++
 4 files changed, 251 insertions(+)
 create mode 100644 1-Introduction/1-intro-to-ML/translations/README.tr.md
 create mode 100644 1-Introduction/1-intro-to-ML/translations/assignment.tr.md
 create mode 100644 1-Introduction/2-history-of-ML/translations/README.tr.md
 create mode 100644 1-Introduction/2-history-of-ML/translations/assignment.tr.md

diff --git a/1-Introduction/1-intro-to-ML/translations/README.tr.md b/1-Introduction/1-intro-to-ML/translations/README.tr.md
new file mode 100644
index 0000000000..cfa7e1274a
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@@ -0,0 +1,114 @@
+# Makine Öğrenimine Giriş
+
+[![ML, AI, Derin öğrenme - Farkları nelerdir?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, Derin öğrenme - Farkları nelerdir?")
+
+> 🎥  Makine öğrenimi, yapay zeka ve derin öğrenme arasındaki farkı tartışan bir video için yukarıdaki resme tıklayın.
+
+## [Ders öncesi sınav](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=tr)
+
+### Introduction
+
+Yeni başlayanlar için klasik makine öğrenimi üzerine olan bu kursa hoş geldiniz! İster bu konuda tamamen yeni olun, ister belli bir alandaki bilgilerini tazelemek isteyen deneyimli bir makine öğrenimi uygulayıcısı olun, aramıza katılmanızdan mutluluk duyarız! Makine öğrenimi çalışmanız için samimi bir başlangıç ​​noktası oluşturmak istiyoruz ve [geri bildiriminizi](https://github.com/microsoft/ML-For-Beginners/discussions) değerlendirmekten, yanıtlamaktan ve hayata geçirmekten memnuniyet duyarız.
+
+[![Makine Öğrenimine Giriş](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Makine Öğrenimine Giriş")
+
+> 🎥 Video için yukarıdaki resme tıklayın: MIT'den John Guttag, makine öğrenimini tanıtıyor
+### Makine Öğrenimine Başlamak
+
+Bu müfredata başlamadan önce, bilgisayarınızın yerel olarak (Jupyter) not defterlerini çalıştırmak için hazır olması gerekir.
+
+- **Makinenizi bu videolar rehberliğinde yapılandırın**. Bu [video setinde](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) makinenizi nasıl kuracağınız hakkında daha fazla bilgi edinin.
+- **Python öğrenin**. Ayrıca, veri bilimciler için faydalı bir programlama dili olan ve bu derslerde kullandığımız [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) programlama dili hakkında temel bilgilere sahip olmanız da önerilir.
+- **Node.js ve JavaScript'i öğrenin**. Web uygulamaları oluştururken de bu kursta JavaScript'i birkaç kez kullanıyoruz, bu nedenle [node](https://nodejs.org), [npm](https://www.npmjs.com/) ve ayrıca hem Python hem de JavaScript geliştirme için kullanılabilen [Visual Studio Code](https://code.visualstudio.com/) yüklü olmalıdır.
+- **GitHub hesabı oluşturun**. Bizi burada [GitHub](https://github.com) üzerinde bulduğunuza göre, zaten bir hesabınız olabilir, ancak mevcut değilse, bir tane hesap oluşturun ve ardından bu müfredatı kendi başınıza kullanmak için çatallayın (fork). (Bize de yıldız vermekten çekinmeyin 😊)
+- **Scikit-learn'ü keşfedin**. Bu derslerde referans verdiğimiz, bir dizi ML kütüphanesinden oluşan [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) hakkında bilgi edinin.
+
+### Makine öğrenimi nedir?
+
+'Makine öğrenimi' terimi, günümüzün en popüler ve sık kullanılan terimlerinden biridir. Hangi alanda çalışırsanız çalışın, teknolojiyle ilgili bir tür aşinalığınız varsa, bu terimi en az bir kez duymuş olma ihtimaliniz yüksektir. Bununla birlikte, makine öğreniminin mekanikleri, yani çalışma prensipleri, çoğu insan için bir gizemdir. Makine öğrenimine yeni başlayan biri için konu bazen bunaltıcı gelebilir. Bu nedenle, makine öğreniminin gerçekte ne olduğunu anlamak ve pratik örnekler üzerinden adım adım öğrenmek önemlidir.
+
+![ML heyecan eÄŸrisi](../images/hype.png)
+
+> Google Trendler, 'makine öğrenimi' teriminin son 'heyecan eğrisini' gösteriyor
+
+Büyüleyici gizemlerle dolu bir evrende yaşıyoruz. Stephen Hawking, Albert Einstein ve daha pek çoğu gibi büyük bilim adamları, hayatlarını çevremizdeki dünyanın gizemlerini ortaya çıkaran anlamlı bilgiler aramaya adadılar. Öğrenmenin insani yönü de budur: insan evladı yeni şeyler öğrenir ve yetişkinliğe doğru büyüdükçe her yıl kendi dünyasının yapısını ortaya çıkarır.
+
+Bir çocuğun beyni ve duyuları, çevrelerindeki gerçekleri algılar ve çocuğun, öğrenilen kalıpları tanımlamak için mantıksal kurallar oluşturmasına yardımcı olan gizli yaşam kalıplarını yavaş yavaş öğrenir. İnsan beyninin öğrenme süreci, insanı bu dünyanın en gelişmiş canlısı yapar. Gizli kalıpları keşfederek sürekli öğrenmek ve sonra bu kalıplar üzerinde yenilik yapmak, yaşamımız boyunca kendimizi giderek daha iyi hale getirmemizi sağlar. Bu öğrenme kapasitesi ve gelişen kabiliyet, [beyin plastisitesi](https://www.simplypsychology.org/brain-plasticity.html) adı verilen bir kavramla ilgilidir. Yüzeysel olarak, insan beyninin öğrenme süreci ile makine öğrenimi kavramları arasında bazı motivasyonel benzerlikler çizebiliriz.
+
+[İnsan beyni](https://www.livescience.com/29365-human-brain.html) gerçek dünyadaki şeyleri algılar, algılanan bilgileri işler, mantıksal kararlar verir ve koşullara göre belirli eylemler gerçekleştirir. Akıllıca davranmak dediğimiz şey buydu işte. Bir makineye akıllı davranış sürecinin bir kopyasını programladığımızda buna yapay zeka (İngilizce haliyle artificial intelligence, kısaca **AI**) denir.
+
+Terimler karıştırılabilse de, makine öğrenimi (İngilizce haliyle machine learning, kısaca **ML**), yapay zekanın önemli bir alt kümesidir. **ML, mantıklı karar verme sürecini desteklemek için anlamlı bilgileri ortaya çıkarmak ve algılanan verilerden gizli kalıpları bulmak için özel algoritmalar kullanmakla ilgilenir**.
+
+![AI, ML, derin öğrenme, veri bilimi](../images/ai-ml-ds.png)
+
+> Yapay zeka, makine öğrenimi, derin öğrenme ve veri bilimi arasındaki ilişkileri gösteren bir diyagram. Bu infografik, [şu grafikten](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-) ilham alan [Jen Looper](https://twitter.com/jenlooper) tarafından hazırlanmıştır.
+
+> AI (Artificial Intelligence): Yapay zekâ  
+> ML(Machine Learning): Makine öğrenimi  
+> Deep Learning: Derin Öğrenme  
+> Data Science: Veri bilimi  
+
+## Bu kursta neler öğreneceksiniz
+
+Bu müfredatta, yalnızca yeni başlayanların bilmesi gereken makine öğreniminin temel kavramlarını ele alacağız. 'Klasik makine öğrenimi' dediğimiz şeyi, öncelikle birçok öğrencinin temel bilgileri öğrenmek için kullandığı mükemmel bir kütüphane olan Scikit-learn'ü kullanarak ele alıyoruz. Daha geniş yapay zeka veya derin öğrenme kavramlarını anlamak için, güçlü bir temel makine öğrenimi bilgisi vazgeçilmezdir ve bu yüzden onu burada sunmak istiyoruz.
+
+Bu kursta şunları öğreneceksiniz:
+
+- makine öğreniminin temel kavramları
+- ML'nin tarihi
+- ML ve adillik
+- regresyon ML teknikleri
+- sınıflandırma ML teknikleri
+- kümeleme ML teknikleri
+- doÄŸal dil iÅŸleme ML teknikleri
+- zaman serisi tahmini ML teknikleri
+- pekiştirmeli öğrenme
+- ML için gerçek-dünya uygulamaları
+
+## Neyi kapsamayacağız
+
+- derin öğrenme
+- sinir ağları
+- yapay zeka
+  
+Daha iyi bir öğrenme deneyimi sağlamak için, farklı bir müfredatta tartışacağımız sinir ağları, 'derin öğrenme' (sinir ağlarını kullanarak çok katmanlı modeller oluşturma) ve yapay zekânın karmaşıklıklarından kaçınacağız. Ayrıca, bu daha geniş alanın bu yönüne odaklanmak için yakında çıkacak bir veri bilimi müfredatı sunacağız.
+
+## Neden makine öğrenimi üzerinde çalışmalısınız?
+
+Sistemler perspektifinden makine öğrenimi, akıllı kararlar almaya yardımcı olmak için verilerden gizli kalıpları öğrenebilen otomatik sistemlerin oluşturulması olarak tanımlanır.
+
+Bu motivasyon, insan beyninin dış dünyadan algıladığı verilere dayanarak belirli şeyleri nasıl öğrendiğinden bir miktar esinlenmiştir.
+
+✅ Bir işletmenin, sabit kurallara dayalı bir karar aracı oluşturmak yerine neden makine öğrenimi stratejilerini kullanmayı denemek isteyebileceklerini bir an için düşünün.
+
+### Makine öğrenimi uygulamaları
+
+Makine öğrenimi uygulamaları artık neredeyse her yerde ve akıllı telefonlarımız, internete bağlı cihazlarımız ve diğer sistemlerimiz tarafından üretilen, toplumlarımızda akan veriler kadar yaygın hale gelmiş durumda. Son teknoloji makine öğrenimi algoritmalarının muazzam potansiyelini göz önünde bulunduran araştırmacılar, bu algoritmaların çok boyutlu ve çok disiplinli gerçek hayat problemlerini çözme yeteneklerini araştırıyorlar ve oldukça olumlu sonuçlar alıyorlar.
+
+**Makine öğrenimini birçok şekilde kullanabilirsiniz**:
+
+- Bir hastanın tıbbi geçmişinden veya raporlarından hastalık olasılığını tahmin etmek
+- Hava olaylarını tahmin etmek için hava durumu verilerini kullanmak
+- Bir metnin duygu durumunu anlamak
+- Propagandanın yayılmasını durdurmak için sahte haberleri tespit etmek
+
+Finans, ekonomi, yer bilimi, uzay araştırmaları, biyomedikal mühendislik, bilişsel bilim ve hatta beşeri bilimlerdeki alanlar, kendi alanlarının zorlu ve ağır veri işleme sorunlarını çözmek için makine öğrenimini tekniklerini kullanmaya başladılar.
+
+Makine öğrenimi, gerçek dünyadan veya oluşturulan verilerden anlamlı içgörüler bularak örüntü bulma sürecini otomatikleştirir. Diğerlerinin yanı sıra iş, sağlık ve finansal uygulamalarda son derece değerli olduğunu kanıtlamıştır.
+
+Yakın gelecekte, yaygın olarak benimsenmesi nedeniyle makine öğreniminin temellerini anlamak, tüm alanlardan insanlar için bir zorunluluk olacak.
+
+---
+## 🚀 Meydan Okuma
+
+Kağıt üzerinde veya [Excalidraw](https://excalidraw.com/) gibi çevrimiçi bir uygulama kullanarak AI, makine öğrenimi, derin öğrenme ve veri bilimi arasındaki farkları anladığınızdan emin olun. Bu tekniklerin her birinin çözmede iyi olduğu bazı problem fikirleri ekleyin.
+
+## [Ders sonrası test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=tr)
+
+## İnceleme ve Bireysel Çalışma
+
+Bulutta makine öğrenimi algoritmalarıyla nasıl çalışabileceğiniz hakkında daha fazla bilgi edinmek için bu [Eğitim Patikasını](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa) izleyin.
+
+## Ödev
+
+[Haydi başlayalım!](assignment.tr.md)
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+# Haydi Başlayalım
+
+## Talimatlar
+
+Bu not-verilmeyen ödevde, Python bilgilerinizi tazelemeli, geliştirme ortamınızı çalışır duruma getirmeli ve not defterlerini çalıştırabilmelisiniz.
+
+Bu [Python Eğitim Patikasını](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) bitirin ve ardından bu tanıtım videolarını izleyerek sistem kurulumunuzu yapın :
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
\ No newline at end of file
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+# Makine öğreniminin tarihi
+
+![Bir taslak-notta makine öğrenimi geçmişinin özeti](../../../sketchnotes/ml-history.png)
+> [Tomomi Imura](https://www.twitter.com/girlie_mac) tarafından hazırlanan taslak-not
+
+## [Ders öncesi test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=tr)
+
+Bu derste, makine öğrenimi ve yapay zeka tarihindeki önemli kilometre taşlarını inceleyeceğiz.
+
+Bir alan olarak yapay zekanın (AI) tarihi, makine öğreniminin tarihi ile iç içedir, çünkü makine öğrenimini destekleyen algoritmalar ve bilgi-işlem kapasitesindeki ilerlemeler, yapay zekanın gelişimini beslemektedir. Ayrı bilim alanlanları olarak bu alanlar 1950'lerde belirginleşmeye başlarken, önemli [algoritmik, istatistiksel, matematiksel, hesaplamalı ve teknik keşiflerin](https://wikipedia.org/wiki/Timeline_of_machine_learning) bir kısmı bu dönemden önce gelmiş ve bir kısmı da bu dönem ile örtüşmüştür. Aslında, insanlar [yüzlerce yıldır](https://wikipedia.org/wiki/History_of_artificial_intelligence) bu soruları düşünüyorlar: bu makale bir 'düşünen makine' fikrinin tarihsel entelektüel temellerini tartışıyor.
+
+## Önemli keşifler
+
+- 1763, 1812 - [Bayes Teoremi](https://tr.wikipedia.org/wiki/Bayes_teoremi) ve öncülleri. Bu teorem ve uygulamaları, önceki bilgilere dayalı olarak meydana gelen bir olayın olasılığını tanımlayan çıkarımın temelini oluşturur.
+- 1805 - [En Küçük Kareler Teorisi](https://tr.wikipedia.org/wiki/En_k%C3%BC%C3%A7%C3%BCk_kareler_y%C3%B6ntemi), Fransız matematikçi Adrien-Marie Legendre tarafından bulunmuştur. Regresyon ünitemizde öğreneceğiniz bu teori, makine öğrenimi modelini veriye uydurmada yardımcı olur.
+- 1913 - Rus matematikçi Andrey Markov'un adını taşıyan [Markov Zincirleri](https://tr.wikipedia.org/wiki/Markov_zinciri), önceki bir duruma dayalı olası olaylar dizisini tanımlamak için kullanılır.
+- 1957 - [Algılayıcı (Perceptron)](https://tr.wikipedia.org/wiki/Perceptron), derin öğrenmedeki ilerlemelerin temelini oluşturan Amerikalı psikolog Frank Rosenblatt tarafından icat edilen bir tür doğrusal sınıflandırıcıdır.
+- 1967 - [En Yakın Komşu](https://wikipedia.org/wiki/Nearest_neighbor), orijinal olarak rotaları haritalamak için tasarlanmış bir algoritmadır. Bir ML bağlamında kalıpları tespit etmek için kullanılır.
+- 1970 - [Geri Yayılım](https://wikipedia.org/wiki/Backpropagation), [ileri beslemeli sinir ağlarını](https://wikipedia.org/wiki/Feedforward_neural_network) eğitmek için kullanılır.
+- 1982 - [Tekrarlayan Sinir Ağları](https://wikipedia.org/wiki/Recurrent_neural_network), zamansal grafikler oluşturan ileri beslemeli sinir ağlarından türetilen yapay sinir ağlarıdır.
+
+✅ Biraz araştırma yapın. Makine öğrenimi ve yapay zeka tarihinde önemli olan başka hangi tarihler öne çıkıyor?
+
+## 1950: Düşünen makineler
+
+[2019'da halk tarafından](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) 20. yüzyılın en büyük bilim adamı seçilen gerçekten dikkate değer bir kişi olan Alan Turing'in, 'düşünebilen makine' kavramının temellerini attığı kabul edilir. Kendisine karşı çıkanlara yanıt olması için ve bu kavramın deneysel kanıtlarını bulma ihtiyacı sebebiyle, NLP derslerimizde keşfedeceğiniz [Turing Testi'ni](https://www.bbc.com/news/technology-18475646) oluşturdu.
+
+## 1956: Dartmouth Yaz Araştırma Projesi
+
+"Yapay zeka üzerine Dartmouth Yaz Araştırma Projesi", bir alan olarak yapay zeka için çığır açan bir olaydı ve burada 'yapay zeka' terimi ortaya çıktı ([kaynak](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> Öğrenmenin her yönü veya zekanın diğer herhangi bir özelliği, prensipte o kadar kesin bir şekilde tanımlanabilir ki, onu simüle etmek için bir makine yapılabilir.
+
+Baş araştırmacı, matematik profesörü John McCarthy, "öğrenmenin her yönünün veya zekanın diğer herhangi bir özelliğinin prensipte oldukça kesin bir şekilde tanımlanabileceği varsayımına dayanarak, onu simüle etmek için bir makine yapılabileceği" varsayımının doğru olmasını umarak ilerliyordu. Katılımcılar arasında bu alanın bir diğer önderi olan Marvin Minsky de vardı.
+
+Çalıştay, "sembolik yöntemlerin yükselişi, sınırlı alanlara odaklanan sistemler (ilk uzman sistemler) ve tümdengelimli sistemlere karşı tümevarımlı sistemler" dahil olmak üzere çeşitli tartışmaları başlatmış ve teşvik etmiştir. ([kaynak](https://tr.wikipedia.org/wiki/Dartmouth_Konferans%C4%B1)).
+
+## 1956 - 1974: "Altın yıllar"
+
+1950'lerden 70'lerin ortalarına kadar, yapay zekanın birçok sorunu çözebileceği umuduyla iyimserlik arttı. 1967'de Marvin Minsky kendinden emin bir şekilde "Bir nesil içinde... 'yapay zeka' yaratma sorunu büyük ölçüde çözülecek" dedi. (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+Doğal dil işleme araştırmaları gelişti, aramalar iyileştirildi ve daha güçlü hale getirildi, ve basit görevlerin sade dil talimatları kullanılarak tamamlandığı 'mikro dünyalar' kavramı yaratıldı.
+
+Araştırmalar, devlet kurumları tarafından iyi finanse edildi, hesaplamalar ve algoritmalarda ilerlemeler kaydedildi ve akıllı makinelerin prototipleri yapıldı. Bu makinelerden bazıları şunlardır:
+
+* [Robot Shakey](https://wikipedia.org/wiki/Shakey_the_robot), manevra yapabilir ve görevleri 'akıllıca' nasıl yerine getireceğine karar verebilir.
+
+    ![Shakey, akıllı bir robot](../images/shakey.jpg)
+    > 1972'de Shakey
+
+* Erken bir 'sohbet botu' olan Eliza, insanlarla sohbet edebilir ve ilkel bir 'terapist' gibi davranabilirdi. NLP derslerinde Eliza hakkında daha fazla bilgi edineceksiniz.
+
+    ![Eliza, bir bot](../images/eliza.png)
+    > Bir sohbet robotu olan Eliza'nın bir versiyonu
+
+* "Dünya Blokları", blokların üst üste koyulabilecekleri, sıralanabilecekleri ve karar vermeyi öğreten makinelerdeki deneylerin test edilebileceği bir mikro dünyaya örnekti. [SHRDLU](https://wikipedia.org/wiki/SHRDLU) gibi kütüphanelerle oluşturulan gelişmeler, dil işlemeyi ilerletmeye yardımcı oldu.
+
+    [![SHRDLU ile Dünya Blokları](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "SHRDLU ile Dünya Blokları" )
+    
+    > 🎥 Video için yukarıdaki resme tıklayın: SHRDLU ile Dünya Blokları
+
+## 1974 - 1980: "Yapay Zekâ Kışı"
+
+1970'lerin ortalarına gelindiğinde, 'akıllı makineler' yapmanın karmaşıklığının hafife alındığı ve mevcut hesaplama gücü göz önüne alındığında, verilen vaatlerin abartıldığı ortaya çıktı. Finansman kurudu ve alana olan güven azaldı. Güveni etkileyen bazı sorunlar şunlardı:
+
+- **Kısıtlıklar**. Hesaplama gücü çok sınırlıydı.
+- **Kombinasyonel patlama**. Hesaplama gücü ve yeteneğinde paralel bir evrim olmaksızın, bilgisayarlardan daha fazla soru istendikçe, eğitilmesi gereken parametre miktarı katlanarak arttı.
+- **Veri eksikliği**. Algoritmaları test etme, geliştirme ve iyileştirme sürecini engelleyen bir veri kıtlığı vardı.
+- **Doğru soruları mı soruyoruz?**. Sorulan sorular sorgulanmaya başlandı. Araştırmacılar mevcut yaklaşımları eleştirmeye başladı:
+  - Turing testleri, diğer fikirlerin yanı sıra, "Çin odası teorisi" aracılığıyla sorgulanmaya başlandı. Bu teori, "dijital bir bilgisayar, programlanarak dili anlıyormuş gibi gösterilebilir fakat gerçek bir dil anlayışı elde edilemez" savını öne sürmektedir. ([kaynak](https://plato.stanford.edu/entries/chinese-room/)
+  - "Terapist" ELIZA gibi yapay zekaların topluma tanıtılmasının etiğine meydan okundu.
+
+Aynı zamanda, çeşitli yapay zekâ düşünce okulları oluşmaya başladı. ["dağınık" ile "düzenli AI"](https://wikipedia.org/wiki/Neats_and_scruffies) uygulamaları arasında bir ikilem kuruldu. _Dağınık_ laboratuvarlar, istenen sonuçları elde edene kadar programlar üzerinde saatlerce ince ayar yaptı. _Düzenli_ laboratuvarlar "mantık ve biçimsel problem çözmeye odaklandı". ELIZA ve SHRDLU, iyi bilinen _dağınık_ sistemlerdi. 1980'lerde, ML sistemlerinin sonuçlarını tekrarlanabilir hale getirmek için talep ortaya çıktıkça, sonuçları daha açıklanabilir olduğu için _düzenli_ yaklaşım yavaş yavaş ön plana çıktı.
+
+## 1980'ler: Uzman sistemler
+
+Alan büyüdükçe, şirketlere olan faydası daha net hale geldi ve 1980'lerde 'uzman sistemlerin' yaygınlaşması da bu şekilde meydana geldi. "Uzman sistemler, yapay zeka (AI) yazılımlarının gerçek anlamda başarılı olan ilk formları arasındaydı." ([kaynak](https://tr.wikipedia.org/wiki/Uzman_sistemler)).
+
+Bu sistem türü aslında kısmen iş gereksinimlerini tanımlayan bir kural aracından ve yeni gerçekleri çıkarmak için kurallar sisteminden yararlanan bir çıkarım aracından oluşan bir _melezdir_.
+
+Bu çağda aynı zamanda sinir ağlarına artan ilgi de görülmüştür.
+
+## 1987 - 1993: Yapay Zeka 'Soğuması'
+
+Özelleşmiş uzman sistem donanımının yaygınlaşması, talihsiz bir şekilde bunları aşırı özelleşmiş hale getirdi. Kişisel bilgisayarların yükselişi de bu büyük, özelleşmiş, merkezi sistemlerle rekabet etti. Bilgisayarın demokratikleşmesi başlamıştı ve sonunda modern büyük veri patlamasının yolunu açtı.
+
+## 1993 - 2011
+
+Bu çağ, daha önce veri ve hesaplama gücü eksikliğinden kaynaklanan bazı sorunları çözebilmek için ML ve AI için yeni bir dönemi getirdi. Veri miktarı hızla artmaya başladı ve özellikle 2007'de akıllı telefonun ortaya çıkmasıyla birlikte iyisiyle kötüsüyle daha yaygın bir şekilde ulaşılabilir hale geldi. Hesaplama gücü katlanarak arttı ve algoritmalar da onunla birlikte gelişti. Geçmişin başıboş günleri gitmiş, yerine giderek olgunlaşan gerçek bir disipline dönüşüm başlamıştı.
+
+## Åžimdi
+
+Günümüzde makine öğrenimi ve yapay zeka hayatımızın neredeyse her alanına dokunuyor. Bu çağ, bu algoritmaların insan yaşamı üzerindeki risklerinin ve potansiyel etkilerinin dikkatli bir şekilde anlaşılmasını gerektirmektedir. Microsoft'tan Brad Smith'in belirttiği gibi, "Bilgi teknolojisi, gizlilik ve ifade özgürlüğü gibi temel insan hakları korumalarının kalbine giden sorunları gündeme getiriyor. Bu sorunlar, bu ürünleri yaratan teknoloji şirketlerinin sorumluluğunu artırıyor. Bizim açımızdan bakıldığında, düşünceli hükümet düzenlemeleri ve kabul edilebilir kullanımlar etrafında normların geliştirilmesi için de bir çağrı niteliği taşıyor." ([kaynak](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/) )).
+
+Geleceğin neler getireceğini birlikte göreceğiz, ancak bu bilgisayar sistemlerini ve çalıştırdıkları yazılım ve algoritmaları anlamak önemlidir. Bu müfredatın, kendi kararlarınızı verebilmeniz için daha iyi bir anlayış kazanmanıza yardımcı olacağını umuyoruz.
+
+[![Derin öğrenmenin tarihi](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "Derin öğrenmenin tarihi")
+> 🎥 Video için yukarıdaki resme tıklayın: Yann LeCun bu derste derin öğrenmenin tarihini tartışıyor
+
+---
+## 🚀Meydan okuma
+
+Bu tarihi anlardan birine girin ve arkasındaki insanlar hakkında daha fazla bilgi edinin. Büyüleyici karakterler var ve kültürel bir boşlukta hiçbir bilimsel keşif yaratılmadı. Ne keşfedersiniz?
+
+## [Ders sonrası test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=tr)
+
+## İnceleme ve Bireysel Çalışma
+
+Ä°ÅŸte izlenmesi ve dinlenmesi gerekenler:
+
+[Amy Boyd'un yapay zekanın evrimini tartıştığı bu podcast](http://runasradio.com/Shows/Show/739)
+
+[![Amy Boyd ile Yapay Zekâ'nın tarihi](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "Amy Boyd ile Yapay Zekâ'nın tarihi")
+
+## Ödev
+
+[Bir zaman çizelgesi oluşturun](assignment.tr.md)
\ No newline at end of file
diff --git a/1-Introduction/2-history-of-ML/translations/assignment.tr.md b/1-Introduction/2-history-of-ML/translations/assignment.tr.md
new file mode 100644
index 0000000000..f0e877636f
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.tr.md
@@ -0,0 +1,11 @@
+# Bir zaman çizelgesi oluşturun
+
+## Talimatlar
+
+[Bu repoyu](https://github.com/Digital-Humanities-Toolkit/timeline-builder) kullanarak; algoritmaların, matematiğin, istatistiğin, AI veya ML'in veya bunların bir kombinasyonunun tarihinin bazı yönlerinin bir zaman çizelgesini oluşturun. Bir kişiye, bir fikre veya bir düşüncenin uzun bir zamanına odaklanabilirsiniz. Multimedya öğeleri eklediğinizden emin olun.
+
+## DeÄŸerlendirme Listesi
+
+|  | Takdir edilesi                                         | Yeterli                                | İyileştirilmesi Lazım                                                |
+| -------- | ------------------------------------------------- | --------------------------------------- | ---------------------------------------------------------------- |
+|     Kriterler     | Zaman çizelgesi bir GitHub sayfası olarak yayınlanmış | Kod eksik ve henüz yayınlanmamış | Zaman çizelgesi eksik, iyi araştırılmamış ve yayınlanmamış |
\ No newline at end of file

From 603c255e5733d26cd518dc1bd6eb33f862cddd5c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Emre=20=C3=87a=C4=9Fatay=20K=C3=B6se?=
 <emrecgty@hotmail.com>
Date: Mon, 12 Jul 2021 05:12:52 +0300
Subject: [PATCH 113/368] fix: erraneous translation of some object keys

---
 quiz-app/src/assets/translations/tr.json | 42 ++++++++++++------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/quiz-app/src/assets/translations/tr.json b/quiz-app/src/assets/translations/tr.json
index c07894a0d9..050bbd2a29 100644
--- a/quiz-app/src/assets/translations/tr.json
+++ b/quiz-app/src/assets/translations/tr.json
@@ -126,7 +126,7 @@
 								"isCorrect": "false"
 							},
 							{
-								"cevapText": "1950'ler",
+								"answerText": "1950'ler",
 								"isCorrect": "true"
 							},
 							{
@@ -179,7 +179,7 @@
 						"questionText": "'Düzensiz' bir yapay zeka sistemi örneği nedir?",
 						"answerOptions": [
 							{
-								"cevapText": "ELIZA",
+								"answerText": "ELIZA",
 								"isCorrect": "true"
 							},
 							{
@@ -200,7 +200,7 @@
 								"isCorrect": "true"
 							},
 							{
-								"cevapText": "Jibo",
+								"answerText": "Jibo",
 								"isCorrect": "false"
 							},
 							{
@@ -734,7 +734,7 @@
 								"isCorrect": "true"
 							},
 							{
-								"cevapText": "düz",
+								"answerText": "düz",
 								"isCorrect": "false"
 							}
 						]
@@ -1042,7 +1042,7 @@
 								"isCorrect": "true"
 							},
 							{
-								"cevapText": "NLP",
+								"answerText": "NLP",
 								"isCorrect": "false"
 							}
 						]
@@ -1108,7 +1108,7 @@
 						"questionText": "Sınıflandırıcılarınızın probleminizi çözmesine yardımcı olmak için tekrarlayan veya yardımcı olmayan verileri temizlemek önemlidir.",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "true"
 							},
 							{
@@ -1144,7 +1144,7 @@
 						"questionText": "Dengeli, temiz veriler en iyi sınıflandırma sonuçlarını verir",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "true"
 							},
 							{
@@ -1174,7 +1174,7 @@
 						"questionText": "Sınıflandırma bir:",
 						"answerOptions": [
 							{
-								"cevapText": "NLP türüdür",
+								"answerText": "NLP türüdür",
 								"isCorrect": "false"
 							},
 							{
@@ -1385,7 +1385,7 @@
 						"questionText": "Bir web uygulamasına bir model gömmek, onun çevrimdışı özellikli olmasına yardımcı olur",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "true"
 							},
 							{
@@ -1683,7 +1683,7 @@
 						"questionText": "K-Means'i kullanmak için önce 'k' değerini belirlemelisiniz",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "true"
 							},
 							{
@@ -1763,7 +1763,7 @@
 								"isCorrect": "false"
 							},
 							{
-								"cevapText": "Eliza",
+								"answerText": "Eliza",
 								"isCorrect": "true"
 							},
 							{
@@ -2135,7 +2135,7 @@
 						"questionText": "Üzerinde işlem yapmadan önce verilerinizi ve zaaflarını anlamak önemlidir.",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "true"
 							},
 							{
@@ -2188,7 +2188,7 @@
 						"questionText": "Verileri bir Etiket sütunu kullanarak kategorilere ayırmak faydalı olabilir.",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "true"
 							},
 							{
@@ -2241,7 +2241,7 @@
 						"questionText": "Duygu analizini test etmek için, aynı inceleme için gözden geçirenin puanıyla eşleştiğinden emin olun.",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "true"
 							},
 							{
@@ -2522,7 +2522,7 @@
 						"questionText": "Ödül fonksiyonu, ortamın her durumu için bir puan döndürür.",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "true"
 							},
 							{
@@ -2562,7 +2562,7 @@
 								"isCorrect": "true"
 							},
 							{
-								"cevapText": "-0.25",
+								"answerText": "-0.25",
 								"isCorrect": "false"
 							},
 							{
@@ -2575,7 +2575,7 @@
 						"questionText": "Dersimizde öğrenme süreci boyunca keşif kullanmak, sömürü kullanmaktan daha iyiydi.",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "false"
 							},
 							{
@@ -2594,7 +2594,7 @@
 						"questionText": "Satranç ve Go, süreğen durumları olan oyunlardır.",
 						"answerOptions": [
 							{
-								"cevapText": "doÄŸru",
+								"answerText": "doÄŸru",
 								"isCorrect": "false"
 							},
 							{
@@ -2729,7 +2729,7 @@
 								"isCorrect": "false"
 							},
 							{
-								"cevapText": "NLP",
+								"answerText": "NLP",
 								"isCorrect": "false"
 							}
 						]
@@ -2769,7 +2769,7 @@
 								"isCorrect": "true"
 							},
 							{
-								"cevapText": "NLP",
+								"answerText": "NLP",
 								"isCorrect": "false"
 							}
 						]
@@ -2786,7 +2786,7 @@
 								"isCorrect": "false"
 							},
 							{
-								"cevapText": "NLP",
+								"answerText": "NLP",
 								"isCorrect": "false"
 							}
 						]

From 5fb6fec85a791a8923338bff7ce3e030ca4d42f6 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Mon, 12 Jul 2021 20:07:59 +0900
Subject: [PATCH 114/368] add japanese translation (2-Regression)

---
 2-Regression/translations/README.ja.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md
index dd58ff9a48..2ff2336638 100644
--- a/2-Regression/translations/README.ja.md
+++ b/2-Regression/translations/README.ja.md
@@ -17,10 +17,10 @@
 
 ### レッスン
 
-1. [商売�具](../1-Tools/README.md)
-2. [データ管�](../2-Data/README.md)
-3. [線形回帰�多項�回帰](../3-Linear/README.md)
-4. [ロジスティック回帰](../4-Logistic/README.md)
+1. [商売�具](../1-Tools/README.ja.md)
+2. [データ管�](../2-Data/README.ja.md)
+3. [線形回帰�多項�回帰](../3-Linear/README.ja.md)
+4. [ロジスティック回帰](../4-Logistic/README.ja.md)
 
 ---
 ### クレジット

From 62eb776b7fe94d86c9c1585cafaa0e9e6a031161 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Mon, 12 Jul 2021 23:59:13 +0900
Subject: [PATCH 115/368] add japanese translation of 2-Regression/2-Data

---
 2-Regression/2-Data/translations/README.ja.md | 144 +++++++++---------
 1 file changed, 74 insertions(+), 70 deletions(-)

diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index 122d5badd2..7780747816 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -1,55 +1,55 @@
 # Scikit-learnを用��回帰モデル�構築: データ�準備��視化
 
-> ![Data visualization infographic](../images/data-visualization.png)
+> ![データ��視化�関�るインフォグラフィック](../images/data-visualization.png)
 > [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
 
 ## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
 
 ## イントロダクション
 
-Now that you are set up with the tools you need to start tackling machine learning model building with Scikit-learn, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.
-
+Scikit-learnを使��機械学習モデル�構築を行����必��ツール�用����������データ�対�る����を始�る準備�整����。データを扱�MLソリューションを�用�る際���データセット�潜在能力を�切�引�出����正������を�る����常�����。
 
 ��レッスン���以下���を学���。
 
 - モデルを構築�る���データ処�方法����
 - データ��視化���るMatplotlib�使�方����
 
-## Asking the right question of your data
+## データ�対��正������を�る
+
+��よ��質��答�る��よ�����よ��MLアルゴリズムを活用�る��決�り��。���返���る回答�質��データ�性質�大���存���。
 
-The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.
+��レッスン����用��れ�[データ]((../../data/US-pumpkins.csv))を見����ょ�。��.csvファイル��VS Code�開��������。���確����る��空欄����り�文字列や数値データ�混在���る�����り��。���'Package'���奇妙�列�� 'sacks' や 'bins' ���異�る���値�混在�����。��よ���データ��ょ����混乱状態��り��。
 
-Take a look at the [data](../data/US-pumpkins.csv) provided for this lesson. You can open this .csv file in VS Code. A quick skim immediately shows that there are blanks and a mix of strings and numeric data. There's also a strange column called 'Package' where the data is a mix between 'sacks', 'bins' and other values. The data, in fact, is a bit of a mess.
+実際�����MLモデル�作�����使�るよ��整��データセットを�������る�����り�り��ん。��レッスン���Python�標準ライブラリを使��生�データセットを準備�る方法を学���。���データを�視化�る���様々�テクニックを学���。
 
-In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. In this lesson, you will learn how to prepare a raw dataset using standard Python libraries. You will also learn various techniques to visualize the data.
+## ケーススタディ: カボ�ャ�市場
 
-## Case study: 'the pumpkin market'
+ルート�`date`フォルダ�中� [US-pumpkins.csv](../../data/US-pumpkins.csv) ������.csvファイル��り��。��ファイル���カボ�ャ�市場�関�る1757行�データ��都市���グループ分��れ�入�����。�れ��米国農務���布���る [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) �ら抽出��生データ��。
 
-In this folder you will find a .csv file in the root `data` folder called [US-pumpkins.csv](../data/US-pumpkins.csv) which includes 1757 lines of data about the market for pumpkins, sorted into groupings by city. This is raw data extracted from the [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distributed by the United States Department of Agriculture.
+### データ�準備
 
-### Preparing data
+��データ�パブリックドメイン��。米国農務��ウェブサイト�ら�都市���個別ファイルをダウンロード�る�������。ファイル�多��り����よ�������都市�データを1��スプレッドシート�連�����。次��データを詳��見����ょ�。
 
-This data is in the public domain. It can be downloaded in many separate files, per city, from the USDA web site. To avoid too many separate files, we have concatenated all the city data into one spreadsheet, thus we have already _prepared_ the data a bit. Next, let's take a closer look at the data.
+### カボ�ャ�データ - �期��論
 
-### The pumpkin data - early conclusions
+��データ����何�気付������り���？文字列�数字�空白�奇妙�値�混在������味を�解���れ��ら����気付�������。
 
-What do you notice about this data? You already saw that there is a mix of strings, numbers, blanks and strange values that you need to make sense of.
+回帰を使�����データ���よ������������？「�る月�販売�れるカボ�ャ�価格を予測�る����������ょ��？データをも�一度見��る����課題�必��データ構造を作る���������変更�必���。
 
-What question can you ask of this data, using a Regression technique? What about "Predict the price of a pumpkin for sale during a given month". Looking again at the data, there are some changes you need to make to create the data structure necessary for the task. 
-## Exercise - analyze the pumpkin data
+## エクササイズ - カボ�ャ�データを分�
 
-Let's use [Pandas](https://pandas.pydata.org/), (the name stands for `Python Data Analysis`) a tool very useful for shaping data, to analyze and prepare this pumpkin data.
+データを整形�る���常�便利� [Pandas](https://pandas.pydata.org/) (Python Data Analysis�略) を使�����カボ�ャ�データを分���り整�����ょ�。
 
-### First, check for missing dates
+### 最���日付�欠�������確��る
 
-You will first need to take steps to check for missing dates:
+日付�欠�������確��る���������ステップ��り��:
 
-1. Convert the dates to a month format (these are US dates, so the format is `MM/DD/YYYY`).
-2. Extract the month to a new column.
+1. 日付を月�形��変��る（�れ�米国�日付����形�� `MM/DD/YYYY` ��る）。
+2. 新��列���月を抽出�る。
 
-Open the _notebook.ipynb_ file in Visual Studio Code and import the spreadsheet in to a new Pandas dataframe.
+Visual Studio Code� _notebook.ipynb_ ファイルを開��スプレッドシートを Pandas DataFrame ���イン�ート���。
 
-1. Use the `head()` function to view the first five rows.
+1. `head()` 関数を使��最��5行を確����。
 
     ```python
     import pandas as pd
@@ -57,30 +57,32 @@ Open the _notebook.ipynb_ file in Visual Studio Code and import the spreadsheet
     pumpkins.head()
     ```
 
-    ✅ What function would you use to view the last five rows?
+    ✅ 最後�5行を表示�る�����よ��関数を使用����？
+
 
-1. Check if there is missing data in the current dataframe:
+1. �在�データフレーム�欠�データ��る����を�ェック���。
 
     ```python
     pumpkins.isnull().sum()
     ```
 
-    There is missing data, but maybe it won't matter for the task at hand.
+    欠�データ��り�����今回�タスク��影響�������。
 
-1. To make your dataframe easier to work with, drop several of its columns, using `drop()`, keeping only the columns you need: 
+
+1. データフレームを扱�や���る����`drop()` 関数を使�������列を削除��必��列��を残�よ�����。
 
     ```python
     new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
     pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
     ```
 
-### Second, determine average price of pumpkin
+### 次��カボ�ャ�平�価格を決定���。
 
-Think about how to determine the average price of a pumpkin in a given month. What columns would you pick for this task? Hint: you'll need 3 columns.
+�る月����ゃ�平�価格を決定�る方法を考�����ょ�。��タスク�������列�必����？ヒント：3��列�必���り��。
 
-Solution: take the average of the `Low Price` and `High Price` columns to populate the new Price column, and convert the Date column to only show the month. Fortunately, according to the check above, there is no missing data for dates or prices.
+解決策：「最低価格��「最高価格��平�値を���新��「price�列を作���「日付�列を月��表示�るよ��変����。幸������上記�確����果�よる�日付や価格�欠�データ��り��ん���。
 
-1. To calculate the average, add the following code:
+1. 平�値を算出�る����以下�コードを追加���。
 
     ```python
     price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
@@ -89,37 +91,39 @@ Solution: take the average of the `Low Price` and `High Price` columns to popula
     
     ```
 
-   ✅ Feel free to print any data you'd like to check using `print(month)`.
+   ✅ `print(month)` ��を使��自由�データを確���������。
+
 
-2. Now, copy your converted data into a fresh Pandas dataframe:
+2. 変���データをPandas�新��データフレーム�コピー���。
 
     ```python
     new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
     ```
 
-    Printing out your dataframe will show you a clean, tidy dataset on which you can build your new regression model.
+    データフレームを出力�る��新��回帰モデルを構築�る���綺麗�整頓�れ�データセット�表示�れ��。
+
+### �も�待����������������。
 
-### But wait! There's something odd here
+`Package` 列を�る��カボ�ャ�様々�形�販売�れ����。「1 1/9ブッシェル��売られ��るも��「1/2ブッシェル��売られ��るも�����ゃ1個���売られ��るも��1�ンド���売られ��るも��幅���大��箱�売られ��るも���様々��。
 
-If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in '1 1/9 bushel' measures, and some in '1/2 bushel' measures, some per pumpkin, some per pound, and some in big boxes with varying widths.
 
-> Pumpkins seem very hard to weigh consistently
+> ���ゃ���を一定��る����も難��よ���。
 
-Digging into the original data, it's interesting that anything with `Unit of Sale` equalling 'EACH' or 'PER BIN' also have the `Package` type per inch, per bin, or 'each'. Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string 'bushel' in their `Package` column.
+元�データを調���る��「Unit of Sale��「EACH����「PER BIN������るも���「Package��「per inch��「per bin��「each������る��興味深���。カボ�ャ�計����一貫性を���る���常�難��よ�����`Package`列�「bushel����文字列を��カボ�ャ��を�択��フィルタリング�����ょ�。
 
-1. Add a filter at the top of the file, under the initial .csv import:
+1. ファイル�一番上�フィルタを追加���。
 
     ```python
     pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
     ```
 
-    If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel.
+    今�データを出力���る��ブッシェル���カボ�ャを�む415行���データ��得られ���������り��。
 
-### But wait! There's one more thing to do
+### �も�待�������も�����やる������り��。
 
-Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, so do some math to standardize it.
+行���ブッシェル���異�る���気付�����？1ブッシェル��り�価格を表示�る�����計算��価格を標準化�る必���り��。
 
-1. Add these lines after the block creating the new_pumpkins dataframe:
+1. new_pumpkinsデータフレームを作��るブロック�後�以下�行を追加���。
 
     ```python
     new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
@@ -127,34 +131,34 @@ Did you notice that the bushel amount varies per row? You need to normalize the
     new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
     ```
 
-✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!
+✅ [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308) �よる��ブッシェル����体�を測るも�����農産物�種類�よ��異�り��。例���トマト�1ブッシェル��56�ンド�����る��れ����。葉��や野�����少���もスペースを�る�����れん��1ブッシェル�����20�ンド��。�ん��複雑����ブッシェル�ら�ンド���算��倒���や���ブッシェル���価格を決���ょ�。����カボ�ャ�ブッシェル�����議論��データ�性質を�解�る����������る�を示�����。
 
-Now, you can analyze the pricing per unit based on their bushel measurement. If you print out the data one more time, you can see how it's standardized.
+�れ��ブッシェル�測定値�基����ユニット���価格を分��る�������。も�1度データを出力���る��標準化�れ��る�����り��。
 
-✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.
+✅ �ーフブッシェル�売られ��るカボ�ャ���も高価����気付�����？������り���？���カボ�ャ�大��カボ�ャよりも�る��高価��。��ら�大��カボ�ャ中身���体���り�考�る�空洞�部分�多���れる�考�られ��。
 
-## Visualization Strategies
+## �視化戦略
 
-Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover. 
+データサイエンティスト�役割�一���扱�データ�質や性質を示�����。������データ��������を示�興味深�ビジュアライゼーション（プロット�グラフ��ャート）を作��る���よ��り��。���る����他�方法��発見����関係性やギャップを視覚的�示��������。
 
-Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.
+����視化�る���データ����機械学習�手法を判断�る�������。例���散布図�直線�沿���るよ��見�る場���線形回帰��用�る手法�良�候補�一����考�られ��。
 
-One data visualization libary that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson).
+Jupyter notebook����利用��るテータ�視化ライブラリ�一�� [Matplotlib](https://matplotlib.org/) ��り�� (��レッスン�も紹介�����)。
 
-> Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
+> [��ら��ュートリアル](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa) �データ��視化���より深�体験�る�������。
 
-## Exercise - experiment with Matplotlib
+## エクササイズ - Matplotlib�実験
 
-Try to create some basic plots to display the new dataframe you just created. What would a basic line plot show?
+先��作���データフレームを表示�る��������基本的�プロットを作���������。折れ線グラフ�ら何�読��れる��ょ��？
 
-1. Import Matplotlib at the top of the file, under the Pandas import:
+1. ファイル�先頭�Pandas�イン�ート�下� Matplotlibをイン�ート���。
 
     ```python
     import matplotlib.pyplot as plt
     ```
 
-1. Rerun the entire notebook to refresh.
-1. At the bottom of the notebook, add a cell to plot the data as a box:
+1. ノートブック全体を�実行��リフレッシュ���。
+2. ノートブック�下部��データをプロット�る���セルを追加���。
 
     ```python
     price = new_pumpkins.Price
@@ -163,39 +167,39 @@ Try to create some basic plots to display the new dataframe you just created. Wh
     plt.show()
     ```
 
-    ![A scatterplot showing price to month relationship](../images/scatterplot.png)
+    ![価格�月�関係を示�散布図](../images/scatterplot.png)
 
-    Is this a useful plot? Does anything about it surprise you?
+    �れ�役�立�プロット���？���驚������り���？
 
-    It's not particularly useful as all it does is display in your data as a spread of points in a given month.
+    �れ�データを�る月����データ�広�り���表示���る������特�役�立�も����り��ん。
 
-### Make it useful
+### 活用��るよ���る
 
-To get charts to display useful data, you usually need to group the data somehow. Let's try creating a plot where the y axis shows the months and the data demonstrates the distribution of data.
+グラフ�有用�データを表示�る���通常�データを何ら��方法�グループ化�る必���り��。�����X軸�月を表��データ�分布を示�よ��プロットを作�����ょ�。
 
-1. Add a cell to create a grouped bar chart:
+1. セルを追加��グループ化�れ�棒グラフを作����。
 
     ```python
     new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
     plt.ylabel("Pumpkin Price")
     ```
 
-    ![A bar chart showing price to month relationship](../images/barchart.png)
+    ![値段�月�関係を表��棒グラフ](../images/barchart.png)
 
-    This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?
+    ��プロット�方��より有用�データを�視化������カボ�ャ�価格�最も高��る���9月�10月��る��を示���るよ���。��プロット�����期待�応�るも����？��よ��点�期待通り���？�����よ��点�期待�答�られ����ん�？
 
 ---
 
-## 🚀Challenge
+## 🚀�ャレンジ
 
-Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
+Matplotlib��供�る様々�タイプ�ビジュアライゼーションを探�����ょ�。回帰��題����タイプ�最も����る��ょ��？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
 
-## Review & Self Study
+## レビュー & 自主学習
 
-Take a look at the many ways to visualize data. Make a list of the various libraries available and note which are best for given types of tasks, for example 2D visualizations vs. 3D visualizations. What do you discover?
+データを�視化�る���様々�方法を見����ょ�。様々�ライブラリをリストアップ��例��2Dビジュアライゼーション�3Dビジュアライゼーション�よ���特定�タイプ�タスク�最��も�をメモ���。��よ��発見��り����？
 
-## Assignment
+## 課題
 
-[Exploring visualization](assignment.md)
+[ビジュアライゼーション�探求](assignment.md)

From 3360c6716d6a9c033435c26c3e55036491868e8f Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Tue, 13 Jul 2021 00:10:12 +0900
Subject: [PATCH 116/368] add japanese translation of 2-Regression/2-Data

---
 2-Regression/2-Data/translations/README.ja.md | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index 7780747816..1570be3c5e 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -1,6 +1,7 @@
 # Scikit-learnを用��回帰モデル�構築: データ�準備��視化
 
 > ![データ��視化�関�るインフォグラフィック](../images/data-visualization.png)
+> 
 > [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
 
 ## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
@@ -18,7 +19,7 @@ Scikit-learnを使��機械学習モデル�構築を行����必�
 
 ��よ��質��答�る��よ�����よ��MLアルゴリズムを活用�る��決�り��。���返���る回答�質��データ�性質�大���存���。
 
-��レッスン����用��れ�[データ]((../../data/US-pumpkins.csv))を見����ょ�。��.csvファイル��VS Code�開��������。���確����る��空欄����り�文字列や数値データ�混在���る�����り��。���'Package'���奇妙�列�� 'sacks' や 'bins' ���異�る���値�混在�����。��よ���データ��ょ����混乱状態��り��。
+��レッスン����用��れ�[データ]((../../data/US-pumpkins.csv))を見����ょ�。��.csvファイル��VS Code�開��������。���確����る��空欄����り�文字列や数値データ�混在���る�����り��。���「Package����奇妙�列��「sacks�や 「bins����異�る���値�混在�����。��よ���データ��ょ����混乱状態��り��。
 
 実際�����MLモデル�作�����使�るよ��整��データセットを�������る�����り�り��ん。��レッスン���Python�標準ライブラリを使��生�データセットを準備�る方法を学���。���データを�視化�る���様々�テクニックを学���。
 
@@ -141,7 +142,7 @@ Visual Studio Code� _notebook.ipynb_ ファイルを開��スプレッド
 
 データサイエンティスト�役割�一���扱�データ�質や性質を示�����。������データ��������を示�興味深�ビジュアライゼーション（プロット�グラフ��ャート）を作��る���よ��り��。���る����他�方法��発見����関係性やギャップを視覚的�示��������。
 
-����視化�る���データ����機械学習�手法を判断�る�������。例���散布図�直線�沿���るよ��見�る場���線形回帰��用�る手法�良�候補�一����考�られ��。
+����視化�る���データ����機械学習�手法を判断�る�������。例���散布図�直線�沿���るよ��見�る場����用�る手法�候補�一����線形回帰�考�られ��。
 
 Jupyter notebook����利用��るテータ�視化ライブラリ�一�� [Matplotlib](https://matplotlib.org/) ��り�� (��レッスン�も紹介�����)。
 
@@ -171,11 +172,11 @@ Jupyter notebook����利用��るテータ�視化ライブラリ�
 
     �れ�役�立�プロット���？���驚������り���？
 
-    �れ�データを�る月����データ�広�り���表示���る������特�役�立�も����り��ん。
+    �れ�データを�る月�����データ�広�り���表示���る������特�役�立�も����り��ん。
 
 ### 活用��るよ���る
 
-グラフ�有用�データを表示�る���通常�データを何ら��方法�グループ化�る必���り��。�����X軸�月を表��データ�分布を示�よ��プロットを作�����ょ�。
+グラフ�有用�データを表示�る���通常�データを何ら��方法�グループ化�る必���り��。�����X軸を月����データ�分布を示�よ��プロットを作�����ょ�。
 
 1. セルを追加��グループ化�れ�棒グラフを作����。
 

From 4be82a4530c550a5b404fa6ded6ec0e82a8c83a9 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Mon, 12 Jul 2021 19:56:29 +0200
Subject: [PATCH 117/368] Ch2-1 readme/assignment translation completed

---
 .../1-Tools/translations/README.it.md         | 55 ++++++++++---------
 .../1-Tools/translations/assignment.it.md     | 13 +++++
 2 files changed, 41 insertions(+), 27 deletions(-)
 create mode 100644 2-Regression/1-Tools/translations/assignment.it.md

diff --git a/2-Regression/1-Tools/translations/README.it.md b/2-Regression/1-Tools/translations/README.it.md
index 94c0a5c6ef..48c61d3404 100644
--- a/2-Regression/1-Tools/translations/README.it.md
+++ b/2-Regression/1-Tools/translations/README.it.md
@@ -32,21 +32,21 @@ In questa lezione, si imparerà come:
 
    > Si prenda confidenza con Python tramite questa collezione di [moduli di apprendimento](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
 
-3. **Installare Scikit-learn**, Seguendo [quest istruczionsi](https://scikit-learn.org/stable/install.html). Visto che ci si deve assicurare di usare Python 3, ci si raccomanda di usare un ambiente virtuale. Si noti che se si installa questa libreria in un M1 Mac, ci sono istruzioni speciali nella pagina di cui al riferimento qui sotto.
+3. **Installare Scikit-learn**, seguendo [queste istruzioni](https://scikit-learn.org/stable/install.html). Visto che ci si deve assicurare di usare Python 3, ci si raccomanda di usare un ambiente virtuale. Si noti che se si installa questa libreria in un M1 Mac, ci sono istruzioni speciali nella pagina di cui al riferimento qui sopra.
 
 1. **Installare Jupyter Notebook**. Servirà [installare il pacchetto Jupyter](https://pypi.org/project/jupyter/). 
 
-## Ambiente di crezione ML
+## Ambiente di creazione ML
 
 Si useranno **notebook** per sviluppare il codice Python e creare modelli di machine learning. Questo tipo di file è uno strumento comune per i data scientist, e viene identificato dal suffisso o estensione `.ipynb`.
 
-I notebook sono un ambiente interattivo che consente allo sviluppatore di scrivere codie, aggiungere note e scrivere documentazione attorno al codice il che è particolarmente utile per progetti sperimentali o orientati alla ricerca.
+I notebook sono un ambiente interattivo che consente allo sviluppatore di scrivere codice, aggiungere note e scrivere documentazione attorno al codice il che è particolarmente utile per progetti sperimentali o orientati alla ricerca.
 
 ### Esercizio - lavorare con un notebook
 
 In questa cartella, si troverà il file _notebook.ipynb_. 
 
-1. Aprure _notebook.ipynb_ in Visual Studio Code.
+1. Aprire _notebook.ipynb_ in Visual Studio Code.
 
    Un server Jupyter verrà lanciato con Python 3+. Si troveranno aree del notebook che possono essere `eseguite`, pezzi di codice. Si può eseguire un blocco di codice selezionando l'icona che assomiglia a un bottone di riproduzione.
 
@@ -63,7 +63,7 @@ In questa cartella, si troverà il file _notebook.ipynb_.
     hello notebook
     ```
 
-![VS Code con un notebook aperto](images/notebook.png)
+![VS Code con un notebook aperto](../images/notebook.png)
 
 Si può inframezzare il codice con commenti per auto documentare il notebook.
 
@@ -71,25 +71,25 @@ Si può inframezzare il codice con commenti per auto documentare il notebook.
 
 ## Scikit-learn installato e funzionante
 
-Adesso che Python è impostato nel proprio ambiente locale, e si è familiari con i notebook Jupyter, si acquisterà ora confidenza con Scikit-learn (si pronuncia con la `si` di `science`). Scikit-learn fornisce una [API estensiva](https://scikit-learn.org/stable/modules/classes.html#api-ref) che aiuta a eseguire attività ML.
+Adesso che Python è impostato nel proprio ambiente locale, e si è familiari con i notebook Jupyter, si acquisterà ora confidenza con Scikit-learn (si pronuncia con la `si` della parola inglese `science`). Scikit-learn fornisce una [API estensiva](https://scikit-learn.org/stable/modules/classes.html#api-ref) che aiuta a eseguire attività ML.
 
 Stando al loro [sito web](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn è una libreria di machine learning open source che supporta l'apprendimento assistito (supervised learning) e non assistito (unsuperivised learnin). Fornisce anche strumenti vari per l'adattamento del modello, la pre-elaborazione dei dati, la selezione e la valutazione dei modelli e molte altre utilità."
 
 In questo corso, si userà Scikit-learn e altri strumenti per costruire modelli di machine learning per eseguire quelle che vengono chiamate attività di 'machine learning tradizionale'. Si sono deliberamente evitate le reti neurali e il deep learning visto che saranno meglio trattati nel prossimo programma di studi 'AI per Principianti'.
 
-Scikit-Learn rende semplice costruire modelli e valutarli per l'uso. Si concentra principalmente sull'utilizzo di dati numerici e contiene diversi insiemi di dati già pronti per l'uso come strumenti di apprendimento. Include anche modelli pre-costruiti per gli studenti da provare. Si esplora ora il processo di caricamento dei dati preconfezionati, e utilizzando un modello di stimatore incorporato prima il modello ML con Scikit-Learn con alcuni dati di base.
+Scikit-learn rende semplice costruire modelli e valutarli per l'uso. Si concentra principalmente sull'utilizzo di dati numerici e contiene diversi insiemi di dati già pronti per l'uso come strumenti di apprendimento. Include anche modelli pre-costruiti per gli studenti da provare. Si esplora ora il processo di caricamento dei dati preconfezionati, e, utilizzando un modello di stimatore incorporato, un primo modello ML con Scikit-Learn con alcuni dati di base.
 
-# Esercizio - Il Primo notebook Scikit-learn
+## Esercizio - Il Primo notebook Scikit-learn
 
 > Questo tutorial è stato ispirato dall'[esempio di regressione lineare](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) nel sito web di Scikit-learn.
 
 Nel file _notebook.ipynb_ associato a questa lezione, svuotare tutte le celle usando l'icona cestino ('trash can').
 
-In questa sezione, di lavorerà con un piccolo insieme di dati sul diabete che è incorporato in Scikit-learn per scopi di apprendimento. Si immagini di voler testare un trattamento per i pazienti diabetici. I modelli di machine learning potrebbero essere di aiuto nel determinare quali pazienti risponderebbero meglio al trattamento, in base a combinazioni di variabili. Anche un modello di regressione molto semplice, quando visualizzato, potrebbe mostrare informazioni sulle variabili che aiuteranno a organizzare le proprie sperimentazioni cliniche teoriche.
+In questa sezione, di lavorerà con un piccolo insieme di dati sul diabete che è incorporato in Scikit-learn per scopi di apprendimento. Si immagini di voler testare un trattamento per i pazienti diabetici. I modelli di machine learning potrebbero essere di aiuto nel determinare quali pazienti risponderebbero meglio al trattamento, in base a combinazioni di variabili. Anche un modello di regressione molto semplice, quando visualizzato, potrebbe mostrare informazioni sulle variabili che aiuteranno a organizzare le sperimentazioni cliniche teoriche.
 
-✅ Esistono molti tipi di metodi di regressione e quale scegliere dipende dalla risposta che si sta cercando. Se si vuole prevedere l'altezza probabile per una persona di una data età, si dovrebbe usare la regressione lineare, visto che si sta cercando un **valore numerico**. Se si è interessati a scoprire se un tipo di cucina dovrebbe essere considerato vegano o no, si sta cercando un' **assegnazione di categoria** quindi si dovrebbe usare la regressione logistica. Si imparerà di più sulla regressione logistica in seguito. Si pensi ad alcune domande che si possono chiedere ai dati e quale di questi metodi sarebbe più appropriato.
+✅ Esistono molti tipi di metodi di regressione e quale scegliere dipende dalla risposta che si sta cercando. Se si vuole prevedere l'altezza probabile per una persona di una data età, si dovrebbe usare la regressione lineare, visto che si sta cercando un **valore numerico**. Se si è interessati a scoprire se un tipo di cucina dovrebbe essere considerato vegano o no, si sta cercando un'**assegnazione di categoria** quindi si dovrebbe usare la regressione logistica. Si imparerà di più sulla regressione logistica in seguito. Si pensi ad alcune domande che si possono chiedere ai dati e quale di questi metodi sarebbe più appropriato.
 
-Si inizia su questo compito.
+Si inizia con questa attività.
 
 ### Importare le librerie
 
@@ -109,26 +109,26 @@ Importare alcune librerie che saranno di aiuto per le proprie attività.
    from sklearn import datasets, linear_model, model_selection
    ```
 
-   Qui sopra vengono importati `matplottlib`, `numpy` da `sklearn` si importa `datasets`, `linear_model` e `model_selection`. `model_selection` viene usato per dividere i dati negli insiemi di addestramento e test.
+   Qui sopra vengono importati `matplottlib`, e `numpy`, da `sklearn` si importa `datasets`, `linear_model` e `model_selection`. `model_selection` viene usato per dividere i dati negli insiemi di addestramento e test.
 
 ### L'insieme di dati riguardante il diabete
 
-L'[insieme dei dati sul diabetes](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) include 442 campioni di dati sul diabete, con 10 variabili caratteristiche, alcune delle quali includono:
+L'[insieme dei dati sul diabete](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) include 442 campioni di dati sul diabete, con 10 variabili caratteristiche, alcune delle quali includono:
 
-age (età): età in anni
-bmi: indice di massa corporea (body mass index)
-bp: media pressione sanguinea
-s1 tc: Cellule T (un tipo di leucocito)
+- age (età): età in anni
+- bmi: indice di massa corporea (body mass index)
+- bp: media pressione sanguinea
+- s1 tc: Cellule T (un tipo di leucocito)
 
 ✅ Questo insieme di dati include il concetto di "sesso" come caratteristica variabile importante per la ricerca sul diabete. Molti insiemi di dati medici includono questo tipo di classificazione binaria. Si rifletta su come categorizzazioni come questa potrebbe escludere alcune parti di una popolazione dai trattamenti.
 
-Ora si caricano i dati di x e y.
+Ora si caricano i dati di X e y.
 
 > 🎓 Si ricordi, questo è apprendimento supervisionato (supervised learning), e serve dare un nome all'obiettivo 'y'.
 
 In una nuova cella di codice, caricare l'insieme di dati sul diabete chiamando `load_diabetes()`. Il parametro `return_X_y=True` segnala che `X` sarà una matrice di dati e `y` sarà l'obiettivo della regressione. 
 
-1. Si aggiungono alcuni comandi di stampa per msotrare la forma della matrice di dati e suoi primi elementi:
+1. Si aggiungono alcuni comandi di stampa per msotrare la forma della matrice di dati e i suoi primi elementi:
 
     ```python
     X, y = datasets.load_diabetes(return_X_y=True)
@@ -146,17 +146,17 @@ In una nuova cella di codice, caricare l'insieme di dati sul diabete chiamando `
     -0.04340085 -0.00259226  0.01990842 -0.01764613]
     ```
 
-    ✅ Si rifletta sulla relazione tra i dati e l'obiettivo di regressione. La regressione lineare prevede le relazioni tra la caratteristica X e la variabile di destinazione Y. Si può trovare l'[obiettivo](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) per l'insieme di dati sul diabete nella documentazione? Cosa dimostra questo insieme di dati, dato questo obiettivo?
+    ✅ Si rifletta sulla relazione tra i dati e l'obiettivo di regressione. La regressione lineare prevede le relazioni tra la caratteristica X e la variabile di destinazione y. Si può trovare l'[obiettivo](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) per l'insieme di dati sul diabete nella documentazione? Cosa dimostra questo insieme di dati, dato quell'obiettivo?
 
-2. Successivamente, selezionare una porzione di questo insieme di dati da tracciare sistemandola in un nuovo array usando la funzione di numpy's `newaxis`. Verrà usata la regressione lineare per generare una linea tra i quali in questi dati secondo il modello che determina.
+2. Successivamente, selezionare una porzione di questo insieme di dati da tracciare sistemandola in un nuovo array usando la funzione di numpy's `newaxis`. Verrà usata la regressione lineare per generare una linea tra i valori in questi dati secondo il modello che determina.
 
    ```python
    X = X[:, np.newaxis, 2]
    ```
 
-   ✅ A picere, stampare i dati per verificarne la forma.
+   ✅ A piacere, stampare i dati per verificarne la forma.
 
-3. Ora che si hanno dei dati pronti per essere tracciati, è possibile vedere se una macchina può aiutare a determinare una divisione logica tra i numeri in questo insieme di dati. Per fare ciò, è necessario dividere sia i dati (x) che l'obiettivo (Y) in insiemi di test e addestamento. Scikit-Learn ha un modo semplice per farlo; si possono dividere i  dati di prova in un determinato punto.
+3. Ora che si hanno dei dati pronti per essere tracciati, è possibile vedere se una macchina può aiutare a determinare una divisione logica tra i numeri in questo insieme di dati. Per fare ciò, è necessario dividere sia i dati (X) che l'obiettivo (y) in insiemi di test e addestamento. Scikit-learn ha un modo semplice per farlo; si possono dividere i  dati di prova in un determinato punto.
 
    ```python
    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
@@ -177,7 +177,7 @@ In una nuova cella di codice, caricare l'insieme di dati sul diabete chiamando `
     y_pred = model.predict(X_test)
     ```
 
-6. Ora è il momento di mostrare i dati in un tracciato. Matplotlib è uno strumento molto utile per questo compito.Si crei un grafico a dispersione (scatterplot) di tutti i dati del test X e Y e si utilizzi la previsione per disegnare una linea nel luogo più appropriato, tra i raggruppamenti dei dati del modello.
+6. Ora è il momento di mostrare i dati in un tracciato. Matplotlib è uno strumento molto utile per questo compito. Si crei un grafico a dispersione (scatterplot) di tutti i dati del test X e y e si utilizzi la previsione per disegnare una linea nel luogo più appropriato, tra i raggruppamenti dei dati del modello.
 
     ```python
     plt.scatter(X_test, y_test,  color='black')
@@ -199,12 +199,13 @@ Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modific
 
 ## [Qui post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
 
-## Revisione e Auto Apprendimento
+## Riepilogo e Auto Apprendimento
 
-In questo tutorial, si è lavorato con una semplice regressione lineare, piuttosto che un regressione univariata o multipla. Si legga circa le differenze tra questi metodi oppure si dia uno sguardo a [questo video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+In questo tutorial, si è lavorato con una semplice regressione lineare, piuttosto che una regressione univariata o multipla. Ci so informi circa le differenze tra questi metodi oppure si dia uno sguardo a [questo video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
 
 Si legga di più sul concetto di regressione e si pensi a quale tipo di domande potrebbero trovare risposta con questa tecnica. Seguire questo [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) per approfondire la propria conoscenza.
 
 ## Compito
 
-[Un insieme di dati diverso](assignment.md)
+[Un insieme di dati diverso](assignment.it.md)
+
diff --git a/2-Regression/1-Tools/translations/assignment.it.md b/2-Regression/1-Tools/translations/assignment.it.md
new file mode 100644
index 0000000000..51fa1663cb
--- /dev/null
+++ b/2-Regression/1-Tools/translations/assignment.it.md
@@ -0,0 +1,13 @@
+# Regressione con Scikit-learn
+
+## Istruzioni
+
+Dare un'occhiata all'[insieme di dati Linnerud](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud) in Scikit-learn. Questo insieme di dati ha [obiettivi](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset) multipli: "Consiste di tre variabili di esercizio (dati) e tre variabili fisiologiche (obiettivo) raccolte da venti uomini di mezza età in un fitness club".
+
+Con parole proprie, descrivere come creare un modello di Regressione che tracci la relazione tra il punto vita e il numero di addominali realizzati. Fare lo stesso per gli altri punti dati in questo insieme di dati.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| ------------------------------ | ----------------------------------- | ----------------------------- | -------------------------- |
+| Inviare un paragrafo descrittivo | Viene presentato un paragrafo ben scritto | Vengono inviate alcune frasi | Non viene fornita alcuna descrizione |

From 677aa8ac435e0fb3c4d32913d9bd9a688be5bb91 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Tue, 13 Jul 2021 12:02:05 +0800
Subject: [PATCH 118/368] Update README.zh-cn.md

---
 .../2-Data/translations/README.zh-cn.md       | 156 +++++++++---------
 1 file changed, 79 insertions(+), 77 deletions(-)

diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md
index 2c7f23adcd..c3fbf34835 100644
--- a/2-Regression/2-Data/translations/README.zh-cn.md
+++ b/2-Regression/2-Data/translations/README.zh-cn.md
@@ -1,85 +1,86 @@
-# Build a regression model using Scikit-learn: prepare and visualize data
+# 使用Scikit-learn构建回归模型：准备和�视化数� 
 
-> ![Data visualization infographic](./images/data-visualization.png)
-> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+> ![数��视化信�图](../images/data-visualization.png)
+> 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
 
-## Introduction
+## 介�
 
-Now that you are set up with the tools you need to start tackling machine learning model building with Scikit-learn, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.
+既然你已�设置了开始使用Scikit-learn处�机器学习模型构建所需的工具，你就�以开始对数��出问题了。当你处�数�并应用ML解决方案时，了解如何�出正确的问题以正确释放数�集的潜力�常��。
 
-In this lesson, you will learn:
+在本课中，你将学习：
 
-- How to prepare your data for model-building.
-- How to use Matplotlib for data visualization.
+- 如何为模型构建准备数�。
+- 如何使用Matplotlib进行数��视化。 
 
-## Asking the right question of your data
+## 对你的数��出正确的问题 
 
-The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.
+你需�回答的问题将决定你将使用哪�类型的ML算法。你得到的答案的质�将在很大程度上�决于你的数�的性质。
 
-Take a look at the [data](../data/US-pumpkins.csv) provided for this lesson. You can open this .csv file in VS Code. A quick skim immediately shows that there are blanks and a mix of strings and numeric data. There's also a strange column called 'Package' where the data is a mix between 'sacks', 'bins' and other values. The data, in fact, is a bit of a mess.
+查看为本课程�供的[数�](../data/US-pumpkins.csv)。你�以在VS Code中打开这个.csv文件。快速�览一下就会�现有空格，还有字符串和数字数�的混�。还有一个奇怪的列��“Package�，其中的数�是“sacks��“bins�和其他值的混�。事实上，数�有点乱。
 
-In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. In this lesson, you will learn how to prepare a raw dataset using standard Python libraries. You will also learn various techniques to visualize the data.
+事实上，获得一个完全准备好用于创建开箱�用的ML模型的数�集并�是很常�。在本课中，你将学习如何使用标准Python库准备原始数�集。你还将学习��技术��视化数�。
 
-## Case study: 'the pumpkin market'
+## 案例研究：“�瓜市场�
 
-In this folder you will find a .csv file in the root `data` folder called [US-pumpkins.csv](../data/US-pumpkins.csv) which includes 1757 lines of data about the market for pumpkins, sorted into groupings by city. This is raw data extracted from the [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distributed by the United States Department of Agriculture.
+你将在`data`文件夹中找到一个�为[US-pumpkins.csv](../data/US-pumpkins.csv)的.csv 文件，其中包�有关�瓜市场的1757行数�，已 按城市排�分组。这是从美国农业部分�的[特�作物终端市场标准报告](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice)中��的原始数�。
 
-### Preparing data
+### 准备数�
 
-This data is in the public domain. It can be downloaded in many separate files, per city, from the USDA web site. To avoid too many separate files, we have concatenated all the city data into one spreadsheet, thus we have already _prepared_ the data a bit. Next, let's take a closer look at the data.
+这些数�属于公共领域。它�以从美国农业部网站下载，�个城市有许多��的文件。为了��太多�独的文件，我们将所有城市数��并到一个电�表格中，因此我们已�准备了一些数�。接下�，让我们仔细看看数�。
 
-### The pumpkin data - early conclusions
+### �瓜数� - 早期结论 
 
-What do you notice about this data? You already saw that there is a mix of strings, numbers, blanks and strange values that you need to make sense of.
+你对这些数�有什么看法？你已�看到了无法�解的字符串�数字�空格和奇怪值的混�体。
 
-What question can you ask of this data, using a Regression technique? What about "Predict the price of a pumpkin for sale during a given month". Looking again at the data, there are some changes you need to make to create the data structure necessary for the task. 
-## Exercise - analyze the pumpkin data
+你�以使用回归技术对这些数��出什么问题？“预测给定月份内待售�瓜的价格�怎么样？�次查看数�，你需�进行一些更改�能创建任务所需的数�结构。 
+## 练习 - 分��瓜数� 
 
-Let's use [Pandas](https://pandas.pydata.org/), (the name stands for `Python Data Analysis`) a tool very useful for shaping data, to analyze and prepare this pumpkin data.
+让我们使用[Pandas](https://pandas.pydata.org/)，（“Python 数�分��的��）一个�常有用的工具，用于分�和准备�瓜数�。
 
-### First, check for missing dates
+### 首先，检查��的日期
 
-You will first need to take steps to check for missing dates:
+你首先需�采�以下步骤�检查缺少的日期：
 
-1. Convert the dates to a month format (these are US dates, so the format is `MM/DD/YYYY`).
-2. Extract the month to a new column.
+1. 将日期转�为月份格�（这些是美国日期，因此格�为`MM/DD/YYYY`）。
 
-Open the _notebook.ipynb_ file in Visual Studio Code and import the spreadsheet in to a new Pandas dataframe.
+2. 将月份��到新列。
 
-1. Use the `head()` function to view the first five rows.
+在 Visual Studio Code 中打开notebook.ipynb文件，并将电�表格导入到新的Pandas dataframe中。
+
+1. 使用 `head()`函数查看�五行。 
 
     ```python
     import pandas as pd
-    pumpkins = pd.read_csv('../data/US-pumpkins.csv')
+    pumpkins = pd.read_csv('../../data/US-pumpkins.csv')
     pumpkins.head()
     ```
 
-    ✅ What function would you use to view the last five rows?
+    ✅ 使用什么函数�查看最�五行？ 
 
-1. Check if there is missing data in the current dataframe:
+2. 检查当�dataframe中是�缺少数�： 
 
     ```python
     pumpkins.isnull().sum()
     ```
 
-    There is missing data, but maybe it won't matter for the task at hand.
+    有数�丢失，但�能对手头的任务�说无关紧�。
 
-1. To make your dataframe easier to work with, drop several of its columns, using `drop()`, keeping only the columns you need: 
+3. 为了让你的dataframe更容易使用，使用`drop()`删除它的几个列，��留你需�的列： 
 
     ```python
     new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
     pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
     ```
 
-### Second, determine average price of pumpkin
+### 然�，确定�瓜的平�价格 
 
-Think about how to determine the average price of a pumpkin in a given month. What columns would you pick for this task? Hint: you'll need 3 columns.
+考虑如何确定给定月份�瓜的平�价格。你会为此任务选择哪些列？�示：你需�3列。
 
-Solution: take the average of the `Low Price` and `High Price` columns to populate the new Price column, and convert the Date column to only show the month. Fortunately, according to the check above, there is no missing data for dates or prices.
+解决方案：�`Low Price`和`High Price`列的平�值�填充新的Price列，将Date列转���显示月份。幸�的是，根�上�的检查，没有丢失日期或价格的数�。 
 
-1. To calculate the average, add the following code:
+1. �计算平�值，请添加以下代�： 
 
     ```python
     price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
@@ -88,37 +89,37 @@ Solution: take the average of the `Low Price` and `High Price` columns to popula
     
     ```
 
-   ✅ Feel free to print any data you'd like to check using `print(month)`.
+   ✅ 请��使用`print(month)`打�你想检查的任何数�。 
 
-2. Now, copy your converted data into a fresh Pandas dataframe:
+2. 现在，将转��的数��制到新的Pandas dataframe中： 
 
     ```python
     new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
     ```
 
-    Printing out your dataframe will show you a clean, tidy dataset on which you can build your new regression model.
+    打�出的dataframe将�你展示一个干净整�的数�集，你�以在此数�集上构建新的回归模型。
 
-### But wait! There's something odd here
+### 但是等等�这里有点奇怪
 
-If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in '1 1/9 bushel' measures, and some in '1/2 bushel' measures, some per pumpkin, some per pound, and some in big boxes with varying widths.
+如果你看看`Package`(包装)一�，�瓜有很多��的�置。有的以1 1/9蒲�耳的尺寸出售，有的以1/2蒲�耳的尺寸出售，有的以���瓜出售，有的以�磅出售，有的以��宽度的大盒�出售。
 
-> Pumpkins seem very hard to weigh consistently
+> �瓜似乎很难统一称�方�
 
-Digging into the original data, it's interesting that anything with `Unit of Sale` equalling 'EACH' or 'PER BIN' also have the `Package` type per inch, per bin, or 'each'. Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string 'bushel' in their `Package` column.
+深入研究原始数�，有趣的是，任何`Unit of Sale`等于“EACH�或“PER BIN�的东西也具有�英寸��箱或“�个�的`Package`类型。�瓜似乎很难采用统一称�方�，因此让我们通过仅选择`Package`列中带有字符串“蒲�耳�的�瓜�过滤它们。
 
-1. Add a filter at the top of the file, under the initial .csv import:
+1. 在�始.csv导入下添加过滤器： 
 
     ```python
     pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
     ```
 
-    If you print the data now, you can see that you are only getting the 415 or so rows of data containing pumpkins by the bushel.
+    如果你现在打�数�，你�以看到你�获得了 415 行左�包�按蒲�耳计算的�瓜的数�。 
 
-### But wait! There's one more thing to do
+### �是等等� 还有一件事�� 
 
-Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, so do some math to standardize it.
+你是�注�到�行的蒲�耳数���？你需�对定价进行标准化，以便显示�蒲�耳的定价，因此请进行一些数学计算以对其进行标准化。 
 
-1. Add these lines after the block creating the new_pumpkins dataframe:
+1. 在创建 new_pumpkins dataframe的代��之�添加这些行： 
 
     ```python
     new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
@@ -126,34 +127,35 @@ Did you notice that the bushel amount varies per row? You need to normalize the
     new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
     ```
 
-✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!
+✅ 根� [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308)，蒲�耳的���决于产�的类型，因为它是一�体积测�。“例如，一蒲�耳西红柿应该�56 磅……��和蔬���更多空间，��更轻，所以一蒲�耳���有20磅。� 这一切都相当���让我们��费心进行蒲�耳到磅的转�，而是按蒲�耳定价。然而，所有这些对蒲�耳�瓜的研究表明，了解数�的性质是多么���
 
-Now, you can analyze the pricing per unit based on their bushel measurement. If you print out the data one more time, you can see how it's standardized.
+现在，你�以根�蒲�耳测��分����的定价。如果你�打�一次数�，你�以看到它是如何标准化的。 
 
-✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.
+✅ 你有没有注�到�蒲�耳�的�瓜很贵？你能弄清楚为什么�？�示：��瓜比大�瓜贵得多，这�能是因为考虑到一个大的空心馅饼�瓜�用的未使用空间，�蒲�耳的�瓜�多得多。 
 
-## Visualization Strategies
+## �视化策略 
 
-Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover. 
+数�科学家的部分�责是展示他们使用的数�的质�和性质。为此，他们通常会创建有趣的�视化或绘图�图形和图表，以显示数�的��方�。通过这�方�，他们能够直观地展示难以�现的关系和差�。 
 
-Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.
+�视化还�以帮助确定最适�数�的机器学习技术。例如，似乎沿�一�线的散点图表明该数�是线性回归练习的良好候选者。
 
-One data visualization libary that works well in Jupyter notebooks is [Matplotlib](https://matplotlib.org/) (which you also saw in the previous lesson).
+一个在Jupyter notebooks中�行良好的数��视化库是[Matplotlib](https://matplotlib.org/)（你在上一课中也看到过）。 
 
-> Get more experience with data visualization in [these tutorials](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
+> 在[这些教程](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa)中获得更多数��视化�验。 
 
-## Exercise - experiment with Matplotlib
+## 练习 - 使用 Matplotlib 进行实验 
 
-Try to create some basic plots to display the new dataframe you just created. What would a basic line plot show?
+�试创建一些基本图形�显示你刚刚创建的新dataframe。基本线图会显示什么？ 
 
-1. Import Matplotlib at the top of the file, under the Pandas import:
+1. 在文件顶部导入Matplotlib： 
 
     ```python
     import matplotlib.pyplot as plt
     ```
 
-1. Rerun the entire notebook to refresh.
-1. At the bottom of the notebook, add a cell to plot the data as a box:
+2. �新刷新以�行整个notebook。 
+
+3. 在notebook底部，添加一个�元格以绘制数�：
 
     ```python
     price = new_pumpkins.Price
@@ -162,39 +164,39 @@ Try to create some basic plots to display the new dataframe you just created. Wh
     plt.show()
     ```
 
-    ![A scatterplot showing price to month relationship](./images/scatterplot.png)
+    ![显示价格与月份关系的散点图](../images/scatterplot.png)
 
-    Is this a useful plot? Does anything about it surprise you?
+    这是一个有用的图�？有什么让你�惊的�？
 
-    It's not particularly useful as all it does is display in your data as a spread of points in a given month.
+    它并�是特别有用，因为它所�的�是在你的数�中显示为给定月份的点数分布。
 
-### Make it useful
+### 让它有用 
 
-To get charts to display useful data, you usually need to group the data somehow. Let's try creating a plot where the y axis shows the months and the data demonstrates the distribution of data.
+为了让图表显示有用的数�，你通常需�以��方�对数�进行分组。让我们�试创建一个图，其中y轴显示月份，数�显示数�的分布。 
 
-1. Add a cell to create a grouped bar chart:
+1. 添加�元格以创建分组�形图：
 
     ```python
     new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
     plt.ylabel("Pumpkin Price")
     ```
 
-    ![A bar chart showing price to month relationship](./images/barchart.png)
+    ![显示价格与月份关系的�形图](../images/barchart.png)
 
-    This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?
+    这是一个更有用的数��视化�似乎表明�瓜的最高价格出现在9月和10月。这符�你的期望�？为什么？为什么�？
 
 ---
 
-## 🚀Challenge
+## 🚀挑战
 
-Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
+探索Matplotlib�供的��类型的�视化。哪�类型最适�回归问题？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
 
-## Review & Self Study
+## �习与自学
 
-Take a look at the many ways to visualize data. Make a list of the various libraries available and note which are best for given types of tasks, for example 2D visualizations vs. 3D visualizations. What do you discover?
+请看一下�视化数�的多�方法。列出���用的库，并注�哪些库最适�给定类型的任务，例如2D�视化与3D�视化。你�现了什么？
 
-## Assignment
+## 任务
 
-[Exploring visualization](assignment.md)
+[探索�视化](../assignment.md)

From 773edc1f74cf6abbff46e02a9269fe1f5dded212 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Tue, 13 Jul 2021 12:02:54 +0800
Subject: [PATCH 119/368] Update README.zh-cn.md

---
 .../1-Tools/translations/README.zh-cn.md      | 173 +++++++++---------
 1 file changed, 86 insertions(+), 87 deletions(-)

diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index e36c34fe68..819b9766fe 100644
--- a/2-Regression/1-Tools/translations/README.zh-cn.md
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -1,132 +1,131 @@
-# Get started with Python and Scikit-learn for regression models
+# 开始使用Python和Scikit学习回归模型
 
-![Summary of regressions in a sketchnote](../../sketchnotes/ml-regression.png)
+![回归](../../sketchnotes/ml-regression.png)
 
-> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+> 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
-## Introduction
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## 介�
 
-In these four lessons, you will discover how to build regression models. We will discuss what these are for shortly. But before you do anything, make sure you have the right tools in place to start the process!
+在这四节课中，你将了解如何构建回归模型。我们将很快讨论这些是什么。但在你�任何事情之�，请确�你有�适的工具�开始这个过程�
 
-In this lesson, you will learn how to:
+在本课中，你将学习如何：
 
-- Configure your computer for local machine learning tasks.
-- Work with Jupyter notebooks.
-- Use Scikit-learn, including installation.
-- Explore linear regression with a hands-on exercise.
+- 为本地机器学习任务�置你的计算机。
+- 使用Jupyter notebooks。
+- 使用Scikit-learn，包括安装。
+- 通过动手练习探索线性回归。 
 
-## Installations and configurations
+## 安装和�置 
 
-[![Using Python with Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
+[![在 Visual Studio Code中使用 Python](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "在 Visual Studio Code中使用 Python")
 
-> 🎥 Click the image above for a video: using Python within VS Code.
+> 🎥 �击上图观看视频：在VS Code中使用Python。 
 
-1. **Install Python**. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) available as well, to ease the setup for some users.
+1. **安装 Python**。确�你的计算机上安装了[Python](https://www.python.org/downloads/)。你将在许多数�科学和机器学习任务中使用 Python。大多数计算机系统已�安装了Python。也有一些有用的[Python编�包](https://code.visualstudio.com/learn/educations/installers?WT.mc_id=academic-15963-cxa)�用于简化�些用户的设置。 
 
-   Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a [virtual environment](https://docs.python.org/3/library/venv.html).
+   然而，Python的�些用法需�一个版本的软件，而其他用法则需��一个��的版本。 因此，在[虚拟环境](https://docs.python.org/3/library/venv.html)中工作很有用。
 
-2. **Install Visual Studio Code**. Make sure you have Visual Studio Code installed on your computer. Follow these instructions to [install Visual Studio Code](https://code.visualstudio.com/) for the basic installation. You are going to use Python in Visual Studio Code in this course, so you might want to brush up on how to [configure Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa) for Python development.
+2. **安装 Visual Studio Code**。确�你的计算机上安装了Visual Studio Code。按照这些说明[安装 Visual Studio Code](https://code.visualstudio.com/)进行基本安装。在本课程中，你将在Visual Studio Code中使用Python，因此你�能想�习如何[�置 Visual Studio Code](https://docs.microsoft.com/learn/modules/python-install-vscode?WT.mc_id=academic-15963-cxa)用于Python开�。 
 
-   > Get comfortable with Python by working through this collection of [Learn modules](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)
+   > 通过学习这一系列的 [学习模�](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)熟悉Python
 
-3. **Install Scikit-learn**, by following [these instructions](https://scikit-learn.org/stable/install.html). Since you need to ensure that you use Python 3, it's recommended that you use a virtual environment. Note, if you are installing this library on a M1 Mac, there are special instructions on the page linked above.
+3. **按照[这些说明]安装Scikit learn**(https://scikit-learn.org/stable/install.html)。由于你需�确�使用Python3，因此建议你使用虚拟环境。注�，如果你是在M1 Mac上安装这个库，在上�链接的页�上有特别的说明。
 
-1. **Install Jupyter Notebook**. You will need to [install the Jupyter package](https://pypi.org/project/jupyter/). 
+4. **安装Jupyter Notebook**。你需�[安装Jupyter包](https://pypi.org/project/jupyter/)。
 
-## Your ML authoring environment
+## 你的ML工作环境
 
-You are going to use **notebooks** to develop your Python code and create machine learning models. This type of file is a common tool for data scientists, and they can be identified by their suffix or extension `.ipynb`.
+你将使用**notebooks**开�Python代�并创建机器学习模型。这�类型的文件是数�科学家的常用工具，�以通过�缀或扩展�`.ipynb`�识别它们。
 
-Notebooks are an interactive environment that allow the developer to both code and add notes and write documentation around the code which is quite helpful for experimental or research-oriented projects.
+Notebooks是一个交互�环境，�许开�人员编写代�并添加注释并围绕代�编写文档，这对于实验或��研究的项目�常有帮助。 
 
-### Exercise - work with a notebook
+### 练习 - 使用notebook
 
-In this folder, you will find the file _notebook.ipynb_. 
+1. 在Visual Studio Code中打开_notebook.ipynb_。
 
-1. Open _notebook.ipynb_ in Visual Studio Code.
+    Jupyter�务器将以python3+�动。你会�现notebook�以“�行�的区域�代��。你�以通过选择看起��播放按钮的图标��行代��。
 
-   A Jupyter server will start with Python 3+ started. You will find areas of the notebook that can be `run`, pieces of code. You can run a code block, by selecting the icon that looks like a play button.
+2. 选择`md`图标并添加一点markdown，输入文字**#Welcome to your notebook**。 
 
-1. Select the `md` icon and add a bit of markdown, and the following text **# Welcome to your notebook**.
+   接下�，添加一些Python代�。
 
-   Next, add some Python code. 
+1. 在代��中输入**print("hello notebook")**。
 
-1. Type **print('hello notebook')** in the code block.
-1. Select the arrow to run the code.
+2. 选择箭头�行代�。
 
-   You should see the printed statement:
+    你应该看到打�的语�： 
 
     ```output
     hello notebook
     ```
 
-![VS Code with a notebook open](images/notebook.png)
+![打开notebook的VS Code](../images/notebook.png)
 
-You can interleaf your code with comments to self-document the notebook.
+你�以为你的代�添加注释，以便notebook�以自�述。
 
-✅ Think for a minute how different a web developer's working environment is versus that of a data scientist.
+✅ 想一想web开�人员的工作环境与数�科学家的工作环境有多大的��。
 
-## Up and running with Scikit-learn
+## �动并�行Scikit-learn 
 
-Now that Python is set up in your local environment, and you are comfortable with Jupyter notebooks, let's get equally comfortable with Scikit-learn (pronounce it `sci` as in `science`). Scikit-learn provides an [extensive API](https://scikit-learn.org/stable/modules/classes.html#api-ref) to help you perform ML tasks.
+现在Python已在你的本地环境中设置好，并且你对Jupyter notebook感到满�，让我们�样熟悉Scikit-learn（在“science�中�音为“sci�）。 Scikit-learn�供了[大�的API](https://scikit-learn.org/stable/modules/classes.html#api-ref)�帮助你执行ML任务。
 
-According to their [website](https://scikit-learn.org/stable/getting_started.html), "Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities."
+根�他们的[网站](https://scikit-learn.org/stable/getting_started.html)，“Scikit-learn是一个开�机器学习库，支�有监�和无监�学习。它还�供了��模型拟�工具�数�预处��模型选择和评估以�许多其他实用程�。�
 
-In this course, you will use Scikit-learn and other tools to build machine learning models to perform what we call 'traditional machine learning' tasks. We have deliberately avoided neural networks and deep learning, as they are better covered in our forthcoming 'AI for Beginners' curriculum. 
+在本课程中，你将使用Scikit-learn和其他工具�构建机器学习模型，以执行我们所谓的“传统机器学习�任务。我们特���了神�网络和深度学习，因为它们在我们�将推出的“���学者的人工智能�课程中得到了更好的介�。 
 
-Scikit-learn makes it straightforward to build models and evaluate them for use. It is primarily focused on using numeric data and contains several ready-made datasets for use as learning tools. It also includes pre-built models for students to try. Let's explore the process of loading prepackaged data and using a built in estimator  first ML model with Scikit-learn with some basic data.
+Scikit-learn使构建模型和评估它们的使用�得简�。它主�侧�于使用数字数�，并包�几个现�的数�集用作学习工具。它还包括供学生�试的预建模型。让我们探索加载预先打包的数�和使用内置的estimator first ML模型和Scikit-learn以�一些基本数�的过程。 
 
-## Exercise - your first Scikit-learn notebook
+## 练习 - 你的第一个Scikit-learn notebook
 
-> This tutorial was inspired by the [linear regression example](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py) on Scikit-learn's web site.
+> 本教程的�感�自Scikit-learn网站上的[线性回归示例](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py)。
 
-In the _notebook.ipynb_ file associated to this lesson, clear out all the cells by pressing the 'trash can' icon.
+在与本课程相关的_notebook.ipynb_文件中，通过点击“垃圾桶�图标清除所有�元格。 
 
-In this section, you will work with a small dataset about diabetes that is built into Scikit-learn for learning purposes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
+在本节中，你将使用一个关于糖尿病的�数�集，该数�集内置于Scikit-learn中以用于学习目的。想象一下，你想为糖尿病患者测试一�治疗方法。机器学习模型�能会帮助你根���组�确定哪些患者对治疗�应更好。�使是�常基本的回归模型，在�视化时，也�能会显示有助于组织�论临床试验的��信�。 
 
-✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use linear regression, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use logistic regression. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
+✅ 回归方法有很多�，你选择哪一��决于你正在寻找的答案。如果你想预测给定年龄的人的�能身高，你�以使用线性回归，因为你正在寻找**数值**。如果你有兴趣了解���肴是�应被视为素食主义者，那么你正在寻找**类别分�**，以便使用逻辑回归。��你将了解有关逻辑回归的更多信�。想一想你�以对数��出的一些问题，以�这些方法中的哪一个更�适。 
 
-Let's get started on this task.
+让我们开始这项任务。 
 
-### Import libraries
+### 导入库 
 
-For this task we will import some libraries:
+对于此任务，我们将导入一些库：
 
-- **matplotlib**. It's a useful [graphing tool](https://matplotlib.org/) and we will use it to create a line plot.
-- **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) is a useful library for handling numeric data in Python.
-- **sklearn**. This is the Scikit-learn library.
+- **matplotlib**。这是一个有用的[绘图工具](https://matplotlib.org/)，我们将使用它�创建线图。
+- **numpy**。 [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html)是一个有用的库，用于在Python中处�数字数�。
+- **sklearn**。这是Scikit-learn库。
 
-Import some libraries to help with your tasks.
+导入一些库�帮助你完�任务。 
 
-1. Add imports by typing the following code:
+1. 通过输入以下代�添加导入：
 
-   ```python
+    ```python
    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn import datasets, linear_model, model_selection
    ```
 
-   Above you are importing `matplottlib`, `numpy` and you are importing `datasets`, `linear_model` and `model_selection` from `sklearn`. `model_selection` is used for splitting data into training and test sets.
+    在上�的代�中，你正在导入`matplottlib`�`numpy`，你正在从`sklearn`导入`datasets`�`linear_model`和`model_selection`。 `model_selection`用于将数�拆分为训练集和测试集。 
 
-### The diabetes dataset
+### 糖尿病数�集 
 
-The built-in [diabetes dataset](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) includes 442 samples of data around diabetes, with 10 feature variables, some of which include:
+内置的[糖尿病数�集](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset)包�442个围绕糖尿病的数�样本，具有10个特���，其中包括：
 
-age: age in years
-bmi: body mass index
-bp: average blood pressure
-s1 tc: T-Cells (a type of white blood cells)
+age：�数
+bmi：体�指数
+bp：平�血压
+s1 tc：T细胞（一�白细胞） 
 
-✅ This dataset includes the concept of 'sex' as a feature variable important to research around diabetes. Many medical datasets include this type of binary classification. Think a bit about how categorizations such as this might exclude certain parts of a population from treatments.
+✅ 该数�集包括“性别�的概念，作为对糖尿病研究很��的特���。许多医学数�集包括这�类型的二元分类。想一想诸如此类的分类如何将人群的�些部分排除在治疗之外。 
 
-Now, load up the X and y data.
+现在，加载X和y数�。 
 
-> 🎓 Remember, this is supervised learning, and we need a named 'y' target.
+> 🎓 请记�，这是监�学习，我们需�一个命�为“y�的目标。 
 
-In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The input `return_X_y=True` signals that `X` will be a data matrix, and `y` will be the regression target. 
+在新的代��元中，通过调用`load_diabetes()`加载糖尿病数�集。输入`return_X_y=True`表示`X`将是一个数�矩阵，而`y`将是回归目标。
 
-1. Add some print commands to show the shape of the data matrix and its first element:
+1. 添加一些打�命令�显示数�矩阵的形状�其第一个元素： 
 
     ```python
     X, y = datasets.load_diabetes(return_X_y=True)
@@ -134,9 +133,9 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The
     print(X[0])
     ```
 
-    What you are getting back as a response, is a tuple. What you are doing is to assign the two first values of the tuple to `X` and `y` respectively. Learn more [about tuples](https://wikipedia.org/wiki/Tuple).
+    作为�应返回的是一个元组。你正在�的是将元组的�两个值分别分�给`X`和`y`。了解更多 [关于元组](https://wikipedia.org/wiki/Tuple)。
 
-    You can see that this data has 442 items shaped in arrays of 10 elements:
+    你�以看到这个数�有442个项目，组�了10个元素的数组：
 
     ```text
     (442, 10)
@@ -144,38 +143,38 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The
     -0.04340085 -0.00259226  0.01990842 -0.01764613]
     ```
 
-    ✅ Think a bit about the relationship between the data and the regression target. Linear regression predicts relationships between feature X and target variable y. Can you find the [target](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset) for the diabetes dataset in the documentation? What is this dataset demonstrating, given that target?
+    ✅ �微�考一下数�和回归目标之间的关系。线性回归预测特�X和目标��y之间的关系。你能在文档中找到糖尿病数�集的[目标](https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset)�？鉴于该目标，该数�集展示了什么？
 
-2. Next, select a portion of this dataset to plot by arranging it into a new array using numpy's `newaxis` function. We are going to use linear regression to generate a line between values in this data, according to a pattern it determines.
+2. 接下�，通过使用numpy的`newaxis`函数将其排列到一个新数组中�选择�绘制的该数�集的一部分。我们将使用线性回归根�它确定的模�在此数�中的值之间生�一�线。
 
    ```python
    X = X[:, np.newaxis, 2]
    ```
 
-   ✅ At any time, print out the data to check its shape.
+   ✅ �时打�数�以检查其形状。 
 
-3. Now that you have data ready to be plotted, you can see if a machine can help determine a logical split between the numbers in this dataset. To do this, you need to split both the data (X) and the target (y) into test and training sets. Scikit-learn has a straightforward way to do this; you can split your test data at a given point.
+3. 现在你已准备好绘制数�，你�以查看机器是��以帮助确定此数�集中数字之间的逻辑分割。为此你需�将数�(X)和目标(y)拆分为测试集和训练集。Scikit-learn有一个简�的方法��到这一点；你�以在给定点拆分测试数�。
 
    ```python
    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)
    ```
 
-4. Now you are ready to train your model! Load up the linear regression model and train it with your X and y training sets using `model.fit()`:
+4. 现在你已准备好训练你的模型�加载线性回归模型并使用`model.fit()`使用X和y训练集对其进行训练：
 
     ```python
     model = linear_model.LinearRegression()
     model.fit(X_train, y_train)
     ```
 
-    ✅ `model.fit()` is a function you'll see in many ML libraries such as TensorFlow
+    ✅ `model.fit()`是一个你会在许多机器学习库（例如 TensorFlow）中看到的函数 
 
-5. Then, create a prediction using test data, using the function `predict()`. This will be used to draw the line between data groups
+5. 然�，使用函数`predict()`，使用测试数�创建预测。这将用于绘制数�组之间的线
 
     ```python
     y_pred = model.predict(X_test)
     ```
 
-6. Now it's time to show the data in a plot. Matplotlib is a very useful tool for this task. Create a scatterplot of all the X and y test data, and use the prediction to draw a line in the most appropriate place, between the model's data groupings.
+6. 现在是时候在图中显示数�了。Matplotlib是完�此任务的�常有用的工具。创建所有X和y测试数�的散点图，并使用预测在模型的数�分组之间最�适的�置画一�线。
 
     ```python
     plt.scatter(X_test, y_test,  color='black')
@@ -183,24 +182,24 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The
     plt.show()
     ```
 
-   ![a scatterplot showing datapoints around diabetes](./images/scatterplot.png)
+   ![显示糖尿病周围数�点的散点图](../images/scatterplot.png)
 
-   ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.
+   ✅ 想一想这里�生了什么。一�直线穿过许多�数�点，但它到底在�什么？你能看到你应该如何使用这�线�预测一个新的�未�过的数�点对应的y轴值�？�试用语言�述该模型的实际用途。
 
-Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!
+�喜，你构建了第一个线性回归模型，使用它创建了预测，并将其显示在绘图中� 
 
 ---
-## 🚀Challenge
+## 🚀挑战
 
-Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+从这个数�集中绘制一个��的��。�示：编辑这一行：`X = X[:, np.newaxis, 2]`。鉴于此数�集的目标，你能够�现糖尿病作为一�疾病的进展情况�？
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
 
-## Review & Self Study
+## �习与自学 
 
-In this tutorial, you worked with simple linear regression, rather than univariate or multiple linear regression. Read a little about the differences between these methods, or take a look at [this video](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef)
+在本教程中，你使用了简�线性回归，而�是���或多元线性回归。阅读一些关于这些方法之间差异的信�，或查看[此视频](https://www.coursera.org/lecture/quantifying-relationships-regression-models/linear-vs-nonlinear-categorical-variables-ai2Ef) 
 
-Read more about the concept of regression and think about what kinds of questions can be answered by this technique. Take this [tutorial](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa) to deepen your understanding.
+阅读有关回归概念的更多信�，并�考这�技术�以回答哪些类型的问题。用这个[教程](https://docs.microsoft.com/learn/modules/train-evaluate-regression-models?WT.mc_id=academic-15963-cxa)加深你的�解。 
 
-## Assignment 
+## 任务  
 
-[A different dataset](assignment.md)
+[��的数�集](../assignment.md)

From 71197564fa10eea091bd405aee76c455b759c40f Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Tue, 13 Jul 2021 12:06:48 +0800
Subject: [PATCH 120/368] Update README.zh-cn.md

---
 2-Regression/1-Tools/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index 819b9766fe..4dff279524 100644
--- a/2-Regression/1-Tools/translations/README.zh-cn.md
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -1,6 +1,6 @@
 # 开始使用Python和Scikit学习回归模型
 
-![回归](../../sketchnotes/ml-regression.png)
+![回归](../../../sketchnotes/ml-regression.png)
 
 > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 

From 823616599b21693a13b26ad94cc5fe786c8c8d02 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Tue, 13 Jul 2021 12:17:36 +0800
Subject: [PATCH 121/368] [WIP]translate 3.1 to Simplified Chinese

---
 .../1-Web-App/translations/README.zh-cn.md    | 347 ++++++++++++++++++
 1 file changed, 347 insertions(+)
 create mode 100644 3-Web-App/1-Web-App/translations/README.zh-cn.md

diff --git a/3-Web-App/1-Web-App/translations/README.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md
new file mode 100644
index 0000000000..6150aece78
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/README.zh-cn.md
@@ -0,0 +1,347 @@
+# Build a Web App to use a ML Model
+
+In this lesson, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from [NUFORC's database](https://www.nuforc.org).
+
+You will learn:
+
+- How to 'pickle' a trained model
+- How to use that model in a Flask app
+
+We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model 'in the wild', so to speak: in a web app.
+
+To do this, you need to build a web app using Flask.
+
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+
+## Building an app
+
+There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app.
+
+### Considerations
+
+There are many questions you need to ask: 
+
+- **Is it a web app or a mobile app?** If you are building a mobile app or need to use the model in an IoT context, you could use [TensorFlow Lite](https://www.tensorflow.org/lite/) and use the model in an Android or iOS app.
+- **Where will the model reside**? In the cloud or locally?
+- **Offline support**. Does the app have to work offline?
+- **What technology was used to train the model?** The chosen technology may influence the tooling you need to use.
+   - **Using Tensor flow**. If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).  
+   - **Using PyTorch**.  If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model.
+   - **Using Lobe.ai or Azure Custom vision**. If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
+
+You also have the opportunity to build an entire Flask web app that would be able to train the model itself in a web browser. This can also be done using TensorFlow.js in a JavaScript context.
+
+For our purposes, since we have been working with Python-based notebooks, let's explore the steps you need to take to export a trained model from such a notebook to a format readable by a Python-built web app.
+
+## Tool
+
+For this task, you need two tools: Flask and Pickle, both of which run on Python.
+
+✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages.  Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) to practice building with Flask.
+
+✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`.
+
+## Exercise - clean your data
+
+In this lesson you'll use data from 80,000 UFO sightings, gathered by [NUFORC](https://nuforc.org) (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:
+
+- **Long example description**. "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
+- **Short example description**. "the lights chased us".
+
+The [ufos.csv](./data/ufos.csv) spreadsheet includes columns about the `city`, `state` and `country` where the sighting occurred, the object's `shape` and its `latitude` and `longitude`. 
+
+In the blank [notebook](notebook.ipynb) included in this lesson: 
+
+1. import `pandas`, `matplotlib`, and `numpy` as you did in previous lessons and import the ufos spreadsheet. You can take a look at a sample data set:
+
+    ```python
+    import pandas as pd
+    import numpy as np
+    
+    ufos = pd.read_csv('../data/ufos.csv')
+    ufos.head()
+    ```
+
+1. Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the `Country` field.
+
+    ```python
+    ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})
+    
+    ufos.Country.unique()
+    ```
+
+1. Now, you can reduce the amount of data we need to deal with by dropping any null values and only importing sightings between 1-60 seconds:
+
+    ```python
+    ufos.dropna(inplace=True)
+    
+    ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]
+    
+    ufos.info()
+    ```
+
+1. Import Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:
+
+   ✅ LabelEncoder encodes data alphabetically
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])
+    
+    ufos.head()
+    ```
+
+    Your data should look like this:
+
+    ```output
+    	Seconds	Country	Latitude	Longitude
+    2	20.0	3	    53.200000	-2.916667
+    3	20.0	4	    28.978333	-96.645833
+    14	30.0	4	    35.823889	-80.253611
+    23	60.0	4	    45.582778	-122.352222
+    24	3.0	    3	    51.783333	-0.783333
+    ```
+
+## Exercise - build your model
+
+Now you can get ready to train a model by diving the data into the training and testing group. 
+
+1. Select the three features you want to train on as your X vector, and the y vector will be the `Country`. You want to be able to input `Seconds`, `Latitude` and `Longitude` and get a country id to return.
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Seconds','Latitude','Longitude']
+    
+    X = ufos[Selected_features]
+    y = ufos['Country']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    ```
+
+1. Train your model using logistic regression:
+
+    ```python
+    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.linear_model import LogisticRegression
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+The accuracy isn't bad **(around 95%)**, unsurprisingly, as `Country` and `Latitude/Longitude` correlate.
+
+The model you created isn't very revolutionary as you should be able to infer a `Country` from its `Latitude` and `Longitude`, but it's a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app.
+
+## Exercise - 'pickle' your model
+
+Now, it's time to _pickle_ your model! You can do that in a few lines of code. Once it's _pickled_, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude,
+
+```python
+import pickle
+model_filename = 'ufo-model.pkl'
+pickle.dump(model, open(model_filename,'wb'))
+
+model = pickle.load(open('ufo-model.pkl','rb'))
+print(model.predict([[50,44,-12]]))
+```
+
+The model returns **'3'**, which is the country code for the UK. Wild! 👽
+
+## Exercise - build a Flask app
+
+Now you can build a Flask app to call your model and return similar results, but in a more visually pleasing way.
+
+1. Start by creating a folder called **web-app** next to the _notebook.ipynb_ file where your _ufo-model.pkl_ file resides.
+
+1. In that folder create three more folders: **static**, with a folder **css** inside it, and **templates`**. You should now have the following files and directories:
+
+    ```output
+    web-app/
+      static/
+        css/
+        templates/
+    notebook.ipynb
+    ufo-model.pkl
+    ``` 
+
+   ✅ Refer to the solution folder for a view of the finished app
+
+1. The first file to create in _web-app_ folder is **requirements.txt** file. Like _package.json_ in a JavaScript app, this file lists dependencies required by the app. In **requirements.txt** add the lines: 
+
+    ```text
+    scikit-learn
+    pandas
+    numpy
+    flask
+    ```
+
+1. Now, run this file by navigating to _web-app_:
+
+   ```bash
+   cd web-app
+   ```
+
+1. In your terminal type `pip install`, to install the libraries listed in _reuirements.txt_:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+1. Now, you're ready to create three more files to finish the app:
+
+    1. Create **app.py** in the root
+    2. Create **index.html** in _templates_ directory.
+    3. Create **styles.css** in _static/css_ directory.
+
+1. Build out the _styles.css__ file with a few styles:
+
+    ```css
+    body {
+    	width: 100%;
+    	height: 100%;
+    	font-family: 'Helvetica';
+    	background: black;
+    	color: #fff;
+    	text-align: center;
+    	letter-spacing: 1.4px;
+    	font-size: 30px;
+    }
+    
+    input {
+    	min-width: 150px;
+    }
+    
+    .grid {
+    	width: 300px;
+    	border: 1px solid #2d2d2d;
+    	display: grid;
+    	justify-content: center;
+    	margin: 20px auto;
+    }
+    
+    .box {
+    	color: #fff;
+    	background: #2d2d2d;
+    	padding: 12px;
+    	display: inline-block;
+    }
+    ```
+
+1. Next, build out the _index.html_ file:
+
+    ```html
+    <!DOCTYPE html>
+    <html>
+    <head>
+      <meta charset="UTF-8">
+      <title>🛸 UFO Appearance Prediction! 👽</title>
+      <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}"> 
+    </head>
+    
+    <body>
+     <div class="grid">
+    
+      <div class="box">
+    
+      <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
+    
+        <form action="{{ url_for('predict')}}" method="post">
+        	<input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
+          <input type="text" name="latitude" placeholder="Latitude" required="required" />
+    		  <input type="text" name="longitude" placeholder="Longitude" required="required" />
+          <button type="submit" class="btn">Predict country where the UFO is seen</button>
+        </form>
+    
+      
+       <p>{{ prediction_text }}</p>
+    
+     </div>
+    </div>
+    
+    </body>
+    </html>
+    ```
+
+    Take a look at the templating in this file. Notice the 'mustache' syntax around variables that will be provided by the app, like the prediction text: `{{}}`. There's also a form that posts a prediction to the `/predict` route.
+
+    Finally, you're ready to build the python file that drives the consumption of the model and the display of predictions:
+
+1. In `app.py` add:
+
+    ```python
+    import numpy as np
+    from flask import Flask, request, render_template
+    import pickle
+    
+    app = Flask(__name__)
+    
+    model = pickle.load(open("../ufo-model.pkl", "rb"))
+    
+    
+    @app.route("/")
+    def home():
+        return render_template("index.html")
+    
+    
+    @app.route("/predict", methods=["POST"])
+    def predict():
+    
+        int_features = [int(x) for x in request.form.values()]
+        final_features = [np.array(int_features)]
+        prediction = model.predict(final_features)
+    
+        output = prediction[0]
+    
+        countries = ["Australia", "Canada", "Germany", "UK", "US"]
+    
+        return render_template(
+            "index.html", prediction_text="Likely country: {}".format(countries[output])
+        )
+    
+    
+    if __name__ == "__main__":
+        app.run(debug=True)
+    ```
+
+   > 💡 Tip: when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
+
+If you run `python app.py` or `python3 app.py` - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!
+
+Before doing that, take a look at the parts of `app.py`:
+
+1. First, dependencies are loaded and the app starts.
+1. Then, the model is imported.
+1. Then, index.html is rendered on the home route.
+
+On the `/predict` route, several things happen when the form is posted:
+
+1. The form variables are gathered and converted to a numpy array. They are then sent to the model and a prediction is returned.
+2. The Countries that we want displayed are re-rendered as readable text from their predicted country code, and that value is sent back to index.html to be rendered in the template.
+
+Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction. 
+
+In a professional setting, you can see how good communication is necessary between the folks who train the model and those who consume it in a web or mobile app. In our case, it's only one person, you!
+
+---
+
+## 🚀 Challenge: 
+
+Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+
+## Review & Self Study
+
+There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.
+
+## Assignment 
+
+[Try a different model](assignment.md)
+
+

From b33be348ec6ff8c6d3c8474889bcee3ca377fa6d Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Tue, 13 Jul 2021 12:36:27 +0800
Subject: [PATCH 122/368] Create README.zh.md

---
 1-Introduction/translations/README.zh.md | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 1-Introduction/translations/README.zh.md

diff --git a/1-Introduction/translations/README.zh.md b/1-Introduction/translations/README.zh.md
new file mode 100644
index 0000000000..6ed91bac9a
--- /dev/null
+++ b/1-Introduction/translations/README.zh.md
@@ -0,0 +1,22 @@
+# 机器学习入门
+
+课程的本章节将为您介�机器学习领域背�的基本概念�什么是机器学习，并学习它的历�以�曾为此�出贡献的技术研究者门。让我们一起开始探索机器学习的全新世界��
+
+![globe](images/globe.jpg)
+> 图片由 <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a>�供，�自 <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+### 课程安排
+
+1. [机器学习简介](1-intro-to-ML/README.md)
+1. [机器学习�人工智能的历�](2-history-of-ML/README.md)
+1. [公平性与机器学习](3-fairness/README.md)
+1. [机器学习的技术](4-techniques-of-ML/README.md)
+### 致谢
+
+"机器学习简介"由 [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) � [Jen Looper](https://twitter.com/jenlooper)，共�倾 ♥� 而作
+
+"机器学习�人工智能历�" 由 [Jen Looper](https://twitter.com/jenlooper) � [Amy Boyd](https://twitter.com/AmyKateNicho)倾 ♥� 而作
+
+"公平性与机器学习" 由 [Tomomi Imura](https://twitter.com/girliemac) 倾 ♥� 而作 
+
+"机器学习的技术" 由 [Jen Looper](https://twitter.com/jenlooper) � [Chris Noring](https://twitter.com/softchris) 倾 ♥� 而作

From 6015d52f9a9b931b2eccbed6703242280615076f Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Tue, 13 Jul 2021 13:07:15 +0800
Subject: [PATCH 123/368] Rename README.zh.md to README.zh-cn.md

---
 1-Introduction/translations/{README.zh.md => README.zh-cn.md} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 1-Introduction/translations/{README.zh.md => README.zh-cn.md} (100%)

diff --git a/1-Introduction/translations/README.zh.md b/1-Introduction/translations/README.zh-cn.md
similarity index 100%
rename from 1-Introduction/translations/README.zh.md
rename to 1-Introduction/translations/README.zh-cn.md

From 0974b44742a4d64a79435b97155396f81cabd972 Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Tue, 13 Jul 2021 13:10:36 +0800
Subject: [PATCH 124/368] Update README.zh-cn.md

change relative path to translation folders
---
 1-Introduction/translations/README.zh-cn.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/1-Introduction/translations/README.zh-cn.md b/1-Introduction/translations/README.zh-cn.md
index 6ed91bac9a..3eb108327e 100644
--- a/1-Introduction/translations/README.zh-cn.md
+++ b/1-Introduction/translations/README.zh-cn.md
@@ -7,10 +7,10 @@
   
 ### 课程安排
 
-1. [机器学习简介](1-intro-to-ML/README.md)
-1. [机器学习�人工智能的历�](2-history-of-ML/README.md)
-1. [公平性与机器学习](3-fairness/README.md)
-1. [机器学习的技术](4-techniques-of-ML/README.md)
+1. [机器学习简介](1-intro-to-ML/translations/README.zh-cn.md)
+1. [机器学习�人工智能的历�](2-history-of-ML/translations/README.zh-cn.md)
+1. [公平性与机器学习](3-fairness/translations/README.zh-cn.md)
+1. [机器学习的技术](4-techniques-of-ML/translations/README.zh-cn.md)
 ### 致谢
 
 "机器学习简介"由 [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) � [Jen Looper](https://twitter.com/jenlooper)，共�倾 ♥� 而作

From 3c97843a72bfbf281eef0c45330952d29a7dbfa0 Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Tue, 13 Jul 2021 13:16:44 +0800
Subject: [PATCH 125/368] Update README.zh-cn.md
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

update relative path of translations folder of each lessons and align the title。
更新了�个课程的相对路径，并��标题翻译的一致性
---
 1-Introduction/translations/README.zh-cn.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/1-Introduction/translations/README.zh-cn.md b/1-Introduction/translations/README.zh-cn.md
index 3eb108327e..534e244e79 100644
--- a/1-Introduction/translations/README.zh-cn.md
+++ b/1-Introduction/translations/README.zh-cn.md
@@ -7,10 +7,10 @@
   
 ### 课程安排
 
-1. [机器学习简介](1-intro-to-ML/translations/README.zh-cn.md)
-1. [机器学习�人工智能的历�](2-history-of-ML/translations/README.zh-cn.md)
-1. [公平性与机器学习](3-fairness/translations/README.zh-cn.md)
-1. [机器学习的技术](4-techniques-of-ML/translations/README.zh-cn.md)
+1. [机器学习简介](../1-intro-to-ML/translations/README.zh-cn.md)
+1. [机器学习的历�](../2-history-of-ML/translations/README.zh-cn.md)
+1. [机器学习中的公平性](../3-fairness/translations/README.zh-cn.md)
+1. [机器学习技术](../4-techniques-of-ML/translations/README.zh-cn.md)
 ### 致谢
 
 "机器学习简介"由 [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) � [Jen Looper](https://twitter.com/jenlooper)，共�倾 ♥� 而作

From 6e8b5f77ce5964db87ae0a4a927fd6dd72ddafc4 Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Tue, 13 Jul 2021 19:50:34 +0800
Subject: [PATCH 126/368] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E5=9B=BE=E7=89=87?=
 =?UTF-8?q?=E7=9B=B8=E5=AF=B9=E8=B7=AF=E5=BE=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 1-Introduction/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/translations/README.zh-cn.md b/1-Introduction/translations/README.zh-cn.md
index 534e244e79..f1ad8e1ebe 100644
--- a/1-Introduction/translations/README.zh-cn.md
+++ b/1-Introduction/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
 
 课程的本章节将为您介�机器学习领域背�的基本概念�什么是机器学习，并学习它的历�以�曾为此�出贡献的技术研究者门。让我们一起开始探索机器学习的全新世界��
 
-![globe](images/globe.jpg)
+![globe](../images/globe.jpg)
 > 图片由 <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a>�供，�自 <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ### 课程安排

From 168a45bb3f75c6271b6c00b2702ef4d8e52ad0e0 Mon Sep 17 00:00:00 2001
From: "Charles Emmanuel S. Ndiaye" <manusquall@outlook.com>
Date: Tue, 13 Jul 2021 16:16:29 +0000
Subject: [PATCH 127/368] French translation of introduction base README

Propose README.fr.md file for the french translation of introduction base README
---
 1-Introduction/translations/README.fr.md | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 1-Introduction/translations/README.fr.md

diff --git a/1-Introduction/translations/README.fr.md b/1-Introduction/translations/README.fr.md
new file mode 100644
index 0000000000..6320589878
--- /dev/null
+++ b/1-Introduction/translations/README.fr.md
@@ -0,0 +1,22 @@
+# Introduction à l’apprentissage automatique
+
+Dans cette section du programme, vous découvrirez les concepts de base sous-jacents au domaine de l'apprentissage automatique, ce qu’il est, et vous découvrirez son histoire et les techniques que les chercheurs utilisent pour travailler avec lui.  Explorons ensemble ce nouveau monde de Machine Learning !
+
+![globe](images/globe.jpg)
+> Photo par <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> sur <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+### Leçons
+
+1. [Introduction à l’apprentissage automatique](1-intro-to-ML/README.md)
+1. [L’histoire de l’apprentissage automatique et de l’IA](2-history-of-ML/README.md)
+1. [Équité et apprentissage automatique](3-équité/README.md)
+1. [Techniques d’apprentissage automatique](4-techniques-of-ML/README.md)
+### Crédits
+
+"Introduction à l’apprentissage automatique" a été écrit avec ♥� par une équipe de personnes comprenant [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) et [Jen Looper](https://twitter.com/jenlooper)
+
+"L’histoire de l’apprentissage automatique" a été écrit avec ♥� par [Jen Looper](https://twitter.com/jenlooper) et [Amy Boyd](https://twitter.com/AmyKateNicho)
+
+"Équité et apprentissage automatique" a été écrit avec ♥� par [Tomomi Imura](https://twitter.com/girliemac) 
+
+"Techniques d’apprentissage automatique" a été écrit avec ♥� par [Jen Looper](https://twitter.com/jenlooper) et [Chris Noring](https://twitter.com/softchris)

From d8c701ec6f2e83ddf36c32333950ae06740cb003 Mon Sep 17 00:00:00 2001
From: "Charles Emmanuel S. Ndiaye" <manusquall@outlook.com>
Date: Tue, 13 Jul 2021 16:18:23 +0000
Subject: [PATCH 128/368] fix image link

---
 1-Introduction/translations/README.fr.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/translations/README.fr.md b/1-Introduction/translations/README.fr.md
index 6320589878..d9fde67846 100644
--- a/1-Introduction/translations/README.fr.md
+++ b/1-Introduction/translations/README.fr.md
@@ -2,7 +2,7 @@
 
 Dans cette section du programme, vous découvrirez les concepts de base sous-jacents au domaine de l'apprentissage automatique, ce qu’il est, et vous découvrirez son histoire et les techniques que les chercheurs utilisent pour travailler avec lui.  Explorons ensemble ce nouveau monde de Machine Learning !
 
-![globe](images/globe.jpg)
+![globe](../images/globe.jpg)
 > Photo par <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> sur <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ### Leçons

From f7469d105668567a5147adfffc320072b4add770 Mon Sep 17 00:00:00 2001
From: duanyang25 <34642309+duanyang25@users.noreply.github.com>
Date: Wed, 14 Jul 2021 00:40:33 +0800
Subject: [PATCH 129/368] Create README.zh-cn.md

translate README to Simplified Chinese
---
 9-Real-World/translations/README.zh-cn.md | 14 ++++++++++++++
 1 file changed, 14 insertions(+)
 create mode 100644 9-Real-World/translations/README.zh-cn.md

diff --git a/9-Real-World/translations/README.zh-cn.md b/9-Real-World/translations/README.zh-cn.md
new file mode 100644
index 0000000000..58bcb42053
--- /dev/null
+++ b/9-Real-World/translations/README.zh-cn.md
@@ -0,0 +1,14 @@
+# 附言: �典机器学习在现实生活中的应用
+
+在课程的这一章节中，你将会了解一些�典机器学习的现实应用。我们在网络上找�了涉�课程中这些技术的应用的白皮书和文章，从中尽力排除了神�网络�深度学习和AI。让我们一起�探索机器学习是如何被应用在商业系统�生�应用�金��艺术文化和其他领域中�。
+
+![chess](images/chess.jpg)
+
+> 照片由 <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> �摄并�布在 <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> 平�
+  
+## 课程安排
+
+1. [机器学习的现实应用](1-Applications/README.md)
+## 致谢
+
+"机器学习的现实应用" 由 [Jen Looper](https://twitter.com/jenlooper) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom) 两人的团队共�撰写.

From 083a024c836f3972229daf8b063c3caa76e18f85 Mon Sep 17 00:00:00 2001
From: duanyang25 <34642309+duanyang25@users.noreply.github.com>
Date: Wed, 14 Jul 2021 00:53:02 +0800
Subject: [PATCH 130/368] Update README.zh-cn.md

Update relative paths
---
 9-Real-World/translations/README.zh-cn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/9-Real-World/translations/README.zh-cn.md b/9-Real-World/translations/README.zh-cn.md
index 58bcb42053..89cae2cbe4 100644
--- a/9-Real-World/translations/README.zh-cn.md
+++ b/9-Real-World/translations/README.zh-cn.md
@@ -2,13 +2,13 @@
 
 在课程的这一章节中，你将会了解一些�典机器学习的现实应用。我们在网络上找�了涉�课程中这些技术的应用的白皮书和文章，从中尽力排除了神�网络�深度学习和AI。让我们一起�探索机器学习是如何被应用在商业系统�生�应用�金��艺术文化和其他领域中�。
 
-![chess](images/chess.jpg)
+![chess](../images/chess.jpg)
 
 > 照片由 <a href="https://unsplash.com/@childeye?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexis Fauvet</a> �摄并�布在 <a href="https://unsplash.com/s/photos/artificial-intelligence?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a> 平�
   
 ## 课程安排
 
-1. [机器学习的现实应用](1-Applications/README.md)
+1. [机器学习的现实应用](../1-Applications/README.md)
 ## 致谢
 
 "机器学习的现实应用" 由 [Jen Looper](https://twitter.com/jenlooper) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom) 两人的团队共�撰写.

From e062028016ede3c0006a9b7ba9a2f6c57ca382b7 Mon Sep 17 00:00:00 2001
From: edgargonarr <35715904+edgargonarr@users.noreply.github.com>
Date: Tue, 13 Jul 2021 17:41:46 -0500
Subject: [PATCH 131/368] Update README.md

---
 3-Web-App/1-Web-App/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index 6150aece78..7c91acb57e 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -165,7 +165,7 @@ Now you can build a Flask app to call your model and return similar results, but
     web-app/
       static/
         css/
-        templates/
+      templates/
     notebook.ipynb
     ufo-model.pkl
     ``` 

From dbafeac18d3a742da75629f8f1b9d13318a6106b Mon Sep 17 00:00:00 2001
From: "Charles Emmanuel S. Ndiaye" <manusquall@outlook.com>
Date: Wed, 14 Jul 2021 00:39:51 +0000
Subject: [PATCH 132/368] Keep "Machine learning" term in fr translation

Keep "Machine learning" term in fr translation
---
 1-Introduction/translations/README.fr.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/1-Introduction/translations/README.fr.md b/1-Introduction/translations/README.fr.md
index d9fde67846..c27f9bef1b 100644
--- a/1-Introduction/translations/README.fr.md
+++ b/1-Introduction/translations/README.fr.md
@@ -1,22 +1,22 @@
-# Introduction à l’apprentissage automatique
+# Introduction au machine learning
 
-Dans cette section du programme, vous découvrirez les concepts de base sous-jacents au domaine de l'apprentissage automatique, ce qu’il est, et vous découvrirez son histoire et les techniques que les chercheurs utilisent pour travailler avec lui.  Explorons ensemble ce nouveau monde de Machine Learning !
+Dans cette section du programme, vous découvrirez les concepts de base sous-jacents au domaine du machine learning, ce qu’il est, et vous découvrirez son histoire et les techniques que les chercheurs utilisent pour travailler avec lui.  Explorons ensemble ce nouveau monde de ML !
 
 ![globe](../images/globe.jpg)
 > Photo par <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> sur <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ### Leçons
 
-1. [Introduction à l’apprentissage automatique](1-intro-to-ML/README.md)
-1. [L’histoire de l’apprentissage automatique et de l’IA](2-history-of-ML/README.md)
-1. [Équité et apprentissage automatique](3-équité/README.md)
-1. [Techniques d’apprentissage automatique](4-techniques-of-ML/README.md)
+1. [Introduction au machine learning](1-intro-to-ML/README.md)
+1. [L’histoire du machine learning et de l’IA](2-history-of-ML/README.md)
+1. [Équité et machine learning](3-équité/README.md)
+1. [Techniques de machine learning](4-techniques-of-ML/README.md)
 ### Crédits
 
-"Introduction à l’apprentissage automatique" a été écrit avec ♥� par une équipe de personnes comprenant [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) et [Jen Looper](https://twitter.com/jenlooper)
+"Introduction au machine learning" a été écrit avec ♥� par une équipe de personnes comprenant [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) et [Jen Looper](https://twitter.com/jenlooper)
 
-"L’histoire de l’apprentissage automatique" a été écrit avec ♥� par [Jen Looper](https://twitter.com/jenlooper) et [Amy Boyd](https://twitter.com/AmyKateNicho)
+"L’histoire du machine learning" a été écrit avec ♥� par [Jen Looper](https://twitter.com/jenlooper) et [Amy Boyd](https://twitter.com/AmyKateNicho)
 
-"Équité et apprentissage automatique" a été écrit avec ♥� par [Tomomi Imura](https://twitter.com/girliemac) 
+"Équité et machine learning" a été écrit avec ♥� par [Tomomi Imura](https://twitter.com/girliemac) 
 
-"Techniques d’apprentissage automatique" a été écrit avec ♥� par [Jen Looper](https://twitter.com/jenlooper) et [Chris Noring](https://twitter.com/softchris)
+"Techniques de machine learning" a été écrit avec ♥� par [Jen Looper](https://twitter.com/jenlooper) et [Chris Noring](https://twitter.com/softchris)

From 721ec86311781b23a4f35a0c145bfa8d323305f5 Mon Sep 17 00:00:00 2001
From: edgargonarr <35715904+edgargonarr@users.noreply.github.com>
Date: Tue, 13 Jul 2021 23:25:37 -0500
Subject: [PATCH 133/368] Update README.md

In line 178, templates folder seems to be inside of static folder and this condition gives notfoundtemplate error. I suggest delete one tab, I discovered this solution because I sew your solutions folder on github where locations is correct
---
 3-Web-App/1-Web-App/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index 6150aece78..7c91acb57e 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -165,7 +165,7 @@ Now you can build a Flask app to call your model and return similar results, but
     web-app/
       static/
         css/
-        templates/
+      templates/
     notebook.ipynb
     ufo-model.pkl
     ``` 

From 16e145ca87c05085b52db104d8e644490bd1f020 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Wed, 14 Jul 2021 12:40:33 +0800
Subject: [PATCH 134/368] Update README.zh-cn.md

---
 .../1-Web-App/translations/README.zh-cn.md    | 162 +++++++++---------
 1 file changed, 81 insertions(+), 81 deletions(-)

diff --git a/3-Web-App/1-Web-App/translations/README.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md
index 6150aece78..cb8a051c57 100644
--- a/3-Web-App/1-Web-App/translations/README.zh-cn.md
+++ b/3-Web-App/1-Web-App/translations/README.zh-cn.md
@@ -1,58 +1,58 @@
-# Build a Web App to use a ML Model
+# 构建使用ML模型的Web应用程�
 
-In this lesson, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from [NUFORC's database](https://www.nuforc.org).
+在本课中，你将在一个数�集上训练一个ML模型，这个数�集�自世界�地：过去一个世纪的UFO目击事件，��于[NUFORC的数�库](https://www.nuforc.org)。
 
-You will learn:
+你将学会：
 
-- How to 'pickle' a trained model
-- How to use that model in a Flask app
+- 如何“pickle�一个训练有素的模型
+- 如何在Flask应用程�中使用该模型
 
-We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model 'in the wild', so to speak: in a web app.
+我们将继续使用notebook�清�数�和训练我们的模型，但你�以进一步探索在web应用程�中使用模型。
 
-To do this, you need to build a web app using Flask.
+为此，你需�使用Flask构建一个web应用程�。
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
 
-## Building an app
+## 构建应用程�
 
-There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app.
+有多�方法�以构建Web应用程�以使用机器学习模型。你的web架构�能会影�你的模型训练方�。想象一下，你在一家�业工作，其中数�科学�组已�训练了他们希望你在应用程�中使用的模型。 
 
-### Considerations
+### 注�事项 
 
-There are many questions you need to ask: 
+你需�问很多问题： 
 
-- **Is it a web app or a mobile app?** If you are building a mobile app or need to use the model in an IoT context, you could use [TensorFlow Lite](https://www.tensorflow.org/lite/) and use the model in an Android or iOS app.
-- **Where will the model reside**? In the cloud or locally?
-- **Offline support**. Does the app have to work offline?
-- **What technology was used to train the model?** The chosen technology may influence the tooling you need to use.
-   - **Using Tensor flow**. If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).  
-   - **Using PyTorch**.  If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model.
-   - **Using Lobe.ai or Azure Custom vision**. If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
+- **它是web应用程�还是移动应用程�？**如果你正在构建移动应用程�或需�在物�网环境中使用模型，你�以使用[TensorFlow Lite](https://www.tensorflow.org/lite/)并在Android或iOS应用程�中使用该模型。
+- **模型放在哪里？**在云端还是本地？
+- **离线支�**。该应用程�是�必须离线工作？ 
+- **使用什么技术�训练模型？**所选的技术�能会影�你需�使用的工具。 
+   - **使用Tensor flow**。例如，如果你正在使用TensorFlow训练模型，则该生�系统�供了使用[TensorFlow.js](https://www.tensorflow.org/js/)转�TensorFlow模型以便在Web应用程�中使用的能力。
+   - **使用 PyTorch**。如果你使用[PyTorch](https://pytorch.org/)等库构建模型，则�以选择将其导出到[ONNX](https://onnx.ai/)（开放神�网络交�)格�，用于�以使用 [Onnx Runtime](https://www.onnxruntime.ai/)的JavaScript Web 应用程�。此选项将在Scikit-learn-trained模型的未�课程中进行探讨。
+   - **使用Lobe.ai或Azure自定义视觉**。如果你使用ML SaaS（软件��务）系统，例如[Lobe.ai](https://lobe.ai/)或[Azure Custom Vision](https://azure.microsoft.com/services/ cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa)�训练模型，这�类型的软件�供了为许多平�导出模型的方法，包括构建一个定制API，供在线应用程�在云中查询。
 
-You also have the opportunity to build an entire Flask web app that would be able to train the model itself in a web browser. This can also be done using TensorFlow.js in a JavaScript context.
+你还有机会构建一个完整的Flask Web应用程�，该应用程�能够在 Web�览器中训练模型本身。这也�以在JavaScript上下文中使用 TensorFlow.js�完�。 
 
-For our purposes, since we have been working with Python-based notebooks, let's explore the steps you need to take to export a trained model from such a notebook to a format readable by a Python-built web app.
+出于我们的目的，既然我们一直在使用基于Python的notebook，那么就让我们探讨一下将�过训练的模型从notebook导出为Python构建的web应用程��读的格�所需�采�的步骤。
 
-## Tool
+## 工具
 
-For this task, you need two tools: Flask and Pickle, both of which run on Python.
+对于此任务，你需�两个工具：Flask和Pickle，它们都在Python上�行。 
 
-✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages.  Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) to practice building with Flask.
+✅ 什么是 [Flask](https://palletsprojects.com/p/flask/)？ Flask被其创建者定义为“微框架�，它�供了使用Python和模�引擎构建网页的Web框架的基本功能。看看[本学习�元](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa)练习使用Flask构建应用程�。 
 
-✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`.
+✅ 什么是[Pickle](https://docs.python.org/3/library/pickle.html)？ Pickle🥒是一 Python模�，用于�列化和��列化 Python对象结构。当你“pickle�一个模型时，你将其结构�列化或展平以在 Web上使用。�心：pickle本质上�是安全的，所以如果�示“un-pickle�文件，请�心。生产的文件具有�缀`.pkl`。
 
-## Exercise - clean your data
+## 练习 - 清�你的数� 
 
-In this lesson you'll use data from 80,000 UFO sightings, gathered by [NUFORC](https://nuforc.org) (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:
+在本课中，你将使用由 [NUFORC](https://nuforc.org)（国家 UFO 报告中心）收集的80,000次UFO目击数�。这些数�对UFO目击事件有一些有趣的�述，例如：
 
-- **Long example description**. "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
-- **Short example description**. "the lights chased us".
+- **详细�述**。"一�男�从夜间照射在�地上的光�中出现，他�德克�斯仪器公�的�车场跑去"。
+- **简短�述**。 “�光追�我们�。 
 
-The [ufos.csv](./data/ufos.csv) spreadsheet includes columns about the `city`, `state` and `country` where the sighting occurred, the object's `shape` and its `latitude` and `longitude`. 
+[ufos.csv](./data/ufos.csv)电�表格包括有关目击事件�生的`city`�`state`和`country`�对象的`shape`�其`latitude`和`longitude`的列。
 
-In the blank [notebook](notebook.ipynb) included in this lesson: 
+在包�在本课中的空白[notebook](notebook.ipynb)中： 
 
-1. import `pandas`, `matplotlib`, and `numpy` as you did in previous lessons and import the ufos spreadsheet. You can take a look at a sample data set:
+1. �在之�的课程中一样导入`pandas`�`matplotlib`和`numpy`，然�导入ufos电�表格。你�以查看一个示例数�集： 
 
     ```python
     import pandas as pd
@@ -62,7 +62,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
     ufos.head()
     ```
 
-1. Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the `Country` field.
+2. 将ufos数�转�为带有新标题的�dataframe。检查`country`字段中的唯一值。 
 
     ```python
     ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})
@@ -70,7 +70,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
     ufos.Country.unique()
     ```
 
-1. Now, you can reduce the amount of data we need to deal with by dropping any null values and only importing sightings between 1-60 seconds:
+3. 现在，你�以通过删除任何空值并仅导入1-60秒之间的目击数���少我们需�处�的数��：
 
     ```python
     ufos.dropna(inplace=True)
@@ -80,9 +80,9 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
     ufos.info()
     ```
 
-1. Import Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:
+4. 导入Scikit-learn的`LabelEncoder`库，将国家的文本值转�为数字： 
 
-   ✅ LabelEncoder encodes data alphabetically
+   ✅ LabelEncoder按字�顺�编�数� 
 
     ```python
     from sklearn.preprocessing import LabelEncoder
@@ -92,7 +92,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
     ufos.head()
     ```
 
-    Your data should look like this:
+    你的数�应如下所示：
 
     ```output
     	Seconds	Country	Latitude	Longitude
@@ -103,11 +103,11 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
     24	3.0	    3	    51.783333	-0.783333
     ```
 
-## Exercise - build your model
+## 练习 - 建立你的模型 
 
-Now you can get ready to train a model by diving the data into the training and testing group. 
+现在，你�以通过将数�划分为训练和测试组�准备训练模型。 
 
-1. Select the three features you want to train on as your X vector, and the y vector will be the `Country`. You want to be able to input `Seconds`, `Latitude` and `Longitude` and get a country id to return.
+1. 选择�训练的三个特�作为X��，y��将是`Country` 你希望能够输入`Seconds`�`Latitude`和`Longitude`并获得�返回的国家/地区ID。
 
     ```python
     from sklearn.model_selection import train_test_split
@@ -120,7 +120,7 @@ Now you can get ready to train a model by diving the data into the training and
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
     ```
 
-1. Train your model using logistic regression:
+2. 使用逻辑回归训练模型： 
 
     ```python
     from sklearn.metrics import accuracy_score, classification_report 
@@ -134,13 +134,13 @@ Now you can get ready to train a model by diving the data into the training and
     print('Accuracy: ', accuracy_score(y_test, predictions))
     ```
 
-The accuracy isn't bad **(around 95%)**, unsurprisingly, as `Country` and `Latitude/Longitude` correlate.
+准确率还�错**（大约 95%）**，�出所料，因为`Country`和`Latitude/Longitude`相关。
 
-The model you created isn't very revolutionary as you should be able to infer a `Country` from its `Latitude` and `Longitude`, but it's a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app.
+你创建的模型并�是�常具有�命性，因为你应该能够从其`Latitude`和`Longitude`推断出`Country`，但是，�试从清��导出的原始数�进行训练，然�在web应用程�中使用此模型是一个很好的练习。
 
-## Exercise - 'pickle' your model
+## 练习 - “pickle�你的模型
 
-Now, it's time to _pickle_ your model! You can do that in a few lines of code. Once it's _pickled_, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude,
+现在，是时候_pickle_你的模型了�你�以在几行代�中�到这一点。一旦它是 _pickled_，加载你的pickled模型并针对包�秒�纬度和�度值的示例数�数组对其进行测试， 
 
 ```python
 import pickle
@@ -151,15 +151,15 @@ model = pickle.load(open('ufo-model.pkl','rb'))
 print(model.predict([[50,44,-12]]))
 ```
 
-The model returns **'3'**, which is the country code for the UK. Wild! 👽
+该模型返回**'3'**，这是英国的国家代�。👽
 
-## Exercise - build a Flask app
+## 练习 - 构建Flask应用程� 
 
-Now you can build a Flask app to call your model and return similar results, but in a more visually pleasing way.
+现在你�以构建一个Flask应用程��调用你的模型并返回类似的结果，但以一�更美观的方�。
 
-1. Start by creating a folder called **web-app** next to the _notebook.ipynb_ file where your _ufo-model.pkl_ file resides.
+1. 首先在你的 _ufo-model.pkl_ 文件所在的_notebook.ipynb_文件�边创建一个�为**web-app**的文件夹。 
 
-1. In that folder create three more folders: **static**, with a folder **css** inside it, and **templates`**. You should now have the following files and directories:
+2. 在该文件夹中创建�外三个文件夹：**static**，其中有文件夹**css**和**templates`**。 你现在应该拥有以下文件和目录
 
     ```output
     web-app/
@@ -170,9 +170,9 @@ Now you can build a Flask app to call your model and return similar results, but
     ufo-model.pkl
     ``` 
 
-   ✅ Refer to the solution folder for a view of the finished app
+   ✅ 请�阅解决方案文件夹以查看已完�的应用程� 
 
-1. The first file to create in _web-app_ folder is **requirements.txt** file. Like _package.json_ in a JavaScript app, this file lists dependencies required by the app. In **requirements.txt** add the lines: 
+3. 在_web-app_文件夹中创建的第一个文件是**requirements.txt**文件。与JavaScript应用程�中的_package.json_一样，此文件列出了应用程�所需的�赖项。在**requirements.txt**中添加以下几行：  
 
     ```text
     scikit-learn
@@ -181,25 +181,25 @@ Now you can build a Flask app to call your model and return similar results, but
     flask
     ```
 
-1. Now, run this file by navigating to _web-app_:
+4. 现在，进入web-app文件夹： 
 
    ```bash
    cd web-app
    ```
 
-1. In your terminal type `pip install`, to install the libraries listed in _reuirements.txt_:
+5. 在你的终端中输入`pip install`，以安装_reuirements.txt_中列出的库：
 
    ```bash
    pip install -r requirements.txt
    ```
 
-1. Now, you're ready to create three more files to finish the app:
+6. 现在，你已准备好创建�外三个文件�完�应用程�： 
 
-    1. Create **app.py** in the root
-    2. Create **index.html** in _templates_ directory.
-    3. Create **styles.css** in _static/css_ directory.
+    1. 在根目录中创建**app.py** 
+    2. 在_templates_目录中创建**index.html**。 
+    3. 在_static/css_目录中创建**styles.css**。 
 
-1. Build out the _styles.css__ file with a few styles:
+7. 使用一些样�构建_styles.css_文件： 
 
     ```css
     body {
@@ -233,7 +233,7 @@ Now you can build a Flask app to call your model and return similar results, but
     }
     ```
 
-1. Next, build out the _index.html_ file:
+8. 接下�，构建_index.html_文件： 
 
     ```html
     <!DOCTYPE html>
@@ -268,11 +268,11 @@ Now you can build a Flask app to call your model and return similar results, but
     </html>
     ```
 
-    Take a look at the templating in this file. Notice the 'mustache' syntax around variables that will be provided by the app, like the prediction text: `{{}}`. There's also a form that posts a prediction to the `/predict` route.
+    看看这个文件中的模�。请注�应用程�将�供的��周围的“mustache�语法，例如预测文本：`{{}}`。还有一个表��以将预测�布到`/predict`路由。
 
-    Finally, you're ready to build the python file that drives the consumption of the model and the display of predictions:
+    最�，你已准备好构建使用模型和显示预测的python 文件：
 
-1. In `app.py` add:
+9. 在`app.py`中添加:
 
     ```python
     import numpy as np
@@ -309,39 +309,39 @@ Now you can build a Flask app to call your model and return similar results, but
         app.run(debug=True)
     ```
 
-   > 💡 Tip: when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
+   > 💡 �示：当你在使用Flask�行Web应用程�时添加 [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode)时你对应用程�所�的任何更改将立��映，无需�新�动�务器。注����在生产应用程�中�用此模�
 
-If you run `python app.py` or `python3 app.py` - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!
+如果你�行`python app.py`或`python3 app.py` - 你的网络�务器在本地�动，你�以填写一个简短的表格�回答你关于在哪里看到UFO的问题�
 
-Before doing that, take a look at the parts of `app.py`:
+在此之�，先看一下`app.py`的实现： 
 
-1. First, dependencies are loaded and the app starts.
-1. Then, the model is imported.
-1. Then, index.html is rendered on the home route.
+1. 首先，加载�赖项并�动应用程�。
+2. 然�，导入模型。
+3. 然�，在home路由上渲染index.html。 
 
-On the `/predict` route, several things happen when the form is posted:
+在`/predict`路由上，当表�被�布时会�生几件事情： 
 
-1. The form variables are gathered and converted to a numpy array. They are then sent to the model and a prediction is returned.
-2. The Countries that we want displayed are re-rendered as readable text from their predicted country code, and that value is sent back to index.html to be rendered in the template.
+1. 收集表���并转�为numpy数组。然�将它们��到模型并返回预测。
+2. 我们希望显示的国家/地区根�其预测的国家/地区代��新呈现为�读文本，并将该值��回index.html以在模�中呈现。 
 
-Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction. 
+以这�方�使用模型，包括Flask和pickled模型，是相对简�的。最困难的是��解数�是什么形状的，这些数�必须��到模型中�能得到预测。这完全�决于模型是如何训练的。有三个数��输入，以便得到一个预测。
 
-In a professional setting, you can see how good communication is necessary between the folks who train the model and those who consume it in a web or mobile app. In our case, it's only one person, you!
+在一个专业的环境中，你�以看到训练模型的人和在Web或移动应用程�中使用模型的人之间的良好沟通是多么的必�。在我们的情况下，�有一个人，你�
 
 ---
 
-## 🚀 Challenge: 
+## 🚀 挑战: 
 
-Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
+你�以在Flask应用程�中训练模型，而�是在notebook上工作并将模型导入Flask应用程���试在notebook中转�Python代�，�能是在清除数�之�，从应用程�中的一个�为`train`的路径训练模型。采用这�方法的利弊是什么？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
 
-## Review & Self Study
+## �习与自学 
 
-There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.
+有很多方法�以构建一个Web应用程��使用ML模型。列出�以使用JavaScript或Python构建Web应用程�以利用机器学习的方法。考虑架构：模型应该留在应用程�中还是存在于云中？如果是�者，你将如何访问它？为应用的ML Web解决方案绘制架构模型。 
 
-## Assignment 
+## 任务 
 
-[Try a different model](assignment.md)
+[�试��的模型](../assignment.md)
 
 

From e975db0a74cf8ba14780cc2775a1d0d99f13e275 Mon Sep 17 00:00:00 2001
From: lty <247969917@qq.com>
Date: Wed, 14 Jul 2021 18:14:43 +0800
Subject: [PATCH 135/368] Fix a spelling error in README.md

---
 6-NLP/1-Introduction-to-NLP/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index 0d47a1d701..227ad589d2 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -81,7 +81,7 @@ This gave the impression that Eliza understood the statement and was asking a fo
 
 ## Exercise - coding a basic conversational bot
 
-A conversational bot, like Eliza, is a program that elicits user input and seems to understand and respond intelligently. Unlike Eliza, our bot will not have several rules giving it the appearance of having an intelligent conversation. Instead, out bot will have one ability only, to keep the conversation going with random responses that might work in almost any trivial conversation.
+A conversational bot, like Eliza, is a program that elicits user input and seems to understand and respond intelligently. Unlike Eliza, our bot will not have several rules giving it the appearance of having an intelligent conversation. Instead, our bot will have one ability only, to keep the conversation going with random responses that might work in almost any trivial conversation.
 
 ### The plan
 

From 5f225d0063e3bce27429a00df83ac2b973b47925 Mon Sep 17 00:00:00 2001
From: lty <247969917@qq.com>
Date: Wed, 14 Jul 2021 18:19:45 +0800
Subject: [PATCH 136/368] Add 6.1 Chinese and English README.md

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 create mode 100644 6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md

diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
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@@ -0,0 +1,225 @@
+# Introduction to natural language processing
+# 自然语言处�介�
+This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*.
+这节课讲解了*自然语言处�*简�历�和��概念，*自然语言处�*是计算语言学的一个�领域。
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+
+## Introduction
+## 介�
+NLP, as it is commonly known, is one of the best-known areas where machine learning has been applied and used in production software.
+众所周知，自然语言处�(Natural Language Processing, NLP)是机器学习在生产软件中应用最广泛的领域之一。
+
+✅ Can you think of software that you use every day that probably has some NLP embedded? What about your word processing programs or mobile apps that you use regularly?
+
+✅你能想到哪些你日常生活中使用的软件嵌入了�些自然语言处�技术呢？你�常使用的文字处�程�或移动应用程�是�嵌入了自然语言处�技术呢？
+
+You will learn about:
+你将会学习到：
+
+- **The idea of languages**. How languages developed and what the major areas of study have been.
+- **Definition and concepts**. You will also learn definitions and concepts about how computers process text, including parsing, grammar, and identifying nouns and verbs. There are some coding tasks in this lesson, and several important concepts are introduced that you will learn to code later on in the next lessons.
+- **语言的�想**. 语言的�展历程�主�研究领域.
+- **定义和概念**. 你还将学习到有关计算机如何处�文本的定义和概念，包括解��语法以���和动�的识别。本节课程包�一些编�任务并介�了几个��的概念，你将在下一节课中学习编�实现这些概念。
+
+## Computational linguistics
+## 计算语言学
+
+Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. natural language processing (NLP) is a related field focused on how computers can process 'natural', or human, languages.
+计算语言学是一个�过几�年研究和�展的领域，它研究计算机如何使用语言��解语言�翻译语言�使用语言交�。自然语言处�(NLP)是计算语言学中一个专注于计算机如何处�“自然�或人类语言的相关领域，
+
+### Example - phone dictation
+### 例� - 电���识别
+
+If you have ever dictated to your phone instead of typing or asked a virtual assistant a question, your speech was converted into a text form and then processed or *parsed* from the language you spoke. The detected keywords were then processed into a format that the phone or assistant could understand and act on.
+如果你曾�在手机上使用语音输入替代键盘输入或者�语音助手�娜�问，那么你的语音将被转录为文本形��进行处�或者�*解�*。被检测到的关键字最�将被处��手机或语音助手�以�解并采�行动的格�。
+
+![comprehension](images/comprehension.png)
+> Real linguistic comprehension is hard! Image by [Jen Looper](https://twitter.com/jenlooper)
+> 真实的语言�解�分困难�图�：[Jen Looper](https://twitter.com/jenlooper)
+
+### How is this technology made possible?
+### 这项技术是如何实现的？
+
+This is possible because someone wrote a computer program to do this. A few decades ago, some science fiction writers predicted that people would mostly speak to their computers, and the computers would always understand exactly what they meant. Sadly, it turned out to be a harder problem that many imagined, and while it is a much better understood problem today, there are significant challenges in achieving 'perfect' natural language processing when it comes to understanding the meaning of a sentence. This is a particularly hard problem when it comes to understanding humour or detecting emotions such as sarcasm in a sentence.
+有人编写了一个计算机程��实现这项技术。几�年�，一些科幻作家预测人类很大�能会和他们的电脑对�，而电脑总是能准确地�解人类的��。�惜的是，事实�明这是一个比许多人想象中更难实现的问题，虽然今天这个问题已�被�步解决，但在�解��的�义时，�实现“完美�的自然语言处��然存在�大挑战。��中的幽默�解或讽刺等情绪的检测是一个特别困难的问题。
+
+At this point, you may be remembering school classes where the teacher covered the parts of grammar in a sentence. In some countries, students are taught grammar and linguistics as a dedicated subject, but in many, these topics are included as part of learning a language: either your first language in primary school (learning to read and write) and perhaps a second language in post-primary, or high school. Don't  worry if you are not an expert at differentiating nouns from verbs or adverbs from adjectives!
+此时，你�能会想起学校课堂上�师讲解的部分��语法。在�些国家/地区，语法和语言学知识是学生的专题课内容。但在�一些国家/地区，�管是在�学时的第一语言（学习阅读和写作），或者在高年级�高中时学习的第二语言中，语法�语言学知识是作为学习语言的一部分教学的。如果你�能很好地区分��与动�或者区分副�与形容�，请��担心�
+
+If you struggle with the difference between the *simple present* and *present progressive*, you are not alone. This is a challenging thing for many people, even native speakers of a language. The good news is that computers are really good at applying formal rules, and you will learn to write code that can *parse* a sentence as well as a human. The greater challenge you will examine later is understanding the *meaning*, and *sentiment*, of a sentence.
+如果你还为区分*一般现在时*与*现在进行时*而烦�，你并�是一个人。�使是对以这门语言为�语的人在内的很多人�说这都是一项有挑战性的任务。好消�是，计算机�常善于应用标准的规则，你将学会编写�以�人一样"解�"��的代�。��你将�对的更大挑战是�解��的*语义*和*情绪*。
+
+## Prerequisites
+## ��
+
+For this lesson, the main prerequisite is being able to read and understand the language of this lesson. There are no math problems or equations to solve. While the original author wrote this lesson in English, it is also translated into other languages, so you could be reading a translation. There are examples where a number of different languages are used (to compare the different grammar rules of different languages). These are *not* translated, but the explanatory text is, so the meaning should be clear.
+本节教程的主�先决�件是能够阅读和�解本节教程的语言。本节中没有数学问题或方程需�解决。虽然原作者用英文写了这教程，但它也被翻译�其他语言，所以你�能在阅读翻译内容。有使用多���语言的示例（以比较��语言的��语法规则）。这些是*未*翻译的，但解释性文本是翻译内容，所以表义应当是清晰的。
+
+For the coding tasks, you will use Python and the examples are using Python 3.8.
+编程任务中，你将会使用Python语言，示例使用的是Python 3.8版本。
+
+In this section, you will need, and use:
+在本节中你将需�并使用：
+
+- **Python 3 comprehension**.  Programming language comprehension in Python 3, this lesson uses input, loops, file reading, arrays.
+- **Visual Studio Code + extension**. We will use Visual Studio Code and its Python extension. You can also use a Python IDE of your choice.
+- **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob) is a simplified text processing library for Python. Follow the instructions on the TextBlob site to install it on your system (install the corpora as well, as shown below):
+- **Python 3 �解**.  Python 3中的编程语言�解，本课使用输入�循环�文件读��数组。
+- **Visual Studio Code + 扩展**. 我们将使用 Visual Studio Code �其 Python 扩展。你还�以使用你选择的 Python IDE。
+- **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob)是一个简化的 Python 文本处�库。按照 TextBlob 网站上的说明在您的系统上安装它（也安装语料库，如下所示）：
+- 
+   ```bash
+   pip install -U textblob
+   python -m textblob.download_corpora
+   ```
+
+> 💡 Tip: You can run Python directly in VS Code environments. Check the [docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) for more information.
+> 💡 �示:�以在 VS Code 环境中直接�行 Python。 点击[docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa)查看更多信�。
+
+## Talking to machines
+## 与机器对�
+
+The history of trying to make computers understand human language goes back decades, and one of the earliest scientists to consider natural language processing was *Alan Turing*.
+试图让计算机�解人类语言的历��以追溯到几�年�，最早考虑自然语言处�的科学家之一是 *Alan Turing*。
+
+### The 'Turing test'
+### 图�测试
+
+
+When Turing was researching *artificial intelligence* in the 1950's, he considered if a conversational test could be given to a human and computer (via typed correspondence) where the human in the conversation was not sure if they were conversing with another human or a computer.
+当图�在1950年代研究*人工智能*时，他考虑是��以对人和计算机进行对�测试（通过打字对应），其中对�中的人�确定他们是在与�一个人交谈还是与计算机交谈.
+
+If, after a certain length of conversation, the human could not determine that the answers were from a computer or not, then could the computer be said to be *thinking*?
+如果�过一定时间的交谈，人类无法确定答案是��自计算机，那么是��以说计算机正在“�考�？
+
+### The inspiration - 'the imitation game'
+### �感 - “模仿游��
+
+The idea for this came from a party game called *The Imitation Game* where an interrogator is alone in a room and tasked with determining which of two people (in another room) are male and female respectively. The interrogator can send notes, and must try to think of questions where the written answers reveal the gender of the mystery person. Of course, the players in the other room are trying to trick the interrogator by answering questions in such as way as to mislead or confuse the interrogator, whilst also giving the appearance of answering honestly.
+这个想法�自一个�为 *模仿游�* 的派对游�，其中一�审讯者独自一人在一个房间里，负责确定两个人（在�一个房间里）是男性还是女性。审讯者�以传递笔记，并且需�想出能够�示神秘人性别的问题。当然，�一个房间的玩家试图通过回答问题的方��欺骗审讯者，例如误导或迷惑审讯者，�时表现出诚实回答的样�。
+
+### Eliza的研�
+
+In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https:/wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". 
+在 1960 年代，一��� *Joseph Weizenbaum* 的麻��工学院科学家开�了[*Eliza*](https:/wikipedia.org/wiki/ELIZA)，Eliza是一�计算机“治疗师�，它�以�人类�出问题并表现出�解他们的答案。然而，虽然 Eliza �以解���并识别�些语法结构和关键字以给出��的答案，但�能说它*�解*了��。如果 Eliza 看到的��格�为“**I am** <u>sad</u>�，它�能会�新排列并替���中的��以形��应“How long have ** you been** <u>sad</u>"。
+
+This gave the impression that Eliza understood the statement and was asking a follow-on question, whereas in reality, it was changing the tense and adding some words. If Eliza could not identify a keyword that it had a response for, it would instead give a random response that could be applicable to many different statements. Eliza could be easily tricked, for instance if a user wrote "**You are** a <u>bicycle</u>" it might respond with "How long have **I been** a <u>bicycle</u>?", instead of a more reasoned response.
+这给人的�象是伊丽莎�解了这��，并在问一个�续问题，而实际上，它是在改�时�并添加一些�。如果 Eliza 无法识别它有�应的关键字，它会给出一个�机�应，该�应�以适用于许多��的语�。 Eliza 很容易被欺骗，例如，如果用户写了**You are** a <u>bicycle</u>"，它�能会回�"How long have **I been** a <u>bicycle</u>?"，而�是更��的回答。
+
+[![Chatting with Eliza](https://img.youtube.com/vi/RMK9AphfLco/0.jpg)](https://youtu.be/RMK9AphfLco "Chatting with Eliza")
+
+> 🎥 Click the image above for a video about original ELIZA program
+> 🎥 点击上方的图片查看真实的ELIZA程�视频
+
+> Note: You can read the original description of [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) published in 1966 if you have an ACM account. Alternately, read about Eliza on [wikipedia](https://wikipedia.org/wiki/ELIZA)
+> 注�:如果你拥有ACM账户，你�以阅读1996年�表的[Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract)的原始介�。或者，在[wikipedia](https://wikipedia.org/wiki/ELIZA)阅读有关 Eliza 的信� 
+
+## Exercise - coding a basic conversational bot
+## �系 - 编�实现一个基础的对�机器人
+
+A conversational bot, like Eliza, is a program that elicits user input and seems to understand and respond intelligently. Unlike Eliza, our bot will not have several rules giving it the appearance of having an intelligent conversation. Instead, out bot will have one ability only, to keep the conversation going with random responses that might work in almost any trivial conversation.
+� Eliza 一样的对�机器人是一个似乎�以智能地�解和�应用户输入的程�。与 Eliza ��的是，我们的机器人�会用规则让它看起��是在进行智能对�。�而代之的是，我们的对�机器人将�有一�能力，通过几乎在所有�碎对�中都适用的�机�应��对�的进行。
+
+### The plan
+### 计划
+
+Your steps when building a conversational bot:
+�建�天机器人的步骤
+
+1. Print instructions advising the user how to interact with the bot
+2. Start a loop
+   1. Accept user input
+   2. If user has asked to exit, then exit
+   3. Process user input and determine response (in this case, the response is a random choice from a list of possible generic responses)
+   4. Print response
+3. loop back to step 2
+1. 打�指导用户如何与机器人交互的说明
+2. 开�循环
+   1. 获�用户输入
+   2. 如果用户�求退出，就退出
+   3. 处�用户输入并选择一个回答（在这个例�中，回答从一个�能的通用回答列表中�机选择）
+   4. 打�回答
+3. ��步骤2
+
+### Building the bot
+### 构建�天机器人
+
+接下�让我们构建�天机器人。我们将从定义一些短语开始。
+
+1. 使用以下�机�应在 Python 中自己创建此机器人：
+
+    ```python
+    random_responses = ["That is quite interesting, please tell me more.",
+                        "I see. Do go on.",
+                        "Why do you say that?",
+                        "Funny weather we've been having, isn't it?",
+                        "Let's change the subject.",
+                        "Did you catch the game last night?"]
+    ```
+
+    Here is some sample output to guide you (user input is on the lines starting with `>`):
+
+    ```output
+    Hello, I am Marvin, the simple robot.
+    You can end this conversation at any time by typing 'bye'
+    After typing each answer, press 'enter'
+    How are you today?
+    > I am good thanks
+    That is quite interesting, please tell me more.
+    > today I went for a walk     
+    Did you catch the game last night?
+    > I did, but my team lost
+    Funny weather we've been having, isn't it?
+    > yes but I hope next week is better
+    Let's change the subject.
+    > ok, lets talk about music
+    Why do you say that?
+    > because I like music!
+    Why do you say that?
+    > bye
+    It was nice talking to you, goodbye!
+    ```
+
+    该任务的一��能解决方案在[这里](solution/bot.py)
+
+    ✅ Stop and consider
+    ✅ �止并�考
+
+    1. Do you think the random responses would 'trick' someone into thinking that the bot actually understood them?
+    2. What features would the bot need to be more effective?
+    3. If a bot could really 'understand' the meaning of a sentence, would it need to 'remember' the meaning of previous sentences in a conversation too?
+   
+    1. 你认为�机�应会“欺骗��人认为机器人实际上�解他们�？
+    2. 机器人需�哪些功能�能更有效？
+    3. 如果机器人真的�以“�解�一个��的��，它是�也需�“记��对�中����的��？
+
+---
+
+## 🚀Challenge
+## 🚀挑战
+
+Choose one of the "stop and consider" elements above and either try to implement them in code or write a solution on paper using pseudocode.
+选择上�的“�止并�考�元素之一，然��试在代�中实现它们或使用伪代�在纸上编写解决方案。
+
+In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning.
+在下一课中，您将了解解�自然语言和机器学习的许多其他方法。
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+
+## Review & Self Study
+## �习与自学
+
+Take a look at the references below as further reading opportunities.
+看看下�的�考资料作为进一步的阅读机会。
+
+### References
+### �考
+
+1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Spring 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/spr2020/entries/computational-linguistics/>.
+2. Princeton University "About WordNet." [WordNet](https://wordnet.princeton.edu/). Princeton University. 2010. 
+
+## Assignment 
+## 任务
+
+[查找一个机器人](assignment.md)

From a0e4826c25c771f1c8559def049b955347854ade Mon Sep 17 00:00:00 2001
From: lty <247969917@qq.com>
Date: Wed, 14 Jul 2021 18:24:33 +0800
Subject: [PATCH 137/368] Add Chinese README for 6.1

---
 .../translations/README.zh-cn.md              | 69 +------------------
 1 file changed, 2 insertions(+), 67 deletions(-)

diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index 06ed9ca0d1..8dbfebc20a 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -1,69 +1,41 @@
-# Introduction to natural language processing
 # 自然语言处�介�
-This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*.
 这节课讲解了*自然语言处�*简�历�和��概念，*自然语言处�*是计算语言学的一个�领域。
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
 
-## Introduction
+## [课�测验]](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+
 ## 介�
-NLP, as it is commonly known, is one of the best-known areas where machine learning has been applied and used in production software.
 众所周知，自然语言处�(Natural Language Processing, NLP)是机器学习在生产软件中应用最广泛的领域之一。
 
-✅ Can you think of software that you use every day that probably has some NLP embedded? What about your word processing programs or mobile apps that you use regularly?
-
 ✅你能想到哪些你日常生活中使用的软件嵌入了�些自然语言处�技术呢？你�常使用的文字处�程�或移动应用程�是�嵌入了自然语言处�技术呢？
 
-You will learn about:
 你将会学习到：
 
-- **The idea of languages**. How languages developed and what the major areas of study have been.
-- **Definition and concepts**. You will also learn definitions and concepts about how computers process text, including parsing, grammar, and identifying nouns and verbs. There are some coding tasks in this lesson, and several important concepts are introduced that you will learn to code later on in the next lessons.
 - **语言的�想**. 语言的�展历程�主�研究领域.
 - **定义和概念**. 你还将学习到有关计算机如何处�文本的定义和概念，包括解��语法以���和动�的识别。本节课程包�一些编�任务并介�了几个��的概念，你将在下一节课中学习编�实现这些概念。
-
-## Computational linguistics
 ## 计算语言学
 
-Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. natural language processing (NLP) is a related field focused on how computers can process 'natural', or human, languages.
 计算语言学是一个�过几�年研究和�展的领域，它研究计算机如何使用语言��解语言�翻译语言�使用语言交�。自然语言处�(NLP)是计算语言学中一个专注于计算机如何处�“自然�或人类语言的相关领域，
-
-### Example - phone dictation
 ### 例� - 电���识别
 
-If you have ever dictated to your phone instead of typing or asked a virtual assistant a question, your speech was converted into a text form and then processed or *parsed* from the language you spoke. The detected keywords were then processed into a format that the phone or assistant could understand and act on.
 如果你曾�在手机上使用语音输入替代键盘输入或者�语音助手�娜�问，那么你的语音将被转录为文本形��进行处�或者�*解�*。被检测到的关键字最�将被处��手机或语音助手�以�解并采�行动的格�。
 
 ![comprehension](images/comprehension.png)
-> Real linguistic comprehension is hard! Image by [Jen Looper](https://twitter.com/jenlooper)
 > 真实的语言�解�分困难�图�：[Jen Looper](https://twitter.com/jenlooper)
-
-### How is this technology made possible?
 ### 这项技术是如何实现的？
 
-This is possible because someone wrote a computer program to do this. A few decades ago, some science fiction writers predicted that people would mostly speak to their computers, and the computers would always understand exactly what they meant. Sadly, it turned out to be a harder problem that many imagined, and while it is a much better understood problem today, there are significant challenges in achieving 'perfect' natural language processing when it comes to understanding the meaning of a sentence. This is a particularly hard problem when it comes to understanding humour or detecting emotions such as sarcasm in a sentence.
 有人编写了一个计算机程��实现这项技术。几�年�，一些科幻作家预测人类很大�能会和他们的电脑对�，而电脑总是能准确地�解人类的��。�惜的是，事实�明这是一个比许多人想象中更难实现的问题，虽然今天这个问题已�被�步解决，但在�解��的�义时，�实现“完美�的自然语言处��然存在�大挑战。��中的幽默�解或讽刺等情绪的检测是一个特别困难的问题。
 
-At this point, you may be remembering school classes where the teacher covered the parts of grammar in a sentence. In some countries, students are taught grammar and linguistics as a dedicated subject, but in many, these topics are included as part of learning a language: either your first language in primary school (learning to read and write) and perhaps a second language in post-primary, or high school. Don't  worry if you are not an expert at differentiating nouns from verbs or adverbs from adjectives!
 此时，你�能会想起学校课堂上�师讲解的部分��语法。在�些国家/地区，语法和语言学知识是学生的专题课内容。但在�一些国家/地区，�管是在�学时的第一语言（学习阅读和写作），或者在高年级�高中时学习的第二语言中，语法�语言学知识是作为学习语言的一部分教学的。如果你�能很好地区分��与动�或者区分副�与形容�，请��担心�
 
-If you struggle with the difference between the *simple present* and *present progressive*, you are not alone. This is a challenging thing for many people, even native speakers of a language. The good news is that computers are really good at applying formal rules, and you will learn to write code that can *parse* a sentence as well as a human. The greater challenge you will examine later is understanding the *meaning*, and *sentiment*, of a sentence.
 如果你还为区分*一般现在时*与*现在进行时*而烦�，你并�是一个人。�使是对以这门语言为�语的人在内的很多人�说这都是一项有挑战性的任务。好消�是，计算机�常善于应用标准的规则，你将学会编写�以�人一样"解�"��的代�。��你将�对的更大挑战是�解��的*语义*和*情绪*。
-
-## Prerequisites
 ## ��
 
-For this lesson, the main prerequisite is being able to read and understand the language of this lesson. There are no math problems or equations to solve. While the original author wrote this lesson in English, it is also translated into other languages, so you could be reading a translation. There are examples where a number of different languages are used (to compare the different grammar rules of different languages). These are *not* translated, but the explanatory text is, so the meaning should be clear.
 本节教程的主�先决�件是能够阅读和�解本节教程的语言。本节中没有数学问题或方程需�解决。虽然原作者用英文写了这教程，但它也被翻译�其他语言，所以你�能在阅读翻译内容。有使用多���语言的示例（以比较��语言的��语法规则）。这些是*未*翻译的，但解释性文本是翻译内容，所以表义应当是清晰的。
 
-For the coding tasks, you will use Python and the examples are using Python 3.8.
 编程任务中，你将会使用Python语言，示例使用的是Python 3.8版本。
 
-In this section, you will need, and use:
 在本节中你将需�并使用：
 
-- **Python 3 comprehension**.  Programming language comprehension in Python 3, this lesson uses input, loops, file reading, arrays.
-- **Visual Studio Code + extension**. We will use Visual Studio Code and its Python extension. You can also use a Python IDE of your choice.
-- **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob) is a simplified text processing library for Python. Follow the instructions on the TextBlob site to install it on your system (install the corpora as well, as shown below):
 - **Python 3 �解**.  Python 3中的编程语言�解，本课使用输入�循环�文件读��数组。
 - **Visual Studio Code + 扩展**. 我们将使用 Visual Studio Code �其 Python 扩展。你还�以使用你选择的 Python IDE。
 - **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob)是一个简化的 Python 文本处�库。按照 TextBlob 网站上的说明在您的系统上安装它（也安装语料库，如下所示）：
@@ -73,66 +45,40 @@ In this section, you will need, and use:
    python -m textblob.download_corpora
    ```
 
-> 💡 Tip: You can run Python directly in VS Code environments. Check the [docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) for more information.
 > 💡 �示:�以在 VS Code 环境中直接�行 Python。 点击[docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa)查看更多信�。
 
-## Talking to machines
 ## 与机器对�
 
-The history of trying to make computers understand human language goes back decades, and one of the earliest scientists to consider natural language processing was *Alan Turing*.
 试图让计算机�解人类语言的历��以追溯到几�年�，最早考虑自然语言处�的科学家之一是 *Alan Turing*。
-
-### The 'Turing test'
 ### 图�测试
 
-
-When Turing was researching *artificial intelligence* in the 1950's, he considered if a conversational test could be given to a human and computer (via typed correspondence) where the human in the conversation was not sure if they were conversing with another human or a computer.
 当图�在1950年代研究*人工智能*时，他考虑是��以对人和计算机进行对�测试（通过打字对应），其中对�中的人�确定他们是在与�一个人交谈还是与计算机交谈.
 
-If, after a certain length of conversation, the human could not determine that the answers were from a computer or not, then could the computer be said to be *thinking*?
 如果�过一定时间的交谈，人类无法确定答案是��自计算机，那么是��以说计算机正在“�考�？
-
-### The inspiration - 'the imitation game'
 ### �感 - “模仿游��
 
-The idea for this came from a party game called *The Imitation Game* where an interrogator is alone in a room and tasked with determining which of two people (in another room) are male and female respectively. The interrogator can send notes, and must try to think of questions where the written answers reveal the gender of the mystery person. Of course, the players in the other room are trying to trick the interrogator by answering questions in such as way as to mislead or confuse the interrogator, whilst also giving the appearance of answering honestly.
 这个想法�自一个�为 *模仿游�* 的派对游�，其中一�审讯者独自一人在一个房间里，负责确定两个人（在�一个房间里）是男性还是女性。审讯者�以传递笔记，并且需�想出能够�示神秘人性别的问题。当然，�一个房间的玩家试图通过回答问题的方��欺骗审讯者，例如误导或迷惑审讯者，�时表现出诚实回答的样�。
 
 ### Eliza的研�
 
-In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https:/wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". 
 在 1960 年代，一��� *Joseph Weizenbaum* 的麻��工学院科学家开�了[*Eliza*](https:/wikipedia.org/wiki/ELIZA)，Eliza是一�计算机“治疗师�，它�以�人类�出问题并表现出�解他们的答案。然而，虽然 Eliza �以解���并识别�些语法结构和关键字以给出��的答案，但�能说它*�解*了��。如果 Eliza 看到的��格�为“**I am** <u>sad</u>�，它�能会�新排列并替���中的��以形��应“How long have ** you been** <u>sad</u>"。
 
-This gave the impression that Eliza understood the statement and was asking a follow-on question, whereas in reality, it was changing the tense and adding some words. If Eliza could not identify a keyword that it had a response for, it would instead give a random response that could be applicable to many different statements. Eliza could be easily tricked, for instance if a user wrote "**You are** a <u>bicycle</u>" it might respond with "How long have **I been** a <u>bicycle</u>?", instead of a more reasoned response.
 这给人的�象是伊丽莎�解了这��，并在问一个�续问题，而实际上，它是在改�时�并添加一些�。如果 Eliza 无法识别它有�应的关键字，它会给出一个�机�应，该�应�以适用于许多��的语�。 Eliza 很容易被欺骗，例如，如果用户写了**You are** a <u>bicycle</u>"，它�能会回�"How long have **I been** a <u>bicycle</u>?"，而�是更��的回答。
 
 [![Chatting with Eliza](https://img.youtube.com/vi/RMK9AphfLco/0.jpg)](https://youtu.be/RMK9AphfLco "Chatting with Eliza")
 
-> 🎥 Click the image above for a video about original ELIZA program
 > 🎥 点击上方的图片查看真实的ELIZA程�视频
 
-> Note: You can read the original description of [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) published in 1966 if you have an ACM account. Alternately, read about Eliza on [wikipedia](https://wikipedia.org/wiki/ELIZA)
 > 注�:如果你拥有ACM账户，你�以阅读1996年�表的[Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract)的原始介�。或者，在[wikipedia](https://wikipedia.org/wiki/ELIZA)阅读有关 Eliza 的信� 
 
-## Exercise - coding a basic conversational bot
 ## �系 - 编�实现一个基础的对�机器人
 
-A conversational bot, like Eliza, is a program that elicits user input and seems to understand and respond intelligently. Unlike Eliza, our bot will not have several rules giving it the appearance of having an intelligent conversation. Instead, out bot will have one ability only, to keep the conversation going with random responses that might work in almost any trivial conversation.
 � Eliza 一样的对�机器人是一个似乎�以智能地�解和�应用户输入的程�。与 Eliza ��的是，我们的机器人�会用规则让它看起��是在进行智能对�。�而代之的是，我们的对�机器人将�有一�能力，通过几乎在所有�碎对�中都适用的�机�应��对�的进行。
 
-### The plan
 ### 计划
 
-Your steps when building a conversational bot:
 �建�天机器人的步骤
 
-1. Print instructions advising the user how to interact with the bot
-2. Start a loop
-   1. Accept user input
-   2. If user has asked to exit, then exit
-   3. Process user input and determine response (in this case, the response is a random choice from a list of possible generic responses)
-   4. Print response
-3. loop back to step 2
 1. 打�指导用户如何与机器人交互的说明
 2. 开�循环
    1. 获�用户输入
@@ -141,7 +87,6 @@ Your steps when building a conversational bot:
    4. 打�回答
 3. ��步骤2
 
-### Building the bot
 ### 构建�天机器人
 
 接下�让我们构建�天机器人。我们将从定义一些短语开始。
@@ -194,32 +139,22 @@ Your steps when building a conversational bot:
     3. 如果机器人真的�以“�解�一个��的��，它是�也需�“记��对�中����的��？
 
 ---
-
-## 🚀Challenge
 ## 🚀挑战
 
-Choose one of the "stop and consider" elements above and either try to implement them in code or write a solution on paper using pseudocode.
 选择上�的“�止并�考�元素之一，然��试在代�中实现它们或使用伪代�在纸上编写解决方案。
 
-In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning.
 在下一课中，您将了解解�自然语言和机器学习的许多其他方法。
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
 ## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
 
-## Review & Self Study
 ## �习与自学
 
-Take a look at the references below as further reading opportunities.
 看看下�的�考资料作为进一步的阅读机会。
-
-### References
 ### �考
 
 1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Spring 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/spr2020/entries/computational-linguistics/>.
 2. Princeton University "About WordNet." [WordNet](https://wordnet.princeton.edu/). Princeton University. 2010. 
 
-## Assignment 
 ## 任务
 
 [查找一个机器人](assignment.md)

From 4bccc6ae2fe5cdbaf3dd91be4bf53ba3ae3d0236 Mon Sep 17 00:00:00 2001
From: lty <247969917@qq.com>
Date: Wed, 14 Jul 2021 18:31:38 +0800
Subject: [PATCH 138/368] Fix a image path error

---
 6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index 8dbfebc20a..0b5b83f502 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -1,7 +1,7 @@
 # 自然语言处�介�
 这节课讲解了*自然语言处�*简�历�和��概念，*自然语言处�*是计算语言学的一个�领域。
 
-## [课�测验]](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
 
 ## 介�
 众所周知，自然语言处�(Natural Language Processing, NLP)是机器学习在生产软件中应用最广泛的领域之一。
@@ -19,7 +19,7 @@
 
 如果你曾�在手机上使用语音输入替代键盘输入或者�语音助手�娜�问，那么你的语音将被转录为文本形��进行处�或者�*解�*。被检测到的关键字最�将被处��手机或语音助手�以�解并采�行动的格�。
 
-![comprehension](images/comprehension.png)
+![comprehension](../images/comprehension.png)
 > 真实的语言�解�分困难�图�：[Jen Looper](https://twitter.com/jenlooper)
 ### 这项技术是如何实现的？
 
@@ -127,7 +127,6 @@
 
     该任务的一��能解决方案在[这里](solution/bot.py)
 
-    ✅ Stop and consider
     ✅ �止并�考
 
     1. Do you think the random responses would 'trick' someone into thinking that the bot actually understood them?

From 31b963c1d8621b6f05e64e5361924ea3ce8645c4 Mon Sep 17 00:00:00 2001
From: lty <247969917@qq.com>
Date: Wed, 14 Jul 2021 18:33:15 +0800
Subject: [PATCH 139/368] ind

---
 6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index 0b5b83f502..9d1e066596 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -102,8 +102,8 @@
                         "Did you catch the game last night?"]
     ```
 
-    Here is some sample output to guide you (user input is on the lines starting with `>`):
-
+    以下是一些指导你的示例输出（用户输入�于以 `>` 开头的行上）：
+    
     ```output
     Hello, I am Marvin, the simple robot.
     You can end this conversation at any time by typing 'bye'
@@ -128,10 +128,6 @@
     该任务的一��能解决方案在[这里](solution/bot.py)
 
     ✅ �止并�考
-
-    1. Do you think the random responses would 'trick' someone into thinking that the bot actually understood them?
-    2. What features would the bot need to be more effective?
-    3. If a bot could really 'understand' the meaning of a sentence, would it need to 'remember' the meaning of previous sentences in a conversation too?
    
     1. 你认为�机�应会“欺骗��人认为机器人实际上�解他们�？
     2. 机器人需�哪些功能�能更有效？

From 4a7ab265381462067304e844d37d3ed7d3642736 Mon Sep 17 00:00:00 2001
From: lty <247969917@qq.com>
Date: Wed, 14 Jul 2021 18:35:01 +0800
Subject: [PATCH 140/368] ind

---
 6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index 9d1e066596..75cd69da90 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -45,7 +45,7 @@
    python -m textblob.download_corpora
    ```
 
-> 💡 �示:�以在 VS Code 环境中直接�行 Python。 点击[docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa)查看更多信�。
+> 💡 �示：�以在 VS Code 环境中直接�行 Python。 点击[docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa)查看更多信�。
 
 ## 与机器对�
 
@@ -61,7 +61,7 @@
 
 ### Eliza的研�
 
-在 1960 年代，一��� *Joseph Weizenbaum* 的麻��工学院科学家开�了[*Eliza*](https:/wikipedia.org/wiki/ELIZA)，Eliza是一�计算机“治疗师�，它�以�人类�出问题并表现出�解他们的答案。然而，虽然 Eliza �以解���并识别�些语法结构和关键字以给出��的答案，但�能说它*�解*了��。如果 Eliza 看到的��格�为“**I am** <u>sad</u>�，它�能会�新排列并替���中的��以形��应“How long have ** you been** <u>sad</u>"。
+在 1960 年代，一��� *Joseph Weizenbaum* 的麻��工学院科学家开�了[*Eliza*](https:/wikipedia.org/wiki/ELIZA)，Eliza是一�计算机“治疗师�，它�以�人类�出问题并表现出�解他们的答案。然而，虽然 Eliza �以解���并识别�些语法结构和关键字以给出��的答案，但�能说它*�解*了��。如果 Eliza 看到的��格�为“**I am** <u>sad</u>�，它�能会�新排列并替���中的��以形��应“How long have **you been** <u>sad</u>"。
 
 这给人的�象是伊丽莎�解了这��，并在问一个�续问题，而实际上，它是在改�时�并添加一些�。如果 Eliza 无法识别它有�应的关键字，它会给出一个�机�应，该�应�以适用于许多��的语�。 Eliza 很容易被欺骗，例如，如果用户写了**You are** a <u>bicycle</u>"，它�能会回�"How long have **I been** a <u>bicycle</u>?"，而�是更��的回答。
 
@@ -103,7 +103,7 @@
     ```
 
     以下是一些指导你的示例输出（用户输入�于以 `>` 开头的行上）：
-    
+
     ```output
     Hello, I am Marvin, the simple robot.
     You can end this conversation at any time by typing 'bye'

From eb6f65e3a8e8b9ba5ea8b555d314690558b0a236 Mon Sep 17 00:00:00 2001
From: "Charles Emmanuel S. Ndiaye" <manusquall@outlook.com>
Date: Wed, 14 Jul 2021 11:41:50 +0000
Subject: [PATCH 141/368] propose Regression french translation readme

add a README.fr.md for Regression base README
---
 2-Regression/translations/README.fr.md | 33 ++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 2-Regression/translations/README.fr.md

diff --git a/2-Regression/translations/README.fr.md b/2-Regression/translations/README.fr.md
new file mode 100644
index 0000000000..eaed5756e1
--- /dev/null
+++ b/2-Regression/translations/README.fr.md
@@ -0,0 +1,33 @@
+# Modèles de régression pour le machine learning
+## Sujet régional : Modèles de régression des prix des citrouilles en Amérique du Nord 🎃
+
+En Amérique du Nord, les citrouilles sont souvent sculptées en visages effrayants pour Halloween. Découvrons-en plus sur ces légumes fascinants!
+
+![jack-o-lanterns](../images/jack-o-lanterns.jpg)
+> Photo de <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> sur <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## Ce que vous apprendrez
+
+Les leçons de cette section couvrent les types de régression dans le contexte du machine learning. Les modèles de régression peuvent aider à déterminer la _relation_ entre les variables. Ce type de modèle peut prédire des valeurs telles que la longueur, la température ou l'âge, découvrant ainsi les relations entre les variables lors de l'analyse des points de données.
+
+Dans cette série de leçons, vous découvrirez la différence entre la régression linéaire et la régression logistique, et quand vous devriez utiliser l'une ou l'autre.
+
+Dans ce groupe de leçons, vous serez préparé afin de commencer les tâches de machine learning, y compris la configuration de Visual Studio Code pour gérer les blocs-notes, l'environnement commun pour les scientifiques des données. Vous découvrirez Scikit-learn, une bibliothèque pour le machine learning, et vous construirez vos premiers modèles, en vous concentrant sur les modèles de régression dans ce chapitre.
+
+> Il existe des outils low-code utiles qui peuvent vous aider à apprendre à travailler avec des modèles de régression. Essayez [Azure ML pour cette tâche](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+### Cours
+
+1. [Outils du métier](1-Tools/README.md)
+2. [Gestion des données](2-Data/README.md)
+3. [Régression linéaire et polynomiale](3-Linear/README.md)
+4. [Régression logistique](4-Logistic/README.md)
+
+---
+### Crédits
+
+"ML avec régression" a été écrit avec ♥� par [Jen Looper](https://twitter.com/jenlooper)
+
+♥� Les contributeurs du quiz incluent : [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) et [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+L'ensemble de données sur la citrouille est suggéré par [ce projet sur Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) et ses données proviennent des [Rapports standard des marchés terminaux des cultures spécialisées](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distribué par le département américain de l'Agriculture. Nous avons ajouté quelques points autour de la couleur en fonction de la variété pour normaliser la distribution. Ces données sont dans le domaine public.

From ac061faf5fdf6e409e94b2da01bfc0beb825fa29 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 20:40:40 +0800
Subject: [PATCH 142/368] Add files via upload

---
 2-Regression/translations/Readme.zh-cn.md | 34 +++++++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 2-Regression/translations/Readme.zh-cn.md

diff --git a/2-Regression/translations/Readme.zh-cn.md b/2-Regression/translations/Readme.zh-cn.md
new file mode 100644
index 0000000000..853c73b548
--- /dev/null
+++ b/2-Regression/translations/Readme.zh-cn.md
@@ -0,0 +1,34 @@
+# 机器学习中的回归模型
+## 本节主题： 北美�瓜价格的回归模型 🎃
+
+在北美，�瓜�常在万圣节被刻上�人的鬼脸。让我们�深入研究一下这�奇妙的蔬�
+
+![jack-o-lanterns](./images/jack-o-lanterns.jpg)
+> Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+##你会学到什么
+
+这节的课程包括机器学习领域中的多�回归模型。回归模型�以明确多���间的_关系_。这�模型�以用�预测类似长度�温度和年龄之类的值， 通过分�数�点��示��之间的关系。
+
+在本节的一系列课程中，你会学到线性回归和逻辑回归之间的区别，并且你将知�对于特定问题如何在这两�模型中进行选择
+
+在这组课程中，你会准备好包括为管�笔记而设置VS Code��置数�科学家常用的环境等机器学习的�始任务。你会开始上手Scikit-learn学习项目（一个机器学习的百科），并且你会以回归模型为主构建起你的第一�机器学习模型
+
+> 这里有一些代�难度较低但很有用的工具�以帮助你学习使用回归模型。 试一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+
+### Lessons
+
+1. [交易的工具](1-Tools/README.md)
+2. [管�数�](2-Data/README.md)
+3. [线性和多项�回归](3-Linear/README.md)
+4. [逻辑回归](4-Logistic/README.md)
+
+---
+### Credits
+
+"机器学习中的回归" 由[Jen Looper](https://twitter.com/jenlooper)♥� 撰写
+
+♥� 测试的贡献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类
\ No newline at end of file

From aa5048fb0e5dba984d5f7bd949301b3031d513e2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 21:04:12 +0800
Subject: [PATCH 143/368] Update Readme.zh-cn.md

---
 2-Regression/translations/Readme.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/translations/Readme.zh-cn.md b/2-Regression/translations/Readme.zh-cn.md
index 853c73b548..ee1243a206 100644
--- a/2-Regression/translations/Readme.zh-cn.md
+++ b/2-Regression/translations/Readme.zh-cn.md
@@ -31,4 +31,4 @@
 
 ♥� 测试的贡献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom)
 
-�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类
\ No newline at end of file
+�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类添加了围绕颜色的一些数�点。这些数�处在公共的域�上。

From 10a7a93f1d050f22fcb63c8b4f93c29baddab0d0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 21:12:46 +0800
Subject: [PATCH 144/368] Add files via upload

---
 2-Regression/translations/Readme.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/translations/Readme.zh-cn.md b/2-Regression/translations/Readme.zh-cn.md
index ee1243a206..81c2c9d49a 100644
--- a/2-Regression/translations/Readme.zh-cn.md
+++ b/2-Regression/translations/Readme.zh-cn.md
@@ -31,4 +31,4 @@
 
 ♥� 测试的贡献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom)
 
-�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类添加了围绕颜色的一些数�点。这些数�处在公共的域�上。
+�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类添加了围绕颜色的一些数�点。这些数�处在公共的域�上。
\ No newline at end of file

From 11567b58cc5f88bfcd1fca645c1cc83fab48a8e9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 21:13:48 +0800
Subject: [PATCH 145/368] Add files via upload

---
 2-Regression/Readme.zh-cn.md | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 2-Regression/Readme.zh-cn.md

diff --git a/2-Regression/Readme.zh-cn.md b/2-Regression/Readme.zh-cn.md
new file mode 100644
index 0000000000..4972c594ca
--- /dev/null
+++ b/2-Regression/Readme.zh-cn.md
@@ -0,0 +1,34 @@
+# 机器学习中的回归模型
+## 本节主题： 北美�瓜价格的回归模型 🎃
+
+在北美，�瓜�常在万圣节被刻上�人的鬼脸。让我们�深入研究一下这�奇妙的蔬�
+
+![jack-o-lanterns](../images/jack-o-lanterns.jpg)
+> Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+##你会学到什么
+
+这节的课程包括机器学习领域中的多�回归模型。回归模型�以明确多���间的_关系_。这�模型�以用�预测类似长度�温度和年龄之类的值， 通过分�数�点��示��之间的关系。
+
+在本节的一系列课程中，你会学到线性回归和逻辑回归之间的区别，并且你将知�对于特定问题如何在这两�模型中进行选择
+
+在这组课程中，你会准备好包括为管�笔记而设置VS Code��置数�科学家常用的环境等机器学习的�始任务。你会开始上手Scikit-learn学习项目（一个机器学习的百科），并且你会以回归模型为主构建起你的第一�机器学习模型
+
+> 这里有一些代�难度较低但很有用的工具�以帮助你学习使用回归模型。 试一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+
+### Lessons
+
+1. [交易的工具](1-Tools/README.md)
+2. [管�数�](2-Data/README.md)
+3. [线性和多项�回归](3-Linear/README.md)
+4. [逻辑回归](4-Logistic/README.md)
+
+---
+### Credits
+
+"机器学习中的回归" 由[Jen Looper](https://twitter.com/jenlooper)♥� 撰写
+
+♥� 测试的贡献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类添加了围绕颜色的一些数�点。这些数�处在公共的域�上。
\ No newline at end of file

From 7d9da49081ce7960872e84820c3adc205daa3b39 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 21:15:26 +0800
Subject: [PATCH 146/368] Delete Readme.zh-cn.md

---
 2-Regression/Readme.zh-cn.md | 34 ----------------------------------
 1 file changed, 34 deletions(-)
 delete mode 100644 2-Regression/Readme.zh-cn.md

diff --git a/2-Regression/Readme.zh-cn.md b/2-Regression/Readme.zh-cn.md
deleted file mode 100644
index 4972c594ca..0000000000
--- a/2-Regression/Readme.zh-cn.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# 机器学习中的回归模型
-## 本节主题： 北美�瓜价格的回归模型 🎃
-
-在北美，�瓜�常在万圣节被刻上�人的鬼脸。让我们�深入研究一下这�奇妙的蔬�
-
-![jack-o-lanterns](../images/jack-o-lanterns.jpg)
-> Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
-
-##你会学到什么
-
-这节的课程包括机器学习领域中的多�回归模型。回归模型�以明确多���间的_关系_。这�模型�以用�预测类似长度�温度和年龄之类的值， 通过分�数�点��示��之间的关系。
-
-在本节的一系列课程中，你会学到线性回归和逻辑回归之间的区别，并且你将知�对于特定问题如何在这两�模型中进行选择
-
-在这组课程中，你会准备好包括为管�笔记而设置VS Code��置数�科学家常用的环境等机器学习的�始任务。你会开始上手Scikit-learn学习项目（一个机器学习的百科），并且你会以回归模型为主构建起你的第一�机器学习模型
-
-> 这里有一些代�难度较低但很有用的工具�以帮助你学习使用回归模型。 试一下 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
-
-
-### Lessons
-
-1. [交易的工具](1-Tools/README.md)
-2. [管�数�](2-Data/README.md)
-3. [线性和多项�回归](3-Linear/README.md)
-4. [逻辑回归](4-Logistic/README.md)
-
----
-### Credits
-
-"机器学习中的回归" 由[Jen Looper](https://twitter.com/jenlooper)♥� 撰写
-
-♥� 测试的贡献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom)
-
-�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类添加了围绕颜色的一些数�点。这些数�处在公共的域�上。
\ No newline at end of file

From f9f69077acc76f317c8c3ce3aa0e5837ccb2dc40 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 21:16:48 +0800
Subject: [PATCH 147/368] Add files via upload

---
 2-Regression/translations/Readme.zh-cn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/translations/Readme.zh-cn.md b/2-Regression/translations/Readme.zh-cn.md
index 81c2c9d49a..3802d5403f 100644
--- a/2-Regression/translations/Readme.zh-cn.md
+++ b/2-Regression/translations/Readme.zh-cn.md
@@ -3,8 +3,8 @@
 
 在北美，�瓜�常在万圣节被刻上�人的鬼脸。让我们�深入研究一下这�奇妙的蔬�
 
-![jack-o-lanterns](./images/jack-o-lanterns.jpg)
-> Photo by <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> on <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+![jack-o-lantern](../images/jack-o-lanterns.jpg)
+> Foto oleh <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> di <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
 ##你会学到什么
 

From acf14616dea3863653b0d99ab0d32a1f0ca5a8e3 Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Wed, 14 Jul 2021 21:25:34 +0800
Subject: [PATCH 148/368] Update README.zh-cn.md

---
 1-Introduction/3-fairness/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index 02f4177756..3b75ddab94 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -145,7 +145,7 @@
 | 未列出性别 | 0.33                | 0.31                | 1266  |
 
  
-这张桌�告诉我们几件事。首先，我们注�到数�中的未列出性别的人相对较少。数�是有�差的，所以你需��心解释这些数字。
+这个表格告诉我们几件事。首先，我们注�到数�中的未列出性别的人相对较少。数�是有�差的，所以你需��心解释这些数字。
 
 在本例中，我们有3个组和2个度�。当我们考虑我们的系统如何影�贷款申请人的客户群时，这�能就足够了，但是当你想�定义更多的组时，你�能需�将其��到更�的摘�集。为此，你�以添加更多的度�，例如�个�阴性和�阳性的最大差异或最�比率。
  

From eda4113bebeaafd71be55a4a15edc6105b7d1a83 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Wed, 14 Jul 2021 17:11:43 +0200
Subject: [PATCH 149/368] Italian Translation - Chapter 2-2 complete

---
 2-Regression/2-Data/translations/README.it.md | 201 ++++++++++++++++++
 .../2-Data/translations/assignment.it.md      |   8 +
 2 files changed, 209 insertions(+)
 create mode 100644 2-Regression/2-Data/translations/README.it.md
 create mode 100644 2-Regression/2-Data/translations/assignment.it.md

diff --git a/2-Regression/2-Data/translations/README.it.md b/2-Regression/2-Data/translations/README.it.md
new file mode 100644
index 0000000000..3a67cdea68
--- /dev/null
+++ b/2-Regression/2-Data/translations/README.it.md
@@ -0,0 +1,201 @@
+# Costruire un modello di regressione usando Scikit-learn: preparare e visualizzare i dati
+
+> ![Infografica sulla visualizzazione dei dati](../images/data-visualization.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+
+## Introduzione
+
+Ora che si hanno a disposizione gli strumenti necessari per iniziare ad affrontare la creazione di modelli di machine learning con Scikit-learn, si è pronti per iniziare a porre domande sui propri dati. Mentre si lavora con i dati e si applicano soluzioni ML, è molto importante capire come porre la domanda giusta per sbloccare correttamente le potenzialità del proprio insieme di dati.
+
+In questa lezione, si imparerà:
+
+- Come preparare i dati per la creazione del modello.
+- Come utilizzare Matplotlib per la visualizzazione dei dati.
+
+## Fare la domanda giusta ai propri dati
+
+La domanda a cui si deve rispondere determinerà il tipo di algoritmi ML che verranno utilizzati. La qualità della risposta che si riceverà dipenderà fortemente dalla natura dei propri dati.
+
+Si dia un'occhiata ai [dati](../../data/US-pumpkins.csv) forniti per questa lezione. Si può aprire questo file .csv in VS Code. Una rapida scrematura mostra immediatamente che ci sono spazi vuoti e un mix di stringhe e dati numerici. C'è anche una strana colonna chiamata "Package" (pacchetto) in cui i dati sono un mix tra "sacks" (sacchi), "bins" (contenitori) e altri valori. I dati, infatti, sono un po' un pasticcio.
+
+In effetti, non è molto comune ricevere un insieme di dati completamente pronto per creare un modello ML pronto all'uso. In questa lezione si imparerà come preparare un insieme di dati non elaborato utilizzando le librerie standard di Python. Si impareranno anche varie tecniche per visualizzare i dati.
+
+## Caso di studio: 'il mercato della zucca'
+
+In questa cartella si troverà un file .csv nella cartella `data` radice chiamato [US-pumpkins.csv](../../data/US-pumpkins.csv) che include 1757 righe di dati sul mercato delle zucche, ordinate in raggruppamenti per città. Si tratta di dati grezzi estratti dai [Report Standard dei Mercati Terminali delle Colture Speciali](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distribuiti dal Dipartimento dell'Agricoltura degli Stati Uniti.
+
+### Preparazione dati
+
+Questi dati sono di pubblico dominio. Possono essere scaricati in molti file separati, per città, dal sito web dell'USDA. Per evitare troppi file separati, sono stati concatenati tutti i dati della città in un unico foglio di calcolo, quindi un po' i dati sono già stati _preparati_ . Successivamente, si darà un'occhiata più da vicino ai dati.
+
+### I dati della zucca - prime conclusioni
+
+Cosa si nota riguardo a questi dati? Si è già visto che c'è un mix di stringhe, numeri, spazi e valori strani a cui occorre dare un senso.
+
+Che domanda si puà fare a questi dati, utilizzando una tecnica di Regressione? Che dire di "Prevedere il prezzo di una zucca in vendita durante un dato mese". Esaminando nuovamente i dati, ci sono alcune modifiche da apportare per creare la struttura dati necessaria per l'attività.
+
+## Esercizio: analizzare i dati della zucca
+
+Si usa [Pandas](https://pandas.pydata.org/), (il nome sta per `Python Data Analysis`) uno strumento molto utile per dare forma ai dati, per analizzare e preparare questi dati sulla zucca.
+
+### Innanzitutto, controllare le date mancanti
+
+Prima si dovranno eseguire i passaggi per verificare le date mancanti:
+
+1. Convertire le date in un formato mensile (queste sono date statunitensi, quindi il formato è `MM/GG/AAAA`).
+2. Estrarre il mese in una nuova colonna.
+
+Aprire il file _notebook.ipynb_ in Visual Studio Code e importare il foglio di calcolo in un nuovo dataframe Pandas.
+
+1. Usare la funzione `head()` per visualizzare le prime cinque righe.
+
+   ```python
+   import pandas as pd
+   pumpkins = pd.read_csv('../data/US-pumpkins.csv')
+   pumpkins.head()
+   ```
+
+   ✅ Quale funzione si userebbe per visualizzare le ultime cinque righe?
+
+1. Controllare se mancano dati nel dataframe corrente:
+
+   ```python
+   pumpkins.isnull().sum()
+   ```
+
+   Ci sono dati mancanti, ma forse non avrà importanza per l'attività da svolgere.
+
+1. Per rendere più facile lavorare con il dataframe, si scartano molte delle sue colonne, usando `drop()`, mantenendo solo le colonne di cui si ha bisogno:
+
+   ```python
+   new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
+   pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+   ```
+
+### Secondo, determinare il prezzo medio della zucca
+
+Si pensi a come determinare il prezzo medio di una zucca in un dato mese. Quali colonne si sceglierebbero per questa attività? Suggerimento: serviranno 3 colonne.
+
+Soluzione: prendere la media delle colonne `Low Price` e `High Price` per popolare la nuova colonna Price e convertire la colonna Date per mostrare solo il mese. Fortunatamente, secondo il controllo di cui sopra, non mancano dati per date o prezzi.
+
+1. Per calcolare la media, aggiungere il seguente codice:
+
+   ```python
+   price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2
+
+   month = pd.DatetimeIndex(pumpkins['Date']).month
+
+   ```
+
+   ✅ Si possono di stampare tutti i dati che si desidera controllare utilizzando `print(month)`.
+
+2. Ora copiare i dati convertiti in un nuovo dataframe Pandas:
+
+   ```python
+   new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})
+   ```
+
+   La stampa del dataframe mostrerà un insieme di dati pulito e ordinato su cui si può costruire il  nuovo modello di regressione.
+
+### Ma non è finita qui! C'è qualcosa di strano qui.
+
+Osservando la colonna `Package`, le zucche sono vendute in molte configurazioni diverse. Alcune sono venduti in misure '1 1/9 bushel' (bushel = staio) e alcuni in misure '1/2 bushel', alcuni per zucca, alcuni per libbra e alcuni in grandi scatole con larghezze variabili.
+
+> Le zucche sembrano molto difficili da pesare in modo coerente
+
+Scavando nei dati originali, è interessante notare che qualsiasi cosa con `Unit of Sale` (Unità di vendita) uguale a 'EACH' o 'PER BIN' ha anche il tipo di `Package` per 'inch' (pollice), per 'bin' (contenitore) o 'each' (entrambi). Le zucche sembrano essere molto difficili da pesare in modo coerente, quindi si filtrano selezionando solo zucche con la stringa "bushel" nella colonna `Package`.
+
+1. Aggiungere un filtro nella parte superiore del file, sotto l'importazione .csv iniziale:
+
+   ```python
+   pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]
+   ```
+
+   Se si stampano i dati ora, si può vedere che si stanno ricevendo solo le circa 415 righe di dati contenenti zucche per bushel.
+
+### Ma non è finita qui! C'è un'altra cosa da fare.
+
+Si è notato che la quantità di bushel varia per riga? Si deve normalizzare il prezzo in modo da mostrare il prezzo per bushel, quindi si facciano un po' di calcoli per standardizzarlo.
+
+1. Aggiungere queste righe dopo il blocco che crea il dataframe new_pumpkins:
+
+   ```python
+   new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)
+
+   new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)
+   ```
+
+✅ Secondo [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), il peso di un bushel dipende dal tipo di prodotto, poiché è una misura di volume. "Un bushel di pomodori, per esempio, dovrebbe pesare 56 libbre... Foglie e verdure occupano più spazio con meno peso, quindi un bushel di spinaci è solo 20 libbre". È tutto piuttosto complicato! Non occorre preoccuparsi di fare una conversione da bushel a libbra, e invece si valuta a bushel. Tutto questo studio sui bushel di zucche, però, dimostra quanto sia importante capire la natura dei propri dati!
+
+Ora si può analizzare il prezzo per unità in base alla misurazione del bushel. Se si stampano i dati ancora una volta, si può vedere come sono standardizzati.
+
+✅ Si è notato che le zucche vendute a metà bushel sono molto costose? Si riesce a capire perché? Suggerimento: le zucche piccole sono molto più costose di quelle grandi, probabilmente perché ce ne sono molte di più per bushel, dato lo spazio inutilizzato occupato da una grande zucca cava.
+
+## Strategie di Visualizzazione
+
+Parte del ruolo del data scientist è dimostrare la qualità e la natura dei dati con cui sta lavorando. Per fare ciò, si creano spesso visualizzazioni interessanti o tracciati, grafici e diagrammi, che mostrano diversi aspetti dei dati. In questo modo, sono in grado di mostrare visivamente relazioni e lacune altrimenti difficili da scoprire.
+
+Le visualizzazioni possono anche aiutare a determinare la tecnica di machine learning più appropriata per i dati. Un grafico a dispersione che sembra seguire una linea, ad esempio, indica che i dati sono un buon candidato per un esercizio di regressione lineare.
+
+Una libreria di visualizzazione dei dati che funziona bene nei notebook Jupyter è [Matplotlib](https://matplotlib.org/) (che si è visto anche nella lezione precedente).
+
+> Per fare più esperienza con la visualizzazione dei dati si seguano [questi tutorial](https://docs.microsoft.com/learn/modules/explore-analyze-data-with-python?WT.mc_id=academic-15963-cxa).
+
+## Esercizio - sperimentare con Matplotlib
+
+Provare a creare alcuni grafici di base per visualizzare il nuovo dataframe appena creato. Cosa mostrerebbe un grafico a linee di base?
+
+1. Importare Matplotlib nella parte superiore del file, sotto l'importazione di Pandas:
+
+   ```python
+   import matplotlib.pyplot as plt
+   ```
+
+1. Rieseguire l'intero notebook per aggiornare.
+1. Nella parte inferiore del notebook, aggiungere una cella per tracciare i dati come una casella:
+
+   ```python
+   price = new_pumpkins.Price
+   month = new_pumpkins.Month
+   plt.scatter(price, month)
+   plt.show()
+   ```
+
+   ![Un grafico a dispersione che mostra la relazione tra prezzo e mese](../images/scatterplot.png)
+
+   È un tracciato utile? C'è qualcosa che sorprende?
+
+   Non è particolarmente utile in quanto tutto ciò che fa è visualizzare nei propri dati come una diffusione di punti in un dato mese.
+
+### Renderlo utile
+
+Per fare in modo che i grafici mostrino dati utili, di solito è necessario raggruppare i dati in qualche modo. Si prova a creare un grafico in cui l'asse y mostra i prezzi e l'asse x mostra la distribuzione dei dati raggruppati per mese.
+
+1. Aggiungere una cella per creare un grafico a barre raggruppato:
+
+   ```python
+   new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
+   plt.ylabel("Pumpkin Price")
+   ```
+
+   ![Un grafico a barre che mostra la relazione tra prezzo e mese](../images/barchart.png)
+
+   Questa è una visualizzazione dei dati più utile! Sembra indicare che il prezzo più alto per le zucche si verifica a settembre e ottobre. Questo soddisfa le proprie aspettative? Perché o perché no?
+
+---
+
+## 🚀 Sfida
+
+Esplorare i diversi tipi di visualizzazione offerti da Matplotlib. Quali tipi sono più appropriati per i problemi di regressione?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+
+## Revisione e Auto Apprendimento
+
+Dare un'occhiata ai molti modi per visualizzare i dati. Fare un elenco delle varie librerie disponibili e annotare quali sono le migliori per determinati tipi di attività, ad esempio visualizzazioni 2D rispetto a visualizzazioni 3D. Cosa si è scoperto?
+
+## Compito
+
+[Esplorazione della visualizzazione](assignment.it.md)
diff --git a/2-Regression/2-Data/translations/assignment.it.md b/2-Regression/2-Data/translations/assignment.it.md
new file mode 100644
index 0000000000..6fe0cf5024
--- /dev/null
+++ b/2-Regression/2-Data/translations/assignment.it.md
@@ -0,0 +1,8 @@
+# Esplorazione delle visualizzazioni
+
+Sono disponibili diverse librerie per la visualizzazione dei dati. Creare alcune visualizzazioni utilizzando i dati della zucca in questa lezione con matplotlib e seaborn in un notebook di esempio. Con quali librerie è più facile lavorare?
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------- | -------- | ----------------- |
+|          | Viene inviato un notebook con due esplorazioni/visualizzazioni | Viene inviato un notebook con una esplorazione/visualizzazione | Non è stato inviato un notebook |

From 489eb93d5563f0f4e15a5cb23a62159bdc7d4747 Mon Sep 17 00:00:00 2001
From: "Charles Emmanuel S. Ndiaye" <manusquall@outlook.com>
Date: Wed, 14 Jul 2021 15:35:28 +0000
Subject: [PATCH 150/368] Update links for future translated readme

Update links for future translated readme
---
 2-Regression/translations/README.fr.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2-Regression/translations/README.fr.md b/2-Regression/translations/README.fr.md
index eaed5756e1..1b252f3f58 100644
--- a/2-Regression/translations/README.fr.md
+++ b/2-Regression/translations/README.fr.md
@@ -18,10 +18,10 @@ Dans ce groupe de leçons, vous serez préparé afin de commencer les tâches de
 
 ### Cours
 
-1. [Outils du métier](1-Tools/README.md)
-2. [Gestion des données](2-Data/README.md)
-3. [Régression linéaire et polynomiale](3-Linear/README.md)
-4. [Régression logistique](4-Logistic/README.md)
+1. [Outils du métier](1-Tools/translations/README.fr.md)
+2. [Gestion des données](2-Data/translations/README.fr.md)
+3. [Régression linéaire et polynomiale](3-Linear/translations/README.fr.md)
+4. [Régression logistique](4-Logistic/translations/README.fr.md)
 
 ---
 ### Crédits

From c53bc86acdbfe218775faf6bf61903b216d9ba24 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 23:37:01 +0800
Subject: [PATCH 151/368] Rename Readme.zh-cn.md to Readme.zh-ch.md

---
 2-Regression/translations/{Readme.zh-cn.md => Readme.zh-ch.md} | 2 +-
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 rename 2-Regression/translations/{Readme.zh-cn.md => Readme.zh-ch.md} (98%)

diff --git a/2-Regression/translations/Readme.zh-cn.md b/2-Regression/translations/Readme.zh-ch.md
similarity index 98%
rename from 2-Regression/translations/Readme.zh-cn.md
rename to 2-Regression/translations/Readme.zh-ch.md
index 3802d5403f..f25a542aa8 100644
--- a/2-Regression/translations/Readme.zh-cn.md
+++ b/2-Regression/translations/Readme.zh-ch.md
@@ -31,4 +31,4 @@
 
 ♥� 测试的贡献者: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) 和 [Ornella Altunyan](https://twitter.com/ornelladotcom)
 
-�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类添加了围绕颜色的一些数�点。这些数�处在公共的域�上。
\ No newline at end of file
+�瓜数�集�此�� [this project on Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) 并且其数��自 [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) 由美国农业部上传分享。我们根��类添加了围绕颜色的一些数�点。这些数�处在公共的域�上。

From 95aa36df517eff69aabc23a583e384ffced6708d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 23:38:15 +0800
Subject: [PATCH 152/368] Update Readme.zh-ch.md

---
 2-Regression/translations/Readme.zh-ch.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2-Regression/translations/Readme.zh-ch.md b/2-Regression/translations/Readme.zh-ch.md
index f25a542aa8..7ce096c384 100644
--- a/2-Regression/translations/Readme.zh-ch.md
+++ b/2-Regression/translations/Readme.zh-ch.md
@@ -19,10 +19,10 @@
 
 ### Lessons
 
-1. [交易的工具](1-Tools/README.md)
-2. [管�数�](2-Data/README.md)
-3. [线性和多项�回归](3-Linear/README.md)
-4. [逻辑回归](4-Logistic/README.md)
+1. [交易的工具](../1-Tools/README.md)
+2. [管�数�](../2-Data/README.md)
+3. [线性和多项�回归](../3-Linear/README.md)
+4. [逻辑回归](../4-Logistic/README.md)
 
 ---
 ### Credits

From 50a94e9ed8622fd6eb52489db602d710ba822f43 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 23:44:01 +0800
Subject: [PATCH 153/368] Rename Readme.zh-ch.md to README.zh-ch.md

---
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 1 file changed, 0 insertions(+), 0 deletions(-)
 rename 2-Regression/translations/{Readme.zh-ch.md => README.zh-ch.md} (100%)

diff --git a/2-Regression/translations/Readme.zh-ch.md b/2-Regression/translations/README.zh-ch.md
similarity index 100%
rename from 2-Regression/translations/Readme.zh-ch.md
rename to 2-Regression/translations/README.zh-ch.md

From c2f2beed8e8bcbfbfb006dfc62322a54223c39d5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Wed, 14 Jul 2021 23:50:08 +0800
Subject: [PATCH 154/368] Update README.zh-ch.md

---
 2-Regression/translations/README.zh-ch.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2-Regression/translations/README.zh-ch.md b/2-Regression/translations/README.zh-ch.md
index 7ce096c384..24c7a26cd3 100644
--- a/2-Regression/translations/README.zh-ch.md
+++ b/2-Regression/translations/README.zh-ch.md
@@ -19,10 +19,10 @@
 
 ### Lessons
 
-1. [交易的工具](../1-Tools/README.md)
-2. [管�数�](../2-Data/README.md)
-3. [线性和多项�回归](../3-Linear/README.md)
-4. [逻辑回归](../4-Logistic/README.md)
+1. [交易的工具](../1-Tools/translations/README.zh-cn.md)
+2. [管�数�](../2-Data/translations/README.zh-cn.md)
+3. [线性和多项�回归](../3-Linear/translations/README.zh-cn.md)
+4. [逻辑回归](../4-Logistic/translations/README.zh-cn.md)
 
 ---
 ### Credits

From a1c1b414d13c3cee56f35ff57012675886c4e109 Mon Sep 17 00:00:00 2001
From: Colin Zang <zyy0116@gmail.com>
Date: Thu, 15 Jul 2021 00:40:54 +0800
Subject: [PATCH 155/368] Create README.zh-cn.md

---
 translations/README.zh-cn.md | 119 +++++++++++++++++++++++++++++++++++
 1 file changed, 119 insertions(+)
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diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md
new file mode 100644
index 0000000000..3918b00833
--- /dev/null
+++ b/translations/README.zh-cn.md
@@ -0,0 +1,119 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# 针对�学者的机器学习课程
+
+> � 环游世界，并通过世界文化�探索机器学习 �
+
+微软 Azure Cloud 的倡导者们很高兴�以�供这套�二周�二�四节课的关于**机器学习**的课程。在这套课程中，你将学习关于**�典机器学习**的内容，主�将使用 Scikit-learn 这一库。关于深度学习的内容将会尽��� —— 它会被我们�将推出的 "AI for Beginners (针对�学者的 AI 教程)" 所涵盖。你也�以把这些课和我们�将推出的 "Data Science for Beginners (针对�学者的数�科学教程)" 相结��
+
+通过把这些�典的技术应用在�自世界�地的数�，我们将 “环游世界�。�一节课都包括了课�和课�测验�课程内容的文字讲义说明�示例代��作业等。通过这�基于项目的教学方法，你将在构建中学习，这样�以把技能学的更牢�。
+
+**�� 衷心感谢作者们** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan 以� Amy Boyd
+
+**🎨 �时也�感谢我们的�画师** Tomomi Imura, Dasani Madipalli 以� Jen Looper
+
+ **� 特别感谢 � 我们的微软学生大使作者们，内容贡献和内容�核者们**, Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, 和 Snigdha Agarwal 等
+
+---
+# 准备开始
+
+**对于学生们**，为了更好的使用这套课程，把整个仓库 fork 到你自己的 Github 账户中，并自行（或和一个�组一起）完�以下练习：
+
+- 从课�测验开始
+- 阅读课程内容，完�所有的活动，在�次 knowledge check 时暂�并�考
+- 我们建议你基于�解�创建项目（而�是仅仅跑一�示例代�）示例代�的�置在�一个项目的 `/solution` 文件夹中。
+- 进行课�测验
+- 完�课程挑战
+- 完�作业
+- 一节课完��, 访问[讨论版](https://github.com/microsoft/ML-For-Beginners/discussions)，通过天�相应的 PAT Rubric (课程目标)�深化自己的学习�果。你也�以回应其它的 PAT，这样我们�以一起学习。
+
+> 如果希望进一步学习，我们推�跟� [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) 的模�和学习路径。
+
+**对于�师们**，我们对于如何使用这套教程[�供了一些建议](for-teachers.md)。
+
+---
+
+## 项目团队
+
+[![宣传视频](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "宣传视频")
+
+> 🎥 点击上方的图片，�观看一个关于这个项目和它的创造者们的视频�
+
+---
+## 教学方�
+
+此课程基于两个教学原则：学生应该上手进行**项目实践**，并完�**频�的测验**。 此外，为了使整个课程更具有整体性，课程们有一个共�的**主题**。
+
+通过确�课程内容与项目强相关，我们让学习过程对学生更具�引力，概念的学习也被深化了。难度较低的课�测验�以�引学生学习课程，课�的第二次测验进一步��了课堂中的概念。该课程被设计地�活有趣，�以一次性全部学习，或者分开�一部分一部分学习。这些项目由浅入深，从第一周的的�项目开始，在第�二周的周期结�时�得较为��。本课程还包括一个关于机器学习实际应用的�记，�用作�外学分或讨论的基础。
+
+> 在这里，你�以找到我们的[行为守则](CODE_OF_CONDUCT.md)，[对项目作出贡献](CONTRIBUTING.md)以�[翻译](TRANSLATIONS.md)指�。我们欢迎���出有建设性的�馈�
+
+## �一节课都包�：
+
+- �选的笔记
+- �选的补充视频
+- 课�热身测验
+- 文字课程
+- 对于基于项目的课程，包�构建项目的分步指�
+- knowledge checks
+- 一个挑战
+- 补充阅读
+- 作业
+- 课�测验
+
+> **关于测验**：所有的测验都在[这个应用里](https://jolly-sea-0a877260f.azurestaticapps.net)，总共 50 个测验，�个测验三个问题。它们的链接在�节课中，而且这个测验应用�以在本地�行。请�考 `quiz-app` 文件夹中的指�。
+
+
+| 课程编� |                           主体                            |                       课程组                       | 学习目标                                                                                                             |                     课程链接                     |     作者     |
+| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
+|      01       |              机器学习简介              |      [简介](1-Introduction/README.md)       | 了解机器学习背�的基本概念                                                                                |   [课程](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
+|      02       |              机器学习的历�               |      [简介](1-Introduction/README.md)       | 了解该领域的历�                                                                                         |  [课程](1-Introduction/2-history-of-ML/README.md)   |  Jen 和 Amy   |
+|      03       |               机器学习与公平                |      [简介](1-Introduction/README.md)       | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的��哲学问题？ |     [课程](1-Introduction/3-fairness/README.md)     |     Tomomi     |
+|      04       |              机器学习的技术工具               |      [简介](1-Introduction/README.md)       | 机器学习研究者使用哪些技术�构建机器学习模型？                                                                       | [课程](1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen  |
+|      05       |                 回归简介                 |        [回归](2-Regression/README.md)         | 开始使用 Python 和 Scikit-learn 构建回归模型                                                                  |       [课程](2-Regression/1-Tools/README.md)        |      Jen       |
+|      06       |              北美�瓜价格 🎃               |        [回归](2-Regression/README.md)         | �视化�进行数�清�，为机器学习�准备                                                                                  |        [课程](2-Regression/2-Data/README.md)        |      Jen       |
+|      07       |              北美�瓜价格 🎃               |        [回归](2-Regression/README.md)         | 建立线性和多项�回归模型                                                                                   |       [课程](2-Regression/3-Linear/README.md)       |      Jen       |
+|      08       |              北美�瓜价格 🎃               |        [回归](2-Regression/README.md)         | 构建逻辑回归模型                                                                                               |      [课程](2-Regression/4-Logistic/README.md)      |      Jen       |
+|      09       |                        一个网页应用 🔌                         |           [网页应用](3-Web-App/README.md)            | 构建一个 Web 应用程�以使用�过训练的模型                                                                                       |        [课程](3-Web-App/1-Web-App/README.md)        |      Jen       |
+|      10       |               分类简介               |    [分类](4-Classification/README.md)     | 清��准备和�视化数�； 分类简介                                                            |  [课程](4-Classification/1-Introduction/README.md)  | Jen 和 Cassie |
+|      11       |          美味的亚洲和�度美食 �            |    [分类](4-Classification/README.md)     | 分类器简介                                                                                                     | [课程](4-Classification/2-Classifiers-1/README.md)  | Jen 和 Cassie |
+|      12       |          美味的亚洲和�度美食 �            |    [分类](4-Classification/README.md)     | 关于分类器的更多内容                                                                                                                | [课程](4-Classification/3-Classifiers-2/README.md)  | Jen 和 Cassie |
+|      13       |          美味的亚洲和�度美食 �            |    [分类](4-Classification/README.md)     | 使用您的模型构建一个�以「推��的 Web 应用                                                                                    |    [课程](4-Classification/4-Applied/README.md)     |      Jen       |
+|      14       |                 �类简介                 |        [�类](5-Clustering/README.md)         | 清��准备和�视化数�； �类简介                                                                |     [课程](5-Clustering/1-Visualize/README.md)      |      Jen       |
+|      15       |            探索尼日利亚人的音��味 🎧             |        [�类](5-Clustering/README.md)         | 探索 K-Means �类方法                                                                                           |      [课程](5-Clustering/2-K-Means/README.md)       |      Jen       |
+|      16       |       自然语言处� (NLP) 简介  ☕�       |   [自然语言处�](6-NLP/README.md)    | 通过构建一个简�的 bot (机器人) �了解 NLP 的基础知识                                                                             |    [课程](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
+|      17       |                     常�的 NLP 任务 ☕�                     |   [自然语言处�](6-NLP/README.md)    | 通过�解处�语言结构时所需的常�任务�加深对于自然语言处� (NLP) 的�解                          |           [课程](6-NLP/2-Tasks/README.md)           |    Stephen     |
+|      18       |            翻译和情感分� ♥�            |   [自然语言处�](6-NLP/README.md)    | 对简·奥斯汀的文本进行翻译和情感分�                                                                             |   [课程](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
+|      19       |                欧洲的浪漫酒店 ♥�                 |   [自然语言处�](6-NLP/README.md)    | 对于酒店评价进行情感分�（上）                                                                                        |      [课程](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
+|      20       |                欧洲的浪漫酒店 ♥�                 |   [自然语言处�](6-NLP/README.md)    | 对于酒店评价进行情感分�（下）                                                                                         |      [课程](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
+|      21       |          时间�列预测简介           |        [时间�列](7-TimeSeries/README.md)        | 时间�列预测简介 forecasting                                                                                         |    [课程](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
+|      22       | ⚡� 世界用电� ⚡� - 使用 ARIMA 进行时间�列预测 |        [时间�列](7-TimeSeries/README.md)        | 使用 ARIMA 进行时间�列预测                                                                                              |       [课程](7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
+|      23       |           强化学习简介           | [强化学习](8-Reinforcement/README.md) | Q-Learning 强化学习简介                                                                          |    [课程](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
+|      24       |                帮助 Peter �开狼��                | [强化学习](8-Reinforcement/README.md) | 强化学习练习                                                                                                      |       [课程](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
+|  �记   |          现实世界中的机器学习场景和应用          |      [自然场景下的机器学习](9-Real-World/README.md)       | 探索有趣的�典机器学习方法，了解现实世界中机器学习的应用                                                               |    [课程](9-Real-World/1-Applications/README.md)    |      团队      |
+## 离线访问
+
+您�以使用 [Docsify](https://docsify.js.org/#/) 离线�行此文档。 Fork 这个仓库，并在你的本地机器上[安装 Docsify](https://docsify.js.org/#/quickstart)，并在这个仓库的根文件夹中�行 `docsify serve`。你�以通过 localhost 的 3000 端�访问此文档：`localhost:3000`。
+## PDF 文档们
+
+点击[这里](pdf/readme.pdf)查找课程的 PDF 文档们。
+
+## 需�你的帮助�
+
+想贡献一份翻译�？请阅读我们的[翻译指�](TRANSLATIONS.md)并在[此处](https://github.com/microsoft/ML-For-Beginners/issues/71)添加你的��。
+
+## 其他课程
+
+我们的团队还制作了其他课程��以看一下：
+
+- [针对�学者的 Web 开�课程](https://aka.ms/webdev-beginners)
+- [针对�学者的物�网课程](https://aka.ms/iot-beginners)
+

From f9fba092689ecf930fd556463810d598551a6aa4 Mon Sep 17 00:00:00 2001
From: Colin Zang <zyy0116@gmail.com>
Date: Thu, 15 Jul 2021 01:07:14 +0800
Subject: [PATCH 156/368] Update README.md

---
 README.md | 62 +++++++++++++++++++++++++++----------------------------
 1 file changed, 31 insertions(+), 31 deletions(-)

diff --git a/README.md b/README.md
index 34c09b5706..4645edb3f8 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
 [![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
 [![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](../http://makeapullrequest.com)
 
 [![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
 [![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
@@ -37,13 +37,13 @@ Travel with us around the world as we apply these classic techniques to data fro
 
 > For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
 
-**Teachers**, we have [included some suggestions](for-teachers.md) on how to use this curriculum.
+**Teachers**, we have [included some suggestions](../for-teachers.md) on how to use this curriculum.
 
 ---
 
 ## Meet the Team
 
-[![Promo video](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+[![Promo video](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
 
 > 🎥 Click the image above for a video about the project and the folks who created it!
 
@@ -54,7 +54,7 @@ We have chosen two pedagogical tenets while building this curriculum: ensuring t
 
 By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.
 
-> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
+> Find our [Code of Conduct](../CODE_OF_CONDUCT.md), [Contributing](../CONTRIBUTING.md), and [Translation](../TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
 ## Each lesson includes:
 
 - optional sketchnote
@@ -73,42 +73,42 @@ By ensuring that the content aligns with projects, the process is made more enga
 
 | Lesson Number |                           Topic                            |                       Lesson Grouping                       | Learning Objectives                                                                                                             |                     Linked Lesson                     |     Author     |
 | :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              Introduction to machine learning              |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
-|      02       |              The History of machine learning               |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
-|      03       |               Fairness and machine learning                |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
-|      04       |              Techniques for machine learning               |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                 Introduction to regression                 |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
-|      06       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
-|      07       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        A Web App 🔌                         |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               Introduction to classification               |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](4-Classification/1-Introduction/README.md)  | Jen and Cassie |
-|      11       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
-|      12       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
-|      13       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 Introduction to clustering                 |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       Introduction to natural language processing  ☕�       |   [Natural language processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     Common NLP Tasks ☕�                     |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            Translation and sentiment analysis ♥�            |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews, 1                                                                                        |      [lesson](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          Introduction to time series forecasting           |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
-|      22       | ⚡� World Power Usage ⚡� - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |       [lesson](7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           Introduction to reinforcement learning           | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                Help Peter avoid the wolf! �                | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  Postscript   |          Real-World ML scenarios and applications          |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](9-Real-World/1-Applications/README.md)    |      Team      |
+|      01       |              Introduction to machine learning              |      [Introduction](../1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](../1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
+|      02       |              The History of machine learning               |      [Introduction](../1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](../1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
+|      03       |               Fairness and machine learning                |      [Introduction](../1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](../1-Introduction/3-fairness/README.md)     |     Tomomi     |
+|      04       |              Techniques for machine learning               |      [Introduction](../1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](../1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
+|      05       |                 Introduction to regression                 |        [Regression](../2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](../2-Regression/1-Tools/README.md)        |      Jen       |
+|      06       |              North American pumpkin prices 🎃               |        [Regression](../2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](../2-Regression/2-Data/README.md)        |      Jen       |
+|      07       |              North American pumpkin prices 🎃               |        [Regression](../2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](../2-Regression/3-Linear/README.md)       |      Jen       |
+|      08       |              North American pumpkin prices 🎃               |        [Regression](../2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](../2-Regression/4-Logistic/README.md)      |      Jen       |
+|      09       |                        A Web App 🔌                         |           [Web App](../3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](../3-Web-App/1-Web-App/README.md)        |      Jen       |
+|      10       |               Introduction to classification               |    [Classification](../4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](../4-Classification/1-Introduction/README.md)  | Jen and Cassie |
+|      11       |           Delicious Asian and Indian cuisines �            |    [Classification](../4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](../4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
+|      12       |           Delicious Asian and Indian cuisines �            |    [Classification](../4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](../4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
+|      13       |           Delicious Asian and Indian cuisines �            |    [Classification](../4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](../4-Classification/4-Applied/README.md)     |      Jen       |
+|      14       |                 Introduction to clustering                 |        [Clustering](../5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](../5-Clustering/1-Visualize/README.md)      |      Jen       |
+|      15       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](../5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](../5-Clustering/2-K-Means/README.md)       |      Jen       |
+|      16       |       Introduction to natural language processing  ☕�       |   [Natural language processing](../6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
+|      17       |                     Common NLP Tasks ☕�                     |   [Natural language processing](../6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](../6-NLP/2-Tasks/README.md)           |    Stephen     |
+|      18       |            Translation and sentiment analysis ♥�            |   [Natural language processing](../6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
+|      19       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](../6-NLP/README.md)    | Sentiment analysis with hotel reviews, 1                                                                                        |      [lesson](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
+|      20       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](../6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
+|      21       |          Introduction to time series forecasting           |        [Time series](../7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](../7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
+|      22       | ⚡� World Power Usage ⚡� - time series forecasting with ARIMA |        [Time series](../7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |       [lesson](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
+|      23       |           Introduction to reinforcement learning           | [Reinforcement learning](../8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](../8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
+|      24       |                Help Peter avoid the wolf! �                | [Reinforcement learning](../8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](../8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
+|  Postscript   |          Real-World ML scenarios and applications          |      [ML in the Wild](../9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](../9-Real-World/1-Applications/README.md)    |      Team      |
 ## Offline access
 
 You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
 
 ## PDFs
 
-Find a pdf of the curriculum with links [here](pdf/readme.pdf)
+Find a pdf of the curriculum with links [here](../pdf/readme.pdf)
 
 ## Help Wanted!
 
-Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
+Would you like to contribute a translation? Please read our [translation guidelines](../TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
 
 ## Other Curricula
 

From 928febc4932997c91418b83851c7a372a7e5e908 Mon Sep 17 00:00:00 2001
From: Colin Zang <zyy0116@gmail.com>
Date: Thu, 15 Jul 2021 01:09:11 +0800
Subject: [PATCH 157/368] Updated README.zh-cn.md

---
 README.md                    | 62 ++++++++++++++++++------------------
 translations/README.zh-cn.md | 62 ++++++++++++++++++------------------
 2 files changed, 62 insertions(+), 62 deletions(-)

diff --git a/README.md b/README.md
index 4645edb3f8..34c09b5706 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
 [![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
 [![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](../http://makeapullrequest.com)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
 [![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
 [![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
@@ -37,13 +37,13 @@ Travel with us around the world as we apply these classic techniques to data fro
 
 > For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
 
-**Teachers**, we have [included some suggestions](../for-teachers.md) on how to use this curriculum.
+**Teachers**, we have [included some suggestions](for-teachers.md) on how to use this curriculum.
 
 ---
 
 ## Meet the Team
 
-[![Promo video](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+[![Promo video](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
 
 > 🎥 Click the image above for a video about the project and the folks who created it!
 
@@ -54,7 +54,7 @@ We have chosen two pedagogical tenets while building this curriculum: ensuring t
 
 By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.
 
-> Find our [Code of Conduct](../CODE_OF_CONDUCT.md), [Contributing](../CONTRIBUTING.md), and [Translation](../TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
+> Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
 ## Each lesson includes:
 
 - optional sketchnote
@@ -73,42 +73,42 @@ By ensuring that the content aligns with projects, the process is made more enga
 
 | Lesson Number |                           Topic                            |                       Lesson Grouping                       | Learning Objectives                                                                                                             |                     Linked Lesson                     |     Author     |
 | :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              Introduction to machine learning              |      [Introduction](../1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](../1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
-|      02       |              The History of machine learning               |      [Introduction](../1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](../1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
-|      03       |               Fairness and machine learning                |      [Introduction](../1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](../1-Introduction/3-fairness/README.md)     |     Tomomi     |
-|      04       |              Techniques for machine learning               |      [Introduction](../1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](../1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                 Introduction to regression                 |        [Regression](../2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](../2-Regression/1-Tools/README.md)        |      Jen       |
-|      06       |              North American pumpkin prices 🎃               |        [Regression](../2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](../2-Regression/2-Data/README.md)        |      Jen       |
-|      07       |              North American pumpkin prices 🎃               |        [Regression](../2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](../2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              North American pumpkin prices 🎃               |        [Regression](../2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](../2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        A Web App 🔌                         |           [Web App](../3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](../3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               Introduction to classification               |    [Classification](../4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](../4-Classification/1-Introduction/README.md)  | Jen and Cassie |
-|      11       |           Delicious Asian and Indian cuisines �            |    [Classification](../4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](../4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
-|      12       |           Delicious Asian and Indian cuisines �            |    [Classification](../4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](../4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
-|      13       |           Delicious Asian and Indian cuisines �            |    [Classification](../4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](../4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 Introduction to clustering                 |        [Clustering](../5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](../5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](../5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](../5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       Introduction to natural language processing  ☕�       |   [Natural language processing](../6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     Common NLP Tasks ☕�                     |   [Natural language processing](../6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](../6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            Translation and sentiment analysis ♥�            |   [Natural language processing](../6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](../6-NLP/README.md)    | Sentiment analysis with hotel reviews, 1                                                                                        |      [lesson](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](../6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          Introduction to time series forecasting           |        [Time series](../7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](../7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
-|      22       | ⚡� World Power Usage ⚡� - time series forecasting with ARIMA |        [Time series](../7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |       [lesson](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           Introduction to reinforcement learning           | [Reinforcement learning](../8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](../8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                Help Peter avoid the wolf! �                | [Reinforcement learning](../8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](../8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  Postscript   |          Real-World ML scenarios and applications          |      [ML in the Wild](../9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](../9-Real-World/1-Applications/README.md)    |      Team      |
+|      01       |              Introduction to machine learning              |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
+|      02       |              The History of machine learning               |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
+|      03       |               Fairness and machine learning                |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
+|      04       |              Techniques for machine learning               |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
+|      05       |                 Introduction to regression                 |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
+|      06       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
+|      07       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
+|      08       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |
+|      09       |                        A Web App 🔌                         |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](3-Web-App/1-Web-App/README.md)        |      Jen       |
+|      10       |               Introduction to classification               |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](4-Classification/1-Introduction/README.md)  | Jen and Cassie |
+|      11       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
+|      12       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
+|      13       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](4-Classification/4-Applied/README.md)     |      Jen       |
+|      14       |                 Introduction to clustering                 |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](5-Clustering/1-Visualize/README.md)      |      Jen       |
+|      15       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](5-Clustering/2-K-Means/README.md)       |      Jen       |
+|      16       |       Introduction to natural language processing  ☕�       |   [Natural language processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
+|      17       |                     Common NLP Tasks ☕�                     |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](6-NLP/2-Tasks/README.md)           |    Stephen     |
+|      18       |            Translation and sentiment analysis ♥�            |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
+|      19       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews, 1                                                                                        |      [lesson](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
+|      20       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
+|      21       |          Introduction to time series forecasting           |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
+|      22       | ⚡� World Power Usage ⚡� - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |       [lesson](7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
+|      23       |           Introduction to reinforcement learning           | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
+|      24       |                Help Peter avoid the wolf! �                | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
+|  Postscript   |          Real-World ML scenarios and applications          |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](9-Real-World/1-Applications/README.md)    |      Team      |
 ## Offline access
 
 You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
 
 ## PDFs
 
-Find a pdf of the curriculum with links [here](../pdf/readme.pdf)
+Find a pdf of the curriculum with links [here](pdf/readme.pdf)
 
 ## Help Wanted!
 
-Would you like to contribute a translation? Please read our [translation guidelines](../TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
+Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
 
 ## Other Curricula
 
diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md
index 3918b00833..f46a50d6c2 100644
--- a/translations/README.zh-cn.md
+++ b/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
 [![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
 [![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
 [![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
-[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](../http://makeapullrequest.com)
 
 [![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
 [![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
@@ -37,13 +37,13 @@
 
 > 如果希望进一步学习，我们推�跟� [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) 的模�和学习路径。
 
-**对于�师们**，我们对于如何使用这套教程[�供了一些建议](for-teachers.md)。
+**对于�师们**，我们对于如何使用这套教程[�供了一些建议](../for-teachers.md)。
 
 ---
 
 ## 项目团队
 
-[![宣传视频](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "宣传视频")
+[![宣传视频](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "宣传视频")
 
 > 🎥 点击上方的图片，�观看一个关于这个项目和它的创造者们的视频�
 
@@ -54,7 +54,7 @@
 
 通过确�课程内容与项目强相关，我们让学习过程对学生更具�引力，概念的学习也被深化了。难度较低的课�测验�以�引学生学习课程，课�的第二次测验进一步��了课堂中的概念。该课程被设计地�活有趣，�以一次性全部学习，或者分开�一部分一部分学习。这些项目由浅入深，从第一周的的�项目开始，在第�二周的周期结�时�得较为��。本课程还包括一个关于机器学习实际应用的�记，�用作�外学分或讨论的基础。
 
-> 在这里，你�以找到我们的[行为守则](CODE_OF_CONDUCT.md)，[对项目作出贡献](CONTRIBUTING.md)以�[翻译](TRANSLATIONS.md)指�。我们欢迎���出有建设性的�馈�
+> 在这里，你�以找到我们的[行为守则](../CODE_OF_CONDUCT.md)，[对项目作出贡献](../CONTRIBUTING.md)以�[翻译](../TRANSLATIONS.md)指�。我们欢迎���出有建设性的�馈�
 
 ## �一节课都包�：
 
@@ -74,41 +74,41 @@
 
 | 课程编� |                           主体                            |                       课程组                       | 学习目标                                                                                                             |                     课程链接                     |     作者     |
 | :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              机器学习简介              |      [简介](1-Introduction/README.md)       | 了解机器学习背�的基本概念                                                                                |   [课程](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
-|      02       |              机器学习的历�               |      [简介](1-Introduction/README.md)       | 了解该领域的历�                                                                                         |  [课程](1-Introduction/2-history-of-ML/README.md)   |  Jen 和 Amy   |
-|      03       |               机器学习与公平                |      [简介](1-Introduction/README.md)       | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的��哲学问题？ |     [课程](1-Introduction/3-fairness/README.md)     |     Tomomi     |
-|      04       |              机器学习的技术工具               |      [简介](1-Introduction/README.md)       | 机器学习研究者使用哪些技术�构建机器学习模型？                                                                       | [课程](1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen  |
-|      05       |                 回归简介                 |        [回归](2-Regression/README.md)         | 开始使用 Python 和 Scikit-learn 构建回归模型                                                                  |       [课程](2-Regression/1-Tools/README.md)        |      Jen       |
-|      06       |              北美�瓜价格 🎃               |        [回归](2-Regression/README.md)         | �视化�进行数�清�，为机器学习�准备                                                                                  |        [课程](2-Regression/2-Data/README.md)        |      Jen       |
-|      07       |              北美�瓜价格 🎃               |        [回归](2-Regression/README.md)         | 建立线性和多项�回归模型                                                                                   |       [课程](2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              北美�瓜价格 🎃               |        [回归](2-Regression/README.md)         | 构建逻辑回归模型                                                                                               |      [课程](2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        一个网页应用 🔌                         |           [网页应用](3-Web-App/README.md)            | 构建一个 Web 应用程�以使用�过训练的模型                                                                                       |        [课程](3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               分类简介               |    [分类](4-Classification/README.md)     | 清��准备和�视化数�； 分类简介                                                            |  [课程](4-Classification/1-Introduction/README.md)  | Jen 和 Cassie |
-|      11       |          美味的亚洲和�度美食 �            |    [分类](4-Classification/README.md)     | 分类器简介                                                                                                     | [课程](4-Classification/2-Classifiers-1/README.md)  | Jen 和 Cassie |
-|      12       |          美味的亚洲和�度美食 �            |    [分类](4-Classification/README.md)     | 关于分类器的更多内容                                                                                                                | [课程](4-Classification/3-Classifiers-2/README.md)  | Jen 和 Cassie |
-|      13       |          美味的亚洲和�度美食 �            |    [分类](4-Classification/README.md)     | 使用您的模型构建一个�以「推��的 Web 应用                                                                                    |    [课程](4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 �类简介                 |        [�类](5-Clustering/README.md)         | 清��准备和�视化数�； �类简介                                                                |     [课程](5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            探索尼日利亚人的音��味 🎧             |        [�类](5-Clustering/README.md)         | 探索 K-Means �类方法                                                                                           |      [课程](5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       自然语言处� (NLP) 简介  ☕�       |   [自然语言处�](6-NLP/README.md)    | 通过构建一个简�的 bot (机器人) �了解 NLP 的基础知识                                                                             |    [课程](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     常�的 NLP 任务 ☕�                     |   [自然语言处�](6-NLP/README.md)    | 通过�解处�语言结构时所需的常�任务�加深对于自然语言处� (NLP) 的�解                          |           [课程](6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            翻译和情感分� ♥�            |   [自然语言处�](6-NLP/README.md)    | 对简·奥斯汀的文本进行翻译和情感分�                                                                             |   [课程](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                欧洲的浪漫酒店 ♥�                 |   [自然语言处�](6-NLP/README.md)    | 对于酒店评价进行情感分�（上）                                                                                        |      [课程](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                欧洲的浪漫酒店 ♥�                 |   [自然语言处�](6-NLP/README.md)    | 对于酒店评价进行情感分�（下）                                                                                         |      [课程](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          时间�列预测简介           |        [时间�列](7-TimeSeries/README.md)        | 时间�列预测简介 forecasting                                                                                         |    [课程](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
-|      22       | ⚡� 世界用电� ⚡� - 使用 ARIMA 进行时间�列预测 |        [时间�列](7-TimeSeries/README.md)        | 使用 ARIMA 进行时间�列预测                                                                                              |       [课程](7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           强化学习简介           | [强化学习](8-Reinforcement/README.md) | Q-Learning 强化学习简介                                                                          |    [课程](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                帮助 Peter �开狼��                | [强化学习](8-Reinforcement/README.md) | 强化学习练习                                                                                                      |       [课程](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  �记   |          现实世界中的机器学习场景和应用          |      [自然场景下的机器学习](9-Real-World/README.md)       | 探索有趣的�典机器学习方法，了解现实世界中机器学习的应用                                                               |    [课程](9-Real-World/1-Applications/README.md)    |      团队      |
+|      01       |              机器学习简介              |      [简介](../1-Introduction/README.md)       | 了解机器学习背�的基本概念                                                                                |   [课程](../1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
+|      02       |              机器学习的历�               |      [简介](../1-Introduction/README.md)       | 了解该领域的历�                                                                                         |  [课程](../1-Introduction/2-history-of-ML/README.md)   |  Jen 和 Amy   |
+|      03       |               机器学习与公平                |      [简介](../1-Introduction/README.md)       | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的��哲学问题？ |     [课程](../1-Introduction/3-fairness/README.md)     |     Tomomi     |
+|      04       |              机器学习的技术工具               |      [简介](../1-Introduction/README.md)       | 机器学习研究者使用哪些技术�构建机器学习模型？                                                                       | [课程](../1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen  |
+|      05       |                 回归简介                 |        [回归](../2-Regression/README.md)         | 开始使用 Python 和 Scikit-learn 构建回归模型                                                                  |       [课程](../2-Regression/1-Tools/README.md)        |      Jen       |
+|      06       |              北美�瓜价格 🎃               |        [回归](../2-Regression/README.md)         | �视化�进行数�清�，为机器学习�准备                                                                                  |        [课程](../2-Regression/2-Data/README.md)        |      Jen       |
+|      07       |              北美�瓜价格 🎃               |        [回归](../2-Regression/README.md)         | 建立线性和多项�回归模型                                                                                   |       [课程](../2-Regression/3-Linear/README.md)       |      Jen       |
+|      08       |              北美�瓜价格 🎃               |        [回归](../2-Regression/README.md)         | 构建逻辑回归模型                                                                                               |      [课程](../2-Regression/4-Logistic/README.md)      |      Jen       |
+|      09       |                        一个网页应用 🔌                         |           [网页应用](../3-Web-App/README.md)            | 构建一个 Web 应用程�以使用�过训练的模型                                                                                       |        [课程](../3-Web-App/1-Web-App/README.md)        |      Jen       |
+|      10       |               分类简介               |    [分类](../4-Classification/README.md)     | 清��准备和�视化数�； 分类简介                                                            |  [课程](../4-Classification/1-Introduction/README.md)  | Jen 和 Cassie |
+|      11       |          美味的亚洲和�度美食 �            |    [分类](../4-Classification/README.md)     | 分类器简介                                                                                                     | [课程](../4-Classification/2-Classifiers-1/README.md)  | Jen 和 Cassie |
+|      12       |          美味的亚洲和�度美食 �            |    [分类](../4-Classification/README.md)     | 关于分类器的更多内容                                                                                                                | [课程](../4-Classification/3-Classifiers-2/README.md)  | Jen 和 Cassie |
+|      13       |          美味的亚洲和�度美食 �            |    [分类](../4-Classification/README.md)     | 使用您的模型构建一个�以「推��的 Web 应用                                                                                    |    [课程](../4-Classification/4-Applied/README.md)     |      Jen       |
+|      14       |                 �类简介                 |        [�类](../5-Clustering/README.md)         | 清��准备和�视化数�； �类简介                                                                |     [课程](../5-Clustering/1-Visualize/README.md)      |      Jen       |
+|      15       |            探索尼日利亚人的音��味 🎧             |        [�类](../5-Clustering/README.md)         | 探索 K-Means �类方法                                                                                           |      [课程](../5-Clustering/2-K-Means/README.md)       |      Jen       |
+|      16       |       自然语言处� (NLP) 简介  ☕�       |   [自然语言处�](../6-NLP/README.md)    | 通过构建一个简�的 bot (机器人) �了解 NLP 的基础知识                                                                             |    [课程](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
+|      17       |                     常�的 NLP 任务 ☕�                     |   [自然语言处�](../6-NLP/README.md)    | 通过�解处�语言结构时所需的常�任务�加深对于自然语言处� (NLP) 的�解                          |           [课程](../6-NLP/2-Tasks/README.md)           |    Stephen     |
+|      18       |            翻译和情感分� ♥�            |   [自然语言处�](../6-NLP/README.md)    | 对简·奥斯汀的文本进行翻译和情感分�                                                                             |   [课程](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
+|      19       |                欧洲的浪漫酒店 ♥�                 |   [自然语言处�](../6-NLP/README.md)    | 对于酒店评价进行情感分�（上）                                                                                        |      [课程](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
+|      20       |                欧洲的浪漫酒店 ♥�                 |   [自然语言处�](../6-NLP/README.md)    | 对于酒店评价进行情感分�（下）                                                                                         |      [课程](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
+|      21       |          时间�列预测简介           |        [时间�列](../7-TimeSeries/README.md)        | 时间�列预测简介 forecasting                                                                                         |    [课程](../7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
+|      22       | ⚡� 世界用电� ⚡� - 使用 ARIMA 进行时间�列预测 |        [时间�列](../7-TimeSeries/README.md)        | 使用 ARIMA 进行时间�列预测                                                                                              |       [课程](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
+|      23       |           强化学习简介           | [强化学习](../8-Reinforcement/README.md) | Q-Learning 强化学习简介                                                                          |    [课程](../8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
+|      24       |                帮助 Peter �开狼��                | [强化学习](../8-Reinforcement/README.md) | 强化学习练习                                                                                                      |       [课程](../8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
+|  �记   |          现实世界中的机器学习场景和应用          |      [自然场景下的机器学习](../9-Real-World/README.md)       | 探索有趣的�典机器学习方法，了解现实世界中机器学习的应用                                                               |    [课程](../9-Real-World/1-Applications/README.md)    |      团队      |
 ## 离线访问
 
 您�以使用 [Docsify](https://docsify.js.org/#/) 离线�行此文档。 Fork 这个仓库，并在你的本地机器上[安装 Docsify](https://docsify.js.org/#/quickstart)，并在这个仓库的根文件夹中�行 `docsify serve`。你�以通过 localhost 的 3000 端�访问此文档：`localhost:3000`。
 ## PDF 文档们
 
-点击[这里](pdf/readme.pdf)查找课程的 PDF 文档们。
+点击[这里](../pdf/readme.pdf)查找课程的 PDF 文档们。
 
 ## 需�你的帮助�
 
-想贡献一份翻译�？请阅读我们的[翻译指�](TRANSLATIONS.md)并在[此处](https://github.com/microsoft/ML-For-Beginners/issues/71)添加你的��。
+想贡献一份翻译�？请阅读我们的[翻译指�](../TRANSLATIONS.md)并在[此处](https://github.com/microsoft/ML-For-Beginners/issues/71)添加你的��。
 
 ## 其他课程
 

From 38c3dfa0c332e03c8e4cefb52c7af5c014b9237e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Thu, 15 Jul 2021 01:19:49 +0800
Subject: [PATCH 158/368] rename

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rename to 2-Regression/translations/README.zh-cn.md

From 109b2078e8b6eacb37a9174745ee26cac2cb086f Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Wed, 14 Jul 2021 20:30:41 +0200
Subject: [PATCH 159/368] Italian Translation - Chapter 2-3 complete

---
 .../2-Data/translations/assignment.it.md      |   1 +
 .../3-Linear/translations/README.it.md        | 339 ++++++++++++++++++
 .../3-Linear/translations/assignment.it.md    |  11 +
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diff --git a/2-Regression/2-Data/translations/assignment.it.md b/2-Regression/2-Data/translations/assignment.it.md
index 6fe0cf5024..14527fcae7 100644
--- a/2-Regression/2-Data/translations/assignment.it.md
+++ b/2-Regression/2-Data/translations/assignment.it.md
@@ -1,6 +1,7 @@
 # Esplorazione delle visualizzazioni
 
 Sono disponibili diverse librerie per la visualizzazione dei dati. Creare alcune visualizzazioni utilizzando i dati della zucca in questa lezione con matplotlib e seaborn in un notebook di esempio. Con quali librerie è più facile lavorare?
+
 ## Rubrica
 
 | Criteri | Ottimo | Adeguato | Necessita miglioramento |
diff --git a/2-Regression/3-Linear/translations/README.it.md b/2-Regression/3-Linear/translations/README.it.md
new file mode 100644
index 0000000000..1aafa6014a
--- /dev/null
+++ b/2-Regression/3-Linear/translations/README.it.md
@@ -0,0 +1,339 @@
+# Costruire un modello di regressione usando Scikit-learn: regressione in due modi
+
+![Infografica di regressione lineare e polinomiale](../images/linear-polynomial.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+
+### Introduzione
+
+Finora si è esplorato cos'è la regressione con dati di esempio raccolti dall'insieme di dati relativo ai prezzi della zucca, che verrà usato in questa lezione. Lo si è anche visualizzato usando Matplotlib.
+
+Ora si è  pronti per approfondire la regressione per machine learning. In questa lezione si imparerà di più su due tipi di regressione: _regressione lineare di base_ e _regressione polinomiale_, insieme ad alcuni dei calcoli alla base di queste tecniche.
+
+> In questo programma di studi, si assume una conoscenza minima della matematica, e si cerca di renderla accessibile agli studenti provenienti da altri campi, quindi si faccia attenzione a note, 🧮 didascalie, diagrammi e altri strumenti di apprendimento che aiutano la comprensione.
+
+### Prerequisito
+
+Si dovrebbe ormai avere familiarità con la struttura dei dati della zucca che si sta esaminando. Lo si può trovare precaricato e prepulito nel file _notebook.ipynb_ di questa lezione. Nel file, il prezzo della zucca viene visualizzato per bushel (staio) in un nuovo dataframe. Assicurasi di poter eseguire questi notebook nei kernel in Visual Studio Code.
+
+### Preparazione
+
+Come promemoria, si stanno caricando questi dati in modo da porre domande su di essi.
+
+- Qual è il momento migliore per comprare le zucche?
+- Che prezzo ci si può  aspettare da una cassa di zucche in miniatura?
+- Si devono acquistare in cestini da mezzo bushel o a scatola da 1 1/9 bushel? Si continua a scavare in questi dati.
+
+Nella lezione precedente, è stato creato un dataframe Pandas e si è popolato con parte dell'insieme di dati originale, standardizzando il prezzo per lo bushel. In questo modo, tuttavia, si sono potuti raccogliere solo circa 400 punti dati e solo per i mesi autunnali.
+
+Si dia  un'occhiata ai dati precaricati nel notebook di accompagnamento di questa lezione. I dati sono precaricati e viene tracciato un grafico a dispersione iniziale per mostrare i dati mensili. Forse si può ottenere qualche dettaglio in più sulla natura dei dati pulendoli ulteriormente.
+
+## Una linea di regressione lineare
+
+Come si è appreso nella lezione 1, l'obiettivo di un esercizio di regressione lineare è essere in grado di tracciare una linea per:
+
+- **Mostrare le relazioni tra variabili**.
+- **Fare previsioni**. Fare previsioni accurate su dove cadrebbe un nuovo punto dati in relazione a quella linea.
+
+È tipico della **Regressione dei Minimi Quadrati** disegnare questo tipo di linea. Il termine "minimi quadrati" significa che tutti i punti dati che circondano la linea di regressione sono elevati al quadrato e quindi sommati. Idealmente, quella somma finale è la più piccola possibile, perché si vuole un basso numero di errori, o `minimi quadrati`.
+
+Lo si fa perché si vuole modellare una linea che abbia la distanza cumulativa minima da tutti i  punti dati. Si esegue anche il quadrato dei termini prima di aggiungerli poiché  interessa la grandezza piuttosto che la direzione.
+
+> **🧮 Mostrami la matematica**
+>
+> Questa linea, chiamata _linea di miglior adattamento_ , può essere espressa da [un'equazione](https://en.wikipedia.org/wiki/Simple_linear_regression):
+>
+> ```
+> Y = a + bX
+> ```
+>
+> `X` è la "variabile esplicativa". `Y` è la "variabile dipendente". La pendenza della linea è `b` e `a` è l'intercetta di y, che si riferisce al valore di `Y` quando `X = 0`.
+>
+> ![calcolare la pendenza](../images/slope.png)
+>
+> Prima, calcolare la pendenza `b`. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+>
+> In altre parole, facendo riferimento alla domanda originale per i dati sulle zucche: "prevedere il prezzo di una zucca per bushel per mese", `X` si riferisce al prezzo e `Y` si riferirisce al mese di vendita.
+>
+> ![completare l'equazione](../images/calculation.png)
+>
+> Si calcola il valore di Y. Se si sta pagando circa $4, deve essere aprile! Infografica di [Jen Looper](https://twitter.com/jenlooper)
+>
+> La matematica che calcola la linea deve dimostrare la pendenza della linea, che dipende anche dall'intercetta, o dove `Y` si trova quando `X = 0`.
+>
+> Si può osservare il metodo di calcolo per questi valori sul sito web [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) . Si visiti anche [questo calcolatore dei minimi quadrati](https://www.mathsisfun.com/data/least-squares-calculator.html) per vedere come i valori dei numeri influiscono sulla linea.
+
+## Correlazione
+
+Un altro termine da comprendere è il **Coefficiente di Correlazione** tra determinate variabili X e Y. Utilizzando un grafico a dispersione, è possibile visualizzare rapidamente questo coefficiente. Un grafico con punti dati sparsi in una linea ordinata ha un'alta correlazione, ma un grafico con punti dati sparsi ovunque tra X e Y ha una bassa correlazione.
+
+Un buon modello di regressione lineare sarà quello che ha un Coefficiente di Correlazione alto (più vicino a 1 rispetto a 0) utilizzando il Metodo di Regressione dei Minimi Quadrati con una linea di regressione.
+
+✅ Eseguire il notebook che accompagna questa lezione e guardare il grafico a dispersione City to Price. I dati che associano la città al prezzo per le vendite di zucca sembrano avere una correlazione alta o bassa, secondo la propria interpretazione visiva del grafico a dispersione?
+
+
+## Preparare i dati per la regressione
+
+Ora che si ha una comprensione della matematica alla base di questo esercizio, si crea un modello di regressione per vedere se si può prevedere quale pacchetto di zucche avrà i migliori prezzi per zucca. Qualcuno che acquista zucche per una festa con tema un campo di zucche potrebbe desiderare che queste informazioni siano in grado di ottimizzare i propri acquisti di pacchetti di zucca per il campo.
+
+Dal momento che si utilizzerà Scikit-learn, non c'è motivo di farlo a mano (anche se si potrebbe!). Nel blocco di elaborazione dati principale del notebook della lezione, aggiungere una libreria da Scikit-learn per convertire automaticamente tutti i dati di tipo stringa in numeri:
+
+```python
+from sklearn.preprocessing import LabelEncoder
+
+new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
+```
+
+Se si guarda ora il dataframe new_pumpkins, si vede che tutte le stringhe ora sono numeriche. Questo rende più difficile la lettura per un umano ma molto più comprensibile per Scikit-learn!
+Ora si possono prendere decisioni più consapevoli (non solo basate sull'osservazione di un grafico a dispersione) sui dati più adatti alla regressione.
+
+Si provi a trovare una buona correlazione tra due punti nei propri dati per costruire potenzialmente un buon modello predittivo. A quanto pare, c'è solo una debole correlazione tra la città e il prezzo:
+
+```python
+print(new_pumpkins['City'].corr(new_pumpkins['Price']))
+0.32363971816089226
+```
+
+Tuttavia, c'è una correlazione leggermente migliore tra il pacchetto e il suo prezzo. Ha senso, vero? Normalmente, più grande è la scatola dei prodotti, maggiore è il prezzo.
+
+```python
+print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
+0.6061712937226021
+```
+
+Una buona domanda da porre a questi dati sarà: "Che prezzo posso aspettarmi da un determinato pacchetto di zucca?"
+
+Si costruisce questo modello di regressione
+
+## Costruire un modello lineare
+
+Prima di costruire il  modello, si esegue un altro riordino  dei dati. Si eliminano tutti i dati nulli e si controlla ancora una volta che aspetto hanno i dati.
+
+```python
+new_pumpkins.dropna(inplace=True)
+new_pumpkins.info()
+```
+
+Quindi, si crea un nuovo dataframe da questo set minimo e lo si stampa:
+
+```python
+new_columns = ['Package', 'Price']
+lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+lin_pumpkins
+```
+
+```output
+	Package	Price
+70	0	13.636364
+71	0	16.363636
+72	0	16.363636
+73	0	15.454545
+74	0	13.636364
+...	...	...
+1738	2	30.000000
+1739	2	28.750000
+1740	2	25.750000
+1741	2	24.000000
+1742	2	24.000000
+415 rows × 2 columns
+```
+
+1. Ora si possono  assegnare i dati delle coordinate X e y:
+
+   ```python
+   X = lin_pumpkins.values[:, :1]
+   y = lin_pumpkins.values[:, 1:2]
+   ```
+
+Cosa sta succedendo qui? Si sta usando [la notazione slice Python](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295) per creare array per popolare `X` e `y`.
+
+2. Successivamente, si avvia le routine di creazione del modello di regressione:
+
+   ```python
+   from sklearn.linear_model import LinearRegression
+   from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+   from sklearn.model_selection import train_test_split
+   
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   lin_reg = LinearRegression()
+   lin_reg.fit(X_train,y_train)
+   
+   pred = lin_reg.predict(X_test)
+   
+   accuracy_score = lin_reg.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   Poiché la correlazione non è particolarmente buona, il modello prodotto non è molto accurato.
+
+   ```output
+   Model Accuracy:  0.3315342327998987
+   ```
+
+3. Si può visualizzare la linea tracciata nel processo:
+
+   ```python
+   plt.scatter(X_test, y_test,  color='black')
+   plt.plot(X_test, pred, color='blue', linewidth=3)
+   
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+   
+   plt.show()
+   ```
+
+   ![Un grafico a dispersione che mostra il rapporto tra pacchetto e prezzo](../images/linear.png)
+
+4. Si testa il modello contro una varietà ipotetica:
+
+   ```python
+   lin_reg.predict( np.array([ [2.75] ]) )
+   ```
+
+   Il prezzo restituito per questa varietà mitologica è:
+
+   ```output
+   array([[33.15655975]])
+   ```
+
+Quel numero ha senso, se la logica della linea di regressione è vera.
+
+🎃 Congratulazioni, si è appena creato un modello che può aiutare a prevedere il prezzo di alcune varietà di zucche. La zucca per le festività sarà bellissima. Ma probabilmente si può creare un modello migliore!
+
+## Regressione polinomiale
+
+Un altro tipo di regressione lineare è la regressione polinomiale. Mentre a volte c'è una relazione lineare tra le variabili - più grande è il volume della zucca, più alto è il prezzo - a volte queste relazioni non possono essere tracciate come un piano o una linea retta.
+
+✅ Ecco [alcuni altri esempi](https://online.stat.psu.edu/stat501/lesson/9/9.8) di dati che potrebbero utilizzare la regressione polinomiale
+
+Si dia un'altra occhiata alla relazione tra Varietà e Prezzo nel tracciato precedente. Questo grafico a dispersione deve essere necessariamente analizzato da una linea retta? Forse no. In questo caso, si può provare la regressione polinomiale.
+
+✅ I polinomi sono espressioni matematiche che possono essere costituite da una o più variabili e coefficienti
+
+La regressione polinomiale crea una linea curva per adattare meglio i dati non lineari.
+
+1. Viene ricreato un dataframe popolato con un segmento dei dati della zucca originale:
+
+   ```python
+   new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
+   poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+   
+   poly_pumpkins
+   ```
+
+Un buon modo per visualizzare le correlazioni tra i dati nei dataframe è visualizzarli in un grafico "coolwarm":
+
+2. Si usa il metodo `Background_gradient()` con `coolwarm` come valore dell'argomento:
+
+   ```python
+   corr = poly_pumpkins.corr()
+   corr.style.background_gradient(cmap='coolwarm')
+   ```
+
+   Questo codice crea una mappa di calore:
+   ![Una mappa di calore che mostra la correlazione dei dati](../images/heatmap.png)
+
+Guardando questo grafico, si può visualizzare la buona correlazione tra Pacchetto e Prezzo. Quindi si dovrebbe essere in grado di creare un modello un po' migliore dell'ultimo.
+
+### Creare una pipeline
+
+Scikit-learn include un'API utile per la creazione di modelli di regressione polinomiale: l'[API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline) `make_pipeline`. Viene creata una 'pipeline' che è una catena di stimatori. In questo caso, la pipeline include caratteristiche polinomiali o previsioni che formano un percorso non lineare.
+
+1. Si costruiscono le colonne X e y:
+
+   ```python
+   X=poly_pumpkins.iloc[:,3:4].values
+   y=poly_pumpkins.iloc[:,4:5].values
+   ```
+
+2. Si crea la pipeline chiamando il metodo `make_pipeline()` :
+
+   ```python
+   from sklearn.preprocessing import PolynomialFeatures
+   from sklearn.pipeline import make_pipeline
+   
+   pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())
+   
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   
+   pipeline.fit(np.array(X_train), y_train)
+   
+   y_pred=pipeline.predict(X_test)
+   ```
+
+### Creare una sequenza
+
+A questo punto, è necessario creare un nuovo dataframe con dati _ordinati_ in modo che la pipeline possa creare una sequenza.
+
+Si aggiunge il seguente codice:
+
+```python
+df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})
+df.sort_values(by='x',inplace = True)
+points = pd.DataFrame(df).to_numpy()
+
+plt.plot(points[:, 0], points[:, 1],color="blue", linewidth=3)
+plt.xlabel('Package')
+plt.ylabel('Price')
+plt.scatter(X,y, color="black")
+plt.show()
+```
+
+Si è  creato un nuovo dataframe chiamato `pd.DataFrame`. Quindi si sono ordinati i valori chiamando `sort_values()`. Alla fine si è  creato un grafico polinomiale:
+
+![Un grafico polinomiale che mostra la relazione tra pacchetto e prezzo](../images/polynomial.png)
+
+Si può vedere una linea curva che si adatta meglio ai  dati.
+
+Si verifica la precisione del modello:
+
+```python
+accuracy_score = pipeline.score(X_train,y_train)
+print('Model Accuracy: ', accuracy_score)
+```
+
+E voilà!
+
+```output
+Model Accuracy:  0.8537946517073784
+```
+
+Ecco, meglio! Si prova a prevedere un prezzo:
+
+### Fare una previsione
+
+E possibile inserire un nuovo valore e ottenere una previsione?
+
+Si chiami `predict()` per fare una previsione:
+
+```python
+pipeline.predict( np.array([ [2.75] ]) )
+```
+
+Viene data questa previsione:
+
+```output
+array([[46.34509342]])
+```
+
+Ha senso, visto il tracciato! Se questo è un modello migliore del precedente, guardando gli stessi dati, si deve preventivare queste zucche più costose!
+
+Ben fatto! Sono stati  creati due modelli di regressione in una lezione. Nella sezione finale sulla regressione, si imparerà a conoscere la regressione logistica per determinare le categorie.
+
+---
+
+## 🚀 Sfida
+
+Testare diverse variabili in questo notebook per vedere come la correlazione corrisponde all'accuratezza del modello.
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+
+## Revisione e Auto Apprendimento
+
+In questa lezione si è appreso della regressione lineare. Esistono altri tipi importanti di regressione. Leggere le tecniche Stepwise, Ridge, Lazo ed Elasticnet. Un buon corso per studiare per saperne di più è il [corso Stanford Statistical Learning](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
+
+## Compito
+
+[Costruire un modello](assignment.it.md)
diff --git a/2-Regression/3-Linear/translations/assignment.it.md b/2-Regression/3-Linear/translations/assignment.it.md
new file mode 100644
index 0000000000..e5aaaa77eb
--- /dev/null
+++ b/2-Regression/3-Linear/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Creare un Modello di Regressione
+
+## Istruzioni
+
+In questa lezione è stato mostrato come costruire un modello utilizzando sia la Regressione Lineare che Polinomiale. Usando questa conoscenza, trovare un insieme di dati o utilizzare uno degli insiemi integrati di Scikit-Learn per costruire un modello nuovo. Spiegare nel proprio notebook perché si è scelto una determinata tecnica e dimostrare la precisione del modello. Se non è accurato, spiegare perché.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------------------ | -------------------------- | ------------------------------- |
+|          | presenta un notebook completo con una soluzione ben documentata | La soluzione è incompleta | La soluzione è difettosa o contiene bug |

From 078846d8064d7fc2453e7c69a936b342426979bf Mon Sep 17 00:00:00 2001
From: edgargonarr <35715904+edgargonarr@users.noreply.github.com>
Date: Wed, 14 Jul 2021 14:22:26 -0500
Subject: [PATCH 160/368] Update README.md

The table for classification_report is not correctly aligned
---
 4-Classification/2-Classifiers-1/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index 15800922a6..bdff6bc993 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -217,7 +217,7 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
     print(classification_report(y_test,y_pred))
     ```
 
-    | precision    | recall | f1-score | support |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |              | precision | recall   | f1-score| support |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
     | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
     | chinese      | 0.73   | 0.71     | 0.72    | 229  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
     | indian       | 0.91   | 0.93     | 0.92    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |

From ce68d9bf2214b9194a85fb3ab25227bd9bd20381 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 14 Jul 2021 17:31:56 -0400
Subject: [PATCH 161/368] Update README.md

---
 quiz-app/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/quiz-app/README.md b/quiz-app/README.md
index 042d53ca11..83b30d1d94 100644
--- a/quiz-app/README.md
+++ b/quiz-app/README.md
@@ -1,6 +1,6 @@
 # Quizzes
 
-These quizzes are the pre- and post-lecture quizzes for the web development for ml curriculum at https://aka.ms/ml-beginners
+These quizzes are the pre- and post-lecture quizzes for the ML curriculum at https://aka.ms/ml-beginners
 
 ## Project setup
 

From 1be8c54849e3213b590f3ad8a986271c6aea0a07 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Wed, 14 Jul 2021 23:38:49 +0200
Subject: [PATCH 162/368] Create README.fr.md

---
 .../1-intro-to-ML/translations/README.fr.md   | 109 ++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 1-Introduction/1-intro-to-ML/translations/README.fr.md

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
new file mode 100644
index 0000000000..511f376468
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -0,0 +1,109 @@
+# Introduction au machine learning
+
+[![ML, AI, deep learning - Quelle est la différence ?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - What's the difference?")
+
+> 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
+
+## [Quizz de pré-conférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+
+### Introduction
+
+Bienvenue à ce cours sur le machine learning classique pour débutant ! Que vous soyez complètement nouveau sur ce sujet ou que vous soyez un professonnel du ML expérimenté cherchant à peaufiner vos connaissances, nous sommes heureux de vous avoir avec nous ! Nous voulons créer un tremplin chaleureux pour vos études en ML et serions ravis d'évaluer, de répondre et d'apprendre de vos retours d'[expériences](https://github.com/microsoft/ML-For-Beginners/discussions).
+
+[![Introduction au ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
+
+> 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo: John Guttag du MIT introduit le machine learning
+### Débuter avec le machine learning
+
+Avant de commencer avec ce cours, vous aurez besoin d'un ordinateur configuré et prêt à faire tourner des notebooks (jupyter) localement.
+
+- **Configurer votre ordinateur avec ces vidéos**. Apprendre comment configurer votre ordinateur avec cette [série de vidéos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6).
+- **Apprendre Python**. Il est aussi recommandé d'avoir une connaissance basique de [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), un langage de programmaton utile pour les data scientist que nous utilisons tout au long de ce cours.
+- **Apprendre Node.js et Javascript**. Nous utilisons aussi Javascript par moment dans ce cours afin de construire des applications WEB, vous aurez donc besoin de [node](https://nodejs.org) et [npm](https://www.npmjs.com/) installé, ainsi que de [Visual Studio Code](https://code.visualstudio.com/) pour développer en Python et Javascript.
+- **Créer un compte GitHub**. Comme vous nous avez trouvé sur [GitHub](https://github.com), vous y avez sûrement un compte, mais si non, créez en un et répliquez ce cours afin de l'utiliser à votre grés. (N'oublier pas de nous donner une étoile aussi 😊)
+- **Explorer Scikit-learn**. Familiariser vous avec [Scikit-learn](https://scikit-learn.org/stable/user_guide.html), un ensemble de librairies ML que nous mentionnons dans nos leçons.
+
+### Qu'est-ce que le machine learning
+
+Le terme `machine learning` est un des mots les plus populaire et le plus utilisé ces derniers temps. Il y a une probabilité accrue que vous l'ayez entendu au moins une fois si vous avez une appétence pour la technologie indépendamment du domaine dans lequel vous travaillez. Le fonctionnement du machine learning, cependant, reste un mystère pour la plupart des personnes. Pour un débutant en machine learning, le sujet peut nous submerger. Ainsi, il est important de comprendre ce qu'est le machine learning et de l'apprendre petit à petit au travers d'exemples pratiques.   
+
+![ml hype curve](images/hype.png)
+
+> Google Trends montre la récente 'courbe de popularité' pour le mot 'machine learning'
+
+Nous vivons dans un univers rempli de mystères fascinants. De grands scientifiques comme Stephen Hawking, Albert Einstein et pleins d'autres ont dévoués leur vie à la recherche d'informations utiles afin de dévoiler les mystères qui nous entourent. C'est la condition humaine pour apprendre : un enfant apprend de nouvelles choses et découvre la structure du monde année après année jusqu'à qu'ils deviennent adultes.
+
+Le cerveau d'un enfant et ses sens perçoivent l'environnement qui les entourent et apprennent graduellement des schémas secrets de la vie qui vont l'aider à fabriquer des règles logiques afin d'identifier les schémas appris. Le processus d'apprentissage du cerveau humain est ce que rend les hommes comme la créature la plus sophistiquée du monde vivant. Apprendre continuellement par la découverte de schémas cachés et ensuite innover sur ces schémas nous permet de nous améliorer tout au long de notre vie. Cette capacité d'apprendre et d'évoluer est liée au concept de [plasticité neuronale](https://www.simplypsychology.org/brain-plasticity.html), nous pouvons tirer quelques motivations similaires entre le processus d'apprentissage du cerveau humain et le concept de machine learning.
+
+Le [cerveau humain](https://www.livescience.com/29365-human-brain.html) perçoit des choses du monde réel, assimile les informations perçues, fait des décisions rationnelles et entreprend certaines actions selon le contexte. C'est ce que l'on appelle se comporter intelligemment. Lorsque nous programmons une reproduction du processus de ce comportement à une machine, c'est ce que l'on appelle intelligence artificielle (IA).
+
+Bien que le terme peut être confu, machine learning (ML) est un important sous-ensemble de l'intelligence artificielle. **ML se réfère à l'utilisation d'algorithmes spécialisés afin de découvrir des informations utiles et de trouver des schémas cachés depuis des données perçues pour corroborer un processus de décision rationnel**.
+
+![AI, ML, deep learning, data science](images/ai-ml-ds.png)
+
+> Un diagramme montrant les relations entre AI, ML, deep learning et data science. Infographie par [Jen Looper](https://twitter.com/jenlooper) et inspiré par [ce graphique](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
+
+## Ce que vous allez apprendre dans ce cours
+
+Dans ce cours, nous allons nous concentrer sur les concepts clés du machine learning qu'un débutant se doit de connaître. Nous parlerons ce que l'on appelle le 'machine learning classique' en utilisant principalement Scikit-learn, une excellente librairie que beaucoup d'étudiants utilisent afin d'apprendre les bases. Afin de comprendre les concepts plus larges de l'intelligence artificielle ou du deep learning, une profonde connaissance en machine learning est indispensable, et c'est ce que nous aimerions fournir ici.
+
+Dans ce cours, vous allez apprendre :
+
+- Les concepts clés du machine learning
+- L'histoire du ML
+- ML et équité (fairness)
+- Les techniques de régression ML
+- Les techniques de classification ML
+- Les techniques de regroupement (clustering) ML
+- Les techniques du traitement automatique des langues (NLP) ML
+- Les techniques de prédictions à partir de séries chronologiques ML
+- Apprentissage renforcé
+- D'applications réels du ML
+
+## Ce que nous ne couvrirons pas
+
+- Deep learning
+- Neural networks
+- IA
+
+Afin d'avoir la meilleur expérience d'apprentissage, nous éviterons les complexités des réseaux neuronaux, du 'deep learning' (construire un modèle utilisant plusieurs couches de réseaux neuronaux) et IA, dont nous parlerons dans un cours différent. Nous offirons aussi un cours à venir sur la data science pour concentrer sur cet aspect de champs très large.
+
+## Pourquoi etudier le machine learning ?
+
+Le machine learning, depuis une perspective systémique, est défini comme la création de systèmes automatiques pouvant apprendre des schémas cachés depuis des données afin d'aider à prendre des décisions intelligentes.
+
+Ce but est faiblement inspiré de la manière dont le cerveau humain apprend certaines choses depuis les données qu'il perçoit du monde extérieur.
+
+✅ Penser une minute aux raisons qu'une entreprise aurait d'essayer d'utiliser des stratégies de machine learning au lieu de créer des règles codés en dur.
+
+### Les applications du machine learning
+
+Les applications du machine learning sont maintenant pratiquement partout, et sont aussi omniprésentes que les données qui circulent autour de notre société (générés par nos smartphones, appareils connectés ou autres systèmes). En prenant en considération l'immense potentiel des algorithmes dernier cri de machine learning, les chercheurs ont pu exploités leurs capacités afin de résoudre des problèmes multidimensionnels et interdisciplinaires de la vie avec d'important retours positifs 
+
+**Vous pouvez utiliser le machine learning de plusieurs manières** :
+
+- Afin de prédire la possibilité d'avoir une maladie à partir des données médicales d'un patient.
+- Pour tirer parti des données météorologiques afin de prédire les événements météorologiques.
+- Afin de comprendre le sentiment d'un texte.
+- Afin de détecter les fake news pour stopper la propagation de la propagande.
+
+La finance, l'économie, les sciences de la terre, l'exploration spatiale, le génie biomédical, les sciences cognitives et même les domaines des sciences humaines ont adapté le machine learning pour résoudre les problèmes ardus et lourds de traitement des données dans leur domaine respectif.
+
+Le machine learning automatise le processus de découverte de modèles en trouvant des informations significatives à partir de données réelles ou générées. Il s'est avéré très utile dans les applications commerciales, de santé et financières, entre autres.
+
+Dans un avenir proche, comprendre les bases du machine learning sera indispensable pour les personnes de tous les domaines en raison de son adoption généralisée.
+
+---
+## 🚀 Challenge
+
+Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
+
+## [Quizz de post-conférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+
+## Révision et auto-apprentissage
+
+Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de ML dans le cloud, suivez ce [Parcours d'apprentissage](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine- learning/?WT.mc_id=academic-15963-cxa).
+
+## Devoir
+
+[Être opérationnel](assignment.md)

From 9439709d5b2a3a1311d6fbbd3f44bb56ae53112b Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Wed, 14 Jul 2021 23:47:41 +0200
Subject: [PATCH 163/368] Editing assignment link

---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 511f376468..19af588dea 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -106,4 +106,4 @@ Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de M
 
 ## Devoir
 
-[Être opérationnel](assignment.md)
+[Être opérationnel](../assignment.md)

From 5775c4b0690771414a7bdfb4f013beaaafa2df7f Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 14 Jul 2021 20:16:08 -0400
Subject: [PATCH 164/368] table tidy-up

---
 4-Classification/2-Classifiers-1/README.md | 66 +++++++++++-----------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index bdff6bc993..0db1aebaad 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -21,15 +21,14 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     The data looks like this:
 
-    ```output
-    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
-    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
-    | 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
-    | 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
-    | 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
-    | 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
-    | 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
-    ```
+|     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+| --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+| 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+  
 
 1. Now, import several more libraries:
 
@@ -68,13 +67,13 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     Your features look like this:
 
-    | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     |
-    | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
-    |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+| almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | 
+|      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+|      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+|      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+|      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+|      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
 
 Now you are ready to train your model!
 
@@ -200,13 +199,13 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
 
     The result is printed - Indian cuisine is its best guess, with good probability:
 
-    |          |        0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-    |   indian | 0.715851 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |  chinese | 0.229475 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | japanese | 0.029763 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |   korean | 0.017277 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |     thai | 0.007634 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |          |        0 | 
+    | -------: | -------: | 
+    |   indian | 0.715851 |
+    |  chinese | 0.229475 |
+    | japanese | 0.029763 |
+    |   korean | 0.017277 |
+    |     thai | 0.007634 |
 
     ✅ Can you explain why the model is pretty sure this is an Indian cuisine?
 
@@ -217,22 +216,23 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
     print(classification_report(y_test,y_pred))
     ```
 
-    |              | precision | recall   | f1-score| support |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-    | chinese      | 0.73   | 0.71     | 0.72    | 229  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | indian       | 0.91   | 0.93     | 0.92    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | japanese     | 0.70   | 0.75     | 0.72    | 220  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | korean       | 0.86   | 0.76     | 0.81    | 242  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | thai         | 0.79   | 0.85     | 0.82    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | accuracy     | 0.80   | 1199     |         |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |              | precision | recall | f1-score | support |
+    | ------------ | ------ | -------- | ------- | ---- |
+    | chinese      | 0.73   | 0.71     | 0.72    | 229  |
+    | indian       | 0.91   | 0.93     | 0.92    | 254  |
+    | japanese     | 0.70   | 0.75     | 0.72    | 220  |
+    | korean       | 0.86   | 0.76     | 0.81    | 242  |
+    | thai         | 0.79   | 0.85     | 0.82    | 254  |
+    | accuracy     | 0.80   | 1199     |         |      |
+    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |
+    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |
 
 ## 🚀Challenge
 
 In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes.
 
 ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+
 ## Review & Self Study
 
 Dig a little more into the math behind logistic regression in [this lesson](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)

From 59cefad031d40dba0abdc293a4ed912a492934ff Mon Sep 17 00:00:00 2001
From: ahaliu1 <247969917@qq.com>
Date: Thu, 15 Jul 2021 11:07:53 +0800
Subject: [PATCH 165/368] Fix two path error

---
 6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index 75cd69da90..3d122be65c 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -125,7 +125,7 @@
     It was nice talking to you, goodbye!
     ```
 
-    该任务的一��能解决方案在[这里](solution/bot.py)
+    该任务的一��能解决方案在[这里](../solution/bot.py)
 
     ✅ �止并�考
    
@@ -152,4 +152,4 @@
 
 ## 任务
 
-[查找一个机器人](assignment.md)
+[查找一个机器人](../assignment.md)

From 4b18d4f145dbe5c584da60ee82993e0610ff439d Mon Sep 17 00:00:00 2001
From: ahaliu1 <247969917@qq.com>
Date: Thu, 15 Jul 2021 11:08:36 +0800
Subject: [PATCH 166/368] Fix two file path error

---
 6-NLP/1-Introduction-to-NLP/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index 227ad589d2..ea244c7409 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -133,7 +133,7 @@ Let's create the bot next. We'll start by defining some phrases.
     It was nice talking to you, goodbye!
     ```
 
-    One possible solution to the task is [here](solution/bot.py)
+    One possible solution to the task is [here](../solution/bot.py)
 
     ✅ Stop and consider
 
@@ -162,4 +162,4 @@ Take a look at the references below as further reading opportunities.
 
 ## Assignment 
 
-[Search for a bot](assignment.md)
+[Search for a bot](../assignment.md)

From 27d16669d59fc09c8af6b651ff4bf9414b37029d Mon Sep 17 00:00:00 2001
From: Vishvanathan K <vishvanathank@outlook.in>
Date: Thu, 15 Jul 2021 10:24:23 +0530
Subject: [PATCH 167/368] Update Markdown error

---
 8-Reinforcement/1-QLearning/README.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md
index bfa07ffe38..6301c46e12 100644
--- a/8-Reinforcement/1-QLearning/README.md
+++ b/8-Reinforcement/1-QLearning/README.md
@@ -229,8 +229,7 @@ We are now ready to implement the learning algorithm. Before we do that, we also
     We add a few `eps` to the original vector in order to avoid division by 0 in the initial case, when all components of the vector are identical.
 
 Run them learning algorithm through 5000 experiments, also called **epochs**: (code block 8)
-
-    ```python
+```python
     for epoch in range(5000):
     
         # Pick initial point
@@ -255,11 +254,11 @@ Run them learning algorithm through 5000 experiments, also called **epochs**: (c
             ai = action_idx[a]
             Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())
             n+=1
-    ```
+```
 
-    After executing this algorithm, the Q-Table should be updated with values that define the attractiveness of different actions at each step. We can try to visualize the Q-Table by plotting a vector at each cell that will point in the desired direction of movement. For simplicity, we draw a small circle instead of an arrow head.
+After executing this algorithm, the Q-Table should be updated with values that define the attractiveness of different actions at each step. We can try to visualize the Q-Table by plotting a vector at each cell that will point in the desired direction of movement. For simplicity, we draw a small circle instead of an arrow head.
 
-    <img src="images/learned.png"/>
+<img src="images/learned.png"/>
 
 ## Checking the policy
 

From 829ac30282d356b1b4b601452c1ab48cd1da390e Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Thu, 15 Jul 2021 09:30:48 +0200
Subject: [PATCH 168/368] Changed url for images

---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 19af588dea..a65367b5bb 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -27,7 +27,7 @@ Avant de commencer avec ce cours, vous aurez besoin d'un ordinateur configuré e
 
 Le terme `machine learning` est un des mots les plus populaire et le plus utilisé ces derniers temps. Il y a une probabilité accrue que vous l'ayez entendu au moins une fois si vous avez une appétence pour la technologie indépendamment du domaine dans lequel vous travaillez. Le fonctionnement du machine learning, cependant, reste un mystère pour la plupart des personnes. Pour un débutant en machine learning, le sujet peut nous submerger. Ainsi, il est important de comprendre ce qu'est le machine learning et de l'apprendre petit à petit au travers d'exemples pratiques.   
 
-![ml hype curve](images/hype.png)
+![ml hype curve](../images/hype.png)
 
 > Google Trends montre la récente 'courbe de popularité' pour le mot 'machine learning'
 
@@ -39,7 +39,7 @@ Le [cerveau humain](https://www.livescience.com/29365-human-brain.html) perçoit
 
 Bien que le terme peut être confu, machine learning (ML) est un important sous-ensemble de l'intelligence artificielle. **ML se réfère à l'utilisation d'algorithmes spécialisés afin de découvrir des informations utiles et de trouver des schémas cachés depuis des données perçues pour corroborer un processus de décision rationnel**.
 
-![AI, ML, deep learning, data science](images/ai-ml-ds.png)
+![AI, ML, deep learning, data science](../images/ai-ml-ds.png)
 
 > Un diagramme montrant les relations entre AI, ML, deep learning et data science. Infographie par [Jen Looper](https://twitter.com/jenlooper) et inspiré par [ce graphique](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 

From 091d096eb19e3d8229e9556e3217c6c7823bb7fa Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Thu, 15 Jul 2021 09:33:50 +0200
Subject: [PATCH 169/368] Corrected Quizz to Quiz

---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index a65367b5bb..828e7c62f2 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
 
 > 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
 
-## [Quizz de pré-conférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz de pré-conférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
 
 ### Introduction
 
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
 
 Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
 
-## [Quizz de post-conférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz de post-conférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
 
 ## Révision et auto-apprentissage
 

From a0c925fdaf878c8c1017fdef25b339297b9e9c38 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Thu, 15 Jul 2021 12:22:53 +0200
Subject: [PATCH 170/368] Correction de traduction
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Post-conférence -> Postlecture
* Pré-conférence -> Prélecture
* Hidden patterns -> Schémas non observés
Comme discuté dans le groupe
---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 828e7c62f2..fd396d6990 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
 
 > 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
 
-## [Quiz de pré-conférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz prélecture](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
 
 ### Introduction
 
@@ -33,11 +33,11 @@ Le terme `machine learning` est un des mots les plus populaire et le plus utilis
 
 Nous vivons dans un univers rempli de mystères fascinants. De grands scientifiques comme Stephen Hawking, Albert Einstein et pleins d'autres ont dévoués leur vie à la recherche d'informations utiles afin de dévoiler les mystères qui nous entourent. C'est la condition humaine pour apprendre : un enfant apprend de nouvelles choses et découvre la structure du monde année après année jusqu'à qu'ils deviennent adultes.
 
-Le cerveau d'un enfant et ses sens perçoivent l'environnement qui les entourent et apprennent graduellement des schémas secrets de la vie qui vont l'aider à fabriquer des règles logiques afin d'identifier les schémas appris. Le processus d'apprentissage du cerveau humain est ce que rend les hommes comme la créature la plus sophistiquée du monde vivant. Apprendre continuellement par la découverte de schémas cachés et ensuite innover sur ces schémas nous permet de nous améliorer tout au long de notre vie. Cette capacité d'apprendre et d'évoluer est liée au concept de [plasticité neuronale](https://www.simplypsychology.org/brain-plasticity.html), nous pouvons tirer quelques motivations similaires entre le processus d'apprentissage du cerveau humain et le concept de machine learning.
+Le cerveau d'un enfant et ses sens perçoivent l'environnement qui les entourent et apprennent graduellement des schémas non observés de la vie qui vont l'aider à fabriquer des règles logiques afin d'identifier les schémas appris. Le processus d'apprentissage du cerveau humain est ce que rend les hommes comme la créature la plus sophistiquée du monde vivant. Apprendre continuellement par la découverte de schémas non observés et ensuite innover sur ces schémas nous permet de nous améliorer tout au long de notre vie. Cette capacité d'apprendre et d'évoluer est liée au concept de [plasticité neuronale](https://www.simplypsychology.org/brain-plasticity.html), nous pouvons tirer quelques motivations similaires entre le processus d'apprentissage du cerveau humain et le concept de machine learning.
 
 Le [cerveau humain](https://www.livescience.com/29365-human-brain.html) perçoit des choses du monde réel, assimile les informations perçues, fait des décisions rationnelles et entreprend certaines actions selon le contexte. C'est ce que l'on appelle se comporter intelligemment. Lorsque nous programmons une reproduction du processus de ce comportement à une machine, c'est ce que l'on appelle intelligence artificielle (IA).
 
-Bien que le terme peut être confu, machine learning (ML) est un important sous-ensemble de l'intelligence artificielle. **ML se réfère à l'utilisation d'algorithmes spécialisés afin de découvrir des informations utiles et de trouver des schémas cachés depuis des données perçues pour corroborer un processus de décision rationnel**.
+Bien que le terme peut être confu, machine learning (ML) est un important sous-ensemble de l'intelligence artificielle. **ML se réfère à l'utilisation d'algorithmes spécialisés afin de découvrir des informations utiles et de trouver des schémas non observés depuis des données perçues pour corroborer un processus de décision rationnel**.
 
 ![AI, ML, deep learning, data science](../images/ai-ml-ds.png)
 
@@ -70,7 +70,7 @@ Afin d'avoir la meilleur expérience d'apprentissage, nous éviterons les comple
 
 ## Pourquoi etudier le machine learning ?
 
-Le machine learning, depuis une perspective systémique, est défini comme la création de systèmes automatiques pouvant apprendre des schémas cachés depuis des données afin d'aider à prendre des décisions intelligentes.
+Le machine learning, depuis une perspective systémique, est défini comme la création de systèmes automatiques pouvant apprendre des schémas non observés depuis des données afin d'aider à prendre des décisions intelligentes.
 
 Ce but est faiblement inspiré de la manière dont le cerveau humain apprend certaines choses depuis les données qu'il perçoit du monde extérieur.
 
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
 
 Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
 
-## [Quiz de post-conférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz postlecture](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
 
 ## Révision et auto-apprentissage
 

From bd08fb25c2e3f49a95aefabfd976ea77d44775d6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Thu, 15 Jul 2021 19:04:56 +0800
Subject: [PATCH 171/368] Add files via upload

---
 .../translations/README.zh-cn.md              | 294 ++++++++++++++++++
 1 file changed, 294 insertions(+)
 create mode 100644 4-Classification/1-Introduction/translations/README.zh-cn.md

diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
new file mode 100644
index 0000000000..d85c266cad
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -0,0 +1,294 @@
+# 对分类方法的介�
+
+在这四节课程中，你将会学习机器学习中一个基本的�点 - _分类_. 我们会在关于亚洲和�度的神奇的美食的数�集上�试使用多�分类算法。希望你有点饿了。
+
+![一个桃��](../images/pinch.png)
+
+>在学习的课程中赞�泛亚地区的美食�� 图片由 [Jen Looper](https://twitter.com/jenlooper)�供
+
+分类算法是[监�学习](https://wikipedia.org/wiki/Supervised_learning) 的一�。它与回归算法在很多方�都有相�之处。如果机器学习所有的目标都是使用数�集�预测数值或物�的�字，那么分类算法通常�以分为两类 _二元分类_ 和 _多元分类_。
+
+[![对分类算法的介�](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "对分类算法的介�")
+
+> 🎥 点击上方给的图片�以跳转到一个视频-MIT的John对分类算法的介�
+
+请记�：
+
+- **线性回归**  帮助你预测��之间的关系并对一个新的数�点会�在哪�线上�出精确的预测。因此，你�以预测 _�瓜在�月的价格和�月的价格_。
+- **逻辑回归**  帮助你�现“二元范畴�：�在当�这个价格， _这个�瓜是�是橙色_？
+
+分类方法采用多�算法�确定其他�以用�确定一个数�点的标签或类别的方法。让我们�研究一下这个数�集，看看我们能�通过观察�肴的原料�确定它的�头。
+
+## [课程�的�问题](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+
+分类是机器学习研究者和数�科学家使用的一�基本方法。从基本的二元分类（这是�是一份垃圾邮件？）到��的图片分类和使用计算机视觉的分割技术，它都是将数�分类并�出相关问题的有效工具。
+
+![二元分类 vs 多元分类](../images/binary-multiclass.png)
+
+> 需�分类算法解决的二元分类和多元分类问题的对比. 信�图由[Jen Looper](https://twitter.com/jenlooper)�供
+
+在开始清洗数��数��视化和调整数�以适应机器学习的任务�，让我们�了解一下多��用�数�分类的机器学习方法。
+
+派生自[统计数学](https://wikipedia.org/wiki/Statistical_classification)，分类算法使用�典的机器学习的一些特�，比如通过'�烟者'�'体�'和'年龄'�推断 _罹患��疾病的�能性_。作为一个与你刚刚实践过的回归算法很相似的监�学习算法，你的数�是被标记过的并且算法通过采集这些标签�进行分类和预测并进行输出。
+
+✅ 花一点时间�想象一下一个关于�肴的数�集。一个多元分类的模型应该能回答什么问题？一个二元分类的模型�应该能回答什么？如果你想确定一个给定的�肴是�会用到葫芦巴（一��物，��用�调味）该怎么�？如果你想知�给你一个装满了八角茴香�花椰�和辣根的购物袋你能��出一�代表性的�度��该怎么�？
+
+[![Crazy mystery baskets](https://img.youtube.com/vi/GuTeDbaNoEU/0.jpg)](https://youtu.be/GuTeDbaNoEU "疯狂的神秘篮�")
+
+> 🎥 点击图�观看视频。整个'Chopped'节目的��都是建立在神秘的篮�上，在这个节目中厨师必须利用�机给定的食���。��一个机器学习模型能起到��的作用
+
+## ��-分类器
+
+我们关于这个�肴数�集想��出的问题其实是一个 **多元问题**，因为我们有很多潜在的具有代表性的�肴。给定一系列食�数�，数�能够符�这些类别中的哪一类？
+
+Scikit-learn项目�供多�对数�进行分类的算法，你需�根�问题的具体类型�进行选择。在下两节课程中你会学到这些算法中的几个。
+
+## 练习 - 清洗并平衡你的数�
+
+在你开始进行这个项目�的第一个上手的任务就是清洗和 **平衡**你的数��得到更好的结果。从当�目录的根目录中的 _nodebook.ipynb_ 开始。
+
+第一个需�安装的东西是 [imblearn](https://imbalanced-learn.org/stable/)这是一个Scikit-learn项目中的一个包，它�以让你更好的平衡数� (关于这个任务你很快你就会学到更多)。
+
+1. 安装 `imblearn`, �行命令 `pip install`:
+
+    ```python
+    pip install imblearn
+    ```
+
+1. 为了导入和�视化数�你需�导入下�的这些包, 你还需�从`imblearn`导入`SMOTE`
+
+    ```python
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import matplotlib as mpl
+    import numpy as np
+    from imblearn.over_sampling import SMOTE
+    ```
+
+    现在你已�准备好导入数�了。
+
+1. 下一项任务是导入数�:
+
+    ```python
+    df  = pd.read_csv('../data/cuisines.csv')
+    ```
+
+   使用函数 `read_csv()` 会读�csv文件的内容 _cusines.csv_ 并将内容放置在 ��`df`中。
+   
+1. 检查数�的形状是�正确:
+
+    ```python
+    df.head()
+    ```
+
+   �五行输出应该是这样的:
+
+    ```output
+    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+    | 0   | 65         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 1   | 66         | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 2   | 67         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 3   | 68         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 4   | 69         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+    ```
+
+1. 调用函数 `info()` �以获得有关这个数�集的信�:
+
+    ```python
+    df.info()
+    ```
+
+    Your out resembles:
+
+    ```output
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 2448 entries, 0 to 2447
+    Columns: 385 entries, Unnamed: 0 to zucchini
+    dtypes: int64(384), object(1)
+    memory usage: 7.2+ MB
+    ```
+
+    ## 练习 - 了解这些�肴
+
+现在任务�得更有趣了，让我们�探索如何将数�分�给�个�肴
+
+1. 调用函数 `barh()`�以绘制出数�的�形图:
+
+    ```python
+    df.cuisine.value_counts().plot.barh()
+    ```
+
+    ![�肴数�分�](../images/cuisine-dist.png)
+
+    这里有有�的一些�肴，但是数�的分�是�平�的。但是你�以修正这一现象�在这样�之���微探索一下。
+
+1. 找出对于�个�肴有多少数�是有效的并将其打�出�:
+
+    ```python
+    thai_df = df[(df.cuisine == "thai")]
+    japanese_df = df[(df.cuisine == "japanese")]
+    chinese_df = df[(df.cuisine == "chinese")]
+    indian_df = df[(df.cuisine == "indian")]
+    korean_df = df[(df.cuisine == "korean")]
+    
+    print(f'thai df: {thai_df.shape}')
+    print(f'japanese df: {japanese_df.shape}')
+    print(f'chinese df: {chinese_df.shape}')
+    print(f'indian df: {indian_df.shape}')
+    print(f'korean df: {korean_df.shape}')
+    ```
+
+    输出应该是这样的 :
+
+    ```output
+    thai df: (289, 385)
+    japanese df: (320, 385)
+    chinese df: (442, 385)
+    indian df: (598, 385)
+    korean df: (799, 385)
+    ```
+## 探索有关食�的内容
+
+现在你�以在数�中探索的更深一点并了解���肴的代表性食�。你需�将��出现的�容易造�混淆的数�清�出去，那么让我们�学习解决这个问题。
+
+1. 在Python中创建一个函数 `create_ingredient()` �创建一个食�的数�帧。这个函数会去掉数�中无用的列并按食�的数�进行分类。
+
+    ```python
+    def create_ingredient_df(df):
+        ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
+        ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
+        ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+        inplace=False)
+        return ingredient_df
+    ```
+现在你�以使用这个函数�得到�想的���肴最��的10�食�。
+
+1. 调用函数 `create_ingredient()` 然�通过函数`barh()`�绘制图�:
+
+    ```python
+    thai_ingredient_df = create_ingredient_df(thai_df)
+    thai_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![thai](../images/thai.png)
+
+1. 对日本的数�进行相�的�作:
+
+    ```python
+    japanese_ingredient_df = create_ingredient_df(japanese_df)
+    japanese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![日本](../images/japanese.png)
+
+1. 现在处�中国的数�:
+
+    ```python
+    chinese_ingredient_df = create_ingredient_df(chinese_df)
+    chinese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![中国](../images/chinese.png)
+
+1. 绘制�度食�的数�:
+
+    ```python
+    indian_ingredient_df = create_ingredient_df(indian_df)
+    indian_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![�度](../images/indian.png)
+
+1. 最�，绘制韩国的食�的数�:
+
+    ```python
+    korean_ingredient_df = create_ingredient_df(korean_df)
+    korean_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![韩国](../images/korean.png)
+
+1. 现在，去除在��的�肴间最普�的容易造�混乱的食�，调用函数 `drop()`: 
+
+   大家都喜欢米饭�大蒜和生姜
+
+    ```python
+    feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)
+    labels_df = df.cuisine #.unique()
+    feature_df.head()
+    ```
+
+## 平衡数�集
+
+现在你已�清�过数�集了, 使用 [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html) - "Synthetic Minority Over-sampling Technique" - �平衡数�集。
+
+1. 调用函数 `fit_resample()`, 此方法通过�入数��生�新的样本
+
+    ```python
+    oversample = SMOTE()
+    transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
+    ```
+
+    通过对数�集的平衡，当你对数�进行分类时能够得到更好的结果。现在考虑一个二元分类的问题，如果你的数�集中的大部分数�都属于其中一个类别，那么机器学习的模型就会因为在那个类别的数�更多而判断那个类别更为常�。平衡数�能够去除�公平的数�点。
+
+1. 现在你�以查看�个食�的标签数�:
+
+    ```python
+    print(f'new label count: {transformed_label_df.value_counts()}')
+    print(f'old label count: {df.cuisine.value_counts()}')
+    ```
+
+    输出应该是这样的 :
+
+    ```output
+    new label count: korean      799
+    chinese     799
+    indian      799
+    japanese    799
+    thai        799
+    Name: cuisine, dtype: int64
+    old label count: korean      799
+    indian      598
+    chinese     442
+    japanese    320
+    thai        289
+    Name: cuisine, dtype: int64
+    ```
+
+    现在这个数�集�仅干净�平衡而且还很“美味� ! 
+
+1. 最�一步是�存你处�过�的平衡的数�（包括标签和特�），将其�存为一个�以被输出到文件中的数�帧。
+
+    ```python
+    transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
+    ```
+
+1. 你�以通过调用函数 `transformed_df.head()` 和 `transformed_df.info()`�检查一下你的数�。 接下��将数��存以供在未�的课程中使用:
+
+    ```python
+    transformed_df.head()
+    transformed_df.info()
+    transformed_df.to_csv("../data/cleaned_cuisine.csv")
+    ```
+
+    这个全新的CSV文件�以在数�根目录中被找到。
+
+---
+
+## 🚀�练习
+
+本项目的全部课程�有很多有趣的数�集。 探索一下 `data`文件夹，看看这里�有没有适�二元分类�多元分类算法的数�集，�想一下你对这些数�集有没有什么想问的问题。
+
+## [课�练习](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+
+## Review & Self Study
+
+探索一下 SMOTE的API文档。�考一下它最适�于什么样的情况�它能够解决什么样的问题。
+
+## Assignment 
+
+[探索一下分类方法](../assignment.md)
+{"mode":"full","isActive":false}
+
+

From bb1821234ebf62cb6f3b9827d5e7b0216b7b00ce Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Thu, 15 Jul 2021 21:04:43 +0800
Subject: [PATCH 172/368] Add files via upload

---
 .../1-Introduction/translations/README.zh-cn.md           | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
index d85c266cad..1dbc3598d3 100644
--- a/4-Classification/1-Introduction/translations/README.zh-cn.md
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -282,13 +282,11 @@ Scikit-learn项目�供多�对数�进行分类的算法，你需�根�
 
 ## [课�练习](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
 
-## Review & Self Study
+## 回顾 & 自学
 
 探索一下 SMOTE的API文档。�考一下它最适�于什么样的情况�它能够解决什么样的问题。
 
-## Assignment 
+## 课�作业 
 
 [探索一下分类方法](../assignment.md)
-{"mode":"full","isActive":false}
-
-
+{"mode":"full","isActive":false}
\ No newline at end of file

From be8738beb8e54799061b1cf274d89a98afd8d3a2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Thu, 15 Jul 2021 21:07:09 +0800
Subject: [PATCH 173/368] Add files via upload


From 05b028a5b2755a92c3c1f43fb7dd1be6cae01615 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=96=87=E4=BD=93=E4=B8=A4=E5=BC=80=E8=8A=B1=E7=94=9F?=
 <56857145+loap-a@users.noreply.github.com>
Date: Thu, 15 Jul 2021 21:07:43 +0800
Subject: [PATCH 174/368] Update README.zh-cn.md

---
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diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
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--- a/4-Classification/1-Introduction/translations/README.zh-cn.md
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -289,4 +289,3 @@ Scikit-learn项目�供多�对数�进行分类的算法，你需�根�
 ## 课�作业 
 
 [探索一下分类方法](../assignment.md)
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From baa5cabaae13c85ac031ba836f37c416e8b1a875 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Thu, 15 Jul 2021 15:41:26 +0200
Subject: [PATCH 175/368] Corrected to accepted words
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Postconférence
Préconférence
---
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diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index fd396d6990..08ad7a09d2 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
 
 > 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
 
-## [Quiz prélecture](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz préconférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
 
 ### Introduction
 
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
 
 Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
 
-## [Quiz postlecture](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz postconférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
 
 ## Révision et auto-apprentissage
 

From f49981f258791b6a7735f090367bae716c2bfba3 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Thu, 15 Jul 2021 15:55:38 +0200
Subject: [PATCH 176/368] Update README.fr.md

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 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 08ad7a09d2..5fae99cd4f 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
 
 > 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
 
-## [Quiz préconférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz de préconférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
 
 ### Introduction
 
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
 
 Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
 
-## [Quiz postconférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz de postconférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
 
 ## Révision et auto-apprentissage
 

From 846b62d0f7f9c5ac76ae9cf31c9c75c0e4b3426e Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Thu, 15 Jul 2021 17:41:04 +0200
Subject: [PATCH 177/368] Italian Translation - Chapter 2 complete

---
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 create mode 100644 2-Regression/4-Logistic/translations/assignment.it.md

diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md
new file mode 100644
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@@ -0,0 +1,299 @@
+# Regressione logistica per prevedere le categorie
+
+![Infografica di regressione lineare e logistica](../images/logistic-linear.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+
+## Introduzione
+
+In questa lezione finale sulla Regressione, una delle tecniche _classiche_ di base di machine learning, si darà un'occhiata alla Regressione Logistica. Si dovrebbe utilizzare questa tecnica per scoprire modelli per prevedere le categorie binarie. Questa caramella è al cioccolato o no? Questa malattia è contagiosa o no? Questo cliente sceglierà questo prodotto o no?
+
+In questa lezione, si imparerà:
+
+- Una nuova libreria per la visualizzazione dei dati
+- Tecniche per la regressione logistica
+
+✅ Con questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) si potrà approfondire la comprensione del lavoro con questo tipo di regressione
+## Prerequisito
+
+Avendo lavorato con i dati della zucca, ora si ha abbastanza familiarità con essi per rendersi conto che esiste una categoria binaria con cui è possibile lavorare: `Color` (Colore).
+
+Si costruisce un modello di regressione logistica per prevedere, date alcune variabili, di _che colore sarà probabilmente una data zucca_ (arancione 🎃 o bianca 👻).
+
+> Perché si parla di classificazione binaria in un gruppo di lezioni sulla regressione? Solo per comodità linguistica, poiché la regressione logistica è in [realtà un metodo di classificazione](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), anche se lineare. Si scopriranno altri modi per classificare i dati nel prossimo gruppo di lezioni.
+
+## Definire la domanda
+
+Allo scopo, verrà espressa come binaria: 'Arancio' o 'Non Arancio'. C'è anche una categoria "striped" (a strisce) nell'insieme di dati, ma ci sono pochi casi, quindi non verrà presa in considerazione. Comunque scompare una volta rimossi i valori null dall'insieme di dati.
+
+> 🎃 Fatto divertente, a volte le zucche bianche vengono chiamate zucche "fantasma" Non sono molto facili da intagliare, quindi non sono così popolari come quelle arancioni ma hanno un bell'aspetto!
+
+## Informazioni sulla regressione logistica
+
+La regressione logistica differisce dalla regressione lineare, che si è appresa in precedenza, in alcuni importanti modi.
+
+### Classificazione Binaria
+
+La regressione logistica non offre le stesse caratteristiche della regressione lineare. La prima offre una previsione su una categoria binaria ("arancione o non arancione") mentre la seconda è in grado di prevedere valori continui, ad esempio data l'origine di una zucca e il momento del raccolto, di _quanto aumenterà il suo prezzo_.
+
+![Modello di classificazione della zucca](../images/pumpkin-classifier.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+### Altre classificazioni:
+
+Esistono altri tipi di regressione logistica, inclusi multinomiale e ordinale:
+
+- **Multinomiale**, che implica avere più di una categoria: "arancione, bianco e a strisce".
+- **Ordinale**, che coinvolge categorie ordinate, utile se si volessero ordinare i risultati in modo logico, come le zucche che sono ordinate per un numero finito di dimensioni (mini,sm,med,lg,xl,xxl).
+
+![Regressione multinomiale contro ordinale](../images/multinomial-ordinal.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+### È ancora lineare
+
+Anche se questo tipo di Regressione riguarda le "previsioni di categoria", funziona ancora meglio quando esiste una chiara relazione lineare tra la variabile dipendente (colore) e le altre variabili indipendenti (il resto dell'insieme di dati, come il nome della città e le dimensioni) . È bene avere un'idea se c'è qualche linearità che divide queste variabili o meno.
+
+### Le variabili NON devono essere correlate
+
+Si ricorda come la regressione lineare ha funzionato meglio con più variabili correlate? La regressione logistica è l'opposto: le variabili non devono essere allineate. Funziona per questi dati che hanno correlazioni alquanto deboli.
+
+### Servono molti dati puliti
+
+La regressione logistica fornirà risultati più accurati se si utilizzano più dati; quindi si tenga a mente che, essendo l'insieme di dati sulla zucca piccolo, non è ottimale per questo compito
+
+✅ Si pensi ai tipi di dati che si prestano bene alla regressione logistica
+
+## Esercizio: riordinare i dati
+
+Innanzitutto, si puliscono un po 'i dati, eliminando i valori null e selezionando solo alcune delle colonne:
+
+1. Aggiungere il seguente codice:
+
+   ```python
+   from sklearn.preprocessing import LabelEncoder
+
+   new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']
+
+   new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+
+   new_pumpkins.dropna(inplace=True)
+
+   new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
+   ```
+
+   Si può sempre dare un'occhiata al nuovo dataframe:
+
+   ```python
+   new_pumpkins.info
+   ```
+
+### Visualizzazione - griglia affiancata
+
+A questo punto si è caricato di nuovo il [notebook iniziale](../notebook.ipynb) con i dati della zucca e lo si è pulito in modo da preservare un insieme di dati contenente alcune variabili, incluso `Color`. Si visualizza il dataframe nel notebook utilizzando una libreria diversa: [Seaborn](https://seaborn.pydata.org/index.html), che è costruita su Matplotlib, usata in precedenza.
+
+Seaborn offre alcuni modi accurati per visualizzare i dati. Ad esempio, si possono confrontare le distribuzioni dei dati per ogni punto in una griglia affiancata.
+
+1. Si crea una griglia di questo tipo istanziando `PairGrid`, usando i dati della zucca `new_pumpkins`, poi chiamando `map()`:
+
+   ```python
+   import seaborn as sns
+
+   g = sns.PairGrid(new_pumpkins)
+   g.map(sns.scatterplot)
+   ```
+
+   ![Una griglia di dati visualizzati](../images/grid.png)
+
+   Osservando i dati fianco a fianco, si può vedere come i dati di Color si riferiscono alle altre colonne.
+
+   ✅ Data questa griglia del grafico a dispersione, quali sono alcune esplorazioni interessanti che si possono immaginare?
+
+### Usare un grafico a sciame
+
+Poiché Color è una categoria binaria (arancione o no), viene chiamata "dati categoriali" e richiede "un [approccio più specializzato](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) alla visualizzazione". Esistono altri modi per visualizzare la relazione di questa categoria con altre variabili.
+
+È possibile visualizzare le variabili fianco a fianco con i grafici di Seaborn.
+
+1. Si provi un grafico a "sciame" per mostrare la distribuzione dei valori:
+
+   ```python
+   sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
+   ```
+
+   ![Uno sciame di dati visualizzati](../images/swarm.png)
+
+### Grafico violino
+
+Un grafico di tipo "violino" è utile in quanto è possibile visualizzare facilmente il modo in cui sono distribuiti i dati nelle due categorie. I grafici di tipo violino non funzionano così bene con insieme di dati più piccoli poiché la distribuzione viene visualizzata in modo più "liscio".
+
+1. Chiamare `catplot()` passando i parametri `x=Color`, `kind="violin"` :
+
+   ```python
+   sns.catplot(x="Color", y="Item Size",
+               kind="violin", data=new_pumpkins)
+   ```
+
+   ![una tabella di un grafico di tipo violino](../images/violin.png)
+
+   ✅ Provare a creare questo grafico e altri grafici Seaborn, utilizzando altre variabili.
+
+Ora che si ha un'idea della relazione tra le categorie binarie di colore e il gruppo più ampio di dimensioni, si esplora la regressione logistica per determinare il probabile colore di una data zucca.
+
+> **🧮 Mostrami la matematica**
+>
+> Si ricorda come la regressione lineare usava spesso i minimi quadrati ordinari per arrivare a un valore? La regressione logistica si basa sul concetto di "massima verosimiglianza" utilizzando [le funzioni sigmoidi](https://wikipedia.org/wiki/Sigmoid_function). Una "Funzione Sigmoide" su un grafico ha l'aspetto di una forma a "S". Prende un valore e lo mappa da qualche parte tra 0 e 1. La sua curva è anche chiamata "curva logistica". La sua formula si presenta così:
+>
+> ![funzione logistica](../images/sigmoid.png)
+>
+> dove il punto medio del sigmoide si trova nel punto 0 di x, L è il valore massimo della curva e k è la pendenza della curva. Se l'esito della funzione è maggiore di 0,5, all'etichetta in questione verrà assegnata la classe '1' della scelta binaria. In caso contrario, sarà classificata come '0'.
+
+## Costruire il modello
+
+Costruire un modello per trovare queste classificazioni binarie è sorprendentemente semplice in Scikit-learn.
+
+1. Si selezionano le variabili da utilizzare nel modello di classificazione e si dividono gli insiemi di training e test chiamando `train_test_split()`:
+
+   ```python
+   from sklearn.model_selection import train_test_split
+
+   Selected_features = ['Origin','Item Size','Variety','City Name','Package']
+
+   X = new_pumpkins[Selected_features]
+   y = new_pumpkins['Color']
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+   ```
+
+1. Ora si può addestrare il modello, chiamando `fit()` con i dati di addestramento e stamparne il risultato:
+
+   ```python
+   from sklearn.model_selection import train_test_split
+   from sklearn.metrics import accuracy_score, classification_report
+   from sklearn.linear_model import LogisticRegression
+
+   model = LogisticRegression()
+   model.fit(X_train, y_train)
+   predictions = model.predict(X_test)
+
+   print(classification_report(y_test, predictions))
+   print('Predicted labels: ', predictions)
+   print('Accuracy: ', accuracy_score(y_test, predictions))
+   ```
+
+   Si dia un'occhiata al tabellone segnapunti del modello. Non è male, considerando che si hanno solo circa 1000 righe di dati:
+
+   ```output
+                      precision    recall  f1-score   support
+
+              0       0.85      0.95      0.90       166
+              1       0.38      0.15      0.22        33
+
+       accuracy                           0.82       199
+      macro avg       0.62      0.55      0.56       199
+   weighted avg       0.77      0.82      0.78       199
+
+   Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+    0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+    1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
+    0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
+    0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+    0 0 0 1 0 1 0 0 1 0 0 0 1 0]
+   ```
+
+## Migliore comprensione tramite una matrice di confusione
+
+Sebbene si possano ottenere [i termini](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) del rapporto dei punteggi stampando gli elementi di cui sopra, si potrebbe essere in grado di comprendere più facilmente il modello utilizzando una [matrice di confusione](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix) che aiuti a capire come lo stesso sta funzionando.
+
+> 🎓 Una '[matrice di confusione](https://it.wikipedia.org/wiki/Matrice_di_confusione)' (o 'matrice di errore') è una tabella che esprime i veri contro i falsi positivi e negativi del modello, misurando così l'accuratezza delle previsioni.
+
+1. Per utilizzare una metrica di confusione, si `chiama confusion_matrix()`:
+
+   ```python
+   from sklearn.metrics import confusion_matrix
+   confusion_matrix(y_test, predictions)
+   ```
+
+   Si dia un'occhiata alla matrice di confusione del modello:
+
+   ```output
+   array([[162,   4],
+          [ 33,   0]])
+   ```
+
+Cosa sta succedendo qui? Si supponga che al modello venga chiesto di classificare gli elementi tra due categorie binarie, la categoria "zucca" e la categoria "non una zucca".
+
+- Se il modello prevede qualcosa come una zucca e appartiene alla categoria 'zucca' in realtà lo si chiama un vero positivo, mostrato dal numero in alto a sinistra.
+- Se il modello prevede qualcosa come non una zucca e appartiene alla categoria 'zucca' in realtà si chiama falso positivo, mostrato dal numero in alto a destra.
+- Se il modello prevede qualcosa come una zucca e appartiene alla categoria 'non-una-zucca' in realtà si chiama falso negativo, mostrato dal numero in basso a sinistra.
+- Se il modello prevede qualcosa come non una zucca e appartiene alla categoria 'non-una-zucca' in realtà lo si chiama un vero negativo, mostrato dal numero in basso a destra.
+
+![Matrice di Confusione](../images/confusion-matrix.png)
+
+> Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+Come si sarà intuito, è preferibile avere un numero maggiore di veri positivi e veri negativi e un numero inferiore di falsi positivi e falsi negativi, il che implica che il modello funziona meglio.
+
+✅ Domanda: Secondo la matrice di confusione, come si è comportato il modello? Risposta: Non male; ci sono un buon numero di veri positivi ma anche diversi falsi negativi.
+
+I termini visti in precedenza vengono rivisitati con l'aiuto della mappatura della matrice di confusione di TP/TN e FP/FN:
+
+🎓 Precisione: TP/(TP + FN) La frazione di istanze rilevanti tra le istanze recuperate (ad es. quali etichette erano ben etichettate)
+
+🎓 Richiamo: TP/(TP + FP) La frazione di istanze rilevanti che sono state recuperate, ben etichettate o meno
+
+🎓 f1-score: (2 * precisione * richiamo)/(precisione + richiamo) Una media ponderata della precisione e del richiamo, dove il migliore è 1 e il peggiore è 0
+
+🎓 Supporto: il numero di occorrenze di ciascuna etichetta recuperata
+
+🎓 Accuratezza: (TP + TN)/(TP + TN + FP + FN) La percentuale di etichette prevista accuratamente per un campione.
+
+🎓 Macro Media: il calcolo delle metriche medie non ponderate per ciascuna etichetta, senza tener conto dello squilibrio dell'etichetta.
+
+🎓 Media ponderata: il calcolo delle metriche medie per ogni etichetta, tenendo conto dello squilibrio dell'etichetta pesandole in base al loro supporto (il numero di istanze vere per ciascuna etichetta).
+
+✅ Si riesce a pensare a quale metrica si dovrebbe guardare se si vuole che il modello riduca il numero di falsi negativi?
+
+## Visualizzare la curva ROC di questo modello
+
+Questo non è un cattivo modello; la sua precisione è nell'intervallo dell'80%, quindi idealmente si potrebbe usare per prevedere il colore di una zucca dato un insieme di variabili.
+
+Si rende un'altra visualizzazione per vedere il cosiddetto punteggio 'ROC':
+
+```python
+from sklearn.metrics import roc_curve, roc_auc_score
+
+y_scores = model.predict_proba(X_test)
+# calculate ROC curve
+fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
+sns.lineplot([0, 1], [0, 1])
+sns.lineplot(fpr, tpr)
+```
+Usando di nuovo Seaborn, si traccia la [Caratteristica Operativa di Ricezione](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) o il ROC del modello. Le curve ROC vengono spesso utilizzate per ottenere una visualizzazione dell'output di un classificatore in termini di veri e falsi positivi. "Le curve ROC in genere presentano un tasso di veri positivi sull'asse Y e un tasso di falsi positivi sull'asse X". Pertanto, la ripidità della curva e lo spazio tra la linea del punto medio e la curva contano: si vuole una curva che si sposti rapidamente verso l'alto e oltre la linea. In questo caso, ci sono falsi positivi con cui iniziare, quindi la linea si dirige correttamente:
+
+![ROC](../images/ROC.png)
+
+Infine, si usa l'[`API roc_auc_score`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) di Scikit-learn per calcolare l'effettiva "Area sotto la curva" (AUC):
+
+```python
+auc = roc_auc_score(y_test,y_scores[:,1])
+print(auc)
+```
+Il risultato è `0.6976998904709748`. Dato che l'AUC varia da 0 a 1, si desidera un punteggio elevato, poiché un modello corretto al 100% nelle sue previsioni avrà un AUC di 1; in questo caso, il modello è _abbastanza buono_.
+
+Nelle lezioni future sulle classificazioni si imparerà come eseguire l'iterazione per migliorare i punteggi del  modello. Ma per ora, congratulazioni! Si sono completate queste lezioni di regressione!
+
+---
+## 🚀 Sfida
+
+C'è molto altro da svelare riguardo alla regressione logistica! Ma il modo migliore per imparare è sperimentare. Trovare un insieme di dati che si presti a questo tipo di analisi e costruire un modello con esso. Cosa si è appreso? suggerimento: provare [Kaggle](https://kaggle.com) per ottenere insiemi di dati interessanti.
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+
+## Revisione e Auto Apprendimento
+
+Leggere le prime pagine di [questo articolo da Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) su alcuni usi pratici della regressione logistica. Si pensi alle attività più adatte per l'uno o l'altro tipo di attività di regressione studiate fino a questo punto. Cosa funzionerebbe meglio?
+
+## Compito
+
+[Ritentare questa regressione](assignment.it.md)
diff --git a/2-Regression/4-Logistic/translations/assignment.it.md b/2-Regression/4-Logistic/translations/assignment.it.md
new file mode 100644
index 0000000000..7b9b201619
--- /dev/null
+++ b/2-Regression/4-Logistic/translations/assignment.it.md
@@ -0,0 +1,10 @@
+# Riprovare un po' di Regressione
+
+## Istruzioni
+
+Nella lezione è stato usato un sottoinsieme dei dati della zucca. Ora si torna ai dati originali e si prova a usarli tutti, puliti e standardizzati, per costruire un modello di regressione logistica.
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ----------------------------------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------- |
+|          | Un notebook viene presentato con un modello ben spiegato con buone prestazioni | Un notebook viene presentato con un modello dalle prestazioni minime | Un notebook viene presentato con un modello con scarse  o nessuna prestazione |

From 93bcc7ff1b5416161067655086fdb7710405c30e Mon Sep 17 00:00:00 2001
From: JudyZhangYifan <judyzhangyifan@gmail.com>
Date: Thu, 15 Jul 2021 17:54:36 -0400
Subject: [PATCH 178/368] 8-Reinforcement intro README translation

---
 8-Reinforcement/translations/README.zh-cn.md | 53 ++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 8-Reinforcement/translations/README.zh-cn.md

diff --git a/8-Reinforcement/translations/README.zh-cn.md b/8-Reinforcement/translations/README.zh-cn.md
new file mode 100644
index 0000000000..c033d1db06
--- /dev/null
+++ b/8-Reinforcement/translations/README.zh-cn.md
@@ -0,0 +1,53 @@
+# 强化学习介�
+
+强化学习（Reinforcement learning，RL）被视为基础机器学习除监�学习以�无监�学习之外的范�之一。强化学习是完全关于决策的，它�以�供正确的决策或者至少能从他们中学习。
+
+想象你现在有一个例如股票市场的模拟环境。如果你施加了一�给定的规章制度的�，将会�生什么呢？这�规章制度会带�积�还是消�的影�呢？如果产生了负�影�的�，那么你就需�接�这� _负强化_ ，从中学习并改�方针。如果产生了正�的�果，那么你就需�基于这� _正强化_ 越�越好。
+
+![彼得与狼](../images/peter.png)
+
+> 彼得和他的朋�们需�逃离饥饿的狼�（图片�自：[Jen Looper](https://twitter.com/jenlooper)）
+
+## 区域主题：彼得与狼（俄罗斯）
+
+[彼得与狼](https://zh.wikipedia.org/wiki/%E5%BD%BC%E5%BE%97%E5%92%8C%E7%8B%BC) 是���作曲家[普罗科�耶夫](https://zh.wikipedia.org/wiki/%E8%B0%A2%E5%B0%94%E7%9B%96%C2%B7%E6%99%AE%E7%BD%97%E7%A7%91%E8%8F%B2%E8%80%B6%E5%A4%AB)写的一部交�童�。它讲述的是少先队员彼得勇敢地离家到森林空地去追�狼的故事。在本节中，我们将训练�以帮助彼得的机器学习算法：
+
+- **探索** 周边区域并构建一张最佳的导航地图
+- **学习** 如何使用滑�并在上���平衡，以便更加快速地移动。
+
+[![彼得与狼](https://img.youtube.com/vi/Fmi5zHg4QSM/0.jpg)](https://www.youtube.com/watch?v=Fmi5zHg4QSM)
+
+> 🎥 点击上图��普罗科�耶夫的《彼得与狼》
+
+## 强化学习
+
+在之�的章节中，你已�看到了两个机器学习问题的例�：
+
+- **有监�的**——我们有数�集�以为我们想�解决的问题�出示例解决方案。[分类模型](../../4-Classification/README.md)与[回归模型](../../2-Regression/translations/README.zh-cn.md)都是有监�的任务。
+- **无监�的**——我们的训练数�没有标签。无监�学习的一个主�例�就是[�类分�](../../5-Clustering/README.md)。
+
+在本节中，我们会�你介�一�新的学习问题。这�问题�需�有标签的训练数�，它们有以下几类问题：
+
+- **[�监�学习](https://wikipedia.org/wiki/Semi-supervised_learning)**——我们有很多没有标签的数��以用于预先训练模型。
+- **[强化学习](https://wikipedia.org/wiki/Reinforcement_learning)**——一个智能体（agent）在�些模拟环境中进行实验并以此学习如何表现。
+
+### 例� - 电脑游�
+
+�设你想�教会电脑如何玩一个例如国际象棋或者[超级马里奥](https://wikipedia.org/wiki/Super_Mario)的游�。对于电脑�说，我们需�让它预测在�个游�状�下它的动作�能使它�功地玩游�。虽然这看上去�是个分类问题，但是事实并�如此——因为我们没有包�（游�）状�和相应动作的数�集。虽然我们�能有一些现有的国际象棋比赛数�或者玩家玩超级马里奥的记录，但是那些数�很�能无法包�足够多的潜在（游�）状�。
+
+**强化学习** (RL) �是寻找现有的游�数�，而是基于一�*想让电脑玩* 多次并观察结果的想法。因此，我们需��以下两件事�应用强化学习：
+
+- **环境** 和 **模拟器** ——�以让我们多次玩游�。这个模拟器将定义所有游�的规则��能的状�以�动作。
+
+- **奖励函数** ——会告诉我们在�个动作或游�中的表现如何。
+
+其他机器学习和强化学习（RL）的主�差别就是在RL中我们通常无法在完�游�之�知�我们是赢还是输。因此，我们无法评价游�中的�一个特定动作是好是�——我们�会在游�结�时�得到奖励。我们的目标是设计一��以在�确定�件下帮我们训练模型的算法。接下�我们将�学习一��**Q-learning**的RL算法。
+
+## 课程
+
+1. [强化学习与Q-Learning介�](../1-QLearning/README.md)
+2. [使用Gym模拟环境](../2-Gym/README.md)
+
+## Credits
+
+"强化学习介�"由[Dmitry Soshnikov](http://soshnikov.com)撰写 ♥� 

From 034b28edb4c39e4d56ba5a8377988f2c448c227d Mon Sep 17 00:00:00 2001
From: unknown <shengjie.peng@mobilemd.cn>
Date: Fri, 16 Jul 2021 11:24:28 +0800
Subject: [PATCH 179/368] Translated 1-intro-to-ML assignment.md into
 Simplified Chinese

---
 .../1-intro-to-ML/translations/assignment.zh-cn.md       | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md

diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..fd59f6919a
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.zh-cn.md
@@ -0,0 +1,9 @@
+# �动和�行
+
+## 说明
+
+在这个�评分的作业中，你应该温习一下 Python，将 Python 环境能够�行起�，并且�以�行 notebooks。
+
+学习这个 [Python 学习路径](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)，然�通过这些介�性的视频将你的系统环境设置好：
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6

From 89ea1f05fa130042de972a6d4cfac97a7fca12b0 Mon Sep 17 00:00:00 2001
From: unknown <shengjie.peng@mobilemd.cn>
Date: Fri, 16 Jul 2021 14:05:43 +0800
Subject: [PATCH 180/368] Translated 2-history-of-ML assignment.md into
 Simplified Chinese

---
 .../2-history-of-ML/translations/assignment.zh-cn.md  | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md

diff --git a/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md b/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..adf3ee15ae
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# 建立一个时间轴
+
+## 说明
+
+使用这个 [仓库](https://github.com/Digital-Humanities-Toolkit/timeline-builder)，创建一个关于算法�数学�统计学�人工智能�机器学习的�个方�或者�以综�多个以上学科�讲。你�以��介��个人，�个想法，或者一个�久�衰的�想。请确�添加了多媒体元素在你的时间线中。
+
+## 评判标准
+
+| 标准         | 优秀                               | 中规中矩               | �需努力                                    |
+| ------------ | ---------------------------------- | ---------------------- | ------------------------------------------ |
+|              | 有一个用 GitHub page 展示的 timeline | 代�还�完整并且没有部署 | 时间线�完整，没有�过充分的研究，并且没有部署 |

From 8f99e59b7722930941719b50905f854483b87fe7 Mon Sep 17 00:00:00 2001
From: unknown <shengjie.peng@mobilemd.cn>
Date: Fri, 16 Jul 2021 15:16:20 +0800
Subject: [PATCH 181/368] Translated 3-fairness assignment.md into Simplified
 Chinese

---
 .../3-fairness/translations/assignment.zh-cn.md       | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/3-fairness/translations/assignment.zh-cn.md

diff --git a/1-Introduction/3-fairness/translations/assignment.zh-cn.md b/1-Introduction/3-fairness/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..a812419948
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# 探索 Fairlearn
+
+## 说明
+
+在这节课中，你了解了 Fairlearn，一个“开�的，社区驱动的项目，旨在帮助数�科学家们�高人工智能系统的公平性�。在这项作业中，探索 Fairlearn [笔记本](https://fairlearn.org/v0.6.2/auto_examples/index.html)中的一个例�，之�你�以用论文或者 ppt 的形��述你学习�的�现。
+
+## 评判标准
+
+| 标准 | 优秀 | 中规中矩 | �需努力 |
+| -------- | --------- | -------- | ----------------- |
+|          |  �交了一篇论文或者ppt 关于讨论 Fairlearn 系统�挑选�行的例��和�行这个例��所得出�的心得结论        |   �交了一篇没有结论的论文       |  没有�交论文                 |

From 01c53fb14613d1fcb248e0bd4500a4674ea95fea Mon Sep 17 00:00:00 2001
From: unknown <shengjie.peng@mobilemd.cn>
Date: Fri, 16 Jul 2021 17:28:11 +0800
Subject: [PATCH 182/368] Translated 4-techniques-of-ML assignment.md into
 Simplified Chinese

---
 .../translations/assignment.zh-cn.md                  | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/4-techniques-of-ML/translations/assignment.zh-cn.md

diff --git a/1-Introduction/4-techniques-of-ML/translations/assignment.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..ba28b55497
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# 采访一�数�科学家
+
+## 说明
+
+在你的公��你所在的社群�或者在你的朋�和�学中，找到一�从事数�科学专业工作的人，与他或她交�一下。写一篇关于他们工作日常的�短文（500字左�）。他们是专家，还是说他们是“全栈�开�者？
+
+## 评判标准
+
+| 标准 | 优秀                                                                            | 中规中矩                                                           | �需努力     |
+| -------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------ | --------------------- |
+|          | �交一篇清晰�述了�业属性且字数符�规范的word文档 | �交的文档�业属性�述得�清晰或者字数��规范 | 啥都没有交 |

From 39bf19bd05443b2b56111eb20de6ef02937d114d Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Fri, 16 Jul 2021 11:39:42 +0200
Subject: [PATCH 183/368] Modifying Quiz translation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

pre-lecture: Quiz préalable
post-lecture: Quiz de validation des connaissances
---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 5fae99cd4f..1e27ca3271 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
 
 > 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
 
-## [Quiz de préconférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
 
 ### Introduction
 
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
 
 Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
 
-## [Quiz de postconférence](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
 
 ## Révision et auto-apprentissage
 

From d5ef7e07218b043b967282b0656767f61684b771 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Fri, 16 Jul 2021 11:48:42 +0200
Subject: [PATCH 184/368] Create assignment.fr.md

---
 .../1-intro-to-ML/translations/assignment.fr.md        | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 1-Introduction/1-intro-to-ML/translations/assignment.fr.md

diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.fr.md b/1-Introduction/1-intro-to-ML/translations/assignment.fr.md
new file mode 100644
index 0000000000..0d703d26c3
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.fr.md
@@ -0,0 +1,10 @@
+# Être opérationnel
+
+
+## Instructions
+
+Dans ce devoir non noté, vous devez vous familiariser avec Python et rendre votre environnement opérationnel et capable d'exécuter des notebook.
+
+Suivez ce [parcours d'apprentissage Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), puis configurez votre système en parcourant ces vidéos introductives :
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6

From 2a0c80f8c0d687016e8a737a537ae935e36723b4 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Fri, 16 Jul 2021 11:49:44 +0200
Subject: [PATCH 185/368] Changed url to assignment translation

---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 1e27ca3271..a178790cff 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -106,4 +106,4 @@ Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de M
 
 ## Devoir
 
-[Être opérationnel](../assignment.md)
+[Être opérationnel](assignment.fr.md)

From c3074d622c0fe323c9f3d94a58c345f684431246 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Fri, 16 Jul 2021 14:39:00 +0200
Subject: [PATCH 186/368] Update README.fr.md

---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index a178790cff..e762c7f6dc 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -8,7 +8,7 @@
 
 ### Introduction
 
-Bienvenue à ce cours sur le machine learning classique pour débutant ! Que vous soyez complètement nouveau sur ce sujet ou que vous soyez un professonnel du ML expérimenté cherchant à peaufiner vos connaissances, nous sommes heureux de vous avoir avec nous ! Nous voulons créer un tremplin chaleureux pour vos études en ML et serions ravis d'évaluer, de répondre et d'apprendre de vos retours d'[expériences](https://github.com/microsoft/ML-For-Beginners/discussions).
+Bienvenue à ce cours sur le machine learning classique pour débutant ! Que vous soyez complètement nouveau sur ce sujet ou que vous soyez un professionnel du ML expérimenté cherchant à peaufiner vos connaissances, nous sommes heureux de vous avoir avec nous ! Nous voulons créer un tremplin chaleureux pour vos études en ML et serions ravis d'évaluer, de répondre et d'apprendre de vos retours d'[expériences](https://github.com/microsoft/ML-For-Beginners/discussions).
 
 [![Introduction au ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
 
@@ -45,7 +45,7 @@ Bien que le terme peut être confu, machine learning (ML) est un important sous-
 
 ## Ce que vous allez apprendre dans ce cours
 
-Dans ce cours, nous allons nous concentrer sur les concepts clés du machine learning qu'un débutant se doit de connaître. Nous parlerons ce que l'on appelle le 'machine learning classique' en utilisant principalement Scikit-learn, une excellente librairie que beaucoup d'étudiants utilisent afin d'apprendre les bases. Afin de comprendre les concepts plus larges de l'intelligence artificielle ou du deep learning, une profonde connaissance en machine learning est indispensable, et c'est ce que nous aimerions fournir ici.
+Dans ce cours, nous allons nous concentrer sur les concepts clés du machine learning qu'un débutant se doit de connaître. Nous parlerons de ce que l'on appelle le 'machine learning classique' en utilisant principalement Scikit-learn, une excellente librairie que beaucoup d'étudiants utilisent afin d'apprendre les bases. Afin de comprendre les concepts plus larges de l'intelligence artificielle ou du deep learning, une profonde connaissance en machine learning est indispensable, et c'est ce que nous aimerions fournir ici.
 
 Dans ce cours, vous allez apprendre :
 

From 0ea938006f66fa8a4453342f765f41b82f923a8b Mon Sep 17 00:00:00 2001
From: manusquall <manusquall@gmail.com>
Date: Fri, 16 Jul 2021 12:55:26 +0000
Subject: [PATCH 187/368] add vscode option folder to gitignore, add fr option
 to quiz-app vue

---
 .gitignore                                |    2 +
 quiz-app/package-lock.json                |  170 +-
 quiz-app/src/App.vue                      |    1 +
 quiz-app/src/assets/translations/fr.json  | 2811 +++++++++++++++++++++
 quiz-app/src/assets/translations/index.js |    2 +
 5 files changed, 2895 insertions(+), 91 deletions(-)
 create mode 100644 quiz-app/src/assets/translations/fr.json

diff --git a/.gitignore b/.gitignore
index a80a15e326..51f47a5aa3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -33,6 +33,8 @@ bld/
 
 # Visual Studio 2015/2017 cache/options directory
 .vs/
+# Visual Studio Code cache/options directory
+.vscode/
 # Uncomment if you have tasks that create the project's static files in wwwroot
 #wwwroot/
 
diff --git a/quiz-app/package-lock.json b/quiz-app/package-lock.json
index e9aebee3c4..8f51a0baaf 100644
--- a/quiz-app/package-lock.json
+++ b/quiz-app/package-lock.json
@@ -1087,16 +1087,6 @@
         "postcss": "^7.0.0"
       }
     },
-    "@kazupon/vue-i18n-loader": {
-      "version": "0.5.0",
-      "resolved": "https://registry.npmjs.org/@kazupon/vue-i18n-loader/-/vue-i18n-loader-0.5.0.tgz",
-      "integrity": "sha512-Tp2mXKemf9/RBhI9CW14JjR9oKjL2KH7tV6S0eKEjIBuQBAOFNuPJu3ouacmz9hgoXbNp+nusw3MVQmxZWFR9g==",
-      "dev": true,
-      "requires": {
-        "js-yaml": "^3.13.1",
-        "json5": "^2.1.1"
-      }
-    },
     "@mrmlnc/readdir-enhanced": {
       "version": "2.2.1",
       "resolved": "https://registry.npmjs.org/@mrmlnc/readdir-enhanced/-/readdir-enhanced-2.2.1.tgz",
@@ -1720,6 +1710,16 @@
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           "dev": true
         },
+        "ansi-styles": {
+          "version": "4.3.0",
+          "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-4.3.0.tgz",
+          "integrity": "sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "color-convert": "^2.0.1"
+          }
+        },
         "cacache": {
           "version": "13.0.1",
           "resolved": "https://registry.npmjs.org/cacache/-/cacache-13.0.1.tgz",
@@ -1746,6 +1746,53 @@
             "unique-filename": "^1.1.1"
           }
         },
+        "chalk": {
+          "version": "4.1.1",
+          "resolved": "https://registry.npmjs.org/chalk/-/chalk-4.1.1.tgz",
+          "integrity": "sha512-diHzdDKxcU+bAsUboHLPEDQiw0qEe0qd7SYUn3HgcFlWgbDcfLGswOHYeGrHKzG9z6UYf01d9VFMfZxPM1xZSg==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "ansi-styles": "^4.1.0",
+            "supports-color": "^7.1.0"
+          }
+        },
+        "color-convert": {
+          "version": "2.0.1",
+          "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-2.0.1.tgz",
+          "integrity": "sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "color-name": "~1.1.4"
+          }
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+        "color-name": {
+          "version": "1.1.4",
+          "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz",
+          "integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==",
+          "dev": true,
+          "optional": true
+        },
+        "has-flag": {
+          "version": "4.0.0",
+          "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-4.0.0.tgz",
+          "integrity": "sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==",
+          "dev": true,
+          "optional": true
+        },
+        "loader-utils": {
+          "version": "2.0.0",
+          "resolved": "https://registry.npmjs.org/loader-utils/-/loader-utils-2.0.0.tgz",
+          "integrity": "sha512-rP4F0h2RaWSvPEkD7BLDFQnvSf+nK+wr3ESUjNTyAGobqrijmW92zc+SO6d4p4B1wh7+B/Jg1mkQe5NYUEHtHQ==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "big.js": "^5.2.2",
+            "emojis-list": "^3.0.0",
+            "json5": "^2.1.2"
+          }
+        },
         "source-map": {
           "version": "0.6.1",
           "resolved": "https://registry.npmjs.org/source-map/-/source-map-0.6.1.tgz",
@@ -1762,6 +1809,16 @@
             "minipass": "^3.1.1"
           }
         },
+        "supports-color": {
+          "version": "7.2.0",
+          "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-7.2.0.tgz",
+          "integrity": "sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "has-flag": "^4.0.0"
+          }
+        },
         "terser-webpack-plugin": {
           "version": "2.3.8",
           "resolved": "https://registry.npmjs.org/terser-webpack-plugin/-/terser-webpack-plugin-2.3.8.tgz",
@@ -1778,6 +1835,18 @@
             "terser": "^4.6.12",
             "webpack-sources": "^1.4.3"
           }
+        },
+        "vue-loader-v16": {
+          "version": "npm:vue-loader@16.3.0",
+          "resolved": "https://registry.npmjs.org/vue-loader/-/vue-loader-16.3.0.tgz",
+          "integrity": "sha512-UDgni/tUVSdwHuQo+vuBmEgamWx88SuSlEb5fgdvHrlJSPB9qMBRF6W7bfPWSqDns425Gt1wxAUif+f+h/rWjg==",
+          "dev": true,
+          "optional": true,
+          "requires": {
+            "chalk": "^4.1.0",
+            "hash-sum": "^2.0.0",
+            "loader-utils": "^2.0.0"
+          }
         }
       }
     },
@@ -10953,87 +11022,6 @@
         }
       }
     },
-    "vue-loader-v16": {
-      "version": "npm:vue-loader@16.1.2",
-      "resolved": "https://registry.npmjs.org/vue-loader/-/vue-loader-16.1.2.tgz",
-      "integrity": "sha512-8QTxh+Fd+HB6fiL52iEVLKqE9N1JSlMXLR92Ijm6g8PZrwIxckgpqjPDWRP5TWxdiPaHR+alUWsnu1ShQOwt+Q==",
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-        "hash-sum": "^2.0.0",
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-        "ansi-styles": {
-          "version": "4.3.0",
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       "version": "3.4.9",
       "resolved": "https://registry.npmjs.org/vue-router/-/vue-router-3.4.9.tgz",
diff --git a/quiz-app/src/App.vue b/quiz-app/src/App.vue
index 78482d496f..6baabd0ca0 100644
--- a/quiz-app/src/App.vue
+++ b/quiz-app/src/App.vue
@@ -6,6 +6,7 @@
       <select v-model="locale">
         <option>en</option>
         <option>tr</option>
+        <option>fr</option>
       </select>
     </nav>
     <div id="app">
diff --git a/quiz-app/src/assets/translations/fr.json b/quiz-app/src/assets/translations/fr.json
new file mode 100644
index 0000000000..ec8111c061
--- /dev/null
+++ b/quiz-app/src/assets/translations/fr.json
@@ -0,0 +1,2811 @@
+[
+							{
+    "title": "Machine Learning pour les débutants: quiz",
+    "complete": "Félicitations, vous avez terminé le quiz!",
+    "error": "Désolé, essayez à nouveau",
+    "quizzes": [
+							{
+            "id": 1,
+            "title": "Introduction au machine learning: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Les applications de machine learning sont toutes autour de nous",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle est la différence technique entre le ml classique et le deep learning?",
+                    "answerOptions": [
+							{
+							"answerText": "ML classique a été inventé en premier",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'utilisation de réseaux de neurones",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Le deep learning est utilisé dans les robots",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pourquoi une entreprise pourrait-elle vouloir utiliser des stratégies ML?",
+                    "answerOptions": [
+							{
+							"answerText": "Pour automatiser la résolution de problèmes multidimensionnels",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Pour personnaliser une expérience de magasinage basée sur le type de client",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 2,
+            "title": "Introduction au machine learning: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Les algorithmes de machine learning sont destinés à simuler",
+                    "answerOptions": [
+							{
+							"answerText": "Des machines intelligentes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Le cerveau humain",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Des orangutans",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce qu'un exemple de technique classique de ML?",
+                    "answerOptions": [
+							{
+							"answerText": "Le traitement des langues naturelles",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Le deep learning",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Des neural networks",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pourquoi tout le monde devrait-il apprendre les bases du ML?",
+                    "answerOptions": [
+							{
+							"answerText": "L'apprentissage ml est amusant et accessible à tout le monde",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les stratégies ML sont utilisées dans de nombreuses industries et domaines",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 3,
+            "title": "Historique du machine learning: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Quand approximativement le terme 'intelligence artificielle' a-t-il été inventé ?",
+                    "answerOptions": [
+							{
+							"answerText": "1980s",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "années 1950",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "années 1930",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qui était l'un des premiers pionniers du machine learning?",
+                    "answerOptions": [
+							{
+							"answerText": "Alan Turing",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bill Gates",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Shakey the Robot",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle est l'une des raisons pour lesquelles l'avancement de l'AI a ralenti dans les années 1970?",
+                    "answerOptions": [
+							{
+							"answerText": "Puissance de calcul limitée",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Pas assez d'ingénieurs qualifiés",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Conflits entre pays",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 4,
+            "title": "Historique du machine learning: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce qu'un exemple de système d'IA \" Scruffy \" AI?",
+                    "answerOptions": [
+							{
+							"answerText": "ELIZA",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "HACKML",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "SSYSTEM",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est l'exemple d'une technologie qui a été développée pendant les « années d'or » ?",
+                    "answerOptions": [
+							{
+							"answerText": "Blocks World",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Jibo",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Robot Dogs",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel événement était fondé sur la création et l'expansion du domaine de l'intelligence artificielle?",
+                    "answerOptions": [
+							{
+							"answerText": "Turing Test",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Projet de recherche d'été de Dartmouth",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "AI Winter",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 5,
+            "title": "L'équité et machine learning: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "L'injustice dans le machine learning peut arriver",
+                    "answerOptions": [
+							{
+							"answerText": "intentionnellement",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Indormalement",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le terme \" injustice \" en ml connotes:",
+                    "answerOptions": [
+							{
+							"answerText": "nuit à un groupe de personnes",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Dommage à une personne",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "nuit à la majorité des gens",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Les cinq principaux types de préjudices incluent",
+                    "answerOptions": [
+							{
+							"answerText": "Allocation, qualité de service, stéréotypage, dénigration et sous-représentation",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Elocation, qualité de service, stéréotypage, dénigration et sous-représentation",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Allocation, qualité de service, stéréophonie, dénigration et sous-représentation",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 6,
+            "title": "Equité et machine learning: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "L'injustice dans un modèle peut être causée par",
+                    "answerOptions": [
+							{
+							"answerText": "dépassement de données historiques",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Sous-solliance sur les données historiques",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Trop d'alignement sur les données historiques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pour atténuer l'injustice, tu peux",
+                    "answerOptions": [
+							{
+							"answerText": "Identifier les préjudices et les groupes affectés",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Définir les métriques d'équité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "à la fois ce qui précède",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "FairLearn est un paquet qui peut",
+                    "answerOptions": [
+							{
+							"answerText": "Comparez plusieurs modèles en utilisant des métriques d'équité et de performance",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Choisissez le meilleur modèle pour vos besoins",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Aidez-vous à décider de ce qui est juste et ce qui n'est pas",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 7,
+            "title": "Outils et techniques: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Lors de la construction d'un modèle, vous devriez:",
+                    "answerOptions": [
+							{
+							"answerText": "Préparez vos données, puis formez votre modèle",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Choisissez une méthode de formation, puis préparez vos données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Tune Paramètres, puis formez votre modèle",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Vos données ___ vont avoir une incidence sur la qualité de votre modèle ML",
+                    "answerOptions": [
+							{
+							"answerText": "Quantité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Forme",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une variable de fonctionnalité est la suivante:",
+                    "answerOptions": [
+							{
+							"answerText": "une qualité de vos données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une propriété mesurable de vos données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Une ligne de vos données",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 8,
+            "title": "Outils et techniques: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Vous devez visualiser vos données car",
+                    "answerOptions": [
+							{
+							"answerText": "Vous pouvez découvrir des valeurs aberrantes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Vous pouvez découvrir une cause potentielle de biais",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "tous les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Sélectionnez vos données en:",
+                    "answerOptions": [
+							{
+							"answerText": "Entraînement et ensembles de Turing",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Entraînement et ensembles de test",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Ensembles de validation et d'évaluation",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une commande commune de démarrer le processus de formation dans diverses bibliothèques ML est la suivante:",
+                    "answerOptions": [
+							{
+							"answerText": "Model.travel",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Model.train",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Model.fit",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 9,
+            "title": "Introduction à la régression: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Laquelle de ces variables est une variable numérique?",
+                    "answerOptions": [
+							{
+							"answerText": "Hauteur",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Genre",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Couleur des cheveux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Laquelle de ces variables est une variable catégorique?",
+                    "answerOptions": [
+							{
+							"answerText": "rythme cardiaque",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Type de sang",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Poids",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Lequel de ces problèmes est un problème basé sur l'analyse de régression?",
+                    "answerOptions": [
+							{
+							"answerText": "Prédire les marques d'examen final d'un étudiant",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Prédire le type de sang d'une personne",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Prédire si un email est spam ou non",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 10,
+            "title": "Introduction à la régression: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Si la précision de la formation du modèle d'apprentissage de votre machine est de 95% et que la précision des tests est de 30%, quel type de condition est appelé?",
+                    "answerOptions": [
+							{
+							"answerText": "Surface",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "sous-facture",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Double ajustement",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le processus d'identification des fonctionnalités significatives d'un ensemble de fonctionnalités est appelé:",
+                    "answerOptions": [
+							{
+							"answerText": "Extraction de fonctionnalités",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Réduction de la dimensionnalité de fonctionnalité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sélection de fonctionnalités",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le processus de division d'un jeu de données dans un certain rapport d'entraînement et de test de jeu de données à l'aide de la méthode / la fonction Train_Test_split () '' Train_Test_Split () 'est appelée:",
+                    "answerOptions": [
+							{
+							"answerText": "Validation croisée",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "validation de maintien",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "laissez une validation d'une sortie",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 11,
+            "title": "Préparer et visualiser des données pour la régression: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Lequel de ces modules Python est utilisé pour tracer la visualisation des données?",
+                    "answerOptions": [
+							{
+							"answerText": "Numpy",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Scikit-apprendre",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Matplotlib",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Si vous souhaitez comprendre la propagation ou les autres caractéristiques des points de données de votre ensemble de données, puis effectuez:",
+                    "answerOptions": [
+							{
+							"answerText": "Visualisation des données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Pré-traitement des données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Split test de train",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Lequel d'entre eux fait partie de l'étape de visualisation des données dans un projet de machine learning?",
+                    "answerOptions": [
+							{
+							"answerText": "Intégrant un algorithme d'apprentissage de certains machines",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Créer une représentation picturale des données à l'aide de différentes méthodes de tracé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Normaliser les valeurs d'un jeu de données",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 12,
+            "title": "Préparez et visualisez des données pour la régression: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Lequel de ces extraits de code est correct d'après cette leçon, si vous souhaitez vérifier la présence de valeurs manquantes dans votre ensemble de données ? Supposons que l'ensemble de données soit stocké dans une variable nommée \"ensemble de données\", qui est un objet Pandas DataFrame.",
+                    "answerOptions": [
+							{
+							"answerText": "DataSet.isnull (). Somme ()",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "FindMissing (DataSet)",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Somme (NULL (DataSet))",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Laquelle de ces méthodes de traçage est utile lorsque vous souhaitez comprendre la propagation de différents groupes de fichiers de données de votre jeu de données?",
+                    "answerOptions": [
+							{
+							"answerText": "Terrain de dispersion",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Terrain de ligne",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "barre de bar",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Que peut ne pas vous dire la visualisation des données?",
+                    "answerOptions": [
+							{
+							"answerText": "Relations entre DataPoints",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "La source de l'endroit où le jeu de données est collecté",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Trouver la présence de valeurs aberrantes dans l'ensemble de données",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 13,
+            "title": "régression linéaire et polynomiale: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Matplotlib est un",
+                    "answerOptions": [
+							{
+							"answerText": "Bibliothèque de dessin",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Bibliothèque de visualisation de données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Library Lanchage",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La régression linéaire utilise ce qui suit pour tracer des relations entre variables",
+                    "answerOptions": [
+							{
+							"answerText": "Une ligne droite",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un cercle",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "une courbe",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un bon modèle de régression linéaire a un coefficient de corrélation ___",
+                    "answerOptions": [
+							{
+							"answerText": "Low",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "High",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "flat",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 14,
+            "title": "Régression linéaire et polynomiale: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Si vos données sont non linéaires, essayez un type ___  de régression",
+                    "answerOptions": [
+							{
+							"answerText": "linéaire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "sphérique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "polynôme",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Ce sont tous types de méthodes de régression",
+                    "answerOptions": [
+							{
+							"answerText": "Falsestetep, crête, lasso et élastique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Stealwise, Ridge, Lasso et Elasticnet",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Stealwise, Ridge, Lariat et Elasticnet",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La régression des moindres carrés signifie que toutes les données de données entourant la ligne de régression sont:",
+                    "answerOptions": [
+							{
+							"answerText": "carré puis soustrait",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "multiplié",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "carré puis ajouté",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 15,
+            "title": "Régression logistique: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Utilisez la régression logistique à prédire",
+                    "answerOptions": [
+							{
+							"answerText": "Si une pomme est mûre ou non",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Combien de billets peuvent être vendus dans un mois",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "De quelle couleur le ciel tournera demain à 18 heures",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Types de régression logistique incluent",
+                    "answerOptions": [
+							{
+							"answerText": "multinomial et cardinal",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "multinomial et ordinal",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Principal et ordinal",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Vos données ont des corrélations faibles. Le meilleur type de régression à utiliser est:",
+                    "answerOptions": [
+							{
+							"answerText": "Logistique",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "linéaire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "cardinal",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 16,
+            "title": "Régression logistique: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Sea-né est un type de",
+                    "answerOptions": [
+							{
+							"answerText": "Bibliothèque de visualisation de données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bibliothèque de mappage",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Bibliothèque mathématique",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une matrice de confusion est également connue sous le nom de:",
+                    "answerOptions": [
+							{
+							"answerText": "matrice d'erreur",
+                            "isCorrect": "true"
+                        },
+                        {
+                            "answerText": "Matrix de vérité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "matrice de précision",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un bon modèle aura:",
+                    "answerOptions": [
+							{
+							"answerText": "Un grand nombre de faux positifs et de vrais négatifs dans sa matrice de confusion",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un grand nombre de vrais positifs et vrais négatifs dans sa matrice de confusion",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un grand nombre de vrais positifs et de faux négatifs dans sa matrice de confusion",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        }, {
+            "id": 17,
+            "title": "Construire une application Web: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce que OnNX signifie?",
+                    "answerOptions": [
+							{
+							"answerText": "Exchange de réseau de neurones",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Exchange de réseau de neurones ouverts",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Exchange de réseau neural de sortie",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Comment le ballon est-il défini par ses créateurs?",
+                    "answerOptions": [
+							{
+							"answerText": "mini-cadre",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Grand-cadre",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "micro-cadre",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Que fait le module de cornichon de Python",
+                    "answerOptions": [
+							{
+							"answerText": "Serialise un objet Python",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "désagréalise un objet Python",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Serialise et désémarifier un objet Python",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 18,
+            "title": "Construire une application Web: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Quels sont les outils que nous pouvons utiliser pour héberger un modèle pré-formé sur le Web à l'aide de Python?",
+                    "answerOptions": [
+							{
+							"answerText": "Flacon",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Tensorflow.js",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "ONNX.JS",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce que SaaS signifie?",
+                    "answerOptions": [
+							{
+							"answerText": "Système en tant que service",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Logiciel en tant que service",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Sécurité en tant que service",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce que la bibliothèque de labelencoder de Scikit-apprendre?",
+                    "answerOptions": [
+							{
+							"answerText": "Encode les données par ordre alphabétique",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Encode les données numériquement",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Code des données en série",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 19,
+            "title": "Classification 1: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "La classification est une forme d'apprentissage supervisé qui a beaucoup en commun avec",
+                    "answerOptions": [
+							{
+							"answerText": "Série temporelle",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Techniques de régression",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle question peut aider la classification à répondre?",
+                    "answerOptions": [
+							{
+							"answerText": "Est-ce que ce courrier électronique ou pas?",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Les cochons peuvent voler?",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Quel est le sens de la vie?",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle est la première étape pour utiliser des techniques de classification?",
+                    "answerOptions": [
+							{
+							"answerText": "Création de cours d'un jeu de données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Nettoyer et équilibrer vos données",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Affectation d'un point de données à un groupe ou à un résultat",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 20,
+            "title": "Classification 1: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce qu'une question multiclasse?",
+                    "answerOptions": [
+							{
+							"answerText": "La tâche de classer les points de données dans plusieurs classes",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "La tâche de classifier les points de données dans l'une des plusieurs classes",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "La tâche de nettoyer les points de données de plusieurs manières",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Il est important de nettoyer des données récurrentes ou inutiles pour aider vos classificateurs à résoudre votre problème.",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle est la meilleure raison d'équilibrer vos données?",
+                    "answerOptions": [
+							{
+							"answerText": "Les données déséquilibrées ont l'air mauvais dans les visualisations",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'équilibrage de vos données donne des résultats meilleurs, car un modèle ML n'enfraigne pas vers une classe",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "L'équilibrage de vos données vous donne plus de points de données",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 21,
+            "title": "Classification 2: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Les données équilibrées et propres ont produit les meilleurs résultats de la classification",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Comment choisissez-vous le bon classificateur?",
+                    "answerOptions": [
+							{
+							"answerText": "Comprend quel classificateurs fonctionnent le mieux pour quels scénarios",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Devineuse éduquée et chèque",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La classification est un type de",
+                    "answerOptions": [
+							{
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Apprentissage supervisé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Langage de programmation",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 22,
+            "title": "Classification 2: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce qu'un \"solveur\" ?",
+                    "answerOptions": [
+							{
+							"answerText": "La personne qui vérifie votre travail",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'algorithme à utiliser dans le problème d'optimisation",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Une technique de machine learning",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel classificateur avons-nous utilisé dans cette leçon?",
+                    "answerOptions": [
+							{
+							"answerText": "régression logistique",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Arbres de décision",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "MultiClass one-vs-tout",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Comment savez-vous si l'algorithme de classification fonctionne comme prévu?",
+                    "answerOptions": [
+							{
+							"answerText": "En vérifiant la précision de ses prévisions",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "En le contrôlant contre d'autres algorithmes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "En regardant des données historiques pour la qualité de cet algorithme de résoudre des problèmes similaires",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 23,
+            "title": "Classification 3: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Un bon classificateur initial à essayer est:",
+                    "answerOptions": [
+							{
+							"answerText": "SVC linéaire",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "k-signifie",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "SVC logique",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Contrôles de régularisation:",
+                    "answerOptions": [
+							{
+							"answerText": "L'influence des paramètres",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "L'influence de la vitesse de formation",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'influence des valeurs aberrantes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le classificateur K-voisins peut être utilisé pour:",
+                    "answerOptions": [
+							{
+							"answerText": "Apprentissage supervisé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'apprentissage non supervisé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "tous les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 24,
+            "title": "Classification 3: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Les classificateurs de support-vectoriel peuvent être utilisés pour",
+                    "answerOptions": [
+							{
+							"answerText": "Classification",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "régression",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "tous les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Forêt aléatoire est un type de classificateur ___",
+                    "answerOptions": [
+							{
+							"answerText": "Ensemble",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Dissembliste",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Assemblez",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Adaboost est connu pour:",
+                    "answerOptions": [
+							{
+							"answerText": "Se concentrer sur les poids des éléments incorrectement classifiés",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Se concentrer sur des valeurs aberrantes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Se concentrer sur des données incorrectes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 25,
+            "title": "Classification 4: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Les systèmes de recommandation peuvent être utilisés pour",
+                    "answerOptions": [
+							{
+							"answerText": "Recommander un bon restaurant",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "recommander des modes à essayer",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "tous les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'intégration d'un modèle dans une application Web l'aide à être hors ligne",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "OnNX Runtime peut être utilisé pour",
+                    "answerOptions": [
+							{
+							"answerText": "Exécution de modèles dans une application Web",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Modèles de formation",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Hyperparameter Tuning",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 26,
+            "title": "Classification 4: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "L'application Nettron vous aide:",
+                    "answerOptions": [
+							{
+							"answerText": "Visualiser les données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Visualisez la structure de votre modèle",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Testez votre application Web",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Convertissez votre modèle SCIKIT-HALL pour une utilisation avec OnNX en utilisant:",
+                    "answerOptions": [
+							{
+							"answerText": "Sklearn-App",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sklearn-web",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sklearn-ONNX",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'utilisation de votre modèle dans une application Web s'appelle:",
+                    "answerOptions": [
+							{
+							"answerText": "Inférence",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Interférence",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Assurance",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 27,
+            "title": "Introduction au clustering: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Un exemple de vie réel de regroupement serait",
+                    "answerOptions": [
+							{
+							"answerText": "Définir la table du dîner",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Tri du linge",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Shopping de l'épicerie",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Les techniques de clustering peuvent être utilisées dans ces industries",
+                    "answerOptions": [
+							{
+							"answerText": "Banking",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "e-commerce",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "tous les deux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La clustering est un type de:",
+                    "answerOptions": [
+							{
+							"answerText": "Apprentissage supervisé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "L'apprentissage non supervisé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Apprentissage du renforcement",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 28,
+            "title": "Introduction au clustering: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "La géométrie euclidienne est arrangée",
+                    "answerOptions": [
+							{
+							"answerText": "Planes",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Courbes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Sphères",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La densité de vos données de clustering est liée à sa",
+                    "answerOptions": [
+							{
+							"answerText": "bruit",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Profondeur",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Validité",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'algorithme de regroupement le plus connu est",
+                    "answerOptions": [
+							{
+							"answerText": "k-signifie",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "K-Middle",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "K-Mart",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 29,
+            "title": "K-Means Clustering: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "K-Means est dérivé de:",
+                    "answerOptions": [
+							{
+							"answerText": "Génie électrique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Traitement du signal",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Linguistics informatiques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une bonne score de silhouette signifie:",
+                    "answerOptions": [
+							{
+							"answerText": "Les grappes sont bien séparées et bien définies",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Il y a peu de grappes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Il y a beaucoup de clusters",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La variance est:",
+                    "answerOptions": [
+							{
+							"answerText": "La moyenne des différences carrées de la moyenne",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un problème de regroupement s'il devient trop élevé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "tous les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 30,
+            "title": "K-olth regroupement: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Un diagramme de Voronoi montre:",
+                    "answerOptions": [
+							{
+							"answerText": "Variance d'une cluster",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "La graine d'une grappe et sa région",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "L'inertie d'une cluster",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "L'inertie est",
+                    "answerOptions": [
+							{
+							"answerText": "Une mesure de la manière dont les clusters cohérents internes sont",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Une mesure de la quantité de grappes déplacées",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une mesure de la qualité des grappes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "en utilisant k-moyen, vous devez d'abord déterminer la valeur de 'k'",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 31,
+            "title": "Intro to NLP: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Que signifie NLP pour ces leçons?",
+                    "answerOptions": [
+							{
+							"answerText": "Traitement des langues neurales",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Traitement des langues naturelles",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Traitement linguistique naturel",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Eliza était un bot précoce qui a agi comme un ordinateur",
+                    "answerOptions": [
+							{
+							"answerText": "Thérapeute",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Docteur",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Infirmière",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Le test de Turing 'd'Alan Turing a essayé de déterminer si un ordinateur était",
+                    "answerOptions": [
+							{
+							"answerText": "Inmistingtilisable d'un humain",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Penser",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 32,
+            "title": "Intro to NLP: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Joseph Weizenbaum a inventé le bot",
+                    "answerOptions": [
+							{
+							"answerText": "Elisha",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Eliza",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Eloise",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un bot conversationnel donne une sortie basée sur",
+                    "answerOptions": [
+							{
+							"answerText": "Choisir des choix prédéfinis au hasard",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Analyse de l'entrée et de l'utilisation de l'intelligence de la machine",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "tous les deux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Comment feriez-vous le bot plus efficace?",
+                    "answerOptions": [
+							{
+							"answerText": "En le demandant plus de questions.",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "En l'alignant plus de données et de la formation en conséquence",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Le bot est stupide, il ne peut pas apprendre :(",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 33,
+            "title": "Tâches NLP: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Tokenization",
+                    "answerOptions": [
+							{
+							"answerText": "Splituelle du texte au moyen de la ponctuation",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Splituelle du texte en jetons séparés (mots)",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Splituelle du texte en phrases",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Embeddings",
+                    "answerOptions": [
+							{
+							"answerText": "Convertit numériquement les données de texte afin que les mots puissent se classer",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Intégrance des mots en phrases",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Intégrance des phrases dans les paragraphes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Marquage des parties de la parole",
+                    "answerOptions": [
+							{
+							"answerText": "Divise des phrases par leurs parties de la parole",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "prend des mots togmentés et les étiquettes de leur part de la parole",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Phrases de diagrammes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 34,
+            "title": "Tâches NLP: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Construisez un dictionnaire de la fréquence à laquelle les mots se reproduisent en utilisant:",
+                    "answerOptions": [
+							{
+							"answerText": "Dictionnaire de mots et d'expressions",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Fréquences de mots et de phrases",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bibliothèque de mots et de phrases",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "N-grammes se réfèrent à",
+                    "answerOptions": [
+							{
+							"answerText": "Un texte peut être divisé en séquences de mots d'une longueur définie",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un mot peut être divisé en séquences de caractères d'une longueur de jeu",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un texte peut être divisé en paragraphes d'une longueur définie",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Analyse du sentiment",
+                    "answerOptions": [
+							{
+							"answerText": "analyse une phrase pour la positivité ou la négativité",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "analyse une phrase pour sentimentalité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "analyse une phrase pour la tristesse",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 35,
+            "title": "NLP et traduction: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Traduction naïf",
+                    "answerOptions": [
+							{
+							"answerText": "Traduit uniquement les mots",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Traduit la structure de la phrase",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Traduit le sentiment",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Un corpus * de textes fait référence à",
+                    "answerOptions": [
+							{
+							"answerText": "Un petit nombre de textes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un grand nombre de textes",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un texte standard",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Si un modèle ML a suffisamment de traductions humaines pour construire un modèle sur, il peut",
+                    "answerOptions": [
+							{
+							"answerText": "Traductions abrégées",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Normaliser les traductions",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Améliorer la précision des traductions",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 36,
+            "title": "NLP et traduction: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "La bibliothèque de traduction de texte sous-jacente est:",
+                    "answerOptions": [
+							{
+							"answerText": "Google Translate",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Bing",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un modèle ML personnalisé",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "utiliser `blob.translate` vous avez besoin:",
+                    "answerOptions": [
+							{
+							"answerText": "Une connexion Internet",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un dictionnaire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "JavaScript",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Déterminer le sentiment, une approche ML serait de:",
+                    "answerOptions": [
+							{
+							"answerText": "Appliquez des techniques de régression pour générer manuellement des opinions et des scores et rechercher des modèles",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Appliquez des techniques de PNL pour générer manuellement des opinions et des scores et rechercher des modèles",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Appliquez des techniques de regroupement pour des opinions et des scores générés manuellement et rechercher des modèles",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 37,
+            "title": "NLP 4: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Quelles informations pouvons-nous obtenir du texte écrit ou parlé par un humain?",
+                    "answerOptions": [
+							{
+							"answerText": "motifs et fréquences",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "sentiment et signification",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce que l'analyse du sentiment?",
+                    "answerOptions": [
+							{
+							"answerText": "Une étude sur la question de savoir si un héritage de famille a une valeur sentimentale",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une méthode d'identification systématique, d'extraction, de quantification et d'étude des états affectifs et des informations subjectives",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "La capacité de savoir si quelqu'un est triste ou heureux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle question pourrait être répondue à l'aide d'un jeu de données de critiques hôteliers, de python et d'analyse de sentiment?",
+                    "answerOptions": [
+							{
+							"answerText": "Quels sont les mots et expressions les plus fréquemment utilisés dans les critiques?",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Quel hôtel a la meilleure piscine?",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Y a-t-il un service de voiturier dans cet hôtel?",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 38,
+            "title": "NLP 4: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Quelle est l'essence de la NLP?",
+                    "answerOptions": [
+							{
+							"answerText": "catégoriser la langue humaine en joyeux ou triste",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Interprétation de sens ou de sentiment sans avoir à avoir un homme humain le faire",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Trouver des valeurs aberrantes dans le sentiment et les examiner",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelles sont certaines choses que vous pourriez rechercher lors de la nettoyage des données?",
+                    "answerOptions": [
+							{
+							"answerText": "Personnages dans d'autres langues",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "lignes vierges ou colonnes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Il est important de comprendre vos données et ses fruits avant d'effectuer des opérations à ce sujet.",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 39,
+            "title": "NLP 5: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Pourquoi est-il important de nettoyer les données avant de l'analyser?",
+                    "answerOptions": [
+							{
+							"answerText": "Certaines colonnes pourraient avoir des données manquantes ou incorrectes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les données en désordre peuvent conduire à de fausses conclusions sur le jeu de données",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est un exemple d'une stratégie de nettoyage des données?",
+                    "answerOptions": [
+							{
+							"answerText": "Supprimer des colonnes / rangées qui ne sont pas utiles pour répondre à une question spécifique",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Se débarrasser des valeurs vérifiées qui ne correspondent pas à votre hypothèse",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Déplacement des valeurs aberrantes vers une table séparée et exécutant les calculs de cette table pour voir s'ils correspondent à",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Il peut être utile de classer les données à l'aide d'une colonne Tag.",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 40,
+            "title": "NLP 5: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Quel est l'objectif de l'ensemble de données?",
+                    "answerOptions": [
+							{
+							"answerText": "Pour voir combien de critiques négatives et positives il y a pour les hôtels à travers le monde",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Pour ajouter du sentiment et des colonnes qui vous aideront à choisir le meilleur hôtel",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Analyser pourquoi les gens laissent des critiques spécifiques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quels sont les mots d'arrêt?",
+                    "answerOptions": [
+							{
+							"answerText": "Mots anglais communs qui ne changent pas le sentiment d'une phrase",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "mots que vous pouvez supprimer pour accélérer l'analyse du sentiment",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pour tester l'analyse du sentiment, assurez-vous qu'il correspond au score du critique pour le même examen.",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 41,
+            "title": "Série introduction à temps: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "La prévision de la série chronologique est utile",
+                    "answerOptions": [
+							{
+							"answerText": "Déterminer les coûts futurs",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Prédicter les prix futurs",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "à la fois ce qui précède",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une série chronologique est une séquence prise à:",
+                    "answerOptions": [
+							{
+							"answerText": "points successifs également espacés dans l'espace",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "points successifs également espacés dans le temps",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "points successifs également espacés dans l'espace et le temps",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La série chronologique peut être utilisée dans:",
+                    "answerOptions": [
+							{
+							"answerText": "Prévision de tremblement de terre",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Vision informatique",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Analyse des couleurs",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 42,
+            "title": "Série introduction à TIME: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Tendances de la série chronologique sont",
+                    "answerOptions": [
+							{
+							"answerText": "mesure mesurable augmente et diminue au fil du temps",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "La quantification diminue au fil du temps",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Des lacunes entre augmentations et diminution au fil du temps",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Les valeurs aberrantes sont",
+                    "answerOptions": [
+							{
+							"answerText": "Points proches de la variance de données standard",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Pointes loin de la variance de données standard",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Points dans la variance des données standard",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "La prévision de la série chronologique est la plus utile pour",
+                    "answerOptions": [
+							{
+							"answerText": "Econométrics",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Histoire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Bibliothèques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 43,
+            "title": "Série TIME ARIMA: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Arima signifie",
+                    "answerOptions": [
+							{
+							"answerText": "Moyenne mobile intégrale autonome",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Action mobile intégrée autorégressive",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Moyenne mobile intégrée autorégresive",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Stationarité fait référence à",
+                    "answerOptions": [
+							{
+							"answerText": "Les données dont les attributs ne changent pas lors de la décalage",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les données dont la distribution ne change pas lors de la décalage de temps",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Les données dont la distribution change lors de la décalage",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "différenciation",
+                    "answerOptions": [
+							{
+							"answerText": "Stabilise la tendance et la saisonnalité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Exacerbe la tendance et la saisonnalité",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Élimine la tendance et la saisonnalité",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 44,
+            "title": "Série TIME ARIMA: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Arima est utilisé pour créer un modèle adapté à la forme spéciale des données de la série chronologique",
+                    "answerOptions": [
+							{
+							"answerText": "aussi plat que possible",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "aussi étroitement que possible",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "via ScatterPlots",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Utilisez Sarimax à",
+                    "answerOptions": [
+							{
+							"answerText": "Gérer les modèles d'ARIMA saisonniers",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Gérer des modèles spéciaux Arima",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Gérer les modèles statistiques ARIMA",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": " `La validation` de la promenade implique ",
+                    "answerOptions": [
+							{
+							"answerText": "Réévaluer un modèle progressivement tel qu'il est validé",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Ré-entraînant un modèle progressivement tel qu'il est validé",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Ré-configurez un modèle progressivement tel qu'il est validé",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        }, {
+            "id": 45,
+            "title": "Renforcement 1: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce que l'apprentissage du renforcement?",
+                    "answerOptions": [
+							{
+							"answerText": "Enseigner à quelqu'un quelque chose encore et encore jusqu'à ce qu'ils comprennent",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une technique d'apprentissage qui déchiffre le comportement optimal d'un agent dans certains environnements en exécutant de nombreuses expériences",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Comprendre comment exécuter plusieurs expériences à la fois",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Qu'est-ce qu'une politique?",
+                    "answerOptions": [
+							{
+							"answerText": "une fonction qui renvoie l'action à tout état donné",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Un document qui vous dit si vous pouvez renvoyer ou non un article",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une fonction utilisée à des fins aléatoires",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Une fonction de récompense renvoie un score pour chaque état d'environnement.",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        }, {
+            "id": 46,
+            "title": "Renforcement 1: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Qu'est-ce que q-apprentissage?",
+                    "answerOptions": [
+							{
+							"answerText": "Un mécanisme d'enregistrement de la \"bonté\" de chaque État",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Un algorithme où la politique est définie par une table Q",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Les deux ci-dessus",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Pour quelles valeurs une table Q correspond à la stratégie de marche aléatoire?",
+                    "answerOptions": [
+							{
+							"answerText": "toutes les valeurs égales",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "-0,25",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "toutes les valeurs différentes",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Il valait mieux utiliser l'exploration que l'exploitation pendant le processus d'apprentissage de notre leçon.",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 47,
+            "title": "Renforcement 2: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Les échecs et les jeux sont des jeux avec des états continus.",
+                    "answerOptions": [
+							{
+							"answerText": "vrai",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Faux",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est le problème de la cartpole?",
+                    "answerOptions": [
+							{
+							"answerText": "Un processus d'élimination des valeurs aberrantes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une méthode d'optimisation de votre panier",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Une version simplifiée d'équilibrage",
+                            "isCorrect": "true"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel outil pouvons-nous utiliser pour jouer à différents scénarios d'états potentiels dans un jeu?",
+                    "answerOptions": [
+							{
+							"answerText": "Devinez et chèque",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Environnements de simulation",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Test de transition de l'état",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 48,
+            "title": "Renforcement 2: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Où définissons-nous toutes les actions possibles dans un environnement?",
+                    "answerOptions": [
+							{
+							"answerText": "Méthodes",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "espace d'action",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Liste d'action",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle paire avez-nous utilisée comme valeur de la clé de dictionnaire?",
+                    "answerOptions": [
+							{
+							"answerText": "(état, action) comme clé de la table Q-Table comme valeur",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "State comme clé, action en tant que valeur",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "La valeur de la fonction QValues ​​est la clé, l'action en tant que valeur",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quels sont les hyperparamètres que nous avons utilisés pendant q-apprentissage?",
+                    "answerOptions": [
+							{
+							"answerText": "Valeur de la table Q, récompense actuelle, action aléatoire",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Taux d'apprentissage, facteur de réduction, facteur d'exploration / d'exploitation",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Récompenses cumulatives, taux d'apprentissage, facteur d'exploration",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 49,
+            "title": "Applications du monde réel: quiz préalable",
+            "quiz": [
+							{
+                    "questionText": "Quel est un exemple d'application ML dans l'industrie des finances?",
+                    "answerOptions": [
+							{
+							"answerText": "Personnaliser le voyage client à l'aide de NLP",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Gestion de la richesse à l'aide de la régression linéaire",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Gestion de l'énergie à l'aide de séries chronologiques",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle technique ML peut utiliser les hôpitaux pour gérer la réadmission?",
+                    "answerOptions": [
+							{
+							"answerText": "Clustering",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Série temporelle",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est un exemple d'utilisation des séries chronologiques pour la gestion de l'énergie?",
+                    "answerOptions": [
+							{
+							"answerText": "Animaux de détection de mouvement",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Parkings intelligents",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Suivi des incendies de forêt",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        },
+        {
+            "id": 50,
+            "title": "Applications du monde réel: quiz de validation des connaissances",
+            "quiz": [
+							{
+                    "questionText": "Quelle technique ML peut être utilisée pour détecter la fraude par carte de crédit?",
+                    "answerOptions": [
+							{
+							"answerText": "régression",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Clustering",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quelle technique ML est illustrée dans la gestion forestière?",
+                    "answerOptions": [
+							{
+							"answerText": "Apprentissage du renforcement",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Série temporelle",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "NLP",
+                            "isCorrect": "false"
+                        }
+                    ]
+                },
+                {
+                    "questionText": "Quel est un exemple d'application ML dans l'industrie des soins de santé?",
+                    "answerOptions": [
+							{
+							"answerText": "Prédire le comportement des étudiants en utilisant la régression",
+                            "isCorrect": "false"
+                        },
+                        {
+							"answerText": "Gestion des essais cliniques à l'aide de classificateurs",
+                            "isCorrect": "true"
+                        },
+                        {
+							"answerText": "Sensation de mouvement des animaux utilisant des classificateurs",
+                            "isCorrect": "false"
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}]
\ No newline at end of file
diff --git a/quiz-app/src/assets/translations/index.js b/quiz-app/src/assets/translations/index.js
index e4abf6eb96..85931b8b73 100644
--- a/quiz-app/src/assets/translations/index.js
+++ b/quiz-app/src/assets/translations/index.js
@@ -1,12 +1,14 @@
 // index.js
 import en from './en.json';
 import tr from './tr.json';
+import fr from './fr.json';
 
 //export const defaultLocale = 'en';
 
 const messages = {
 	en: en[0],
 	tr: tr[0],
+	fr: fr[0]
 };
 
 export default messages;

From 4dab6027cc9af7640a5403f6d8f77d1ffcae106c Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Fri, 16 Jul 2021 21:18:52 +0800
Subject: [PATCH 188/368] Update README.zh-cn.md

---
 2-Regression/4-Logistic/translations/README.zh-cn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 52453de5ab..b4397856b5 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -120,7 +120,7 @@ Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�
     sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
     ```
 
-    ![分类散点图�视化数�](images/swarm.png)
+    ![分类散点图�视化数�](../images/swarm.png)
 
 ### ���图
 
@@ -133,7 +133,7 @@ Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�
                 kind="violin", data=new_pumpkins)
     ```
 
-    ![���图](images/violin.png)
+    ![���图](../images/violin.png)
 
     ✅ �试使用其他��创建此图和其他Seaborn图。
 

From 580cfc314c2348e52849a90be12d6b9e74decd5e Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 16 Jul 2021 10:14:21 -0400
Subject: [PATCH 189/368] small link format error

---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index e762c7f6dc..0d07a5e806 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -102,7 +102,7 @@ Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw]
 
 ## Révision et auto-apprentissage
 
-Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de ML dans le cloud, suivez ce [Parcours d'apprentissage](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine- learning/?WT.mc_id=academic-15963-cxa).
+Pour en savoir plus sur la façon dont vous pouvez utiliser les algorithmes de ML dans le cloud, suivez ce [Parcours d'apprentissage](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
 
 ## Devoir
 

From c67123ad7322722cfab87a0666ef8c5417f3c9aa Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 16 Jul 2021 10:23:19 -0400
Subject: [PATCH 190/368] linking assignment

---
 1-Introduction/1-intro-to-ML/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
index 8693ff20c0..45ec79be09 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
@@ -104,4 +104,4 @@
 
 ## 任务
 
-[�动并�行](../assignment.md)
+[�动并�行](assignment.zh-cn.md)

From d07d80730ddc9c8eda3dc294be54fb51130f5e24 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Fri, 16 Jul 2021 10:25:47 -0400
Subject: [PATCH 191/368] linking Chinese language assignments

---
 .../translations/README.zh-cn.md              |  2 +-
 .../3-fairness/translations/README.zh-cn.md   | 22 +++++++++----------
 .../translations/README.zh-cn.md              |  4 ++--
 3 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index 51e66ecd2b..8ca7e690f7 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -113,4 +113,4 @@ Alan Turing，一个真正�出的人，[在2019年被公众投票选出](https
 
 ## 任务
 
-[创建时间线](../assignment.md)
+[创建时间线](assignment.zh-cn.md)
diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index 3b75ddab94..222045441a 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -89,11 +89,11 @@
  
 ✅ **讨论**：�温一些例�，看看它们是�显示出��的�害。
 
-|                         | 分� | �务质� | 刻��象 | 诋� | 代表性过高或过低 |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| 自动招�系统 |     x      |         x          |      x       |             |               x                |
-| 机器翻译     |            |                    |              |             |                                |
-| 照片加标签          |            |                    |              |             |                                |
+|              | 分�  | �务质� | 刻��象 | 诋�  | 代表性过高或过低 |
+| ------------ | :---: | :------: | :------: | :---: | :--------------: |
+| 自动招�系统 |   x   |    x     |    x     |       |        x         |
+| 机器翻译     |       |          |          |       |                  |
+| 照片加标签   |       |          |          |       |                  |
 
 
 ## 检测�公平
@@ -138,11 +138,11 @@
 
 ✅ 在以�关于�类的课程中，你将看到如何在代�中构建这个“混淆矩阵�
 
-|            | �阳性率 | �阴性率 | 数� |
-| ---------- | ------------------- | ------------------- | ----- |
-| 女性      | 0.37                | 0.27                | 54032 |
-| 男性        | 0.31                | 0.35                | 28620 |
-| 未列出性别 | 0.33                | 0.31                | 1266  |
+|            | �阳性率 | �阴性率 | 数�  |
+| ---------- | -------- | -------- | ----- |
+| 女性       | 0.37     | 0.27     | 54032 |
+| 男性       | 0.31     | 0.35     | 28620 |
+| 未列出性别 | 0.33     | 0.31     | 1266  |
 
  
 这个表格告诉我们几件事。首先，我们注�到数�中的未列出性别的人相对较少。数�是有�差的，所以你需��心解释这些数字。
@@ -211,4 +211,4 @@
 
 ## 任务
 
-[探索Fairlearn](../assignment.md) 
+[探索Fairlearn](assignment.zh-cn.md) 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index d01d5bbfe2..373602f303 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -54,7 +54,7 @@
 
 - **训练**。这部分数�集适�你的模型进行训练。这个集�构�了原始数�集的大部分。
 - **测试**。测试数�集是一组独立的数�，通常从原始数�中收集，用于确认构建模型的性能。
-- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../../7-TimeSeries/1-Introduction/README.md)中所述）。 
+- **验�**。验�集是一个较�的独立示例组，用于调整模型的超�数或架构，以改进模型。根�你的数�大�和你�出的问题，你�能�需�构建第三组（正如我们在[时间�列预测](../../../7-TimeSeries/1-Introduction/README.md)中所述）。 
 
 ## 建立模型 
 
@@ -105,4 +105,4 @@
 
 ## 任务
 
-[采访一�数�科学家](../assignment.md)
+[采访一�数�科学家](assignment.zh-cn.md)

From 1ac779f41cfff29cca56e6093b651ada4bef4025 Mon Sep 17 00:00:00 2001
From: Colin Zang <zyy0116@gmail.com>
Date: Fri, 16 Jul 2021 22:44:19 +0800
Subject: [PATCH 192/368] Update README.zh-cn.md

---
 translations/README.zh-cn.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md
index f46a50d6c2..dfe0760cc3 100644
--- a/translations/README.zh-cn.md
+++ b/translations/README.zh-cn.md
@@ -29,11 +29,11 @@
 
 - 从课�测验开始
 - 阅读课程内容，完�所有的活动，在�次 knowledge check 时暂�并�考
-- 我们建议你基于�解�创建项目（而�是仅仅跑一�示例代�）示例代�的�置在�一个项目的 `/solution` 文件夹中。
+- 我们建议你基于�解�创建项目（而�是仅仅跑一�示例代�）。示例代�的�置在�一个项目的 `/solution` 文件夹中。
 - 进行课�测验
 - 完�课程挑战
 - 完�作业
-- 一节课完��, 访问[讨论版](https://github.com/microsoft/ML-For-Beginners/discussions)，通过天�相应的 PAT Rubric (课程目标)�深化自己的学习�果。你也�以回应其它的 PAT，这样我们�以一起学习。
+- 一节课完��, 访问[讨论版](https://github.com/microsoft/ML-For-Beginners/discussions)，通过填写相应的 PAT Rubric (课程目标) �深化自己的学习�果。你也�以回应其它的 PAT，这样我们�以一起学习。
 
 > 如果希望进一步学习，我们推�跟� [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) 的模�和学习路径。
 
@@ -52,7 +52,7 @@
 
 此课程基于两个教学原则：学生应该上手进行**项目实践**，并完�**频�的测验**。 此外，为了使整个课程更具有整体性，课程们有一个共�的**主题**。
 
-通过确�课程内容与项目强相关，我们让学习过程对学生更具�引力，概念的学习也被深化了。难度较低的课�测验�以�引学生学习课程，课�的第二次测验进一步��了课堂中的概念。该课程被设计地�活有趣，�以一次性全部学习，或者分开�一部分一部分学习。这些项目由浅入深，从第一周的的�项目开始，在第�二周的周期结�时�得较为��。本课程还包括一个关于机器学习实际应用的�记，�用作�外学分或讨论的基础。
+通过确�课程内容与项目强相关，我们让学习过程对学生更具�引力，概念的学习也被深化了。难度较低的课�测验�以�引学生学习课程，而课�的第二次测验也进一步��了课堂中的概念。该课程被设计地�活有趣，�以一次性全部学习，或者分开�一部分一部分学习。这些项目由浅入深，从第一周的�项目开始，在第�二周结�时�得较为��。本课程还包括一个关于机器学习实际应用的�记，�用作�外学分或进一步讨论的基础。
 
 > 在这里，你�以找到我们的[行为守则](../CODE_OF_CONDUCT.md)，[对项目作出贡献](../CONTRIBUTING.md)以�[翻译](../TRANSLATIONS.md)指�。我们欢迎���出有建设性的�馈�
 

From 0e93c335f31adf87e090f5362103e165f751ed32 Mon Sep 17 00:00:00 2001
From: Colin Zang <zyy0116@gmail.com>
Date: Fri, 16 Jul 2021 23:21:17 +0800
Subject: [PATCH 193/368] Create README.zh-cn.md

---
 8-Reinforcement/translations/README.zh-cn.md | 53 ++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 8-Reinforcement/translations/README.zh-cn.md

diff --git a/8-Reinforcement/translations/README.zh-cn.md b/8-Reinforcement/translations/README.zh-cn.md
new file mode 100644
index 0000000000..25a53cfc36
--- /dev/null
+++ b/8-Reinforcement/translations/README.zh-cn.md
@@ -0,0 +1,53 @@
+# 强化学习简介
+
+强化学习 (RL, Reinforcement Learning)，是基本的机器学习范�之一（仅次于监�学习 (Supervised Learning) 和无监�学习(Unsupervised Learning)）。强化学习和「策略���相关：它应当产生正确的策略，或从错误的策略中学习。
+
+�设有一个模拟环境，比如说股市。当我们用�一个规则��制这个市场时，会�生什么？这个规则（或者说策略）有积�或消�的影��？如果它的影�是正�的，我们需�从这�_负�强化_中学习，改�我们的策略。如果它的影�是正�的，我们需�在这�_积�强化_的基础上�进一步�展。
+
+![彼得和狼](../images/peter.png)
+
+> 彼得和他的朋�们得从饥饿的狼这儿逃掉�图片�自 [Jen Looper](https://twitter.com/jenlooper)
+
+## 本节主题：彼得与狼（俄罗斯）
+
+[彼得与狼](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) 是俄罗斯作曲家[谢尔盖·普罗科�耶夫](https://en.wikipedia.org/wiki/Sergei_Prokofiev)创作的音�童�。它讲述了彼得勇敢地走出家门，到森林中央追�狼的故事。在本节中，我们将训练帮助 Peter 追狼的机器学习算法：
+
+- **探索**周边区域并构建最佳地图
+- **学习**如何使用滑�并在滑�上��平衡，以便更快地移动。
+
+[![彼得和狼](https://img.youtube.com/vi/Fmi5zHg4QSM/0.jpg)](https://www.youtube.com/watch?v=Fmi5zHg4QSM)
+
+> 🎥 点击上�的图片，�普罗科�耶夫的《彼得与狼》
+
+## 强化学习
+
+在��的部分中，您已�看到了两类机器学习问题的例�：
+
+- **监�**，在有已�标记的，暗�解决方案的数�集的情况下。 [分类](../../4-Classification/README.md) 和 [回归](../../2-Regression/README.md) 是监�学习任务。
+- **无监�**，在我们没有标记训练数�集的情况下。无监�学习的主�例�是 [�类](../../5-Clustering/README.md)。
+
+在本节中，我们将学习一类新的机器学习问题，它�需�已�标记的训练数� —— 比如这两类问题：
+
+- **[�监�学习](https://wikipedia.org/wiki/Semi-supervised_learning)**，在我们有很多未标记的��以用�预训练模型的数�的情况下。
+- **[强化学习](https://wikipedia.org/wiki/Reinforcement_learning)**，在这�方法中，机器通过在��模拟环境中进行实验�学习最佳策略。
+
+### 示例 - 电脑游�
+
+�设我们�教会计算机玩�一款游� —— 例如国际象棋，或者 [超级马里奥](https://wikipedia.org/wiki/Super_Mario)。为了让计算机学会玩游�，我们需�它预测在�个游�「状��下，它应该�什么「�作�。虽然这看起��是一个分类问题，但事实并�如此，因为我们并没有�这样的，包�「状��和状�对应的「�作�的数�集。我们�有一些有�的数�，比如�自国际象棋比赛的记录，或者是玩家玩超级马里奥的记录。这些数��能无法涵盖足够多的「状��。
+
+��于这�需�大�现有的数�的方法，**强化学习**是基于*让计算机多次玩*并观察玩的结果的想法。因此，�使用强化学习方法，我们需�两个�素：
+
+- **环境**和**模拟器**，它们�许我们多次玩游�。该模拟器应该定义所有游�规则，以��能的状�和动作。
+
+- **奖励函数**，它会告诉我们�个�一步（或者�局游�）的表现如何。
+
+其他类型的机器学习和强化学习 (RL) 之间的主�区别在于，在 RL 中，我们通常在完�游�之�，都�知�我们是赢还是输。因此，我们�能说�独的�个动作是�是「好�的 - 我们�会在游�结�时获得奖励。我们的目标是设计算法，使我们能够在这��确定的�件下训练模型。我们将了解一�称为 **Q-learning** 的 RL 算法。
+
+## 课程
+
+1.�强化学习和 Q-Learning 介�】(1-QLearning/README.md)
+2.�使用 Gym 模拟环境】(2-Gym/README.md)
+
+## 本文作者
+
+“强化学习简介� 由 [Dmitry Soshnikov](http://soshnikov.com) 用 ♥� 编写
\ No newline at end of file

From 7240be1e93f701d0c4d1d94d87113febf183fae1 Mon Sep 17 00:00:00 2001
From: manusquall <manusquall@gmail.com>
Date: Sat, 17 Jul 2021 01:59:53 +0000
Subject: [PATCH 194/368] stop at 43

---
 package.json                             |   4 +-
 quiz-app/src/assets/translations/fr.json | 236 +++++++++++------------
 2 files changed, 120 insertions(+), 120 deletions(-)

diff --git a/package.json b/package.json
index b64c6bf14e..3b5c347c91 100644
--- a/package.json
+++ b/package.json
@@ -4,8 +4,8 @@
 	"description": "Machine Learning for Beginners - A Curriculum",
 	"main": "index.js",
 	"scripts": {
-    "convert": "node_modules/.bin/docsify-to-pdf"
-  },
+		"convert": "node_modules/.bin/docsify-to-pdf"
+	},
 	"repository": {
 		"type": "git",
 		"url": "git+https://github.com/microsoft/ML-For-Beginners.git"
diff --git a/quiz-app/src/assets/translations/fr.json b/quiz-app/src/assets/translations/fr.json
index ec8111c061..3ec4ec9e7a 100644
--- a/quiz-app/src/assets/translations/fr.json
+++ b/quiz-app/src/assets/translations/fr.json
@@ -1,18 +1,18 @@
 [
 							{
-    "title": "Machine Learning pour les débutants: quiz",
+    "title": "Machine Learning pour les Débutants: Quiz",
     "complete": "Félicitations, vous avez terminé le quiz!",
     "error": "Désolé, essayez à nouveau",
     "quizzes": [
 							{
             "id": 1,
-            "title": "Introduction au machine learning: quiz préalable",
+            "title": "Introduction au machine learning: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Les applications de machine learning sont toutes autour de nous",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -59,7 +59,7 @@
         },
         {
             "id": 2,
-            "title": "Introduction au machine learning: quiz de validation des connaissances",
+            "title": "Introduction au machine learning: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Les algorithmes de machine learning sont destinés à simuler",
@@ -116,7 +116,7 @@
         },
         {
             "id": 3,
-            "title": "Historique du machine learning: quiz préalable",
+            "title": "Historique du machine learning: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Quand approximativement le terme 'intelligence artificielle' a-t-il été inventé ?",
@@ -173,7 +173,7 @@
         },
         {
             "id": 4,
-            "title": "Historique du machine learning: quiz de validation des connaissances",
+            "title": "Historique du machine learning: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Qu'est-ce qu'un exemple de système d'IA \" Scruffy \" AI?",
@@ -230,7 +230,7 @@
         },
         {
             "id": 5,
-            "title": "L'équité et machine learning: quiz préalable",
+            "title": "L'équité et le machine learning: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "L'injustice dans le machine learning peut arriver",
@@ -287,7 +287,7 @@
         },
         {
             "id": 6,
-            "title": "Equité et machine learning: quiz de validation des connaissances",
+            "title": "L'équité et le machine learning: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "L'injustice dans un modèle peut être causée par",
@@ -324,10 +324,10 @@
                     ]
                 },
                 {
-                    "questionText": "FairLearn est un paquet qui peut",
+                    "questionText": "Fairlearn est un paquet qui peut",
                     "answerOptions": [
 							{
-							"answerText": "Comparez plusieurs modèles en utilisant des métriques d'équité et de performance",
+							"answerText": "Comparer plusieurs modèles en utilisant des métriques d'équité et de performance",
                             "isCorrect": "true"
                         },
                         {
@@ -335,7 +335,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Aidez-vous à décider de ce qui est juste et ce qui n'est pas",
+							"answerText": "Vous aider à décider de ce qui est juste et ce qui ne l'est pas",
                             "isCorrect": "false"
                         }
                     ]
@@ -344,7 +344,7 @@
         },
         {
             "id": 7,
-            "title": "Outils et techniques: quiz préalable",
+            "title": "Outils et techniques: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Lors de la construction d'un modèle, vous devriez:",
@@ -401,7 +401,7 @@
         },
         {
             "id": 8,
-            "title": "Outils et techniques: quiz de validation des connaissances",
+            "title": "Outils et techniques: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Vous devez visualiser vos données car",
@@ -458,7 +458,7 @@
         },
         {
             "id": 9,
-            "title": "Introduction à la régression: quiz préalable",
+            "title": "Introduction à la régression: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Laquelle de ces variables est une variable numérique?",
@@ -515,7 +515,7 @@
         },
         {
             "id": 10,
-            "title": "Introduction à la régression: quiz de validation des connaissances",
+            "title": "Introduction à la régression: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Si la précision de la formation du modèle d'apprentissage de votre machine est de 95% et que la précision des tests est de 30%, quel type de condition est appelé?",
@@ -572,7 +572,7 @@
         },
         {
             "id": 11,
-            "title": "Préparer et visualiser des données pour la régression: quiz préalable",
+            "title": "Préparer et visualiser des données pour la régression: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Lequel de ces modules Python est utilisé pour tracer la visualisation des données?",
@@ -629,7 +629,7 @@
         },
         {
             "id": 12,
-            "title": "Préparez et visualisez des données pour la régression: quiz de validation des connaissances",
+            "title": "Préparer et visualiser des données pour la régression: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Lequel de ces extraits de code est correct d'après cette leçon, si vous souhaitez vérifier la présence de valeurs manquantes dans votre ensemble de données ? Supposons que l'ensemble de données soit stocké dans une variable nommée \"ensemble de données\", qui est un objet Pandas DataFrame.",
@@ -686,7 +686,7 @@
         },
         {
             "id": 13,
-            "title": "régression linéaire et polynomiale: quiz préalable",
+            "title": "Régression linéaire et polynomiale: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Matplotlib est un",
@@ -743,7 +743,7 @@
         },
         {
             "id": 14,
-            "title": "Régression linéaire et polynomiale: quiz de validation des connaissances",
+            "title": "Régression linéaire et polynomiale: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Si vos données sont non linéaires, essayez un type ___  de régression",
@@ -800,7 +800,7 @@
         },
         {
             "id": 15,
-            "title": "Régression logistique: quiz préalable",
+            "title": "Régression logistique: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Utilisez la régression logistique à prédire",
@@ -857,7 +857,7 @@
         },
         {
             "id": 16,
-            "title": "Régression logistique: quiz de validation des connaissances",
+            "title": "Régression logistique: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Sea-né est un type de",
@@ -913,7 +913,7 @@
             ]
         }, {
             "id": 17,
-            "title": "Construire une application Web: quiz préalable",
+            "title": "Construire une application Web: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Qu'est-ce que OnNX signifie?",
@@ -970,7 +970,7 @@
         },
         {
             "id": 18,
-            "title": "Construire une application Web: quiz de validation des connaissances",
+            "title": "Construire une application Web: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Quels sont les outils que nous pouvons utiliser pour héberger un modèle pré-formé sur le Web à l'aide de Python?",
@@ -1027,7 +1027,7 @@
         },
         {
             "id": 19,
-            "title": "Classification 1: quiz préalable",
+            "title": "Classification 1: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "La classification est une forme d'apprentissage supervisé qui a beaucoup en commun avec",
@@ -1084,7 +1084,7 @@
         },
         {
             "id": 20,
-            "title": "Classification 1: quiz de validation des connaissances",
+            "title": "Classification 1: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Qu'est-ce qu'une question multiclasse?",
@@ -1107,7 +1107,7 @@
                     "questionText": "Il est important de nettoyer des données récurrentes ou inutiles pour aider vos classificateurs à résoudre votre problème.",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -1137,13 +1137,13 @@
         },
         {
             "id": 21,
-            "title": "Classification 2: quiz préalable",
+            "title": "Classification 2: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Les données équilibrées et propres ont produit les meilleurs résultats de la classification",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -1190,7 +1190,7 @@
         },
         {
             "id": 22,
-            "title": "Classification 2: quiz de validation des connaissances",
+            "title": "Classification 2: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Qu'est-ce qu'un \"solveur\" ?",
@@ -1247,7 +1247,7 @@
         },
         {
             "id": 23,
-            "title": "Classification 3: quiz préalable",
+            "title": "Classification 3: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Un bon classificateur initial à essayer est:",
@@ -1304,7 +1304,7 @@
         },
         {
             "id": 24,
-            "title": "Classification 3: quiz de validation des connaissances",
+            "title": "Classification 3: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Les classificateurs de support-vectoriel peuvent être utilisés pour",
@@ -1361,7 +1361,7 @@
         },
         {
             "id": 25,
-            "title": "Classification 4: quiz préalable",
+            "title": "Classification 4: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Les systèmes de recommandation peuvent être utilisés pour",
@@ -1384,7 +1384,7 @@
                     "questionText": "L'intégration d'un modèle dans une application Web l'aide à être hors ligne",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -1414,7 +1414,7 @@
         },
         {
             "id": 26,
-            "title": "Classification 4: quiz de validation des connaissances",
+            "title": "Classification 4: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "L'application Nettron vous aide:",
@@ -1471,7 +1471,7 @@
         },
         {
             "id": 27,
-            "title": "Introduction au clustering: quiz préalable",
+            "title": "Introduction au Clustering: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Un exemple de vie réel de regroupement serait",
@@ -1508,18 +1508,18 @@
                     ]
                 },
                 {
-                    "questionText": "La clustering est un type de:",
+                    "questionText": "La Clustering est un type :",
                     "answerOptions": [
 							{
-							"answerText": "Apprentissage supervisé",
+							"answerText": "D'apprentissage supervisé",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "L'apprentissage non supervisé",
+							"answerText": "D'apprentissage non supervisé",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Apprentissage du renforcement",
+							"answerText": "D'apprentissage de renforcement",
                             "isCorrect": "false"
                         }
                     ]
@@ -1528,30 +1528,30 @@
         },
         {
             "id": 28,
-            "title": "Introduction au clustering: quiz de validation des connaissances",
+            "title": "Introduction au Clustering: Quiz de validation des connaissances",
             "quiz": [
 							{
-                    "questionText": "La géométrie euclidienne est arrangée",
+                    "questionText": "La géométrie euclidienne est disposée le long",
                     "answerOptions": [
 							{
-							"answerText": "Planes",
+							"answerText": "De plans",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Courbes",
+							"answerText": "De courbes",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Sphères",
+							"answerText": "De sphères",
                             "isCorrect": "false"
                         }
                     ]
                 },
                 {
-                    "questionText": "La densité de vos données de clustering est liée à sa",
+                    "questionText": "La densité de vos données de clustering est liée à son / sa",
                     "answerOptions": [
 							{
-							"answerText": "bruit",
+							"answerText": "Bruit",
                             "isCorrect": "true"
                         },
                         {
@@ -1568,15 +1568,15 @@
                     "questionText": "L'algorithme de regroupement le plus connu est",
                     "answerOptions": [
 							{
-							"answerText": "k-signifie",
+							"answerText": "k-means",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "K-Middle",
+							"answerText": "K-middle",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "K-Mart",
+							"answerText": "K-mart",
                             "isCorrect": "false"
                         }
                     ]
@@ -1585,7 +1585,7 @@
         },
         {
             "id": 29,
-            "title": "K-Means Clustering: quiz préalable",
+            "title": "K-Means Clustering: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "K-Means est dérivé de:",
@@ -1605,7 +1605,7 @@
                     ]
                 },
                 {
-                    "questionText": "Une bonne score de silhouette signifie:",
+                    "questionText": "Un bon score de silhouette signifie:",
                     "answerOptions": [
 							{
 							"answerText": "Les grappes sont bien séparées et bien définies",
@@ -1642,13 +1642,13 @@
         },
         {
             "id": 30,
-            "title": "K-olth regroupement: quiz de validation des connaissances",
+            "title": "K-Means Clustering: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Un diagramme de Voronoi montre:",
                     "answerOptions": [
 							{
-							"answerText": "Variance d'une cluster",
+							"answerText": "Une variance d'une cluster",
                             "isCorrect": "false"
                         },
                         {
@@ -1682,7 +1682,7 @@
                     "questionText": "en utilisant k-moyen, vous devez d'abord déterminer la valeur de 'k'",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -1695,7 +1695,7 @@
         },
         {
             "id": 31,
-            "title": "Intro to NLP: quiz préalable",
+            "title": "Intro aux NLP: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Que signifie NLP pour ces leçons?",
@@ -1732,14 +1732,14 @@
                     ]
                 },
                 {
-                    "questionText": "Le test de Turing 'd'Alan Turing a essayé de déterminer si un ordinateur était",
+                    "questionText": "Le test Turing d'Alan Turing a essayé de déterminer si un ordinateur était",
                     "answerOptions": [
 							{
-							"answerText": "Inmistingtilisable d'un humain",
+							"answerText": "indiscernable d'un humain",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Penser",
+							"answerText": "Pensif",
                             "isCorrect": "false"
                         },
                         {
@@ -1752,7 +1752,7 @@
         },
         {
             "id": 32,
-            "title": "Intro to NLP: quiz de validation des connaissances",
+            "title": "Intro aux NLP: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Joseph Weizenbaum a inventé le bot",
@@ -1775,7 +1775,7 @@
                     "questionText": "Un bot conversationnel donne une sortie basée sur",
                     "answerOptions": [
 							{
-							"answerText": "Choisir des choix prédéfinis au hasard",
+							"answerText": "Un choix de choix prédéfinis au hasard",
                             "isCorrect": "false"
                         },
                         {
@@ -1789,14 +1789,14 @@
                     ]
                 },
                 {
-                    "questionText": "Comment feriez-vous le bot plus efficace?",
+                    "questionText": "Comment feriez-vous pour que le bot soit plus efficace?",
                     "answerOptions": [
 							{
 							"answerText": "En le demandant plus de questions.",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "En l'alignant plus de données et de la formation en conséquence",
+							"answerText": "En lui fournissant plus de données et en le formant en conséquence",
                             "isCorrect": "true"
                         },
                         {
@@ -1809,21 +1809,21 @@
         },
         {
             "id": 33,
-            "title": "Tâches NLP: quiz préalable",
+            "title": "Tâches NLP: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Tokenization",
                     "answerOptions": [
 							{
-							"answerText": "Splituelle du texte au moyen de la ponctuation",
+							"answerText": "Divise le texte au moyen de la ponctuation",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Splituelle du texte en jetons séparés (mots)",
+							"answerText": "Divise le texte en jetons séparés (mots)",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Splituelle du texte en phrases",
+							"answerText": "Divise le texte en phrases",
                             "isCorrect": "false"
                         }
                     ]
@@ -1836,11 +1836,11 @@
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Intégrance des mots en phrases",
+							"answerText": "Intégre des mots en phrases",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Intégrance des phrases dans les paragraphes",
+							"answerText": "Intégre des phrases dans les paragraphes",
                             "isCorrect": "false"
                         }
                     ]
@@ -1866,7 +1866,7 @@
         },
         {
             "id": 34,
-            "title": "Tâches NLP: quiz de validation des connaissances",
+            "title": "Tâches NLP: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Construisez un dictionnaire de la fréquence à laquelle les mots se reproduisent en utilisant:",
@@ -1923,10 +1923,10 @@
         },
         {
             "id": 35,
-            "title": "NLP et traduction: quiz préalable",
+            "title": "NLP et traduction: Quiz préalable",
             "quiz": [
 							{
-                    "questionText": "Traduction naïf",
+                    "questionText": "La traduction naïve",
                     "answerOptions": [
 							{
 							"answerText": "Traduit uniquement les mots",
@@ -1943,7 +1943,7 @@
                     ]
                 },
                 {
-                    "questionText": "Un corpus * de textes fait référence à",
+                    "questionText": "Un *corpus* de textes fait référence à",
                     "answerOptions": [
 							{
 							"answerText": "Un petit nombre de textes",
@@ -1960,10 +1960,10 @@
                     ]
                 },
                 {
-                    "questionText": "Si un modèle ML a suffisamment de traductions humaines pour construire un modèle sur, il peut",
+                    "questionText": "Si un modèle ML a suffisamment de traductions humaines pour construire un modèle, il peut",
                     "answerOptions": [
 							{
-							"answerText": "Traductions abrégées",
+							"answerText": "Abréger des traductions",
                             "isCorrect": "false"
                         },
                         {
@@ -1980,7 +1980,7 @@
         },
         {
             "id": 36,
-            "title": "NLP et traduction: quiz de validation des connaissances",
+            "title": "NLP et traduction: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "La bibliothèque de traduction de texte sous-jacente est:",
@@ -2000,7 +2000,7 @@
                     ]
                 },
                 {
-                    "questionText": "utiliser `blob.translate` vous avez besoin:",
+                    "questionText": "Pour utiliser `blob.translate` vous avez besoin:",
                     "answerOptions": [
 							{
 							"answerText": "Une connexion Internet",
@@ -2017,7 +2017,7 @@
                     ]
                 },
                 {
-                    "questionText": "Déterminer le sentiment, une approche ML serait de:",
+                    "questionText": "Pour déterminer un sentiment, une approche ML serait de:",
                     "answerOptions": [
 							{
 							"answerText": "Appliquez des techniques de régression pour générer manuellement des opinions et des scores et rechercher des modèles",
@@ -2037,7 +2037,7 @@
         },
         {
             "id": 37,
-            "title": "NLP 4: quiz préalable",
+            "title": "NLP 4: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Quelles informations pouvons-nous obtenir du texte écrit ou parlé par un humain?",
@@ -2094,17 +2094,17 @@
         },
         {
             "id": 38,
-            "title": "NLP 4: quiz de validation des connaissances",
+            "title": "NLP 4: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Quelle est l'essence de la NLP?",
                     "answerOptions": [
 							{
-							"answerText": "catégoriser la langue humaine en joyeux ou triste",
+							"answerText": "catégoriser la langue humaine en joyeuse ou triste",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Interprétation de sens ou de sentiment sans avoir à avoir un homme humain le faire",
+							"answerText": "Interprétation de sens ou de sentiment sans avoir un humain pour le faire",
                             "isCorrect": "true"
                         },
                         {
@@ -2114,7 +2114,7 @@
                     ]
                 },
                 {
-                    "questionText": "Quelles sont certaines choses que vous pourriez rechercher lors de la nettoyage des données?",
+                    "questionText": "Quelles sont certaines choses que vous pourriez rechercher lors du nettoyage des données?",
                     "answerOptions": [
 							{
 							"answerText": "Personnages dans d'autres langues",
@@ -2131,10 +2131,10 @@
                     ]
                 },
                 {
-                    "questionText": "Il est important de comprendre vos données et ses fruits avant d'effectuer des opérations à ce sujet.",
+                    "questionText": "Il est important de comprendre votre donnée et ses faiblesses avant d'effectuer des opérations à ce sujet.",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -2147,7 +2147,7 @@
         },
         {
             "id": 39,
-            "title": "NLP 5: quiz préalable",
+            "title": "NLP 5: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Pourquoi est-il important de nettoyer les données avant de l'analyser?",
@@ -2178,7 +2178,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Déplacement des valeurs aberrantes vers une table séparée et exécutant les calculs de cette table pour voir s'ils correspondent à",
+							"answerText": "Déplacement des valeurs aberrantes vers une table séparée et exécutant les calculs de cette table pour voir s'ils correspondent",
                             "isCorrect": "false"
                         }
                     ]
@@ -2187,7 +2187,7 @@
                     "questionText": "Il peut être utile de classer les données à l'aide d'une colonne Tag.",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -2200,17 +2200,17 @@
         },
         {
             "id": 40,
-            "title": "NLP 5: quiz de validation des connaissances",
+            "title": "NLP 5: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Quel est l'objectif de l'ensemble de données?",
                     "answerOptions": [
 							{
-							"answerText": "Pour voir combien de critiques négatives et positives il y a pour les hôtels à travers le monde",
+							"answerText": "Voir combien de critiques négatives et positives il y a pour les hôtels à travers le monde",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Pour ajouter du sentiment et des colonnes qui vous aideront à choisir le meilleur hôtel",
+							"answerText": "Ajouter du sentiment et des colonnes qui vous aideront à choisir le meilleur hôtel",
                             "isCorrect": "true"
                         },
                         {
@@ -2240,7 +2240,7 @@
                     "questionText": "Pour tester l'analyse du sentiment, assurez-vous qu'il correspond au score du critique pour le même examen.",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -2253,27 +2253,27 @@
         },
         {
             "id": 41,
-            "title": "Série introduction à temps: quiz préalable",
+            "title": "Introduction aux Time Series (séries temporelles): Quiz préalable",
             "quiz": [
 							{
-                    "questionText": "La prévision de la série chronologique est utile",
+                    "questionText": "La prévision de Time Series est utile pour",
                     "answerOptions": [
 							{
 							"answerText": "Déterminer les coûts futurs",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Prédicter les prix futurs",
+							"answerText": "Prédire les prix futurs",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "à la fois ce qui précède",
+							"answerText": "Les deux à la fois",
                             "isCorrect": "true"
                         }
                     ]
                 },
                 {
-                    "questionText": "Une série chronologique est une séquence prise à:",
+                    "questionText": "Une série temporelles est une séquence prise à:",
                     "answerOptions": [
 							{
 							"answerText": "points successifs également espacés dans l'espace",
@@ -2290,7 +2290,7 @@
                     ]
                 },
                 {
-                    "questionText": "La série chronologique peut être utilisée dans:",
+                    "questionText": "La série temporelles peut être utilisée dans les cas de:",
                     "answerOptions": [
 							{
 							"answerText": "Prévision de tremblement de terre",
@@ -2310,17 +2310,17 @@
         },
         {
             "id": 42,
-            "title": "Série introduction à TIME: quiz de validation des connaissances",
+            "title": "Introduction aux Time Series (séries temporelles): Quiz de validation des connaissances",
             "quiz": [
 							{
-                    "questionText": "Tendances de la série chronologique sont",
+                    "questionText": "Les tendances de série temporelles sont",
                     "answerOptions": [
 							{
-							"answerText": "mesure mesurable augmente et diminue au fil du temps",
+							"answerText": "des augmentations et des diminutions mesurables au fil du temps",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "La quantification diminue au fil du temps",
+							"answerText": "La quantification des diminutions au fil du temps",
                             "isCorrect": "false"
                         },
                         {
@@ -2330,14 +2330,14 @@
                     ]
                 },
                 {
-                    "questionText": "Les valeurs aberrantes sont",
+                    "questionText": "Les valeurs aberrantes sont des",
                     "answerOptions": [
 							{
 							"answerText": "Points proches de la variance de données standard",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Pointes loin de la variance de données standard",
+							"answerText": "Points loin de la variance de données standard",
                             "isCorrect": "true"
                         },
                         {
@@ -2347,18 +2347,18 @@
                     ]
                 },
                 {
-                    "questionText": "La prévision de la série chronologique est la plus utile pour",
+                    "questionText": "La prévision de la série temporelle est la plus utile pour",
                     "answerOptions": [
 							{
-							"answerText": "Econométrics",
+							"answerText": "L'conométrics",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Histoire",
+							"answerText": "L'histoire",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Bibliothèques",
+							"answerText": "Les bibliothèques",
                             "isCorrect": "false"
                         }
                     ]
@@ -2367,7 +2367,7 @@
         },
         {
             "id": 43,
-            "title": "Série TIME ARIMA: quiz préalable",
+            "title": "Série TIME ARIMA: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Arima signifie",
@@ -2424,7 +2424,7 @@
         },
         {
             "id": 44,
-            "title": "Série TIME ARIMA: quiz de validation des connaissances",
+            "title": "Série TIME ARIMA: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Arima est utilisé pour créer un modèle adapté à la forme spéciale des données de la série chronologique",
@@ -2480,7 +2480,7 @@
             ]
         }, {
             "id": 45,
-            "title": "Renforcement 1: quiz préalable",
+            "title": "Renforcement 1: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Qu'est-ce que l'apprentissage du renforcement?",
@@ -2520,7 +2520,7 @@
                     "questionText": "Une fonction de récompense renvoie un score pour chaque état d'environnement.",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "true"
                         },
                         {
@@ -2532,7 +2532,7 @@
             ]
         }, {
             "id": 46,
-            "title": "Renforcement 1: quiz de validation des connaissances",
+            "title": "Renforcement 1: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Qu'est-ce que q-apprentissage?",
@@ -2572,7 +2572,7 @@
                     "questionText": "Il valait mieux utiliser l'exploration que l'exploitation pendant le processus d'apprentissage de notre leçon.",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "false"
                         },
                         {
@@ -2585,13 +2585,13 @@
         },
         {
             "id": 47,
-            "title": "Renforcement 2: quiz préalable",
+            "title": "Renforcement 2: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Les échecs et les jeux sont des jeux avec des états continus.",
                     "answerOptions": [
 							{
-							"answerText": "vrai",
+							"answerText": "Vrai",
                             "isCorrect": "false"
                         },
                         {
@@ -2638,7 +2638,7 @@
         },
         {
             "id": 48,
-            "title": "Renforcement 2: quiz de validation des connaissances",
+            "title": "Renforcement 2: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Où définissons-nous toutes les actions possibles dans un environnement?",
@@ -2695,7 +2695,7 @@
         },
         {
             "id": 49,
-            "title": "Applications du monde réel: quiz préalable",
+            "title": "Applications du monde réel: Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Quel est un exemple d'application ML dans l'industrie des finances?",
@@ -2752,7 +2752,7 @@
         },
         {
             "id": 50,
-            "title": "Applications du monde réel: quiz de validation des connaissances",
+            "title": "Applications du monde réel: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Quelle technique ML peut être utilisée pour détecter la fraude par carte de crédit?",

From 6277966cb81afa13415dff1b9582ddd399ad1307 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Sat, 17 Jul 2021 17:38:22 +0300
Subject: [PATCH 195/368] Add Turkish translation of the base README.md file

---
 translations/README.tr.md | 119 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 119 insertions(+)
 create mode 100644 translations/README.tr.md

diff --git a/translations/README.tr.md b/translations/README.tr.md
new file mode 100644
index 0000000000..fa7c6169d4
--- /dev/null
+++ b/translations/README.tr.md
@@ -0,0 +1,119 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# Yeni Başlayanlar için Makine Öğrenimi - Bir Eğitim Programı
+
+> :earth_africa: Dünya kültürleri sayesinde Makine Öğrenimi'ni keşfederken dünyayı gezin :earth_africa:
+
+Microsoft'taki Azure Cloud Destekleyicileri tamamen **Makine Öğrenimi** hakkında olan 12 hafta ve 24 derslik eğitim programını sunmaktan memnuniyet duyar. Bu eğitim programında, kütüphane olarak temelde Scikit-learn kullanarak ve yakında çıkacak olan 'Yeni Başlayanlar için Yapay Zeka' dersinde anlatılan derin öğrenmeden uzak durarak, zaman zaman adlandırıldığı şekliyle, **klasik makine öğrenimi**ni öğreneceksiniz. Bu dersleri yakında çıkacak olan 'Yeni Başlayanlar için Veri Bilimi' eğitim programımızla da birleştirin!
+
+Biz bu klasik teknikleri dünyanın birçok alanından verilere uygularken bizimle dünyayı gezin. Her bir ders, ders başı ve ders sonu kısa sınavlarını, dersi tamamlamak için yazılı yönergeleri, bir çözümü, bir ödevi ve daha fazlasını içerir. Yeni becerilerin 'yerleşmesi' için kanıtlanmış bir yol olan proje temelli pedagojimiz, yaparken öğrenmenizi sağlar.
+
+**:writing_hand: Yazarlarımıza yürekten teşekkürler** Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Ornella Altunyan, and Amy Boyd
+
+**:art: Çizerlerimize de teşekkürler** Tomomi Imura, Dasani Madipalli, and Jen Looper
+
+ **:pray: Microsoft Student Ambassador yazarlarımıza, eleştirmenlerimize ve içeriğe katkıda bulunanlara özel teşekkürler :pray:**, özellikle Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+
+---
+# BaÅŸlarken
+
+**Öğrenciler**, bu eğitim programını kullanmak için, tüm yazılım havuzunu kendi GitHub hesabınıza çatallayın ve alıştırmaları kendiniz veya bir grup ile tamamlayın:
+
+- Bir ders öncesi kısa sınavı ile başlayın
+- Her bilgi kontrolünde durup derinlemesine düşünerek dersi okuyun ve etkinlikleri tamamlayın.
+- Çözüm kodunu çalıştırmaktansa dersleri kavrayarak projeleri yapmaya çalışın; yine de o çözüm kodu her proje yönelimli derste `/solution` klasörlerinde mevcut.
+- Ders sonrası kısa sınavını çözün
+- Meydan okumayı tamamlayın
+- Ödevi tamamlayın
+- Bir ders grubunu tamamladıktan sonra, [Tartışma Panosu](https://github.com/microsoft/ML-For-Beginners/discussions)'nu ziyaret edin ve uygun PAT yönergesini doldurarak "sesli öğrenin" (Yani, tamamen öğrenmeden önce öğrenme süreciniz üzerine derin düşünerek içgözlem ve geridönütlerle kendinizde farkındalık oluşturun.). 'PAT', bir Progress Assessment Tool'dur (Süreç Değerlendirme Aracı), öğrenmenizi daha ileriye taşımak için doldurduğunuz bir yönergedir. Diğer PAT'lere de karşılık verebilirsiniz, böylece beraber öğrenebiliriz.
+
+> İleri çalışma için, bu [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modüllerini ve öğrenme rotalarını takip etmenizi tavsiye ediyoruz.
+
+**Öğretmenler**, bu eğitim programının nasıl kullanılacağı hakkında [bazı öneriler ekledik](for-teachers.md).
+
+---
+
+## Takımla Tanışın
+
+[![Tanıtım videosu](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+
+> :movie_camera: Proje ve projeyi yaratanlar hakkındaki video için yukarıdaki fotoğrafa tıklayın!
+
+---
+## Pedagoji
+
+Bu eğitim programını oluştururken iki pedagojik ilke seçtik: uygulamalı **proje temelli** olduğundan ve **sık kısa sınavlar** içerdiğinden emin olmak. Ayrıca, bu eğitim programında tutarlılık sağlaması için genel bir **tema** var.
+
+İçeriğin projelerle uyumlu olduğuna emin olarak, süreç öğrenciler için daha ilgi çekici hale getirilmiştir ve kavramların akılda kalıcılığı artacaktır. Ayrıca, dersten önce ikincil değerli bir kısa sınav öğrencinin niyetini konuyu öğrenmek yaparken dersten sonra yapılan ikinci bir kısa sınav da akılda kalıcılığı sağlar. Bu eğitim programı esnek ve eğlenceli olacak şekilde hazırlanmıştır ve tümüyle veya kısmen işlenebilir. Projeler kolay başlar ve 12 haftalık zamanın sonuna doğru karmaşıklıkları gittikçe artar. Bu eğitim programı, Makine Öğreniminin gerçek hayattaki uygulamaları üzerine, ek puan veya tartışma için bir temel olarak kullanılabilecek bir ek yazı da içermektedir.
+
+> [Davranış Kuralları](CODE_OF_CONDUCT.md)'mızı, [Katkıda Bulunma](CONTRIBUTING.md) ve [Çeviri](TRANSLATIONS.md) kılavuz ilkelerimizi inceleyin. Yapıcı geridönütlerinizi memnuniyetle karşılıyoruz!
+## Her ders şunları içermektedir:
+
+- isteğe bağlı eskiz notu
+- isteğe bağlı ek video
+- ders öncesi ısınma kısa sınavı
+- yazılı ders
+- proje temelli dersler için, projenin nasıl yapılacağına dair adım adım kılavuz
+- bilgi kontrolleri
+- bir meydan okuma
+- ek okuma
+- ödev
+- ders sonrası kısa sınavı
+
+> **Kısa sınavlar hakkında bir not**: Her biri üç sorudan oluşan ve toplamda 50 tane olan tüm kısa sınavlar [bu uygulamada](https://jolly-sea-0a877260f.azurestaticapps.net) bulunmaktadır. Derslerin içinden de bağlantı yoluyla ulaşılabilirler ancak kısa sınav uygulaması yerelde çalıştırılabilir; `quiz-app` klasöründeki yönergeleri takip edin.
+
+
+| Ders Numarası |                           Konu                             |                     Ders Gruplandırması                     |  Öğrenme Hedefleri                                                                                                              |                         Ders                          |     Yazar      |
+| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
+|      01       |                   Makine Öğrenimi Giriş                    |         [Giriş](1-Introduction/README.md)           | Makine öğreniminin temel kavramlarını öğrenmek                                                                                  |    [ders](1-Introduction/1-intro-to-ML/README.md)     |    Muhammad    |
+|      02       |                   Makine Öğrenimi Tarihi                   |         [Giriş](1-Introduction/README.md)           | Bu alanın altında yatan tarihi öğrenmek                                                                                         |   [ders](1-Introduction/2-history-of-ML/README.md)    |  Jen and Amy   |
+|      03       |                 Eşitlik ve Makine Öğrenimi                 |         [Giriş](1-Introduction/README.md)           | Öğrencilerin ML modelleri yaparken ve uygularken düşünmeleri gereken eşitlik hakkındaki önemli felsefi sorunlar nelerdir?       |      [ders](1-Introduction/3-fairness/README.md)      |     Tomomi     |
+|      04       |               Makine Öğrenimi için Yöntemler               |         [Giriş](1-Introduction/README.md)           | ML araştırmacıları ML modelleri üretmek için hangi yöntemleri kullanırlar?                                                      |  [ders](1-Introduction/4-techniques-of-ML/README.md)  | Chris and Jen  |
+|      05       |                      Regresyona Giriş                      |         [Regresyon](2-Regression/README.md)         | Regresyon modelleri için Python ve Scikit-learn'e başlamak                                                                      |        [ders](2-Regression/1-Tools/README.md)         |      Jen       |
+|      06       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](2-Regression/README.md)         | ML hazırlığı için verileri görselleştirmek ve temizlemek                                                                        |         [ders](2-Regression/2-Data/README.md)         |      Jen       |
+|      07       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](2-Regression/README.md)         | Doğrusal ve polinom regresyon modelleri yapmak                                                                                  |        [ders](2-Regression/3-Linear/README.md)        |      Jen       |
+|      08       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](2-Regression/README.md)         | Lojistik bir regresyon modeli yapmak                                                                                            |       [ders](2-Regression/4-Logistic/README.md)       |      Jen       |
+|      09       |                    Bir Web Uygulaması :electric_plug:                    |        [Web Uygulaması](3-Web-App/README.md)        | Eğittiğiniz modeli kullanmak için bir web uygulaması yapmak                                                                     |         [ders](3-Web-App/1-Web-App/README.md)         |      Jen       |
+|      10       |                   Sınıflandırmaya Giriş                    |     [Sınıflandırma](4-Classification/README.md)     | Verilerinizi temizlemek, hazırlamak, ve görselleştirmek; sınıflandırmaya giriş                                                  |   [ders](4-Classification/1-Introduction/README.md)   | Jen and Cassie |
+|      11       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](4-Classification/README.md)     | Sınıflandırıcılara giriş                                                                                                        |  [ders](4-Classification/2-Classifiers-1/README.md)   | Jen and Cassie |
+|      12       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](4-Classification/README.md)     | Daha fazla sınıflandırıcı                                                                                                       |  [ders](4-Classification/3-Classifiers-2/README.md)   | Jen and Cassie |
+|      13       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](4-Classification/README.md)     | Modelinizi kullanarak tavsiyede bulunan bir web uygulaması yapmak                                                               |     [ders](4-Classification/4-Applied/README.md)      |      Jen       |
+|      14       |                      Kümelemeye Giriş                      |         [Kümeleme](5-Clustering/README.md)          | Verilerinizi temizlemek, hazırlamak, ve görselleştirmek; kümelemeye giriş                                                       |      [ders](5-Clustering/1-Visualize/README.md)       |      Jen       |
+|      15       |          Nijerya'nın Müzik Zevklerini Keşfetme :headphones:           |         [Kümeleme](5-Clustering/README.md)          | K merkezli kümeleme yöntemini keşfetmek                                                                                         |       [ders](5-Clustering/2-K-Means/README.md)        |      Jen       |
+|      16       |                 Doğal Dil İşlemeye Giriş  :coffee:                |        [Doğal Dil İşleme](6-NLP/README.md)          | Basit bir bot yaratarak NLP temellerini öğrenmek                                                                                |     [ders](6-NLP/1-Introduction-to-NLP/README.md)     |    Stephen     |
+|      17       |                   Yaygın NLP Görevleri :coffee:                   |        [Doğal Dil İşleme](6-NLP/README.md)          | Dil yapılarıyla uğraşırken gereken yaygın görevleri anlayarak NLP bilginizi derinleştirmek                                      |            [ders](6-NLP/2-Tasks/README.md)            |    Stephen     |
+|      18       |                  Çeviri ve Duygu Analizi :hearts:                 |        [Doğal Dil İşleme](6-NLP/README.md)          | Jane Austen ile çeviri ve duygu analizi                                                                                         |    [ders](6-NLP/3-Translation-Sentiment/README.md)    |    Stephen     |
+|      19       |               Avrupa'nın Romantik Otelleri :hearts:               |        [Doğal Dil İşleme](6-NLP/README.md)          | Otel değerlendirmeleriyle duygu analizi, 1                                                                                      |       [ders](6-NLP/4-Hotel-Reviews-1/README.md)       |    Stephen     |
+|      20       |               Avrupa'nın Romantik Otelleri :hearts:               |        [Doğal Dil İşleme](6-NLP/README.md)          | Otel değerlendirmeleriyle duygu analizi 2                                                                                       |       [ders](6-NLP/5-Hotel-Reviews-2/README.md)       |    Stephen     |
+|      21       |               Zaman Serisi Tahminine GiriÅŸ                 |        [Zaman Serisi](7-TimeSeries/README.md)       | Zaman serisi tahminine giriÅŸ                                                                                                    |     [ders](7-TimeSeries/1-Introduction/README.md)     |   Francesca    |
+|      22       | :zap: Dünya Güç Kullanımı :zap: - ARIMA ile Zaman Serisi Tahmini |        [Zaman Serisi](7-TimeSeries/README.md)       | ARIMA ile zaman serisi tahmini                                                                                                  |        [ders](7-TimeSeries/2-ARIMA/README.md)         |   Francesca    |
+|      23       |                Pekiştirmeli Öğrenmeye Giriş                |  [Pekiştirmeli Öğrenme](8-Reinforcement/README.md)  | Q-Learning ile pekiştirmeli öğrenmeye giriş                                                                                     |     [ders](8-Reinforcement/1-QLearning/README.md)     |     Dmitry     |
+|      24       |       Peter'ın Kurttan Uzak Durmasına Yardım Edin! :wolf:       |  [Pekiştirmeli Öğrenme](8-Reinforcement/README.md)  | Pekiştirmeli öğrenme spor salonu                                                                                                |        [ders](8-Reinforcement/2-Gym/README.md)        |     Dmitry     |
+|   Ek Yazı     |       Gerçek Hayattan ML Senaryoları ve Uygulamaları       |     [Vahşi Doğada ML](9-Real-World/README.md)       | Klasik makine öğreniminin ilginç ve açıklayıcı gerçek hayat uygulamaları                                                        |     [ders](9-Real-World/1-Applications/README.md)     |      Team      |
+## Çevrimdışı erişim
+
+Bu dokümantasyonu [Docsify](https://docsify.js.org/#/) kullanarak çevrimdışı çalıştırabilirsiniz. Bu depoyu çatallayın, yerel makinenizde [Docsify'ı kurum](https://docsify.js.org/#/quickstart) ve sonra bu deponun kök dizininde `docsify serve` yazın. İnternet sitesi, 3000 portunda `localhost:3000` yerel ana makinenizde sunulacaktır.
+
+## PDF'ler
+
+Eğitim programının bağlantılarla PDF'sine [buradan](pdf/readme.pdf) ulaşabilirsiniz.
+
+## Yardım İsteniyor!
+
+Bir çeviri katkısında bulunmak ister misiniz? Lütfen [çeviri kılavuz ilkelerimizi](TRANSLATIONS.md) okuyun ve [buraya](https://github.com/microsoft/ML-For-Beginners/issues/71) girdiyi ekleyin.
+
+## Diğer Eğitim Programları
+
+Takımımız başka eğitim programları üretiyor! İnceleyin:
+
+- [Yeni Başlayanlar için Web Geliştirme](https://aka.ms/webdev-beginners)
+- [Yeni Başlayanlar için Nesnelerin İnterneti](https://aka.ms/iot-beginners)
+

From aa4ec30a6b1a5b6e253e1195589eb9f6b75dd984 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Sat, 17 Jul 2021 17:58:13 +0300
Subject: [PATCH 196/368] Update some words in the Turkish translation of base
 README.md

---
 translations/README.tr.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/translations/README.tr.md b/translations/README.tr.md
index fa7c6169d4..2311bf67b3 100644
--- a/translations/README.tr.md
+++ b/translations/README.tr.md
@@ -10,7 +10,7 @@
 
 # Yeni Başlayanlar için Makine Öğrenimi - Bir Eğitim Programı
 
-> :earth_africa: Dünya kültürleri sayesinde Makine Öğrenimi'ni keşfederken dünyayı gezin :earth_africa:
+> :earth_africa: Dünya kültürleri sayesinde Makine Öğrenimini keşfederken dünyayı gezin :earth_africa:
 
 Microsoft'taki Azure Cloud Destekleyicileri tamamen **Makine Öğrenimi** hakkında olan 12 hafta ve 24 derslik eğitim programını sunmaktan memnuniyet duyar. Bu eğitim programında, kütüphane olarak temelde Scikit-learn kullanarak ve yakında çıkacak olan 'Yeni Başlayanlar için Yapay Zeka' dersinde anlatılan derin öğrenmeden uzak durarak, zaman zaman adlandırıldığı şekliyle, **klasik makine öğrenimi**ni öğreneceksiniz. Bu dersleri yakında çıkacak olan 'Yeni Başlayanlar için Veri Bilimi' eğitim programımızla da birleştirin!
 
@@ -20,7 +20,7 @@ Biz bu klasik teknikleri dünyanın birçok alanından verilere uygularken bizim
 
 **:art: Çizerlerimize de teşekkürler** Tomomi Imura, Dasani Madipalli, and Jen Looper
 
- **:pray: Microsoft Student Ambassador yazarlarımıza, eleştirmenlerimize ve içeriğe katkıda bulunanlara özel teşekkürler :pray:**, özellikle Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+ **:pray: Microsoft Student Ambassador yazarlarımıza, eleştirmenlerimize ve içeriğe katkıda bulunanlara özel teşekkürler :pray:** özellikle Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
 
 ---
 # BaÅŸlarken
@@ -55,7 +55,7 @@ Bu eğitim programını oluştururken iki pedagojik ilke seçtik: uygulamalı **
 İçeriğin projelerle uyumlu olduğuna emin olarak, süreç öğrenciler için daha ilgi çekici hale getirilmiştir ve kavramların akılda kalıcılığı artacaktır. Ayrıca, dersten önce ikincil değerli bir kısa sınav öğrencinin niyetini konuyu öğrenmek yaparken dersten sonra yapılan ikinci bir kısa sınav da akılda kalıcılığı sağlar. Bu eğitim programı esnek ve eğlenceli olacak şekilde hazırlanmıştır ve tümüyle veya kısmen işlenebilir. Projeler kolay başlar ve 12 haftalık zamanın sonuna doğru karmaşıklıkları gittikçe artar. Bu eğitim programı, Makine Öğreniminin gerçek hayattaki uygulamaları üzerine, ek puan veya tartışma için bir temel olarak kullanılabilecek bir ek yazı da içermektedir.
 
 > [Davranış Kuralları](CODE_OF_CONDUCT.md)'mızı, [Katkıda Bulunma](CONTRIBUTING.md) ve [Çeviri](TRANSLATIONS.md) kılavuz ilkelerimizi inceleyin. Yapıcı geridönütlerinizi memnuniyetle karşılıyoruz!
-## Her ders şunları içermektedir:
+## Her bir ders şunları içermektedir:
 
 - isteğe bağlı eskiz notu
 - isteğe bağlı ek video
@@ -97,10 +97,10 @@ Bu eğitim programını oluştururken iki pedagojik ilke seçtik: uygulamalı **
 |      22       | :zap: Dünya Güç Kullanımı :zap: - ARIMA ile Zaman Serisi Tahmini |        [Zaman Serisi](7-TimeSeries/README.md)       | ARIMA ile zaman serisi tahmini                                                                                                  |        [ders](7-TimeSeries/2-ARIMA/README.md)         |   Francesca    |
 |      23       |                Pekiştirmeli Öğrenmeye Giriş                |  [Pekiştirmeli Öğrenme](8-Reinforcement/README.md)  | Q-Learning ile pekiştirmeli öğrenmeye giriş                                                                                     |     [ders](8-Reinforcement/1-QLearning/README.md)     |     Dmitry     |
 |      24       |       Peter'ın Kurttan Uzak Durmasına Yardım Edin! :wolf:       |  [Pekiştirmeli Öğrenme](8-Reinforcement/README.md)  | Pekiştirmeli öğrenme spor salonu                                                                                                |        [ders](8-Reinforcement/2-Gym/README.md)        |     Dmitry     |
-|   Ek Yazı     |       Gerçek Hayattan ML Senaryoları ve Uygulamaları       |     [Vahşi Doğada ML](9-Real-World/README.md)       | Klasik makine öğreniminin ilginç ve açıklayıcı gerçek hayat uygulamaları                                                        |     [ders](9-Real-World/1-Applications/README.md)     |      Team      |
+|   Ek Yazı     |       Gerçek Hayattan ML Senaryoları ve Uygulamaları       |     [Vahşi Doğada ML](9-Real-World/README.md)       | Klasik makine öğreniminin ilginç ve açıklayıcı gerçek hayat uygulamaları                                                        |     [ders](9-Real-World/1-Applications/README.md)     |     Takım      |
 ## Çevrimdışı erişim
 
-Bu dokümantasyonu [Docsify](https://docsify.js.org/#/) kullanarak çevrimdışı çalıştırabilirsiniz. Bu depoyu çatallayın, yerel makinenizde [Docsify'ı kurum](https://docsify.js.org/#/quickstart) ve sonra bu deponun kök dizininde `docsify serve` yazın. İnternet sitesi, 3000 portunda `localhost:3000` yerel ana makinenizde sunulacaktır.
+Bu dokümantasyonu [Docsify](https://docsify.js.org/#/) kullanarak çevrimdışı çalıştırabilirsiniz. Bu yazılım havuzunu çatallayın, yerel makinenizde [Docsify'ı kurum](https://docsify.js.org/#/quickstart) ve sonra bu yazılım havuzunun kök dizininde `docsify serve` yazın. İnternet sitesi, 3000 portunda `localhost:3000` yerel ana makinenizde sunulacaktır.
 
 ## PDF'ler
 

From 194190f73bee3cee9c3728a46a297462578a34eb Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Sat, 17 Jul 2021 19:18:11 +0200
Subject: [PATCH 197/368] Translation of AI History

README + Assignment
---
 .../2-history-of-ML/translations/README.fr.md | 117 ++++++++++++++++++
 .../translations/assignment.fr.md             |  11 ++
 2 files changed, 128 insertions(+)
 create mode 100644 1-Introduction/2-history-of-ML/translations/README.fr.md
 create mode 100644 1-Introduction/2-history-of-ML/translations/assignment.fr.md

diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
new file mode 100644
index 0000000000..9c74b0b833
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -0,0 +1,117 @@
+# Histoire du Machine Learning (apprentissage automatique)
+
+![Résumé de l'histoire du machine learning dans un sketchnote](../../../sketchnotes/ml-history.png)
+> Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quizz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+
+Dans cette leçon, nous allons parcourir les principales étapes de l'histoire du machine learning et de l'intelligence artificielle.
+
+L'histoire de l'intelligence artificielle, l'IA, en tant que domaine est étroitement liée à l'histoire du machine learning, car les algorithmes et les avancées informatiques qui sous-tendent le ML alimentent le développement de l'IA. Bien que ces domaines en tant que domaines de recherches distincts ont commencé à se cristalliser dans les années 1950, il est important de rappeler que les [découvertes algorithmiques, statistiques, mathématiques, informatiques et techniques](https://wikipedia.org/wiki/Timeline_of_machine_learning) ont précédé et chevauchait cette époque. En fait, le monde réfléchit à ces questions depuis [des centaines d'années](https://fr.wikipedia.org/wiki/Histoire_de_l%27intelligence_artificielle) : cet article traite des fondements intellectuels historiques de l'idée d'une « machine qui pense ».
+
+## Découvertes notables
+
+- 1763, 1812 [théorème de Bayes](https://wikipedia.org/wiki/Bayes%27_theorem) et ses prédécesseurs. Ce théorème et ses applications sous-tendent l'inférence, décrivant la probabilité qu'un événement se produise sur la base de connaissances antérieures.
+- 1805 [Théorie des moindres carrés](https://wikipedia.org/wiki/Least_squares) par le mathématicien français Adrien-Marie Legendre. Cette théorie, que vous découvrirez dans notre unité Régression, aide à l'ajustement des données.
+- 1913 [Chaînes de Markov](https://wikipedia.org/wiki/Markov_chain) du nom du mathématicien russe Andrey Markov sont utilisées pour décrire une séquence d'événements possibles basée sur un état antérieur.
+- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) est un type de classificateur linéaire inventé par le psychologue américain Frank Rosenblatt qui sous-tend les progrès de l'apprentissage en profondeur.
+- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) est un algorithme conçu à l'origine pour cartographier les itinéraires. Dans un contexte ML, il est utilisé pour détecter des modèles.
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) est utilisé pour former des [réseaux de neurones feedforward (propagation avant)](https://fr.wikipedia.org/wiki/R%C3%A9seau_de_neurones_%C3%A0_propagation_avant).
+- 1982 [Réseaux de neurones récurrents](https://wikipedia.org/wiki/Recurrent_neural_network) sont des réseaux de neurones artificiels dérivés de réseaux de neurones à réaction qui créent des graphes temporels.
+
+✅ Faites une petite recherche. Quelles autres dates sont marquantes dans l'histoire du ML et de l'IA ?
+
+## 1950 : Des machines qui pensent
+
+Alan Turing, une personne vraiment remarquable qui a été élue [par le public en 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) comme le plus grand scientifique du 20e siècle, est reconnu pour avoir aidé à jeter les bases du concept d'une "machine qui peut penser". Il a lutté avec ses opposants et son propre besoin de preuves empiriques de sa théorie en créant le [Test de Turing] (https://www.bbc.com/news/technology-18475646), que vous explorerez dans nos leçons de TALN.
+
+## 1956 : Projet de recherche d'été à Dartmouth
+
+« Le projet de recherche d'été de Dartmouth sur l'intelligence artificielle a été un événement fondateur pour l'intelligence artificielle en tant que domaine », et c'est ici que le terme « intelligence artificielle » a été inventé ([source](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+> Chaque aspect de l'apprentissage ou toute autre caractéristique de l'intelligence peut en principe être décrit si précisément qu'une machine peut être conçue pour les simuler.
+
+Le chercheur en tête, le professeur de mathématiques John McCarthy, espérait « procéder sur la base de la conjecture selon laquelle chaque aspect de l'apprentissage ou toute autre caractéristique de l'intelligence peut en principe être décrit avec une telle précision qu'une machine peut être conçue pour les simuler ». Les participants comprenaient une autre sommité dans le domaine, Marvin Minsky.
+
+L'atelier est crédité d'avoir initié et encouragé plusieurs discussions, notamment « l'essor des méthodes symboliques, des systèmes spécialisés sur des domaines limités (premiers systèmes experts) et des systèmes déductifs par rapport aux systèmes inductifs ». ([source](https://fr.wikipedia.org/wiki/Conf%C3%A9rence_de_Dartmouth)).
+
+## 1956 - 1974 : "Les années d'or"
+
+Des années 50 au milieu des années 70, l'optimisme était au rendez-vous en espérant que l'IA puisse résoudre de nombreux problèmes. En 1967, Marvin Minsky a déclaré avec assurance que « Dans une génération... le problème de la création d'"intelligence artificielle" sera substantiellement résolu. » (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+La recherche sur le traitement du langage naturel a prospéré, la recherche a été affinée et rendue plus puissante, et le concept de « micro-mondes » a été créé, où des tâches simples ont été effectuées en utilisant des instructions en langue naturelle.
+
+La recherche a été bien financée par les agences gouvernementales, des progrès ont été réalisés dans le calcul et les algorithmes, et des prototypes de machines intelligentes ont été construits. Certaines de ces machines incluent :
+
+* [Shakey le robot](https://fr.wikipedia.org/wiki/Shakey_le_robot), qui pouvait manœuvrer et décider comment effectuer des tâches « intelligemment ».
+
+    ![Shakey, un robot intelligent](../images/shakey.jpg)
+    > Shaky en 1972
+
+* Eliza, une des premières « chatbot », pouvait converser avec les gens et agir comme une « thérapeute » primitive. Vous en apprendrez plus sur Eliza dans les leçons de TALN.
+
+    ![Eliza, un bot](../images/eliza.png)
+    > Une version d'Eliza, un chatbot
+
+* Le « monde des blocs » était un exemple de micro-monde où les blocs pouvaient être empilés et triés, et où des expériences d'apprentissages sur des machines, dans le but qu'elles prennent des décisions, pouvaient être testées. Les avancées réalisées avec des bibliothèques telles que [SHRDLU](https://fr.wikipedia.org/wiki/SHRDLU) ont contribué à faire avancer le traitement du langage.
+
+    [![Monde de blocs avec SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Monde de blocs avec SHRDLU" )
+    
+    > 🎥 Cliquez sur l'image ci-dessus pour une vidéo : Blocks world with SHRDLU
+
+## 1974 - 1980 : « l'hiver de l'IA »
+
+Au milieu des années 1970, il était devenu évident que la complexité de la fabrication de « machines intelligentes » avait été sous-estimée et que sa promesse, compte tenu de la puissance de calcul disponible, avait été exagérée. Les financements se sont taris et la confiance dans le domaine s'est ralentie. Parmi les problèmes qui ont eu un impact sur la confiance, citons :
+
+- **Restrictions**. La puissance de calcul était trop limitée.
+- **Explosion combinatoire**. Le nombre de paramètres à former augmentait de façon exponentielle à mesure que l'on en demandait davantage aux ordinateurs, sans évolution parallèle de la puissance et de la capacité de calcul.
+- **Pénurie de données**. Il y avait un manque de données qui a entravé le processus de test, de développement et de raffinement des algorithmes.
+- **Posions-nous les bonnes questions ?**. Les questions mêmes, qui étaient posées, ont commencé à être remises en question. Les chercheurs ont commencé à émettre des critiques sur leurs approches :
+  - Les tests de Turing ont été remis en question au moyen, entre autres, de la « théorie de la chambre chinoise » qui postulait que « la programmation d'un ordinateur numérique peut faire croire qu'il comprend le langage mais ne peut pas produire une compréhension réelle ». ([source](https://plato.stanford.edu/entries/chinese-room/))
+  - L'éthique de l'introduction d'intelligences artificielles telles que la "thérapeute" ELIZA dans la société a été remise en cause.
+
+Dans le même temps, diverses écoles de pensée sur l'IA ont commencé à se former. Une dichotomie a été établie entre les pratiques IA ["scruffy" et "neat"](https://wikipedia.org/wiki/Neats_and_scruffies). Les laboratoires _Scruffy_ peaufinaient leurs programmes pendant des heures jusqu'à ce qu'ils obtiennent les résultats souhaités. Les laboratoires _Neat_ "se concentraient sur la logique et la résolution formelle de problèmes". ELIZA et SHRDLU étaient des systèmes _scruffy_ bien connus. Dans les années 1980, alors qu'émergeait la demande de rendre les systèmes ML reproductibles, l'approche _neat_ a progressivement pris le devant de la scène car ses résultats sont plus explicables.
+
+## 1980 : Systèmes experts
+
+Au fur et à mesure que le domaine s'est développé, ses avantages pour les entreprises sont devenus plus clairs, particulièrement via les « systèmes experts » dans les années 1980. "Les systèmes experts ont été parmi les premières formes vraiment réussies de logiciels d'intelligence artificielle (IA)." ([source](https://fr.wikipedia.org/wiki/Syst%C3%A8me_expert)).
+
+Ce type de système est en fait _hybride_, composé en partie d'un moteur de règles définissant les exigences métier et d'un moteur d'inférence qui exploite le système de règles pour déduire de nouveaux faits.
+
+Cette époque a également vu une attention croissante accordée aux réseaux de neurones.
+
+## 1987 - 1993 : IA « Chill »
+
+La prolifération du matériel spécialisé des systèmes experts a eu pour effet malheureux de devenir trop spécialisée. L'essor des ordinateurs personnels a également concurrencé ces grands systèmes spécialisés et centralisés. La démocratisation de l'informatique a commencé et a finalement ouvert la voie à l'explosion des mégadonnées.
+
+## 1993 - 2011
+
+Cette époque a vu naître une nouvelle ère pour le ML et l'IA afin de résoudre certains des problèmes qui n'avaient pu l'être plus tôt par le manque de données et de puissance de calcul. La quantité de données a commencé à augmenter rapidement et à devenir plus largement disponibles, pour le meilleur et pour le pire, en particulier avec l'avènement du smartphone vers 2007. La puissance de calcul a augmenté de façon exponentielle et les algorithmes ont évolué parallèlement. Le domaine a commencé à gagner en maturité alors que l'ingéniosité a commencé à se cristalliser en une véritable discipline.
+
+## À présent
+
+Aujourd'hui, le machine learning et l'IA touchent presque tous les aspects de notre vie. Cette ère nécessite une compréhension approfondie des risques et des effets potentiels de ces algorithmes sur les vies humaines. Comme l'a déclaré Brad Smith de Microsoft, « les technologies de l'information soulèvent des problèmes qui vont au cœur des protections fondamentales des droits de l'homme comme la vie privée et la liberté d'expression. Ces problèmes accroissent la responsabilité des entreprises technologiques qui créent ces produits. À notre avis, ils appellent également à une réglementation gouvernementale réfléchie et au développement de normes autour des utilisations acceptables" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+Reste à savoir ce que l'avenir nous réserve, mais il est important de comprendre ces systèmes informatiques ainsi que les logiciels et algorithmes qu'ils exécutent. Nous espérons que ce programme vous aidera à mieux les comprendre afin que vous puissiez décider par vous-même.
+
+[![L'histoire du Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "L'histoire du Deep Learning")
+> 🎥 Cliquez sur l'image ci-dessus pour une vidéo : Yann LeCun discute de l'histoire du deep learning dans cette conférence
+
+---
+## 🚀Défi
+
+Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ?
+
+## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+
+## Révision et auto-apprentissage
+
+Voici quelques articles à regarder et à écouter :
+
+[Ce podcast où Amy Boyd discute de l'évolution de l'IA](http://runasradio.com/Shows/Show/739)
+
+[![L'histoire de l'iA par Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "L'histoire de l'iA par Amy Boyd")
+
+## Assignment
+
+[Créer une frise chonologique](assignment.fr.md)
diff --git a/1-Introduction/2-history-of-ML/translations/assignment.fr.md b/1-Introduction/2-history-of-ML/translations/assignment.fr.md
new file mode 100644
index 0000000000..f1f06cb7d5
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.fr.md
@@ -0,0 +1,11 @@
+# Créer une frise chronologique
+
+## Instructions
+
+Utiliser [ce repo](https://github.com/Digital-Humanities-Toolkit/timeline-builder), créer une frise chronologique de certains aspects de l'histoire des algorithmes, des mathématiques, des statistiques, de l'IA ou du machine learning, ou une combinaison de ceux-ci. Vous pouvez vous concentrer sur une personne, une idée ou une longue période de réflexion. Assurez-vous d'ajouter des éléments multimédias.
+
+## Rubrique
+
+| Critères | Exemplaire                                                       | Adéquate                             | A améliorer                                                        |
+| -------- | ---------------------------------------------------------------- | ------------------------------------ | ------------------------------------------------------------------ |
+|          | Une chronologie déployée est présentée sous forme de page GitHub | Le code est incomplet et non déployé | La chronologie est incomplète, pas bien recherchée et pas déployée |

From 051fced33be1891a143f3dc6656b3ca8bef90853 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Sat, 17 Jul 2021 20:21:38 +0300
Subject: [PATCH 198/368] Fix file paths for images, lessons, and .md files

---
 translations/README.tr.md | 60 +++++++++++++++++++--------------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/translations/README.tr.md b/translations/README.tr.md
index 2311bf67b3..cc517004ed 100644
--- a/translations/README.tr.md
+++ b/translations/README.tr.md
@@ -37,13 +37,13 @@ Biz bu klasik teknikleri dünyanın birçok alanından verilere uygularken bizim
 
 > İleri çalışma için, bu [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modüllerini ve öğrenme rotalarını takip etmenizi tavsiye ediyoruz.
 
-**Öğretmenler**, bu eğitim programının nasıl kullanılacağı hakkında [bazı öneriler ekledik](for-teachers.md).
+**Öğretmenler**, bu eğitim programının nasıl kullanılacağı hakkında [bazı öneriler ekledik](../for-teachers.md).
 
 ---
 
 ## Takımla Tanışın
 
-[![Tanıtım videosu](ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
+[![Tanıtım videosu](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "Promo video")
 
 > :movie_camera: Proje ve projeyi yaratanlar hakkındaki video için yukarıdaki fotoğrafa tıklayın!
 
@@ -54,7 +54,7 @@ Bu eğitim programını oluştururken iki pedagojik ilke seçtik: uygulamalı **
 
 İçeriğin projelerle uyumlu olduğuna emin olarak, süreç öğrenciler için daha ilgi çekici hale getirilmiştir ve kavramların akılda kalıcılığı artacaktır. Ayrıca, dersten önce ikincil değerli bir kısa sınav öğrencinin niyetini konuyu öğrenmek yaparken dersten sonra yapılan ikinci bir kısa sınav da akılda kalıcılığı sağlar. Bu eğitim programı esnek ve eğlenceli olacak şekilde hazırlanmıştır ve tümüyle veya kısmen işlenebilir. Projeler kolay başlar ve 12 haftalık zamanın sonuna doğru karmaşıklıkları gittikçe artar. Bu eğitim programı, Makine Öğreniminin gerçek hayattaki uygulamaları üzerine, ek puan veya tartışma için bir temel olarak kullanılabilecek bir ek yazı da içermektedir.
 
-> [Davranış Kuralları](CODE_OF_CONDUCT.md)'mızı, [Katkıda Bulunma](CONTRIBUTING.md) ve [Çeviri](TRANSLATIONS.md) kılavuz ilkelerimizi inceleyin. Yapıcı geridönütlerinizi memnuniyetle karşılıyoruz!
+> [Davranış Kuralları](../CODE_OF_CONDUCT.md)'mızı, [Katkıda Bulunma](../CONTRIBUTING.md) ve [Çeviri](../TRANSLATIONS.md) kılavuz ilkelerimizi inceleyin. Yapıcı geridönütlerinizi memnuniyetle karşılıyoruz!
 ## Her bir ders şunları içermektedir:
 
 - isteğe bağlı eskiz notu
@@ -73,42 +73,42 @@ Bu eğitim programını oluştururken iki pedagojik ilke seçtik: uygulamalı **
 
 | Ders Numarası |                           Konu                             |                     Ders Gruplandırması                     |  Öğrenme Hedefleri                                                                                                              |                         Ders                          |     Yazar      |
 | :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |                   Makine Öğrenimi Giriş                    |         [Giriş](1-Introduction/README.md)           | Makine öğreniminin temel kavramlarını öğrenmek                                                                                  |    [ders](1-Introduction/1-intro-to-ML/README.md)     |    Muhammad    |
-|      02       |                   Makine Öğrenimi Tarihi                   |         [Giriş](1-Introduction/README.md)           | Bu alanın altında yatan tarihi öğrenmek                                                                                         |   [ders](1-Introduction/2-history-of-ML/README.md)    |  Jen and Amy   |
-|      03       |                 Eşitlik ve Makine Öğrenimi                 |         [Giriş](1-Introduction/README.md)           | Öğrencilerin ML modelleri yaparken ve uygularken düşünmeleri gereken eşitlik hakkındaki önemli felsefi sorunlar nelerdir?       |      [ders](1-Introduction/3-fairness/README.md)      |     Tomomi     |
-|      04       |               Makine Öğrenimi için Yöntemler               |         [Giriş](1-Introduction/README.md)           | ML araştırmacıları ML modelleri üretmek için hangi yöntemleri kullanırlar?                                                      |  [ders](1-Introduction/4-techniques-of-ML/README.md)  | Chris and Jen  |
-|      05       |                      Regresyona Giriş                      |         [Regresyon](2-Regression/README.md)         | Regresyon modelleri için Python ve Scikit-learn'e başlamak                                                                      |        [ders](2-Regression/1-Tools/README.md)         |      Jen       |
-|      06       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](2-Regression/README.md)         | ML hazırlığı için verileri görselleştirmek ve temizlemek                                                                        |         [ders](2-Regression/2-Data/README.md)         |      Jen       |
-|      07       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](2-Regression/README.md)         | Doğrusal ve polinom regresyon modelleri yapmak                                                                                  |        [ders](2-Regression/3-Linear/README.md)        |      Jen       |
-|      08       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](2-Regression/README.md)         | Lojistik bir regresyon modeli yapmak                                                                                            |       [ders](2-Regression/4-Logistic/README.md)       |      Jen       |
-|      09       |                    Bir Web Uygulaması :electric_plug:                    |        [Web Uygulaması](3-Web-App/README.md)        | Eğittiğiniz modeli kullanmak için bir web uygulaması yapmak                                                                     |         [ders](3-Web-App/1-Web-App/README.md)         |      Jen       |
-|      10       |                   Sınıflandırmaya Giriş                    |     [Sınıflandırma](4-Classification/README.md)     | Verilerinizi temizlemek, hazırlamak, ve görselleştirmek; sınıflandırmaya giriş                                                  |   [ders](4-Classification/1-Introduction/README.md)   | Jen and Cassie |
-|      11       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](4-Classification/README.md)     | Sınıflandırıcılara giriş                                                                                                        |  [ders](4-Classification/2-Classifiers-1/README.md)   | Jen and Cassie |
-|      12       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](4-Classification/README.md)     | Daha fazla sınıflandırıcı                                                                                                       |  [ders](4-Classification/3-Classifiers-2/README.md)   | Jen and Cassie |
-|      13       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](4-Classification/README.md)     | Modelinizi kullanarak tavsiyede bulunan bir web uygulaması yapmak                                                               |     [ders](4-Classification/4-Applied/README.md)      |      Jen       |
-|      14       |                      Kümelemeye Giriş                      |         [Kümeleme](5-Clustering/README.md)          | Verilerinizi temizlemek, hazırlamak, ve görselleştirmek; kümelemeye giriş                                                       |      [ders](5-Clustering/1-Visualize/README.md)       |      Jen       |
-|      15       |          Nijerya'nın Müzik Zevklerini Keşfetme :headphones:           |         [Kümeleme](5-Clustering/README.md)          | K merkezli kümeleme yöntemini keşfetmek                                                                                         |       [ders](5-Clustering/2-K-Means/README.md)        |      Jen       |
-|      16       |                 Doğal Dil İşlemeye Giriş  :coffee:                |        [Doğal Dil İşleme](6-NLP/README.md)          | Basit bir bot yaratarak NLP temellerini öğrenmek                                                                                |     [ders](6-NLP/1-Introduction-to-NLP/README.md)     |    Stephen     |
-|      17       |                   Yaygın NLP Görevleri :coffee:                   |        [Doğal Dil İşleme](6-NLP/README.md)          | Dil yapılarıyla uğraşırken gereken yaygın görevleri anlayarak NLP bilginizi derinleştirmek                                      |            [ders](6-NLP/2-Tasks/README.md)            |    Stephen     |
-|      18       |                  Çeviri ve Duygu Analizi :hearts:                 |        [Doğal Dil İşleme](6-NLP/README.md)          | Jane Austen ile çeviri ve duygu analizi                                                                                         |    [ders](6-NLP/3-Translation-Sentiment/README.md)    |    Stephen     |
-|      19       |               Avrupa'nın Romantik Otelleri :hearts:               |        [Doğal Dil İşleme](6-NLP/README.md)          | Otel değerlendirmeleriyle duygu analizi, 1                                                                                      |       [ders](6-NLP/4-Hotel-Reviews-1/README.md)       |    Stephen     |
-|      20       |               Avrupa'nın Romantik Otelleri :hearts:               |        [Doğal Dil İşleme](6-NLP/README.md)          | Otel değerlendirmeleriyle duygu analizi 2                                                                                       |       [ders](6-NLP/5-Hotel-Reviews-2/README.md)       |    Stephen     |
-|      21       |               Zaman Serisi Tahminine GiriÅŸ                 |        [Zaman Serisi](7-TimeSeries/README.md)       | Zaman serisi tahminine giriÅŸ                                                                                                    |     [ders](7-TimeSeries/1-Introduction/README.md)     |   Francesca    |
-|      22       | :zap: Dünya Güç Kullanımı :zap: - ARIMA ile Zaman Serisi Tahmini |        [Zaman Serisi](7-TimeSeries/README.md)       | ARIMA ile zaman serisi tahmini                                                                                                  |        [ders](7-TimeSeries/2-ARIMA/README.md)         |   Francesca    |
-|      23       |                Pekiştirmeli Öğrenmeye Giriş                |  [Pekiştirmeli Öğrenme](8-Reinforcement/README.md)  | Q-Learning ile pekiştirmeli öğrenmeye giriş                                                                                     |     [ders](8-Reinforcement/1-QLearning/README.md)     |     Dmitry     |
-|      24       |       Peter'ın Kurttan Uzak Durmasına Yardım Edin! :wolf:       |  [Pekiştirmeli Öğrenme](8-Reinforcement/README.md)  | Pekiştirmeli öğrenme spor salonu                                                                                                |        [ders](8-Reinforcement/2-Gym/README.md)        |     Dmitry     |
-|   Ek Yazı     |       Gerçek Hayattan ML Senaryoları ve Uygulamaları       |     [Vahşi Doğada ML](9-Real-World/README.md)       | Klasik makine öğreniminin ilginç ve açıklayıcı gerçek hayat uygulamaları                                                        |     [ders](9-Real-World/1-Applications/README.md)     |     Takım      |
+|      01       |                   Makine Öğrenimi Giriş                    |         [Giriş](../1-Introduction/README.md)           | Makine öğreniminin temel kavramlarını öğrenmek                                                                                  |    [ders](../1-Introduction/1-intro-to-ML/README.md)     |    Muhammad    |
+|      02       |                   Makine Öğrenimi Tarihi                   |         [Giriş](../1-Introduction/README.md)           | Bu alanın altında yatan tarihi öğrenmek                                                                                         |   [ders](../1-Introduction/2-history-of-ML/README.md)    |  Jen and Amy   |
+|      03       |                 Eşitlik ve Makine Öğrenimi                 |         [Giriş](../1-Introduction/README.md)           | Öğrencilerin ML modelleri yaparken ve uygularken düşünmeleri gereken eşitlik hakkındaki önemli felsefi sorunlar nelerdir?       |      [ders](../1-Introduction/3-fairness/README.md)      |     Tomomi     |
+|      04       |               Makine Öğrenimi için Yöntemler               |         [Giriş](../1-Introduction/README.md)           | ML araştırmacıları ML modelleri üretmek için hangi yöntemleri kullanırlar?                                                      |  [ders](../1-Introduction/4-techniques-of-ML/README.md)  | Chris and Jen  |
+|      05       |                      Regresyona Giriş                      |         [Regresyon](../2-Regression/README.md)         | Regresyon modelleri için Python ve Scikit-learn'e başlamak                                                                      |        [ders](../2-Regression/1-Tools/README.md)         |      Jen       |
+|      06       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](../2-Regression/README.md)         | ML hazırlığı için verileri görselleştirmek ve temizlemek                                                                        |         [ders](../2-Regression/2-Data/README.md)         |      Jen       |
+|      07       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](../2-Regression/README.md)         | Doğrusal ve polinom regresyon modelleri yapmak                                                                                  |        [ders](../2-Regression/3-Linear/README.md)        |      Jen       |
+|      08       |            Kuzey Amerika balkabağı fiyatları :jack_o_lantern:             |         [Regresyon](../2-Regression/README.md)         | Lojistik bir regresyon modeli yapmak                                                                                            |       [ders](../2-Regression/4-Logistic/README.md)       |      Jen       |
+|      09       |                    Bir Web Uygulaması :electric_plug:                    |        [Web Uygulaması](../3-Web-App/README.md)        | Eğittiğiniz modeli kullanmak için bir web uygulaması yapmak                                                                     |         [ders](../3-Web-App/1-Web-App/README.md)         |      Jen       |
+|      10       |                   Sınıflandırmaya Giriş                    |     [Sınıflandırma](../4-Classification/README.md)     | Verilerinizi temizlemek, hazırlamak, ve görselleştirmek; sınıflandırmaya giriş                                                  |   [ders](../4-Classification/1-Introduction/README.md)   | Jen and Cassie |
+|      11       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](../4-Classification/README.md)     | Sınıflandırıcılara giriş                                                                                                        |  [ders](../4-Classification/2-Classifiers-1/README.md)   | Jen and Cassie |
+|      12       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](../4-Classification/README.md)     | Daha fazla sınıflandırıcı                                                                                                       |  [ders](../4-Classification/3-Classifiers-2/README.md)   | Jen and Cassie |
+|      13       |                Leziz Asya ve Hint mutfağı :ramen:                |     [Sınıflandırma](../4-Classification/README.md)     | Modelinizi kullanarak tavsiyede bulunan bir web uygulaması yapmak                                                               |     [ders](../4-Classification/4-Applied/README.md)      |      Jen       |
+|      14       |                      Kümelemeye Giriş                      |         [Kümeleme](../5-Clustering/README.md)          | Verilerinizi temizlemek, hazırlamak, ve görselleştirmek; kümelemeye giriş                                                       |      [ders](../5-Clustering/1-Visualize/README.md)       |      Jen       |
+|      15       |          Nijerya'nın Müzik Zevklerini Keşfetme :headphones:           |         [Kümeleme](../5-Clustering/README.md)          | K merkezli kümeleme yöntemini keşfetmek                                                                                         |       [ders](../5-Clustering/2-K-Means/README.md)        |      Jen       |
+|      16       |                 Doğal Dil İşlemeye Giriş  :coffee:                |        [Doğal Dil İşleme](../6-NLP/README.md)          | Basit bir bot yaratarak NLP temellerini öğrenmek                                                                                |     [ders](../6-NLP/1-Introduction-to-NLP/README.md)     |    Stephen     |
+|      17       |                   Yaygın NLP Görevleri :coffee:                   |        [Doğal Dil İşleme](../6-NLP/README.md)          | Dil yapılarıyla uğraşırken gereken yaygın görevleri anlayarak NLP bilginizi derinleştirmek                                      |            [ders](../6-NLP/2-Tasks/README.md)            |    Stephen     |
+|      18       |                  Çeviri ve Duygu Analizi :hearts:                 |        [Doğal Dil İşleme](../6-NLP/README.md)          | Jane Austen ile çeviri ve duygu analizi                                                                                         |    [ders](../6-NLP/3-Translation-Sentiment/README.md)    |    Stephen     |
+|      19       |               Avrupa'nın Romantik Otelleri :hearts:               |        [Doğal Dil İşleme](../6-NLP/README.md)          | Otel değerlendirmeleriyle duygu analizi, 1                                                                                      |       [ders](../6-NLP/4-Hotel-Reviews-1/README.md)       |    Stephen     |
+|      20       |               Avrupa'nın Romantik Otelleri :hearts:               |        [Doğal Dil İşleme](../6-NLP/README.md)          | Otel değerlendirmeleriyle duygu analizi 2                                                                                       |       [ders](../6-NLP/5-Hotel-Reviews-2/README.md)       |    Stephen     |
+|      21       |               Zaman Serisi Tahminine GiriÅŸ                 |        [Zaman Serisi](../7-TimeSeries/README.md)       | Zaman serisi tahminine giriÅŸ                                                                                                    |     [ders](../7-TimeSeries/1-Introduction/README.md)     |   Francesca    |
+|      22       | :zap: Dünya Güç Kullanımı :zap: - ARIMA ile Zaman Serisi Tahmini |        [Zaman Serisi](../7-TimeSeries/README.md)       | ARIMA ile zaman serisi tahmini                                                                                                  |        [ders](../7-TimeSeries/2-ARIMA/README.md)         |   Francesca    |
+|      23       |                Pekiştirmeli Öğrenmeye Giriş                |  [Pekiştirmeli Öğrenme](../8-Reinforcement/README.md)  | Q-Learning ile pekiştirmeli öğrenmeye giriş                                                                                     |     [ders](../8-Reinforcement/1-QLearning/README.md)     |     Dmitry     |
+|      24       |       Peter'ın Kurttan Uzak Durmasına Yardım Edin! :wolf:       |  [Pekiştirmeli Öğrenme](../8-Reinforcement/README.md)  | Pekiştirmeli öğrenme spor salonu                                                                                                |        [ders](../8-Reinforcement/2-Gym/README.md)        |     Dmitry     |
+|   Ek Yazı     |       Gerçek Hayattan ML Senaryoları ve Uygulamaları       |     [Vahşi Doğada ML](../9-Real-World/README.md)       | Klasik makine öğreniminin ilginç ve açıklayıcı gerçek hayat uygulamaları                                                        |     [ders](../9-Real-World/1-Applications/README.md)     |     Takım      |
 ## Çevrimdışı erişim
 
 Bu dokümantasyonu [Docsify](https://docsify.js.org/#/) kullanarak çevrimdışı çalıştırabilirsiniz. Bu yazılım havuzunu çatallayın, yerel makinenizde [Docsify'ı kurum](https://docsify.js.org/#/quickstart) ve sonra bu yazılım havuzunun kök dizininde `docsify serve` yazın. İnternet sitesi, 3000 portunda `localhost:3000` yerel ana makinenizde sunulacaktır.
 
 ## PDF'ler
 
-Eğitim programının bağlantılarla PDF'sine [buradan](pdf/readme.pdf) ulaşabilirsiniz.
+Eğitim programının bağlantılarla PDF'sine [buradan](../pdf/readme.pdf) ulaşabilirsiniz.
 
 ## Yardım İsteniyor!
 
-Bir çeviri katkısında bulunmak ister misiniz? Lütfen [çeviri kılavuz ilkelerimizi](TRANSLATIONS.md) okuyun ve [buraya](https://github.com/microsoft/ML-For-Beginners/issues/71) girdiyi ekleyin.
+Bir çeviri katkısında bulunmak ister misiniz? Lütfen [çeviri kılavuz ilkelerimizi](../TRANSLATIONS.md) okuyun ve [buraya](https://github.com/microsoft/ML-For-Beginners/issues/71) girdiyi ekleyin.
 
 ## Diğer Eğitim Programları
 

From 42c364d5d31d7a57bf64867048afa7a650950032 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Sat, 17 Jul 2021 19:24:02 +0200
Subject: [PATCH 199/368] Remplacement de TALN par NLP
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Remplacer défi par challenge
---
 .../2-history-of-ML/translations/README.fr.md          | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
index 9c74b0b833..78ba70cc2f 100644
--- a/1-Introduction/2-history-of-ML/translations/README.fr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -23,7 +23,7 @@ L'histoire de l'intelligence artificielle, l'IA, en tant que domaine est étroit
 
 ## 1950 : Des machines qui pensent
 
-Alan Turing, une personne vraiment remarquable qui a été élue [par le public en 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) comme le plus grand scientifique du 20e siècle, est reconnu pour avoir aidé à jeter les bases du concept d'une "machine qui peut penser". Il a lutté avec ses opposants et son propre besoin de preuves empiriques de sa théorie en créant le [Test de Turing] (https://www.bbc.com/news/technology-18475646), que vous explorerez dans nos leçons de TALN.
+Alan Turing, une personne vraiment remarquable qui a été élue [par le public en 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) comme le plus grand scientifique du 20e siècle, est reconnu pour avoir aidé à jeter les bases du concept d'une "machine qui peut penser". Il a lutté avec ses opposants et son propre besoin de preuves empiriques de sa théorie en créant le [Test de Turing] (https://www.bbc.com/news/technology-18475646), que vous explorerez dans nos leçons de NLP (TALN en français).
 
 ## 1956 : Projet de recherche d'été à Dartmouth
 
@@ -39,7 +39,7 @@ L'atelier est crédité d'avoir initié et encouragé plusieurs discussions, not
 
 Des années 50 au milieu des années 70, l'optimisme était au rendez-vous en espérant que l'IA puisse résoudre de nombreux problèmes. En 1967, Marvin Minsky a déclaré avec assurance que « Dans une génération... le problème de la création d'"intelligence artificielle" sera substantiellement résolu. » (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
 
-La recherche sur le traitement du langage naturel a prospéré, la recherche a été affinée et rendue plus puissante, et le concept de « micro-mondes » a été créé, où des tâches simples ont été effectuées en utilisant des instructions en langue naturelle.
+La recherche sur le Natural Language Processing (traitement du langage naturel en français) a prospéré, la recherche a été affinée et rendue plus puissante, et le concept de « micro-mondes » a été créé, où des tâches simples ont été effectuées en utilisant des instructions en langue naturelle.
 
 La recherche a été bien financée par les agences gouvernementales, des progrès ont été réalisés dans le calcul et les algorithmes, et des prototypes de machines intelligentes ont été construits. Certaines de ces machines incluent :
 
@@ -48,12 +48,12 @@ La recherche a été bien financée par les agences gouvernementales, des progr
     ![Shakey, un robot intelligent](../images/shakey.jpg)
     > Shaky en 1972
 
-* Eliza, une des premières « chatbot », pouvait converser avec les gens et agir comme une « thérapeute » primitive. Vous en apprendrez plus sur Eliza dans les leçons de TALN.
+* Eliza, une des premières « chatbot », pouvait converser avec les gens et agir comme une « thérapeute » primitive. Vous en apprendrez plus sur Eliza dans les leçons de NLP.
 
     ![Eliza, un bot](../images/eliza.png)
     > Une version d'Eliza, un chatbot
 
-* Le « monde des blocs » était un exemple de micro-monde où les blocs pouvaient être empilés et triés, et où des expériences d'apprentissages sur des machines, dans le but qu'elles prennent des décisions, pouvaient être testées. Les avancées réalisées avec des bibliothèques telles que [SHRDLU](https://fr.wikipedia.org/wiki/SHRDLU) ont contribué à faire avancer le traitement du langage.
+* Le « monde des blocs » était un exemple de micro-monde où les blocs pouvaient être empilés et triés, et où des expériences d'apprentissages sur des machines, dans le but qu'elles prennent des décisions, pouvaient être testées. Les avancées réalisées avec des bibliothèques telles que [SHRDLU](https://fr.wikipedia.org/wiki/SHRDLU) ont contribué à faire avancer le natural language processing.
 
     [![Monde de blocs avec SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "Monde de blocs avec SHRDLU" )
     
@@ -98,7 +98,7 @@ Reste à savoir ce que l'avenir nous réserve, mais il est important de comprend
 > 🎥 Cliquez sur l'image ci-dessus pour une vidéo : Yann LeCun discute de l'histoire du deep learning dans cette conférence
 
 ---
-## 🚀Défi
+## 🚀Challenge
 
 Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ?
 

From 0ae3500513f1f83c74612dc5a7820180bbe9d2a9 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Sat, 17 Jul 2021 23:50:37 +0200
Subject: [PATCH 200/368] Correction orthographique

---
 1-Introduction/2-history-of-ML/translations/README.fr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
index 78ba70cc2f..5debe07e6b 100644
--- a/1-Introduction/2-history-of-ML/translations/README.fr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -110,8 +110,8 @@ Voici quelques articles à regarder et à écouter :
 
 [Ce podcast où Amy Boyd discute de l'évolution de l'IA](http://runasradio.com/Shows/Show/739)
 
-[![L'histoire de l'iA par Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "L'histoire de l'iA par Amy Boyd")
+[![L'histoire de l'IA par Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "L'histoire de l'IA par Amy Boyd")
 
-## Assignment
+## Devoir
 
 [Créer une frise chonologique](assignment.fr.md)

From 8d1ad5cc65ef3c0eb3e863d4a21e87d93987c3d4 Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Sat, 17 Jul 2021 23:57:51 +0200
Subject: [PATCH 201/368] Update assignment.fr.md

---
 1-Introduction/2-history-of-ML/translations/assignment.fr.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/assignment.fr.md b/1-Introduction/2-history-of-ML/translations/assignment.fr.md
index f1f06cb7d5..c562516e47 100644
--- a/1-Introduction/2-history-of-ML/translations/assignment.fr.md
+++ b/1-Introduction/2-history-of-ML/translations/assignment.fr.md
@@ -2,7 +2,7 @@
 
 ## Instructions
 
-Utiliser [ce repo](https://github.com/Digital-Humanities-Toolkit/timeline-builder), créer une frise chronologique de certains aspects de l'histoire des algorithmes, des mathématiques, des statistiques, de l'IA ou du machine learning, ou une combinaison de ceux-ci. Vous pouvez vous concentrer sur une personne, une idée ou une longue période de réflexion. Assurez-vous d'ajouter des éléments multimédias.
+Utiliser [ce repo](https://github.com/Digital-Humanities-Toolkit/timeline-builder), créer une frise chronologique de certains aspects de l'histoire des algorithmes, des mathématiques, des statistiques, de l'IA ou du machine learning, ou une combinaison de ceux-ci. Vous pouvez vous concentrer sur une personne, une idée ou une longue période d'innovations. Assurez-vous d'ajouter des éléments multimédias.
 
 ## Rubrique
 

From 645e67b6f4e19bfd18615f48c85aac48b2dc0b79 Mon Sep 17 00:00:00 2001
From: Fan <ewfian@users.noreply.github.com>
Date: Sun, 18 Jul 2021 15:07:11 +0900
Subject: [PATCH 202/368] fix style mistake in 1-Introduction

---
 1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index 373602f303..318876bd4d 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -72,7 +72,7 @@
 
 训练过程完��（训练大型模型�能需�多次迭代或“时期�），你将能够通过使用测试数��衡�模型的性能�评估模型的质�。此数�是模型先�未分�的原始数�的�集。 你�以打�出有关模型质�的指标表。 
 
-🎓 **模型拟� **
+🎓 **模型拟�**
 
 在机器学习的背景下，模型拟�是指模型在�试分��熟悉的数�时其底层功能的准确性。 
 

From 79dca20d33b24bf1f52c5d6ea3e354603b9db195 Mon Sep 17 00:00:00 2001
From: Fan <ewfian@users.noreply.github.com>
Date: Sun, 18 Jul 2021 15:13:01 +0900
Subject: [PATCH 203/368] fix style mistake in 2-Regression

---
 2-Regression/1-Tools/translations/README.zh-cn.md | 4 ++--
 2-Regression/translations/README.zh-cn.md         | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index 4dff279524..41b167e37c 100644
--- a/2-Regression/1-Tools/translations/README.zh-cn.md
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -46,7 +46,7 @@ Notebooks是一个交互�环境，�许开�人员编写代�并添加注
 
     Jupyter�务器将以python3+�动。你会�现notebook�以“�行�的区域�代��。你�以通过选择看起��播放按钮的图标��行代��。
 
-2. 选择`md`图标并添加一点markdown，输入文字**#Welcome to your notebook**。 
+2. 选择`md`图标并添加一点markdown，输入文字 **# Welcome to your notebook**。
 
    接下�，添加一些Python代�。
 
@@ -80,7 +80,7 @@ Scikit-learn使构建模型和评估它们的使用�得简�。它主�侧
 
 > 本教程的�感�自Scikit-learn网站上的[线性回归示例](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py)。
 
-在与本课程相关的_notebook.ipynb_文件中，通过点击“垃圾桶�图标清除所有�元格。 
+在与本课程相关的 _notebook.ipynb_ 文件中，通过点击“垃圾桶�图标清除所有�元格。 
 
 在本节中，你将使用一个关于糖尿病的�数�集，该数�集内置于Scikit-learn中以用于学习目的。想象一下，你想为糖尿病患者测试一�治疗方法。机器学习模型�能会帮助你根���组�确定哪些患者对治疗�应更好。�使是�常基本的回归模型，在�视化时，也�能会显示有助于组织�论临床试验的��信�。 
 
diff --git a/2-Regression/translations/README.zh-cn.md b/2-Regression/translations/README.zh-cn.md
index 24c7a26cd3..f7c511e620 100644
--- a/2-Regression/translations/README.zh-cn.md
+++ b/2-Regression/translations/README.zh-cn.md
@@ -6,7 +6,7 @@
 ![jack-o-lantern](../images/jack-o-lanterns.jpg)
 > Foto oleh <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> di <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
-##你会学到什么
+## 你会学到什么
 
 这节的课程包括机器学习领域中的多�回归模型。回归模型�以明确多���间的_关系_。这�模型�以用�预测类似长度�温度和年龄之类的值， 通过分�数�点��示��之间的关系。
 

From 488fabcd6d0f2b4c4df121dc9a15e47219f1491c Mon Sep 17 00:00:00 2001
From: Colin Zang <zyy0116@gmail.com>
Date: Sun, 18 Jul 2021 20:43:10 +0800
Subject: [PATCH 204/368] Update README.zh-cn.md

---
 .../translations/README.zh-cn.md              | 93 ++++++++++---------
 1 file changed, 50 insertions(+), 43 deletions(-)

diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index 3d122be65c..e9df88a322 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -1,97 +1,103 @@
 # 自然语言处�介�
-这节课讲解了*自然语言处�*简�历�和��概念，*自然语言处�*是计算语言学的一个�领域。
+这节课讲解了 *自然语言处�* 的简�历�和��概念，*自然语言处�*是计算语言学的一个�领域。
 
 ## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
 
 ## 介�
-众所周知，自然语言处�(Natural Language Processing, NLP)是机器学习在生产软件中应用最广泛的领域之一。
+众所周知，自然语言处� (Natural Language Processing, NLP) 是机器学习在生产软件中应用最广泛的领域之一。
 
-✅你能想到哪些你日常生活中使用的软件嵌入了�些自然语言处�技术呢？你�常使用的文字处�程�或移动应用程�是�嵌入了自然语言处�技术呢？
+✅ 你能想到哪些你日常生活中使用的软件�能嵌入了自然语言处�技术呢？或者，你�常使用的文字处�程�或移动应用程�中是�嵌入了自然语言处�技术呢？
 
 你将会学习到：
 
-- **语言的�想**. 语言的�展历程�主�研究领域.
-- **定义和概念**. 你还将学习到有关计算机如何处�文本的定义和概念，包括解��语法以���和动�的识别。本节课程包�一些编�任务并介�了几个��的概念，你将在下一节课中学习编�实现这些概念。
+- **什么是「语言�**。语言的�展历程，以�相关研究的主�领域。
+- **定义和概念**。你还将了解关于计算机文本处�的概念。包括解� (parsing)�语法 (grammar) 以�识别��与动�。这节课中有一些编程任务；还有一些��概念将在以�的课程中被引入，届时你也会练习通过编程实现其它概念。
+
 ## 计算语言学
 
-计算语言学是一个�过几�年研究和�展的领域，它研究计算机如何使用语言��解语言�翻译语言�使用语言交�。自然语言处�(NLP)是计算语言学中一个专注于计算机如何处�“自然�或人类语言的相关领域，
-### 例� - 电���识别
+计算语言学 (Computational Linguistics) 是一个�过几�年研究和�展的领域，它研究如何让计算机能使用��解�翻译语言并使用语言交�。自然语言处� (NLP) 是计算语言学中一个专注于计算机如何处�「自然的�（或者说，人类的）语言的相关领域。
+
+### 举例：电���识别
 
-如果你曾�在手机上使用语音输入替代键盘输入或者�语音助手�娜�问，那么你的语音将被转录为文本形��进行处�或者�*解�*。被检测到的关键字最�将被处��手机或语音助手�以�解并采�行动的格�。
+如果你曾�在手机上使用语音输入替代键盘输入，或者使用过虚拟语音助手，那么你的语音将被转录（或者�*解�*）为文本形��进行处�。被检测到的关键字最�将被处��手机或语音助手�以�解并�以�此�出行为的格�。
 
 ![comprehension](../images/comprehension.png)
-> 真实的语言�解�分困难�图�：[Jen Looper](https://twitter.com/jenlooper)
+> 真正�义上的语言�解很难�图�：[Jen Looper](https://twitter.com/jenlooper)
+
 ### 这项技术是如何实现的？
 
-有人编写了一个计算机程��实现这项技术。几�年�，一些科幻作家预测人类很大�能会和他们的电脑对�，而电脑总是能准确地�解人类的��。�惜的是，事实�明这是一个比许多人想象中更难实现的问题，虽然今天这个问题已�被�步解决，但在�解��的�义时，�实现“完美�的自然语言处��然存在�大挑战。��中的幽默�解或讽刺等情绪的检测是一个特别困难的问题。
+我们之所以�能完�这样的任务，是因为有人编写了一个计算机程��实现它。几�年�，一些科幻作家预测，在未�，人类很大�能会能够他们的电脑对�，而电脑总是能准确地�解人类的��。�惜的是，事实�明这个问题的解决比我们想象的更困难。虽然今天这个问题已�被�步解决，但在�解��的�义时，�实现 “完美� 的自然语言处��然存在�大挑战 —— �解幽默或是检测感情（比如讽刺）对于计算机�说尤其困难。
+
+现在，你�能会想起课堂上�师讲解的语法。在�些国家/地区，语法和语言学知识是学生的专题课内容。但在�一些国家/地区，�管是从�学习的第一语言（学习阅读和写作），还是之�学习的第二语言中，语法�语言学知识都是作为语言的一部分教学的。所以，如果你�能很好地区分��与动�或者区分副�与形容�，请��担心�
 
-此时，你�能会想起学校课堂上�师讲解的部分��语法。在�些国家/地区，语法和语言学知识是学生的专题课内容。但在�一些国家/地区，�管是在�学时的第一语言（学习阅读和写作），或者在高年级�高中时学习的第二语言中，语法�语言学知识是作为学习语言的一部分教学的。如果你�能很好地区分��与动�或者区分副�与形容�，请��担心�
+你还为难以区分*一般现在时*与*现在进行时*而烦��？没关系的，�使是对以这门语言为�语的人在内的大多数人�说，区分它们都很有挑战性。但是，计算机�常善于应用标准的规则，你将学会编写�以�人一样"解�"��的代�。��你将�对的更大挑战是�解��的*语义*和*情绪*。
 
-如果你还为区分*一般现在时*与*现在进行时*而烦�，你并�是一个人。�使是对以这门语言为�语的人在内的很多人�说这都是一项有挑战性的任务。好消�是，计算机�常善于应用标准的规则，你将学会编写�以�人一样"解�"��的代�。��你将�对的更大挑战是�解��的*语义*和*情绪*。
 ## ��
 
-本节教程的主�先决�件是能够阅读和�解本节教程的语言。本节中没有数学问题或方程需�解决。虽然原作者用英文写了这教程，但它也被翻译�其他语言，所以你�能在阅读翻译内容。有使用多���语言的示例（以比较��语言的��语法规则）。这些是*未*翻译的，但解释性文本是翻译内容，所以表义应当是清晰的。
+本节教程的主�先决�件是能够阅读和�解本节教程的语言。本节中没有数学问题或方程需�解决。虽然原作者用英文写了这教程，但它也被翻译�其他语言，所以你�能在阅读翻译内容。这节课的示例中涉�到很多语言�类（以比较��语言的��语法规则）。这些是*未*翻译的，但对它们的解释是翻译过的，所以你应该能�解它在讲什么。
 
-编程任务中，你将会使用Python语言，示例使用的是Python 3.8版本。
+编程任务中，你将会使用 Python 语言，示例使用的是 Python 3.8 版本。
 
-在本节中你将需�并使用：
+在本节中你将需�并使用如下技能：
 
-- **Python 3 �解**.  Python 3中的编程语言�解，本课使用输入�循环�文件读��数组。
-- **Visual Studio Code + 扩展**. 我们将使用 Visual Studio Code �其 Python 扩展。你还�以使用你选择的 Python IDE。
-- **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob)是一个简化的 Python 文本处�库。按照 TextBlob 网站上的说明在您的系统上安装它（也安装语料库，如下所示）：
+- **Python 3**。你需�能够�解并使用 Python 3. 本课将会使用输入�循环�文件读��数组功能。
+- **Visual Studio Code + 扩展**. 我们将使用 Visual Studio Code �其 Python 扩展。你也�以使用你喜欢的 Python IDE。
+- **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob)是一个精简的 Python 文本处�库。请按照 TextBlob 网站上的说明，在您的系统上安装它（也需�安装语料库，安装代�如下所示）：
 - 
    ```bash
    pip install -U textblob
    python -m textblob.download_corpora
    ```
 
-> 💡 �示：�以在 VS Code 环境中直接�行 Python。 点击[docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa)查看更多信�。
+> 💡 �示：你�以在 VS Code 环境中直接�行 Python。 点击[docs](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa)查看更多信�。
 
 ## 与机器对�
 
-试图让计算机�解人类语言的历��以追溯到几�年�，最早考虑自然语言处�的科学家之一是 *Alan Turing*。
+试图让计算机�解人类语言的�试最早�以追溯到几�年�。*Alan Turing* 是最早研究自然语言处�问题的科学家之一。
+
 ### 图�测试
 
-当图�在1950年代研究*人工智能*时，他考虑是��以对人和计算机进行对�测试（通过打字对应），其中对�中的人�确定他们是在与�一个人交谈还是与计算机交谈.
+当图�在 1950 年代研究*人工智能*时，他想出了这个�维实验：让人类和计算机通过打字的方��交谈，其中人类并�知�对方是人类还是计算机。
+
+如果�过一定时间的交谈，人类无法确定对方是�是计算机，那么是��以认为计算机正在“�考�？
 
-如果�过一定时间的交谈，人类无法确定答案是��自计算机，那么是��以说计算机正在“�考�？
 ### �感 - “模仿游��
 
-这个想法�自一个�为 *模仿游�* 的派对游�，其中一�审讯者独自一人在一个房间里，负责确定两个人（在�一个房间里）是男性还是女性。审讯者�以传递笔记，并且需�想出能够�示神秘人性别的问题。当然，�一个房间的玩家试图通过回答问题的方��欺骗审讯者，例如误导或迷惑审讯者，�时表现出诚实回答的样�。
+这个想法�自一个�为 *模仿游�* 的派对游�，其中一�审讯者独自一人在一个房间里，负责确定在�一个房间里的两人的性别（男性或女性）。审讯者�以传递笔记，并且需�想出能够�示神秘人性别的问题。当然，�一个房间的玩家也�以通过回答问题的方��欺骗审讯者，例如用看似真诚的方�误导或迷惑审讯者。
 
-### Eliza的研�
+### Eliza 的研�
 
-在 1960 年代，一��� *Joseph Weizenbaum* 的麻��工学院科学家开�了[*Eliza*](https:/wikipedia.org/wiki/ELIZA)，Eliza是一�计算机“治疗师�，它�以�人类�出问题并表现出�解他们的答案。然而，虽然 Eliza �以解���并识别�些语法结构和关键字以给出��的答案，但�能说它*�解*了��。如果 Eliza 看到的��格�为“**I am** <u>sad</u>�，它�能会�新排列并替���中的��以形��应“How long have **you been** <u>sad</u>"。
+在 1960 年代的麻��工学院，一��� *Joseph Weizenbaum* 的科学家开�了[*Eliza*](https:/wikipedia.org/wiki/ELIZA)。Eliza 是一�计算机“治疗师�，它�以�人类�出问题并让人类觉得它能�解人类的回答。然而，虽然 Eliza �以解���并识别�些语法结构和关键字以给出��的答案，但�能说它*�解*了��。如果 Eliza 看到的��格�为“**I am** <u>sad</u>�（**我很** <u>难过</u>），它�能会�新排列并替���中的��，回答 “How long have **you been** <u>sad</u>"（**你已�** <u>难过</u> 多久了）。
 
-这给人的�象是伊丽莎�解了这��，并在问一个�续问题，而实际上，它是在改�时�并添加一些�。如果 Eliza 无法识别它有�应的关键字，它会给出一个�机�应，该�应�以适用于许多��的语�。 Eliza 很容易被欺骗，例如，如果用户写了**You are** a <u>bicycle</u>"，它�能会回�"How long have **I been** a <u>bicycle</u>?"，而�是更��的回答。
+看起��是 Eliza �解了这��，还在询问关于这��的问题，而实际上，它�是在改�时�和添加�语。如果 Eliza 没有在回答中�现它知�如何�应的�汇，它会给出一个�机�应，该�应�以适用于许多��的语�。 Eliza 很容易被欺骗，例如，如果用户写了 "**You are** a <u>bicycle</u>"（**你是** 个 <u>自行车</u>），它�能会回� "How long have **I been** a <u>bicycle</u>?"（**我已�是** 一个 <u>自行车</u> 多久了?），而�是更��的回答。
 
-[![Chatting with Eliza](https://img.youtube.com/vi/RMK9AphfLco/0.jpg)](https://youtu.be/RMK9AphfLco "Chatting with Eliza")
+[![跟 Eliza �天](https://img.youtube.com/vi/RMK9AphfLco/0.jpg)](https://youtu.be/RMK9AphfLco "跟 Eliza �天")
 
-> 🎥 点击上方的图片查看真实的ELIZA程�视频
+> 🎥 点击上方的图片查看关于 Eliza 原型的视频
 
-> 注�:如果你拥有ACM账户，你�以阅读1996年�表的[Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract)的原始介�。或者，在[wikipedia](https://wikipedia.org/wiki/ELIZA)阅读有关 Eliza 的信� 
+> �注：如果你拥有 ACM 账户，你�以阅读 1996 年�表的 [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract)的原始介�。或者，在[维基百科](https://wikipedia.org/wiki/ELIZA)上阅读有关 Eliza 的信�。
 
-## �系 - 编�实现一个基础的对�机器人
+## 练习 - 编程实现一个基础的对�机器人
 
-� Eliza 一样的对�机器人是一个似乎�以智能地�解和�应用户输入的程�。与 Eliza ��的是，我们的机器人�会用规则让它看起��是在进行智能对�。�而代之的是，我们的对�机器人将�有一�能力，通过几乎在所有�碎对�中都适用的�机�应��对�的进行。
+� Eliza 一样的对�机器人是一个看起��以智能地�解和�应用户输入的程�。与 Eliza ��的是，我们的机器人�会用规则让它看起��是在进行智能对�。我们的对�机器人将�有一�能力：它�会通过基本上�以糊弄所有普通对�的����机回答，使得谈�能够继续进行。
 
 ### 计划
 
 �建�天机器人的步骤
 
-1. 打�指导用户如何与机器人交互的说明
+1. 打�用户与机器人交互的使用说明
 2. 开�循环
    1. 获�用户输入
    2. 如果用户�求退出，就退出
-   3. 处�用户输入并选择一个回答（在这个例�中，回答从一个�能的通用回答列表中�机选择）
+   3. 处�用户输入并选择一个回答（在这个例�中，从回答列表中�机选择一个回答）
    4. 打�回答
 3. ��步骤2
 
 ### 构建�天机器人
 
-接下�让我们构建�天机器人。我们将从定义一些短语开始。
+接下�让我们建一个�天机器人。我们将从定义一些短语开始。
 
-1. 使用以下�机�应在 Python 中自己创建此机器人：
+1. 使用以下�机的回�（`random_responses`）在 Python 中自己创建此机器人：
 
     ```python
     random_responses = ["That is quite interesting, please tell me more.",
@@ -102,7 +108,7 @@
                         "Did you catch the game last night?"]
     ```
 
-    以下是一些指导你的示例输出（用户输入�于以 `>` 开头的行上）：
+    程��行看起�应该是这样：（用户输入�于以 `>` 开头的行上）
 
     ```output
     Hello, I am Marvin, the simple robot.
@@ -125,18 +131,18 @@
     It was nice talking to you, goodbye!
     ```
 
-    该任务的一��能解决方案在[这里](../solution/bot.py)
+    示例程�在[这里](../solution/bot.py)。这�是一��能的解决方案。
 
-    ✅ �止并�考
+    ✅ �下�，�考一下
    
-    1. 你认为�机�应会“欺骗��人认为机器人实际上�解他们�？
-    2. 机器人需�哪些功能�能更有效？
-    3. 如果机器人真的�以“�解�一个��的��，它是�也需�“记��对�中����的��？
+    1. 你认为这些�机�应能够“欺骗�人类，使人类认为机器人实际上�解了他们的���？
+    2. 机器人需�哪些功能�能更有效的回应？
+    3. 如果机器人真的�以“�解�一个��的��，它是�也需�“记������的��？
 
 ---
 ## 🚀挑战
 
-选择上�的“�止并�考�元素之一，然��试在代�中实现它们或使用伪代�在纸上编写解决方案。
+在上�的「�下�，�考一下���中选择一个问题，�试编程实现它们，或使用伪代�在纸上编写解决方案。
 
 在下一课中，您将了解解�自然语言和机器学习的许多其他方法。
 
@@ -144,7 +150,8 @@
 
 ## �习与自学
 
-看看下�的�考资料作为进一步的阅读机会。
+看看下�的�考资料作为进一步的�考阅读。
+
 ### �考
 
 1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Spring 2020 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/spr2020/entries/computational-linguistics/>.

From 479ebae0be16751f333a429c994a48ef52e18fa2 Mon Sep 17 00:00:00 2001
From: Colin Zang <zyy0116@gmail.com>
Date: Sun, 18 Jul 2021 20:58:42 +0800
Subject: [PATCH 205/368] Create README.zh-cn.md

---
 6-NLP/translations/README.zh-cn.md | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)
 create mode 100644 6-NLP/translations/README.zh-cn.md

diff --git a/6-NLP/translations/README.zh-cn.md b/6-NLP/translations/README.zh-cn.md
new file mode 100644
index 0000000000..db08bd0853
--- /dev/null
+++ b/6-NLP/translations/README.zh-cn.md
@@ -0,0 +1,24 @@
+# 自然语言处�入门
+
+自然语言处� (NLP) 是人工智能的一个�领域，主�研究如何让机器�解和处�人类语言，并用它�执行拼写检查或机器翻译等任务。
+
+## 本节主题：欧洲语言文学和欧洲浪漫酒店 ��
+
+在这部分课程中，您将了解机器学习最广泛的用途之一：自然语言处� (NLP)。�自计算语言学，这一类人工智能会通过语音或文本与人类交�，建立连接人与机器的桥�。
+
+课程中，我们将通过构建�型对�机器人�学习 NLP 的基础知识，以了解机器学习是如何使这个机器人越�越“智能�。您将穿越回 1813 年，与简·奥斯汀的�典�说 **傲慢与��** 中的 Elizabeth Bennett 和 Mr. Darcy �天（该�说于 1813 年出版）。然�，您将通过欧洲的酒店评论�进一步学习情感分�。
+
+![傲慢与��之书，和茶](../images/p&p.jpg)
+> <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">由 Elaine Howlin �摄，</a> �自 <a href="https://unsplash.com/s/照片/傲慢与��？utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## 课程
+
+1. [自然语言处�简介](../1-Introduction-to-NLP/README.md)
+2. [NLP 常�任务与技巧](../2-Tasks/README.md)
+3. [机器学习翻译和情感分�](../3-Translation-Sentiment/README.md)
+4. [准备数�](../4-Hotel-Reviews-1/README.md)
+5. [用于情感分�的工具：NLTK](../5-Hotel-Reviews-2/README.md)
+
+## 作者
+
+这些自然语言处�课程由 [Stephen Howell](https://twitter.com/Howell_MSFT) 用 ☕ 编写
\ No newline at end of file

From 54a3eb726b273b8b955986439dd946172d193fb7 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Sun, 18 Jul 2021 16:35:47 +0300
Subject: [PATCH 206/368] Add 3 files translated to Turkish under
 Classification lesson

---
 .../1-Introduction/translations/README.tr.md  | 297 ++++++++++++++++++
 .../translations/assignment.tr.md             |  11 +
 4-Classification/translations/README.tr.md    |  25 ++
 3 files changed, 333 insertions(+)
 create mode 100644 4-Classification/1-Introduction/translations/README.tr.md
 create mode 100644 4-Classification/1-Introduction/translations/assignment.tr.md
 create mode 100644 4-Classification/translations/README.tr.md

diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
new file mode 100644
index 0000000000..9413d5f34c
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -0,0 +1,297 @@
+# Sınıflandırmaya giriş
+
+Bu dört derste klasik makine öğreniminin temel bir odağı olan _sınıflandırma_ konusunu keşfedeceksiniz. Asya ve Hindistan'ın nefis mutfağının tamamı üzerine hazırlanmış bir veri setiyle çeşitli sınıflandırma algoritmalarını kullanmanın üzerinden geçeceğiz. Umarız açsınızdır!
+
+![sadece bir tutam!](../images/pinch.png)
+
+> Bu derslerede Pan-Asya mutfağını kutlayın! Fotoğraf [Jen Looper](https://twitter.com/jenlooper) tarafından çekilmiştir.
+
+Sınıflandırma, regresyon yöntemleriyle birçok ortak özelliği olan bir [gözetimli öğrenme](https://wikipedia.org/wiki/Supervised_learning) biçimidir. Eğer makine öğrenimi tamamen veri setleri kullanarak değerleri veya nesnelere verilecek isimleri öngörmekse, sınıflandırma genellikle iki gruba ayrılır: _ikili sınıflandırma_ ve _çok sınıflı sınıflandırma_.
+
+[![Sınıflandırmaya giriş](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to classification")
+
+> :movie_camera: Video için yukarıdaki fotoğrafa tıklayın: MIT's John Guttag introduces classification (MIT'den John Guttag sınıflandırmayı tanıtıyor)
+
+Hatırlayın:
+
+- **Doğrusal regresyon** değişkenler arasındaki ilişkileri öngörmenize ve o doğruya ilişkili olarak yeni bir veri noktasının nereye düşeceğine dair doğru öngörülerde bulunmanıza yardımcı oluyordu. Yani, _bir balkabağının fiyatının aralık ayına göre eylül ayında ne kadar olabileceğini_ öngörebilirsiniz örneğin.
+- **Lojistik regresyon** "ikili kategoriler"i keşfetmenizi sağlamıştı: bu fiyat noktasında, _bu balkabağı turuncu mudur, turuncu-değil midir?_
+
+Sınıflandırma, bir veri noktasının etiketini veya sınıfını belirlemek için farklı yollar belirlemek üzere çeşitli algoritmalar kullanır. Bir grup malzemeyi gözlemleyerek kökeninin hangi mutfak olduğunu belirleyip belirleyemeyeceğimizi görmek için bu mutfak verisiyle çalışalım.
+
+## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+
+### GiriÅŸ
+
+Sınıflandırma, makine öğrenimi araştırmacısının ve veri bilimcisinin temel işlerinden biridir. İkili bir değerin temel sınıflandırmasından ("Bu e-posta gereksiz (spam) midir yoksa değil midir?") bilgisayarla görüden yararlanarak karmaşık görüntü sınıflandırma ve bölütlemeye kadar, veriyi sınıf sınıf sıralayabilmek ve soru sorabilmek daima faydalıdır.
+
+Süreci daha bilimsel bir yolla ifade etmek gerekirse, sınıflandırma yönteminiz, girdi bilinmeyenlerinin arasındaki ilişkiyi çıktı bilinmeyenlerine eşlemenizi sağlayan öngörücü bir model oluşturur.
+
+![ikili ve çok sınıflı sınıflandırma karşılaştırması](../images/binary-multiclass.png)
+
+> Sınıflandırma algoritmalarının başa çıkması gereken ikili ve çok sınıflı problemler. Bilgilendirme grafiği [Jen Looper](https://twitter.com/jenlooper) tarafından hazırlanmıştır.
+
+Verimizi temizleme, görselleştirme ve makine öğrenimi görevleri için hazırlama süreçlerine başlamadan önce, veriyi sınıflandırmak için makine öğreniminin leveraj edilebileceği çeşitli yolları biraz öğrenelim.
+
+[İstatistikten](https://wikipedia.org/wiki/Statistical_classification) türetilmiş olarak, klasik makine öğrenimi kullanarak sınıflandırma, _X hastalığının gelişmesi ihtimalini_ belirlemek için `smoker`, `weight`, ve `age` gibi öznitelikler kullanır. Daha önce yaptığınız regresyon alıştırmalarına benzeyen bir gözetimli öğrenme yöntemi olarak, veriniz etiketlenir ve makine öğrenimi algoritmaları o etiketleri, sınıflandırmak ve veri setinin sınıflarını (veya 'özniteliklerini') öngörmek ve onları bir gruba veya bir sonuca atamak için kullanır.
+
+:white_check_mark: Mutfaklarla ilgili bir veri setini biraz düşünün. Çok sınıflı bir model neyi cevaplayabilir? İkili bir model neyi cevaplayabilir? Farz edelim ki verilen bir mutfağın çemen kullanmasının muhtemel olup olmadığını belirlemek istiyorsunuz. Farzedelim ki yıldız anason, enginar, karnabahar ve bayır turpu ile dolu bir alışveriş poşetinden tipik bir Hint yemeği yapıp yapamayacağınızı görmek istiyorsunuz.
+
+[![Çılgın gizem sepetleri](https://img.youtube.com/vi/GuTeDbaNoEU/0.jpg)](https://youtu.be/GuTeDbaNoEU "Crazy mystery baskets")
+
+> :movie_camera: Video için yukarıdaki fotoğrafa tıklayın. Aşçıların rastgele malzeme seçeneklerinden yemek yaptığı 'Chopped' programının tüm olayı 'gizem sepetleri'dir. Kuşkusuz, bir makine öğrenimi modeli onlara yardımcı olurdu!
+
+## Merhaba 'sınıflandırıcı'
+
+Bu mutfak veri setiyle ilgili sormak istediğimiz soru aslında bir **çok sınıflı soru**dur çünkü elimizde farklı potansiyel ulusal mutfaklar var. Verilen bir grup malzeme için, veri bu sınıflardan hangisine uyacak?
+
+Scikit-learn, veriyi sınıflandırmak için kullanmak üzere, çözmek istediğiniz problem çeşidine bağlı olarak, çeşitli farklı algoritmalar sunar. Önümüzdeki iki derste, bu algoritmalardan birkaçını öğreneceksiniz.
+
+## Alıştırma - verinizi temizleyip dengeleyin
+
+Bu projeye başlamadan önce elinizdeki ilk görev, daha iyi sonuçlar almak için, verinizi temizlemek ve **dengelemek**. Bu klasördeki boş _notebook.ipynb_ dosyasıyla başlayın.
+
+Kurmanız gereken ilk şey [imblearn](https://imbalanced-learn.org/stable/). Bu, veriyi daha iyi dengelemenizi sağlayacak bir Scikit-learn paketidir. (Bu görev hakkında birazdan daha fazla bilgi göreceksiniz.)
+
+1. `imblearn` kurun, `pip install` çalıştırın, şu şekilde:
+
+    ```python
+    pip install imblearn
+    ```
+
+1. Verinizi almak ve görselleştirmek için ihtiyaç duyacağınız paketleri alın (import edin), ayrıca `imblearn` paketinden `SMOTE` alın.
+
+    ```python
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import matplotlib as mpl
+    import numpy as np
+    from imblearn.over_sampling import SMOTE
+    ```
+
+    Şimdi okumak için hazırsınız, sonra veriyi alın.
+
+1. Sonraki görev veriyi almak olacak:
+
+    ```python
+    df  = pd.read_csv('../../data/cuisines.csv')
+    ```
+
+   `read_csv()` kullanmak _cusines.csv_ csv dosyasının içeriğini okuyacak ve `df` değişkenine yerleştirecek.
+
+1. Verinin ÅŸeklini kontrol edin:
+
+    ```python
+    df.head()
+    ```
+
+   İlk beş satır şöyle görünüyor:
+
+    ```output
+    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+    | 0   | 65         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 1   | 66         | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 2   | 67         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 3   | 68         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 4   | 69         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+    ```
+
+1. `info()` fonksiyonunu çağırarak bu veri hakkında bilgi edinin:
+
+    ```python
+    df.info()
+    ```
+
+    Çıktınız şuna benzer:
+
+    ```output
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 2448 entries, 0 to 2447
+    Columns: 385 entries, Unnamed: 0 to zucchini
+    dtypes: int64(384), object(1)
+    memory usage: 7.2+ MB
+    ```
+
+## Alıştırma - mutfaklar hakkında bilgi edinmek
+
+Şimdi, işimiz daha da ilginçleşmeye başlıyor. Mutfak mutfak verinin dağılımını keşfedelim
+
+1. `barh()` fonksiyonunu çağırarak veriyi sütunlarla çizdirin:
+
+    ```python
+    df.cuisine.value_counts().plot.barh()
+    ```
+
+    ![mutfak veri dağılımı](../images/cuisine-dist.png)
+
+    Sonlu sayıda mutfak var, ancak verinin dağılımı düzensiz. Bunu düzeltebilirsiniz! Bunu yapmadan önce, biraz daha keşfedelim. 
+
+1. Her mutfak için ne kadar verinin mevcut olduğunu bulun ve yazdırın:
+
+    ```python
+    thai_df = df[(df.cuisine == "thai")]
+    japanese_df = df[(df.cuisine == "japanese")]
+    chinese_df = df[(df.cuisine == "chinese")]
+    indian_df = df[(df.cuisine == "indian")]
+    korean_df = df[(df.cuisine == "korean")]
+    
+    print(f'thai df: {thai_df.shape}')
+    print(f'japanese df: {japanese_df.shape}')
+    print(f'chinese df: {chinese_df.shape}')
+    print(f'indian df: {indian_df.shape}')
+    print(f'korean df: {korean_df.shape}')
+    ```
+
+    çıktı şöyle görünür:
+
+    ```output
+    thai df: (289, 385)
+    japanese df: (320, 385)
+    chinese df: (442, 385)
+    indian df: (598, 385)
+    korean df: (799, 385)
+    ```
+
+## Malzemeleri keÅŸfetme
+
+Şimdi veriyi daha derinlemesine inceleyebilirsiniz ve her mutfak için tipik malzemelerin neler olduğunu öğrenebilirsiniz. Mutfaklar arasında karışıklık yaratan tekrar eden veriyi temizlemelisiniz, dolayısıyla şimdi bu problemle ilgili bilgi edinelim.
+
+1. Python'da, malzeme veri iskeleti yaratmak için `create_ingredient()` diye bir fonksiyon oluşturun. Bu fonksiyon, yardımcı olmayan bir sütunu temizleyerek ve sayılarına göre malzemeleri sıralayarak başlar:
+
+    ```python
+    def create_ingredient_df(df):
+        ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
+        ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
+        ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+        inplace=False)
+        return ingredient_df
+    ```
+
+   Şimdi bu fonksiyonu, her mutfağın en yaygın ilk on malzemesi hakkında hakkında fikir edinmek için kullanabilirsiniz.
+
+1. `create_ingredient()` fonksiyonunu çağırın ve `barh()` fonksiyonunu çağırarak çizdirin:
+
+    ```python
+    thai_ingredient_df = create_ingredient_df(thai_df)
+    thai_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Tayland](../images/thai.png)
+
+1. Japon verisi için de aynısını yapın:
+
+    ```python
+    japanese_ingredient_df = create_ingredient_df(japanese_df)
+    japanese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Japon](../images/japanese.png)
+
+1. Şimdi Çin malzemeleri için yapın:
+
+    ```python
+    chinese_ingredient_df = create_ingredient_df(chinese_df)
+    chinese_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Çin](../images/chinese.png)
+
+1. Hint malzemelerini çizdirin:
+
+    ```python
+    indian_ingredient_df = create_ingredient_df(indian_df)
+    indian_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Hint](../images/indian.png)
+
+1. Son olarak, Kore malzemelerini çizdirin:
+
+    ```python
+    korean_ingredient_df = create_ingredient_df(korean_df)
+    korean_ingredient_df.head(10).plot.barh()
+    ```
+
+    ![Kore](../images/korean.png)
+
+1. Şimdi, `drop()` fonksiyonunu çağırarak, farklı mutfaklar arasında karışıklığa sebep olan en çok ortaklık taşıyan malzemeleri temizleyelim:
+
+   Herkes pirinci, sarımsağı ve zencefili seviyor!
+
+    ```python
+    feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)
+    labels_df = df.cuisine #.unique()
+    feature_df.head()
+    ```
+
+## Veri setini dengeleyin
+
+Veriyi temizlediniz, şimdi [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html) - "Synthetic Minority Over-sampling Technique" ("Sentetik Azınlık Aşırı-Örnekleme/Örneklem-Artırma Tekniği") kullanarak dengeleyelim.
+
+1. `fit_resample()` fonksiyonunu çağırın, bu strateji ara değerlemeyle yeni örnekler üretir.
+
+    ```python
+    oversample = SMOTE()
+    transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
+    ```
+
+    Verinizi dengeleyerek, sınıflandırırken daha iyi sonuçlar alabileceksiniz. Bir ikili sınıflandırma düşünün. Eğer verimizin çoğu tek bir sınıfsa, bir makine öğrenimi modeli, sırf onun için daha fazla veri olduğundan o sınıfı daha sık tahmin edecektir. Veriyi dengelemek herhangi eğri veriyi alır ve bu dengesizliğin ortadan kaldırılmasına yardımcı olur.
+
+1. Şimdi, her bir malzeme için etiket sayısını kontrol edebilirsiniz:
+
+    ```python
+    print(f'new label count: {transformed_label_df.value_counts()}')
+    print(f'old label count: {df.cuisine.value_counts()}')
+    ```
+
+    Çıktınız şöyle görünür:
+
+    ```output
+    new label count: korean      799
+    chinese     799
+    indian      799
+    japanese    799
+    thai        799
+    Name: cuisine, dtype: int64
+    old label count: korean      799
+    indian      598
+    chinese     442
+    japanese    320
+    thai        289
+    Name: cuisine, dtype: int64
+    ```
+
+    Veri şimdi tertemiz, dengeli ve çok lezzetli!
+
+1. Son adım, dengelenmiş verinizi, etiket ve özniteliklerle beraber, yeni bir dosyaya gönderilebilecek yeni bir veri iskeletine kaydetmek:
+
+    ```python
+    transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
+    ```
+
+1. `transformed_df.head()` ve `transformed_df.info()` fonksiyonlarını kullanarak verinize bir kez daha göz atabilirsiniz. Gelecek derslerde kullanabilmek için bu verinin bir kopyasını kaydedin:
+
+    ```python
+    transformed_df.head()
+    transformed_df.info()
+    transformed_df.to_csv("../../data/cleaned_cuisine.csv")
+    ```
+
+    Bu yeni CSV şimdi kök data (veri) klasöründe görülebilir.
+
+---
+
+## :rocket: Meydan okuma
+
+Bu öğretim programı farklı ilgi çekici veri setleri içermekte. `data` klasörlerini inceleyin ve ikili veya çok sınıflı sınıflandırma için uygun olabilecek veri setleri bulunduran var mı, bakın. Bu veri seti için hangi soruları sorabilirdiniz?
+
+## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+
+## Gözden Geçirme & Kendi Kendine Çalışma
+
+SMOTE'nin API'ını keşfedin. En iyi hangi durumlar için kullanılıyor? Hangi problemleri çözüyor?
+
+## Ödev
+
+[Sınıflandırma yöntemlerini keşfedin](assignment.md)
diff --git a/4-Classification/1-Introduction/translations/assignment.tr.md b/4-Classification/1-Introduction/translations/assignment.tr.md
new file mode 100644
index 0000000000..99dfe5c26d
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/assignment.tr.md
@@ -0,0 +1,11 @@
+# Sınıflandırma yöntemlerini keşfedin
+
+## Yönergeler
+
+[Scikit-learn dokümentasyonunda](https://scikit-learn.org/stable/supervised_learning.html) veriyi sınıflandırma yöntemlerini içeren büyük bir liste göreceksiniz. Bu dokümanlar arasında ufak bir çöpçü avı yapın: Hedefiniz, sınıflandırma yöntemleri aramak ve bu eğitim programındaki bir veri seti, sorabileceğiniz bir soru ve bir sınıflandırma yöntemi eşleştirmek. Bir .doc dosyasında elektronik çizelge veya tablo hazırlayın ve veri setinin sınıflandırma algoritmasıyla nasıl çalışacağını açıklayın.
+
+## Rubrik
+
+|  Ölçüt   | Örnek Alınacak Nitelikte                                                                                                            | Yeterli                                                                                                                             | Geliştirme Gerekli                                                                                                                                            |
+| -------- | ----------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+|          | Bir sınıflandırma yönteminin yanısıra 5 algoritmayı inceleyen bir doküman sunulmuş. İnceleme iyi açıklanmış ve detaylı.             | Bir sınıflandırma yönteminin yanısıra 5 algoritmayı inceleyen bir doküman sunulmuş. İnceleme iyi açıklanmış ve detaylı.             | Bir sınıflandırma yönteminin yanısıra 3'ten az algoritmayı inceleyen bir doküman sunulmuş ve inceleme iyi açıklanmış veya detaylı değil.                      |
diff --git a/4-Classification/translations/README.tr.md b/4-Classification/translations/README.tr.md
new file mode 100644
index 0000000000..dc2ed959cb
--- /dev/null
+++ b/4-Classification/translations/README.tr.md
@@ -0,0 +1,25 @@
+# Sınıflandırmaya başlarken
+## Bölgesel konu: Leziz Asya ve Hint Mutfağı :ramen:
+
+Asya ve Hindistan'da yemek gelenekleri fazlaca çeşitlilik gösterir ve çok lezzetlidir! Malzemelerini anlamaya çalışmak için bölgesel mutfak hakkındaki verilere bakalım.
+
+![Taylandlı yemek satıcısı](../images/thai-food.jpg)
+> Fotoğraf <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> tarafından çekilmiştir ve <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>'tadır.
+
+## Öğrenecekleriniz
+
+Bu bölümde, bu eğitim programının tamamen regresyon üzerine olan ilk bölümünde öğrendiğiniz becerilere dayanıp onların üstüne beceriler ekleyeceksiniz ve verileriniz hakkında bilgi sahibi olmanızı sağlayacak diğer sınıflandırıcıları öğreneceksiniz.
+
+> Sınıflandırma modelleriyle çalışmayı öğrenmenizi sağlayacak faydalı düşük kodlu araçlar vardır. [Bu görev için Azure ML](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)'i deneyin.
+
+## Dersler
+
+1. [Sınıflandırmaya giriş](../1-Introduction/README.md)
+2. [Daha fazla sınıflandırıcı](../2-Classifiers-1/README.md)
+3. [Hatta daha fazla sınıflandırıcı](../3-Classifiers-2/README.md)
+4. [Uygulamalı Makine Öğrenimi: bir web uygulaması oluşturun](../4-Applied/README.md)
+## Katkıda bulunanlar
+
+"Sınıflandırmaya başlarken" [Cassie Breviu](https://www.twitter.com/cassieview) ve [Jen Looper](https://www.twitter.com/jenlooper) tarafından :hearts: ile yazılmıştır.
+
+Leziz mutfak veri seti [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)'dan alınmıştır.

From 47eba72c03526c2140409874691fbc48a0fcd2f9 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Sun, 18 Jul 2021 18:36:01 +0300
Subject: [PATCH 207/368] Fix file paths

---
 4-Classification/1-Introduction/translations/README.tr.md | 6 +++---
 4-Classification/translations/README.tr.md                | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
index 9413d5f34c..e4cc372aa4 100644
--- a/4-Classification/1-Introduction/translations/README.tr.md
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -19,7 +19,7 @@ Hatırlayın:
 
 Sınıflandırma, bir veri noktasının etiketini veya sınıfını belirlemek için farklı yollar belirlemek üzere çeşitli algoritmalar kullanır. Bir grup malzemeyi gözlemleyerek kökeninin hangi mutfak olduğunu belirleyip belirleyemeyeceğimizi görmek için bu mutfak verisiyle çalışalım.
 
-## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/?loc=tr)
 
 ### GiriÅŸ
 
@@ -286,7 +286,7 @@ Veriyi temizlediniz, ÅŸimdi [SMOTE](https://imbalanced-learn.org/dev/references/
 
 Bu öğretim programı farklı ilgi çekici veri setleri içermekte. `data` klasörlerini inceleyin ve ikili veya çok sınıflı sınıflandırma için uygun olabilecek veri setleri bulunduran var mı, bakın. Bu veri seti için hangi soruları sorabilirdiniz?
 
-## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/?loc=tr)
 
 ## Gözden Geçirme & Kendi Kendine Çalışma
 
@@ -294,4 +294,4 @@ SMOTE'nin API'ını keşfedin. En iyi hangi durumlar için kullanılıyor? Hangi
 
 ## Ödev
 
-[Sınıflandırma yöntemlerini keşfedin](assignment.md)
+[Sınıflandırma yöntemlerini keşfedin](assignment.tr.md)
diff --git a/4-Classification/translations/README.tr.md b/4-Classification/translations/README.tr.md
index dc2ed959cb..91b7425596 100644
--- a/4-Classification/translations/README.tr.md
+++ b/4-Classification/translations/README.tr.md
@@ -14,7 +14,7 @@ Bu bölümde, bu eğitim programının tamamen regresyon üzerine olan ilk böl
 
 ## Dersler
 
-1. [Sınıflandırmaya giriş](../1-Introduction/README.md)
+1. [Sınıflandırmaya giriş](../1-Introduction/translations/README.tr.md)
 2. [Daha fazla sınıflandırıcı](../2-Classifiers-1/README.md)
 3. [Hatta daha fazla sınıflandırıcı](../3-Classifiers-2/README.md)
 4. [Uygulamalı Makine Öğrenimi: bir web uygulaması oluşturun](../4-Applied/README.md)

From 66f2f1318827f960638fee94c3ee0617f7914dad Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Sun, 18 Jul 2021 19:34:27 +0200
Subject: [PATCH 208/368] Italian Translation - Chapter 2 correction in 2-Data

---
 3-Web-App/1-Web-App/translations/README.it.md | 347 ++++++++++++++++++
 .../1-Web-App/translations/assignment.it.md   |  11 +
 3-Web-App/translations/README.it.md           |  22 ++
 3 files changed, 380 insertions(+)
 create mode 100644 3-Web-App/1-Web-App/translations/README.it.md
 create mode 100644 3-Web-App/1-Web-App/translations/assignment.it.md
 create mode 100644 3-Web-App/translations/README.it.md

diff --git a/3-Web-App/1-Web-App/translations/README.it.md b/3-Web-App/1-Web-App/translations/README.it.md
new file mode 100644
index 0000000000..18e6af1070
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/README.it.md
@@ -0,0 +1,347 @@
+# Crearere un'app web per utilizzare un modello ML
+
+In questa lezione, si addestrerà un modello ML su un insieme di dati fuori dal mondo: _avvistamenti di UFO nel secolo scorso_, provenienti dal [database di NUFORC](https://www.nuforc.org).
+
+Si imparerà:
+
+- Come "scapigliare" un modello addestrato
+- Come usare quel modello in un'app Flask
+
+Si continuerà a utilizzare i notebook per pulire i dati e addestrare il modello, ma si può fare un ulteriore passo avanti esplorando il processo utilizzando un modello "in the wild", per così dire: in un'app web.
+
+Per fare ciò, è necessario creare un'app Web utilizzando Flask.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+
+## Costruire un'app
+
+Esistono diversi modi per creare app Web per utilizzare modelli di machine learning. L'architettura web può influenzare il modo in cui il modello viene addestrato. Si immagini di lavorare in un'azienda in cui il gruppo di data science ha addestrato un modello che vogliono che venga utilizzato in un'app.
+
+### Considerazioni
+
+Ci sono molte domande da porsi:
+
+- **È un'app Web o un'app mobile?** Se si sta creando un'app mobile o si deve usare il modello in un contesto IoT, ci si può avvalere [di TensorFlow Lite](https://www.tensorflow.org/lite/) e usare il modello in un'app Android o iOS.
+- **Dove risiederà il modello**? E' utilizzato in cloud o in locale?
+- **Supporto offline**. L'app deve funzionare offline?
+- **Quale tecnologia è stata utilizzata per addestrare il modello?** La tecnologia scelta può influenzare gli strumenti che è necessario utilizzare.
+   - **Utilizzare** TensorFlow Se si sta addestrando un modello utilizzando TensorFlow, ad esempio, tale ecosistema offre la possibilità di convertire un modello TensorFlow per l'utilizzo in un'app Web utilizzando [TensorFlow.js](https://www.tensorflow.org/js/).
+   - **Utilizzare PyTorch**. Se stai costruendo un modello utilizzando una libreria come PyTorch[,](https://pytorch.org/) si ha la possibilità di esportarlo in formato [ONNX](https://onnx.ai/) ( Open Neural Network Exchange) per l'utilizzo in app Web JavaScript che possono utilizzare il motore di esecuzione [Onnx](https://www.onnxruntime.ai/). Questa opzione verrà esplorata in una lezione futura per un modello addestrato da Scikit-learn
+   - **Utilizzo di Lobe.ai o Azure Custom vision**. Se si sta usando un sistema ML SaaS (Software as a Service) come [Lobe.ai](https://lobe.ai/) o [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) per addestrare un modello, questo tipo di software fornisce modi per esportare il modello per molte piattaforme, inclusa la creazione di un'API su misura da interrogare nel cloud dalla propria applicazione online.
+
+Si hai anche l'opportunità di creare un'intera app Web Flask in grado di addestrare il modello stesso in un browser Web. Questo può essere fatto anche usando TensorFlow.js in un contesto JavaScript.
+
+Per i nostri scopi, poiché si è lavorato con i notebook basati su Python, verranno esplorati i passaggi necessari per esportare un modello addestrato da un tale notebook in un formato leggibile da un'app Web creata in Python.
+
+## Strumento
+
+Per questa attività sono necessari due strumenti: Flask e Pickle, entrambi eseguiti su Python.
+
+✅ Cos'è [Flask](https://palletsprojects.com/p/flask/)? Definito come un "micro-framework" dai suoi creatori, Flask fornisce le funzionalità di base dei framework web utilizzando Python e un motore di modelli per creare pagine web. Si dia un'occhiata a [questo modulo di apprendimento](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) per esercitarsi a costruire con Flask.
+
+✅ Cos'è [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 è un modulo Python che serializza e de-serializza una struttura di oggetti Python. Quando si utilizza pickle in un modello, si serializza o si appiattisce la sua struttura per l'uso sul web. Cautela: pickle non è intrinsecamente sicuro, quindi si faccia  attenzione se viene chiesto di de-serializzare un file. Un file creato con pickle ha il suffisso `.pkl`.
+
+## Esercizio: pulire i dati
+
+In questa lezione verranno utilizzati i dati di 80.000 avvistamenti UFO, raccolti da [NUFORC](https://nuforc.org) (The National UFO Reporting Center). Questi dati hanno alcune descrizioni interessanti di avvistamenti UFO, ad esempio:
+
+- **Descrizione di esempio lunga**. "Un uomo emerge da un raggio di luce che di notte brilla su un campo erboso e corre verso il parcheggio della Texas Instruments".
+- **Descrizione di esempio breve**. "le luci ci hanno inseguito".
+
+Il [foglio](./data/ufos.csv) di calcolo ufos.csv include colonne sulla `città`, `lo stato` e il `paese` in cui è avvenuto l'avvistamento, la `forma dell'oggetto e la` sua `latitudine` e `longitudine`.
+
+Nel [notebook](notebook.ipynb) vuoto incluso in questa lezione:
+
+1. importare `pandas`, `matplotlib` e numpy `come` fatto nelle lezioni precedenti e importare il foglio di calcolo ufos. Si può dare un'occhiata a un insieme di dati di esempio:
+
+   ```python
+   import pandas as pd
+   import numpy as np
+
+   ufos = pd.read_csv('../data/ufos.csv')
+   ufos.head()
+   ```
+
+1. Convertire i dati ufos in un piccolo dataframe con nuove intestazioni Controllare i valori univoci nel campo `Country` .
+
+   ```python
+   ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})
+
+   ufos.Country.unique()
+   ```
+
+1. Ora si può ridurre la quantità di dati da gestire eliminando qualsiasi valore nullo e importando solo avvistamenti tra 1-60 secondi:
+
+   ```python
+   ufos.dropna(inplace=True)
+
+   ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]
+
+   ufos.info()
+   ```
+
+1. Importare la libreria LabelEncoder `di` Scikit-learn per convertire i valori di testo per i paesi in un numero:
+
+   ✅ LabelEncoder codifica i dati in ordine alfabetico
+
+   ```python
+   from sklearn.preprocessing import LabelEncoder
+
+   ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])
+
+   ufos.head()
+   ```
+
+   I dati dovrebbero assomigliare a questo:
+
+   ```output
+   	Seconds	Country	Latitude	Longitude
+   2	20.0	3	    53.200000	-2.916667
+   3	20.0	4	    28.978333	-96.645833
+   14	30.0	4	    35.823889	-80.253611
+   23	60.0	4	    45.582778	-122.352222
+   24	3.0	    3	    51.783333	-0.783333
+   ```
+
+## Esercizio: costruire il proprio modello
+
+Ora ci si può preparare per addestrare un modello portando i dati nel gruppo di addestramento e test.
+
+1. Selezionare le tre caratteristiche su cui lo si vuole allenare come vettore X mentre il vettore y sarà `Country` Se deve essere in grado di inserire `Secondi`, `Latitudine` e `Longitudine` e ottenere un ID nazione da restituire.
+
+   ```python
+   from sklearn.model_selection import train_test_split
+
+   Selected_features = ['Seconds','Latitude','Longitude']
+
+   X = ufos[Selected_features]
+   y = ufos['Country']
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   ```
+
+1. Addestrare il modello usando la regressione logistica:
+
+   ```python
+   from sklearn.metrics import accuracy_score, classification_report
+   from sklearn.linear_model import LogisticRegression
+   model = LogisticRegression()
+   model.fit(X_train, y_train)
+   predictions = model.predict(X_test)
+
+   print(classification_report(y_test, predictions))
+   print('Predicted labels: ', predictions)
+   print('Accuracy: ', accuracy_score(y_test, predictions))
+   ```
+
+La precisione non è male **(circa il 95%)**, non sorprende che `Country` e `Latitude/Longitude` siano correlati.
+
+Il modello creato non è molto rivoluzionario in quanto si dovrebbe essere in grado di dedurre una `nazione` dalla sua `latitudine` e `longitudine`, ma è un buon esercizio provare ad allenare dai dati grezzi che sono stati puliti ed esportati, e quindi utilizzare questo modello in una app web.
+
+## Esercizio: usare pickle con il modello
+
+Ora è il momento di utilizzare pickle _con_ il modello! Lo si può fare in poche righe di codice. Una volta che è stato serializzato con pickle_,_ caricare il modello e testarlo rispetto a un array di dati di esempio contenente valori per secondi, latitudine e longitudine,
+
+```python
+import pickle
+model_filename = 'ufo-model.pkl'
+pickle.dump(model, open(model_filename,'wb'))
+
+model = pickle.load(open('ufo-model.pkl','rb'))
+print(model.predict([[50,44,-12]]))
+```
+
+Il modello restituisce **"3"**, che è il codice paese per il Regno Unito. Jolly 👽
+
+## Esercizio: creare un'app Flask
+
+Ora si può creare un'app Flask per chiamare il modello e restituire risultati simili, ma in un modo visivamente più gradevole.
+
+1. Iniziare creando una cartella chiamata web-app **a** livello del _file_ notebook.ipynb dove risiede _il_ file ufo-model.pkl.
+
+1. In quella cartella creare altre tre cartelle: **static**, con una cartella **css** al suo interno e **templates**. Ora si dovrebbero avere i seguenti file e directory:
+
+   ```output
+   web-app/
+     static/
+       css/
+       templates/
+   notebook.ipynb
+   ufo-model.pkl
+   ```
+
+   ✅ Fare riferimento alla cartella della soluzione per una visualizzazione dell'app finita
+
+1. Il primo file da creare nella cartella dell'_app_ Web è **il** file requirements.txt. Come _package.json_ in un'app JavaScript, questo file elenca le dipendenze richieste dall'app. In **requirements.txt** aggiungere le righe:
+
+   ```text
+   scikit-learn
+   pandas
+   numpy
+   flask
+   ```
+
+1. Ora, eseguire questo file portandosi su _web-app_:
+
+   ```bash
+   cd web-app
+   ```
+
+1. Aprire una finestra di terminale dove risiede requirements.txt e digitare `pip install`, per installare le librerie elencate in _reuirements.txt_:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+1. Ora si è pronti per creare altri tre file per completare l'app:
+
+   1. Crea **app.py** nella directory radice
+   2. Creare **index.html** nella directory _templates_ .
+   3. Crea **sytles.css** nella directory _static/css_ .
+
+1. Inserire nel _file_ styles.css alcuni stili:
+
+   ```css
+   body {
+   	width: 100%;
+   	height: 100%;
+   	font-family: 'Helvetica';
+   	background: black;
+   	color: #fff;
+   	text-align: center;
+   	letter-spacing: 1.4px;
+   	font-size: 30px;
+   }
+
+   input {
+   	min-width: 150px;
+   }
+
+   .grid {
+   	width: 300px;
+   	border: 1px solid #2d2d2d;
+   	display: grid;
+   	justify-content: center;
+   	margin: 20px auto;
+   }
+
+   .box {
+   	color: #fff;
+   	background: #2d2d2d;
+   	padding: 12px;
+   	display: inline-block;
+   }
+   ```
+
+1. Quindi, creare il file _index.html_ :
+
+   ```html
+   <!DOCTYPE html>
+   <html>
+   <head>
+     <meta charset="UTF-8">
+     <title>🛸 UFO Appearance Prediction! 👽</title>
+     <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
+   </head>
+
+   <body>
+    <div class="grid">
+
+     <div class="box">
+
+     <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
+
+       <form action="{{ url_for('predict')}}" method="post">
+       	<input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
+         <input type="text" name="latitude" placeholder="Latitude" required="required" />
+   		  <input type="text" name="longitude" placeholder="Longitude" required="required" />
+         <button type="submit" class="btn">Predict country where the UFO is seen</button>
+       </form>
+
+
+      <p>{{ prediction_text }}</p>
+
+    </div>
+   </div>
+
+   </body>
+   </html>
+   ```
+
+   Dare un'occhiata al modello in questo file. Notare la sintassi con le parentesi graffe attorno alle variabili che verranno fornite dall'app, come il testo di previsione: `{{}}`. C'è anche un modulo che invia una previsione al percorso `/` predict.
+
+   Infine, si è pronti per creare il file python che guida il consumo del modello e la visualizzazione delle previsioni:
+
+1. In `app.py` aggiungere:
+
+   ```python
+   import numpy as np
+   from flask import Flask, request, render_template
+   import pickle
+
+   app = Flask(__name__)
+
+   model = pickle.load(open("../ufo-model.pkl", "rb"))
+
+
+   @app.route("/")
+   def home():
+       return render_template("index.html")
+
+
+   @app.route("/predict", methods=["POST"])
+   def predict():
+
+       int_features = [int(x) for x in request.form.values()]
+       final_features = [np.array(int_features)]
+       prediction = model.predict(final_features)
+
+       output = prediction[0]
+
+       countries = ["Australia", "Canada", "Germany", "UK", "US"]
+
+       return render_template(
+           "index.html", prediction_text="Likely country: {}".format(countries[output])
+       )
+
+
+   if __name__ == "__main__":
+       app.run(debug=True)
+   ```
+
+   > 💡 Suggerimento: quando si aggiunge [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) durante l'esecuzione dell'app Web utilizzando Flask, qualsiasi modifica apportata all'applicazione verrà applicata immediatamente senza la necessità di riavviare il server. Attenzione! Non abilitare questa modalità in un'app di produzione.
+
+Se si esegue `python app.py` o `python3 app.py` , il server web si avvia, localmente, e si può compilare un breve modulo per ottenere una risposta alla domanda scottante su dove sono stati avvistati gli UFO!
+
+Prima di farlo, dare un'occhiata alle parti di `app.py`:
+
+1. Innanzitutto, le dipendenze vengono caricate e l'app si avvia.
+1. Quindi, il modello viene importato.
+1. Quindi, index.html viene visualizzato sulla rotta home.
+
+Sulla rotta `/predict` , accadono diverse cose quando il modulo viene inviato:
+
+1. Le variabili del modulo vengono raccolte e convertite in un array numpy. Vengono quindi inviati al modello e viene restituita una previsione.
+2. Le nazioni che si vogliono visualizzare vengono nuovamente esposte come testo leggibile dal loro codice paese previsto e tale valore viene inviato a index.html per essere visualizzato nel modello.
+
+Usare un modello in questo modo, con Flask e un modello serializzato è relativamente semplice. La cosa più difficile è capire che forma hanno i dati che devono essere inviati al modello per ottenere una previsione. Tutto dipende da come è stato addestrato il modello. Questo ha tre punti dati da inserire per ottenere una previsione.
+
+In un ambiente professionale, si può vedere quanto sia necessaria una buona comunicazione tra le persone che addestrano il modello e coloro che lo consumano in un'app Web o mobile. In questo caso, si ricoprono entrambi i ruoli!
+
+---
+
+## 🚀 Sfida
+
+Invece di lavorare su un notebook e importare il modello nell'app Flask, si può addestrare il modello direttamente nell'app Flask! Provare a convertire il codice Python nel notebook, magari dopo che i dati sono stati puliti, per addestrare il modello dall'interno dell'app su un percorso chiamato /`train`. Quali sono i pro e i contro di seguire questo metodo?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+
+## Revisione e Auto Apprendimento
+
+Esistono molti modi per creare un'app Web per utilizzare i modelli ML. Elencare dei modi in cui si potrebbe utilizzare JavaScript o Python per creare un'app Web per sfruttare machine learning. Considerare l'architettura: il modello dovrebbe rimanere nell'app o risiedere nel cloud? Se quest'ultimo, come accedervi? Disegnare un modello architettonico per una soluzione web ML applicata.
+
+## Compito
+
+[Provare un modello diverso](assignment.md)
+
+
diff --git a/3-Web-App/1-Web-App/translations/assignment.it.md b/3-Web-App/1-Web-App/translations/assignment.it.md
new file mode 100644
index 0000000000..21a1b804c9
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Provare un modello diverso
+
+## Istruzioni
+
+Ora che si è creato un'app Web utilizzando un modello di regressione addestrato, usare uno dei modelli di una lezione precedente sulla regressione per ripetere questa app Web. Si può mantenere lo stile o progettarla in modo diverso per riflettere i dati della zucca. Fare attenzione a modificare gli input in modo che riflettano il metodo di addestramento del proprio modello.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------------------------- | --------------------------------------------------------- | --------------------------------------------------------- | -------------------------------------- |
+| | L'app Web funziona come previsto e viene distribuita nel cloud | L'app Web contiene difetti o mostra risultati imprevisti | L'app web non funziona correttamente |
diff --git a/3-Web-App/translations/README.it.md b/3-Web-App/translations/README.it.md
new file mode 100644
index 0000000000..52d9c5ec1a
--- /dev/null
+++ b/3-Web-App/translations/README.it.md
@@ -0,0 +1,22 @@
+# Creare un'app Web per utilizzare il modello ML
+
+In questa sezione del programma di studi, verrà presentato un argomento ML applicato: come salvare il modello di Scikit-learn come file che può essere utilizzato per fare previsioni all'interno di un'applicazione web. Una volta salvato il modello, si imparerà come utilizzarlo in un'app Web integrata in Flask. Per prima cosa si creerà un modello utilizzando alcuni dati che riguardano gli avvistamenti di UFO! Quindi, si creerà un'app Web che consentirà di inserire un numero di secondi con un valore di latitudine e longitudine per prevedere quale paese ha riferito di aver visto un UFO.
+
+![Parcheggio UFO](images/ufo.jpg)
+
+Foto di <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> su <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+
+## Lezioni
+
+1. [Costruire un'app Web](1-Web-App/README.md)
+
+## Crediti
+
+"Costruire un'app web" è stato scritto con ♥� da [Jen Looper](https://twitter.com/jenlooper).
+
+♥� I quiz sono stati scritti da Rohan Raj.
+
+L'insieme di dati proviene da [Kaggle](https://www.kaggle.com/NUFORC/ufo-sightings).
+
+L'architettura dell'app web è stata suggerita in parte da [questo articolo](https://towardsdatascience.com/how-to-easily-deploy-machine-learning-models-using-flask-b95af8fe34d4) e da [questo](https://github.com/abhinavsagar/machine-learning-deployment) repository di Abhinav Sagar.
\ No newline at end of file

From 889e697dac61751545e9be35aec744fecc785981 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Sun, 18 Jul 2021 19:37:32 +0200
Subject: [PATCH 209/368] Italian Translation - Chapter 2 sentence modified in
 2-Data

---
 2-Regression/2-Data/translations/README.it.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/2-Data/translations/README.it.md b/2-Regression/2-Data/translations/README.it.md
index 3a67cdea68..7b78ac52bb 100644
--- a/2-Regression/2-Data/translations/README.it.md
+++ b/2-Regression/2-Data/translations/README.it.md
@@ -171,7 +171,7 @@ Provare a creare alcuni grafici di base per visualizzare il nuovo dataframe appe
 
 ### Renderlo utile
 
-Per fare in modo che i grafici mostrino dati utili, di solito è necessario raggruppare i dati in qualche modo. Si prova a creare un grafico in cui l'asse y mostra i prezzi e l'asse x mostra la distribuzione dei dati raggruppati per mese.
+Per fare in modo che i grafici mostrino dati utili, di solito è necessario raggruppare i dati in qualche modo. Si prova a creare un grafico che mostra la distribuzione dei dati dove  l'asse x mostra i mesi.
 
 1. Aggiungere una cella per creare un grafico a barre raggruppato:
 

From 8f29d285edc1248e10245534a88c0705e758f9f0 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Sun, 18 Jul 2021 20:55:00 +0200
Subject: [PATCH 210/368] Italian Translation - Chapter 3 complete

---
 3-Web-App/1-Web-App/translations/README.it.md | 116 +++++++++---------
 .../1-Web-App/translations/assignment.it.md   |   4 +-
 3-Web-App/translations/README.it.md           |   8 +-
 3 files changed, 64 insertions(+), 64 deletions(-)

diff --git a/3-Web-App/1-Web-App/translations/README.it.md b/3-Web-App/1-Web-App/translations/README.it.md
index 18e6af1070..82454852a2 100644
--- a/3-Web-App/1-Web-App/translations/README.it.md
+++ b/3-Web-App/1-Web-App/translations/README.it.md
@@ -1,13 +1,13 @@
-# Crearere un'app web per utilizzare un modello ML
+# Creare un'app web per utilizzare un modello ML
 
 In questa lezione, si addestrerà un modello ML su un insieme di dati fuori dal mondo: _avvistamenti di UFO nel secolo scorso_, provenienti dal [database di NUFORC](https://www.nuforc.org).
 
 Si imparerà:
 
-- Come "scapigliare" un modello addestrato
+- Come serializzare/deserializzare un modello addestrato
 - Come usare quel modello in un'app Flask
 
-Si continuerà a utilizzare i notebook per pulire i dati e addestrare il modello, ma si può fare un ulteriore passo avanti esplorando il processo utilizzando un modello "in the wild", per così dire: in un'app web.
+Si continuerà a utilizzare il notebook per pulire i dati e addestrare il modello, ma si può fare un ulteriore passo avanti nel processo esplorando l'utilizzo del modello direttamente in un'app web.
 
 Per fare ciò, è necessario creare un'app Web utilizzando Flask.
 
@@ -15,44 +15,44 @@ Per fare ciò, è necessario creare un'app Web utilizzando Flask.
 
 ## Costruire un'app
 
-Esistono diversi modi per creare app Web per utilizzare modelli di machine learning. L'architettura web può influenzare il modo in cui il modello viene addestrato. Si immagini di lavorare in un'azienda in cui il gruppo di data science ha addestrato un modello che vogliono che venga utilizzato in un'app.
+Esistono diversi modi per creare app Web per utilizzare modelli di machine learning. L'architettura web può influenzare il modo in cui il modello viene addestrato. Si immagini di lavorare in un'azienda nella quale il gruppo di data science ha addestrato un modello che va utilizzato in un'app.
 
 ### Considerazioni
 
 Ci sono molte domande da porsi:
 
-- **È un'app Web o un'app mobile?** Se si sta creando un'app mobile o si deve usare il modello in un contesto IoT, ci si può avvalere [di TensorFlow Lite](https://www.tensorflow.org/lite/) e usare il modello in un'app Android o iOS.
+- **È un'app web o un'app su dispositivo mobile?** Se si sta creando un'app su dispositivo mobile o si deve usare il modello in un contesto IoT, ci si può avvalere [di TensorFlow Lite](https://www.tensorflow.org/lite/) e usare il modello in un'app Android o iOS.
 - **Dove risiederà il modello**? E' utilizzato in cloud o in locale?
 - **Supporto offline**. L'app deve funzionare offline?
 - **Quale tecnologia è stata utilizzata per addestrare il modello?** La tecnologia scelta può influenzare gli strumenti che è necessario utilizzare.
-   - **Utilizzare** TensorFlow Se si sta addestrando un modello utilizzando TensorFlow, ad esempio, tale ecosistema offre la possibilità di convertire un modello TensorFlow per l'utilizzo in un'app Web utilizzando [TensorFlow.js](https://www.tensorflow.org/js/).
-   - **Utilizzare PyTorch**. Se stai costruendo un modello utilizzando una libreria come PyTorch[,](https://pytorch.org/) si ha la possibilità di esportarlo in formato [ONNX](https://onnx.ai/) ( Open Neural Network Exchange) per l'utilizzo in app Web JavaScript che possono utilizzare il motore di esecuzione [Onnx](https://www.onnxruntime.ai/). Questa opzione verrà esplorata in una lezione futura per un modello addestrato da Scikit-learn
+   - **Utilizzare** TensorFlow. Se si sta addestrando un modello utilizzando TensorFlow, ad esempio, tale ecosistema offre la possibilità di convertire un modello TensorFlow per l'utilizzo in un'app Web utilizzando [TensorFlow.js](https://www.tensorflow.org/js/).
+   - **Utilizzare PyTorch**. Se si sta costruendo un modello utilizzando una libreria come PyTorch[,](https://pytorch.org/) si ha la possibilità di esportarlo in formato [ONNX](https://onnx.ai/) ( Open Neural Network Exchange) per l'utilizzo in app Web JavaScript che possono utilizzare il [motore di esecuzione Onnx](https://www.onnxruntime.ai/). Questa opzione verrà esplorata in una lezione futura per un modello addestrato da Scikit-learn
    - **Utilizzo di Lobe.ai o Azure Custom vision**. Se si sta usando un sistema ML SaaS (Software as a Service) come [Lobe.ai](https://lobe.ai/) o [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) per addestrare un modello, questo tipo di software fornisce modi per esportare il modello per molte piattaforme, inclusa la creazione di un'API su misura da interrogare nel cloud dalla propria applicazione online.
 
-Si hai anche l'opportunità di creare un'intera app Web Flask in grado di addestrare il modello stesso in un browser Web. Questo può essere fatto anche usando TensorFlow.js in un contesto JavaScript.
+Si ha anche l'opportunità di creare un'intera app Web Flask in grado di addestrare il modello stesso in un browser Web. Questo può essere fatto anche usando TensorFlow.js in un contesto JavaScript.
 
-Per i nostri scopi, poiché si è lavorato con i notebook basati su Python, verranno esplorati i passaggi necessari per esportare un modello addestrato da un tale notebook in un formato leggibile da un'app Web creata in Python.
+Per questo scopo, poiché si è lavorato con i notebook basati su Python, verranno esplorati i passaggi necessari per esportare un modello addestrato da tale notebook in un formato leggibile da un'app Web creata in Python.
 
-## Strumento
+## Strumenti
 
 Per questa attività sono necessari due strumenti: Flask e Pickle, entrambi eseguiti su Python.
 
-✅ Cos'è [Flask](https://palletsprojects.com/p/flask/)? Definito come un "micro-framework" dai suoi creatori, Flask fornisce le funzionalità di base dei framework web utilizzando Python e un motore di modelli per creare pagine web. Si dia un'occhiata a [questo modulo di apprendimento](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) per esercitarsi a costruire con Flask.
+✅ Cos'è [Flask](https://palletsprojects.com/p/flask/)? Definito come un "micro-framework" dai suoi creatori, Flask fornisce le funzionalità di base dei framework web utilizzando Python e un motore di template per creare pagine web. Si dia un'occhiata a [questo modulo di apprendimento](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) per esercitarsi a sviluppare con Flask.
 
-✅ Cos'è [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 è un modulo Python che serializza e de-serializza una struttura di oggetti Python. Quando si utilizza pickle in un modello, si serializza o si appiattisce la sua struttura per l'uso sul web. Cautela: pickle non è intrinsecamente sicuro, quindi si faccia  attenzione se viene chiesto di de-serializzare un file. Un file creato con pickle ha il suffisso `.pkl`.
+✅ Cos'è [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 è un modulo Python che serializza e de-serializza la struttura di un oggetto Python. Quando si utilizza pickle in un modello, si serializza o si appiattisce la sua struttura per l'uso sul web. Cautela: pickle non è intrinsecamente sicuro, quindi si faccia  attenzione se viene chiesto di de-serializzare un file. Un file creato con pickle ha il suffisso `.pkl`.
 
 ## Esercizio: pulire i dati
 
-In questa lezione verranno utilizzati i dati di 80.000 avvistamenti UFO, raccolti da [NUFORC](https://nuforc.org) (The National UFO Reporting Center). Questi dati hanno alcune descrizioni interessanti di avvistamenti UFO, ad esempio:
+In questa lezione verranno utilizzati i dati di 80.000 avvistamenti UFO, raccolti dal Centro Nazionale per gli Avvistamenti di UFO [NUFORC](https://nuforc.org) (The National UFO Reporting Center). Questi dati hanno alcune descrizioni interessanti di avvistamenti UFO, ad esempio:
 
 - **Descrizione di esempio lunga**. "Un uomo emerge da un raggio di luce che di notte brilla su un campo erboso e corre verso il parcheggio della Texas Instruments".
 - **Descrizione di esempio breve**. "le luci ci hanno inseguito".
 
-Il [foglio](./data/ufos.csv) di calcolo ufos.csv include colonne sulla `città`, `lo stato` e il `paese` in cui è avvenuto l'avvistamento, la `forma dell'oggetto e la` sua `latitudine` e `longitudine`.
+Il foglio di calcolo [ufo.csv](../data/ufos.csv) include colonne su città (`city`), stato (`state`) e nazione (`country`) in cui è avvenuto l'avvistamento, la forma (`shape`) dell'oggetto e la sua latitudine (`latitude`) e longitudine (`longitude`).
 
-Nel [notebook](notebook.ipynb) vuoto incluso in questa lezione:
+Nel [notebook](../notebook.ipynb) vuoto incluso in questa lezione:
 
-1. importare `pandas`, `matplotlib` e numpy `come` fatto nelle lezioni precedenti e importare il foglio di calcolo ufos. Si può dare un'occhiata a un insieme di dati di esempio:
+1. importare `pandas`, `matplotlib` e `numpy` come fatto nelle lezioni precedenti e importare il foglio di calcolo ufo.csv. Si può dare un'occhiata a un insieme di dati campione:
 
    ```python
    import pandas as pd
@@ -80,7 +80,7 @@ Nel [notebook](notebook.ipynb) vuoto incluso in questa lezione:
    ufos.info()
    ```
 
-1. Importare la libreria LabelEncoder `di` Scikit-learn per convertire i valori di testo per i paesi in un numero:
+1. Importare la libreria `LabelEncoder` di Scikit-learn per convertire i valori di testo per le nazioni in un numero:
 
    ✅ LabelEncoder codifica i dati in ordine alfabetico
 
@@ -105,9 +105,9 @@ Nel [notebook](notebook.ipynb) vuoto incluso in questa lezione:
 
 ## Esercizio: costruire il proprio modello
 
-Ora ci si può preparare per addestrare un modello portando i dati nel gruppo di addestramento e test.
+Ora ci si può preparare per addestrare un modello portando i dati nei gruppi di addestramento e test.
 
-1. Selezionare le tre caratteristiche su cui lo si vuole allenare come vettore X mentre il vettore y sarà `Country` Se deve essere in grado di inserire `Secondi`, `Latitudine` e `Longitudine` e ottenere un ID nazione da restituire.
+1. Selezionare le tre caratteristiche su cui lo si vuole allenare come vettore X mentre il vettore y sarà `Country` Si deve essere in grado di inserire secondi (`Seconds`), latitudine (`Latitude`) e longitudine (`Longitude`) e ottenere un ID nazione da restituire.
 
    ```python
    from sklearn.model_selection import train_test_split
@@ -136,11 +136,11 @@ Ora ci si può preparare per addestrare un modello portando i dati nel gruppo di
 
 La precisione non è male **(circa il 95%)**, non sorprende che `Country` e `Latitude/Longitude` siano correlati.
 
-Il modello creato non è molto rivoluzionario in quanto si dovrebbe essere in grado di dedurre una `nazione` dalla sua `latitudine` e `longitudine`, ma è un buon esercizio provare ad allenare dai dati grezzi che sono stati puliti ed esportati, e quindi utilizzare questo modello in una app web.
+Il modello creato non è molto rivoluzionario in quanto si dovrebbe essere in grado di dedurre una nazione (`Country`) dalla sua latitudine e longitudine (`Latitude` e `Longitude`), ma è un buon esercizio provare ad allenare dai dati grezzi che sono stati puliti ed esportati, e quindi utilizzare questo modello in una app web.
 
 ## Esercizio: usare pickle con il modello
 
-Ora è il momento di utilizzare pickle _con_ il modello! Lo si può fare in poche righe di codice. Una volta che è stato serializzato con pickle_,_ caricare il modello e testarlo rispetto a un array di dati di esempio contenente valori per secondi, latitudine e longitudine,
+Ora è il momento di utilizzare _pickle_ con il modello! Lo si può fare in poche righe di codice. Una volta che è stato _serializzato con pickle_, caricare il modello e testarlo rispetto a un array di dati di esempio contenente valori per secondi, latitudine e longitudine,
 
 ```python
 import pickle
@@ -151,13 +151,13 @@ model = pickle.load(open('ufo-model.pkl','rb'))
 print(model.predict([[50,44,-12]]))
 ```
 
-Il modello restituisce **"3"**, che è il codice paese per il Regno Unito. Jolly 👽
+Il modello restituisce **"3"**, che è il codice nazione per il Regno Unito. Fantastico! 👽
 
 ## Esercizio: creare un'app Flask
 
 Ora si può creare un'app Flask per chiamare il modello e restituire risultati simili, ma in un modo visivamente più gradevole.
 
-1. Iniziare creando una cartella chiamata web-app **a** livello del _file_ notebook.ipynb dove risiede _il_ file ufo-model.pkl.
+1. Iniziare creando una cartella chiamata **web-app** a livello del file _notebook.ipynb_ dove risiede il file _ufo-model.pkl_.
 
 1. In quella cartella creare altre tre cartelle: **static**, con una cartella **css** al suo interno e **templates**. Ora si dovrebbero avere i seguenti file e directory:
 
@@ -165,14 +165,14 @@ Ora si può creare un'app Flask per chiamare il modello e restituire risultati s
    web-app/
      static/
        css/
-       templates/
+     templates/
    notebook.ipynb
    ufo-model.pkl
    ```
 
-   ✅ Fare riferimento alla cartella della soluzione per una visualizzazione dell'app finita
+   ✅ Fare riferimento alla cartella della soluzione per una visualizzazione dell'app finita.
 
-1. Il primo file da creare nella cartella dell'_app_ Web è **il** file requirements.txt. Come _package.json_ in un'app JavaScript, questo file elenca le dipendenze richieste dall'app. In **requirements.txt** aggiungere le righe:
+1. Il primo file da creare nella cartella _web-app_ è il file **requirements.txt**. Come _package.json_ in un'app JavaScript, questo file elenca le dipendenze richieste dall'app. In **requirements.txt** aggiungere le righe:
 
    ```text
    scikit-learn
@@ -195,41 +195,41 @@ Ora si può creare un'app Flask per chiamare il modello e restituire risultati s
 
 1. Ora si è pronti per creare altri tre file per completare l'app:
 
-   1. Crea **app.py** nella directory radice
-   2. Creare **index.html** nella directory _templates_ .
-   3. Crea **sytles.css** nella directory _static/css_ .
+   1. Creare **app.py** nella directory radice.
+   2. Creare **index.html** nella directory _templates_.
+   3. Creare **sytles.css** nella directory _static/css_.
 
-1. Inserire nel _file_ styles.css alcuni stili:
+1. Inserire nel file _styles.css_ alcuni stili:
 
    ```css
    body {
-   	width: 100%;
-   	height: 100%;
-   	font-family: 'Helvetica';
-   	background: black;
-   	color: #fff;
-   	text-align: center;
-   	letter-spacing: 1.4px;
-   	font-size: 30px;
+      width: 100%;
+      height: 100%;
+      font-family: 'Helvetica';
+      background: black;
+      color: #fff;
+      text-align: center;
+      letter-spacing: 1.4px;
+      font-size: 30px;
    }
 
    input {
-   	min-width: 150px;
+      min-width: 150px;
    }
 
    .grid {
-   	width: 300px;
-   	border: 1px solid #2d2d2d;
-   	display: grid;
-   	justify-content: center;
-   	margin: 20px auto;
+      width: 300px;
+      border: 1px solid #2d2d2d;
+      display: grid;
+      justify-content: center;
+      margin: 20px auto;
    }
 
    .box {
-   	color: #fff;
-   	background: #2d2d2d;
-   	padding: 12px;
-   	display: inline-block;
+      color: #fff;
+      background: #2d2d2d;
+      padding: 12px;
+      display: inline-block;
    }
    ```
 
@@ -268,7 +268,7 @@ Ora si può creare un'app Flask per chiamare il modello e restituire risultati s
    </html>
    ```
 
-   Dare un'occhiata al modello in questo file. Notare la sintassi con le parentesi graffe attorno alle variabili che verranno fornite dall'app, come il testo di previsione: `{{}}`. C'è anche un modulo che invia una previsione al percorso `/` predict.
+   Dare un'occhiata al template di questo file. Notare la sintassi con le parentesi graffe attorno alle variabili che verranno fornite dall'app, come il testo di previsione: `{{}}`. C'è anche un modulo che invia una previsione alla rotta `/predict`.
 
    Infine, si è pronti per creare il file python che guida il consumo del modello e la visualizzazione delle previsioni:
 
@@ -309,39 +309,39 @@ Ora si può creare un'app Flask per chiamare il modello e restituire risultati s
        app.run(debug=True)
    ```
 
-   > 💡 Suggerimento: quando si aggiunge [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) durante l'esecuzione dell'app Web utilizzando Flask, qualsiasi modifica apportata all'applicazione verrà applicata immediatamente senza la necessità di riavviare il server. Attenzione! Non abilitare questa modalità in un'app di produzione.
+   > 💡 Suggerimento: quando si aggiunge [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) durante l'esecuzione dell'app web utilizzando Flask, qualsiasi modifica apportata all'applicazione verrà recepita immediatamente senza la necessità di riavviare il server. Attenzione! Non abilitare questa modalità in un'app di produzione.
 
 Se si esegue `python app.py` o `python3 app.py` , il server web si avvia, localmente, e si può compilare un breve modulo per ottenere una risposta alla domanda scottante su dove sono stati avvistati gli UFO!
 
 Prima di farlo, dare un'occhiata alle parti di `app.py`:
 
 1. Innanzitutto, le dipendenze vengono caricate e l'app si avvia.
-1. Quindi, il modello viene importato.
-1. Quindi, index.html viene visualizzato sulla rotta home.
+1. Poi il modello viene importato.
+1. Infine index.html viene visualizzato sulla rotta home.
 
 Sulla rotta `/predict` , accadono diverse cose quando il modulo viene inviato:
 
-1. Le variabili del modulo vengono raccolte e convertite in un array numpy. Vengono quindi inviati al modello e viene restituita una previsione.
-2. Le nazioni che si vogliono visualizzare vengono nuovamente esposte come testo leggibile dal loro codice paese previsto e tale valore viene inviato a index.html per essere visualizzato nel modello.
+1. Le variabili del modulo vengono raccolte e convertite in un array numpy. Vengono quindi inviate al modello e viene restituita una previsione.
+2. Le nazioni che si vogliono visualizzare vengono nuovamente esposte come testo leggibile ricavato dal loro codice paese previsto e tale valore viene inviato a index.html per essere visualizzato nel template della pagina web.
 
 Usare un modello in questo modo, con Flask e un modello serializzato è relativamente semplice. La cosa più difficile è capire che forma hanno i dati che devono essere inviati al modello per ottenere una previsione. Tutto dipende da come è stato addestrato il modello. Questo ha tre punti dati da inserire per ottenere una previsione.
 
-In un ambiente professionale, si può vedere quanto sia necessaria una buona comunicazione tra le persone che addestrano il modello e coloro che lo consumano in un'app Web o mobile. In questo caso, si ricoprono entrambi i ruoli!
+In un ambiente professionale, si può vedere quanto sia necessaria una buona comunicazione tra le persone che addestrano il modello e coloro che lo consumano in un'app web o su dispositivo mobile. In questo caso, si ricoprono entrambi i ruoli!
 
 ---
 
 ## 🚀 Sfida
 
-Invece di lavorare su un notebook e importare il modello nell'app Flask, si può addestrare il modello direttamente nell'app Flask! Provare a convertire il codice Python nel notebook, magari dopo che i dati sono stati puliti, per addestrare il modello dall'interno dell'app su un percorso chiamato /`train`. Quali sono i pro e i contro di seguire questo metodo?
+Invece di lavorare su un notebook e importare il modello nell'app Flask, si può addestrare il modello direttamente nell'app Flask! Provare a convertire il codice Python nel notebook, magari dopo che i dati sono stati puliti, per addestrare il modello dall'interno dell'app su un percorso chiamato `/train`. Quali sono i pro e i contro nel seguire questo metodo?
 
 ## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
 
 ## Revisione e Auto Apprendimento
 
-Esistono molti modi per creare un'app Web per utilizzare i modelli ML. Elencare dei modi in cui si potrebbe utilizzare JavaScript o Python per creare un'app Web per sfruttare machine learning. Considerare l'architettura: il modello dovrebbe rimanere nell'app o risiedere nel cloud? Se quest'ultimo, come accedervi? Disegnare un modello architettonico per una soluzione web ML applicata.
+Esistono molti modi per creare un'app web per utilizzare i modelli ML. Elencare dei modi in cui si potrebbe utilizzare JavaScript o Python per creare un'app web per sfruttare machine learning. Considerare l'architettura: il modello dovrebbe rimanere nell'app o risiedere nel cloud? In quest'ultimo casi, come accedervi? Disegnare un modello architettonico per una soluzione web ML applicata.
 
 ## Compito
 
-[Provare un modello diverso](assignment.md)
+[Provare un modello diverso](assignment.it.md)
 
 
diff --git a/3-Web-App/1-Web-App/translations/assignment.it.md b/3-Web-App/1-Web-App/translations/assignment.it.md
index 21a1b804c9..7bc7ffd948 100644
--- a/3-Web-App/1-Web-App/translations/assignment.it.md
+++ b/3-Web-App/1-Web-App/translations/assignment.it.md
@@ -2,10 +2,10 @@
 
 ## Istruzioni
 
-Ora che si è creato un'app Web utilizzando un modello di regressione addestrato, usare uno dei modelli di una lezione precedente sulla regressione per ripetere questa app Web. Si può mantenere lo stile o progettarla in modo diverso per riflettere i dati della zucca. Fare attenzione a modificare gli input in modo che riflettano il metodo di addestramento del proprio modello.
+Ora che si è creato un'app web utilizzando un modello di Regressione addestrato, usare uno dei modelli da una lezione precedente sulla Regressione per rifare questa app web. Si può mantenere lo stile o progettarla in modo diverso per riflettere i dati della zucca. Fare attenzione a modificare gli input in modo che riflettano il metodo di addestramento del proprio modello.
 
 ## Rubrica
 
 | Criteri | Ottimo | Adeguato | Necessita miglioramento |
 | -------------------------- | --------------------------------------------------------- | --------------------------------------------------------- | -------------------------------------- |
-| | L'app Web funziona come previsto e viene distribuita nel cloud | L'app Web contiene difetti o mostra risultati imprevisti | L'app web non funziona correttamente |
+| | L'app web funziona come previsto e viene distribuita nel cloud | L'app web contiene difetti o mostra risultati imprevisti | L'app web non funziona correttamente |
diff --git a/3-Web-App/translations/README.it.md b/3-Web-App/translations/README.it.md
index 52d9c5ec1a..d376b8ec7c 100644
--- a/3-Web-App/translations/README.it.md
+++ b/3-Web-App/translations/README.it.md
@@ -1,15 +1,15 @@
-# Creare un'app Web per utilizzare il modello ML
+# Creare un'app web per utilizzare il modello ML
 
-In questa sezione del programma di studi, verrà presentato un argomento ML applicato: come salvare il modello di Scikit-learn come file che può essere utilizzato per fare previsioni all'interno di un'applicazione web. Una volta salvato il modello, si imparerà come utilizzarlo in un'app Web integrata in Flask. Per prima cosa si creerà un modello utilizzando alcuni dati che riguardano gli avvistamenti di UFO! Quindi, si creerà un'app Web che consentirà di inserire un numero di secondi con un valore di latitudine e longitudine per prevedere quale paese ha riferito di aver visto un UFO.
+In questa sezione del programma di studi, verrà presentato un argomento ML applicato: come salvare il modello di Scikit-learn come file che può essere utilizzato per fare previsioni all'interno di un'applicazione web. Una volta salvato il modello, si imparerà come utilizzarlo in un'app web sviluppata con Flask. Per prima cosa si creerà un modello utilizzando alcuni dati che riguardano gli avvistamenti di UFO! Quindi, si creerà un'app web che consentirà di inserire un numero di secondi con un valore di latitudine e longitudine per prevedere quale paese ha riferito di aver visto un UFO.
 
-![Parcheggio UFO](images/ufo.jpg)
+![Parcheggio UFO](../images/ufo.jpg)
 
 Foto di <a href="https://unsplash.com/@mdherren?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Michael Herren</a> su <a href="https://unsplash.com/s/photos/ufo?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
 
 
 ## Lezioni
 
-1. [Costruire un'app Web](1-Web-App/README.md)
+1. [Costruire un'app web](../1-Web-App/translations/README.it.md)
 
 ## Crediti
 

From 9628c457b884ecd79af96a32537107f701587b1d Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Sun, 18 Jul 2021 21:23:45 +0200
Subject: [PATCH 211/368] Update README.fr.md

---
 1-Introduction/2-history-of-ML/translations/README.fr.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
index 5debe07e6b..6613216277 100644
--- a/1-Introduction/2-history-of-ML/translations/README.fr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -114,4 +114,4 @@ Voici quelques articles à regarder et à écouter :
 
 ## Devoir
 
-[Créer une frise chonologique](assignment.fr.md)
+[Créer une frise chronologique](assignment.fr.md)

From 6b37ceab9682fb8cc8f1cd6bd77011faa8199e95 Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Mon, 19 Jul 2021 11:28:36 +0700
Subject: [PATCH 212/368] Add Indonesian translation for 1-3 README

---
 .../3-fairness/translations/README.id.md      | 215 ++++++++++++++++++
 1 file changed, 215 insertions(+)
 create mode 100644 1-Introduction/3-fairness/translations/README.id.md

diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md
new file mode 100644
index 0000000000..44056ab83f
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/README.id.md
@@ -0,0 +1,215 @@
+# Keadilan dalam Machine Learning 
+
+![Ringkasan dari Keadilan dalam Machine Learning dalam sebuah catatan sketsa](../../sketchnotes/ml-fairness.png)
+> Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+ 
+## Pengantar
+
+Dalam kurikulum ini, kamu akan mulai mengetahui bagaimana Machine Learning bisa memengaruhi kehidupan kita sehari-hari. Bahkan sekarang, sistem dan model terlibat dalam tugas pengambilan keputusan sehari-hari, seperti diagnosis kesehatan atau mendeteksi penipuan. Jadi, penting bahwa model-model ini bekerja dengan baik untuk memberikan hasil yang adil bagi semua orang.
+
+Bayangkan apa yang bisa terjadi ketika data yang kamu gunakan untuk membangun model ini tidak memiliki demografi tertentu, seperti ras, jenis kelamin, pandangan politik, agama, atau secara tidak proporsional mewakili demografi tersebut. Bagaimana jika keluaran dari model diinterpretasikan lebih menyukai beberapa demografis tertentu? Apa konsekuensi untuk aplikasinya? 
+
+Dalam pelajaran ini, kamu akan:
+
+- Meningkatkan kesadaran dari pentingnya keadilan dalam Machine Learning.
+- Mempelajari tentang berbagai kerugian terkait keadilan.
+- Learn about unfairness assessment and mitigation.
+- Mempelajari tentang mitigasi dan penilaian ketidakadilan.
+
+## Prasyarat
+
+Sebagai prasyarat, silakan ikuti jalur belajar "Prinsip AI yang Bertanggung Jawab" dan tonton video di bawah ini dengan topik: 
+
+Pelajari lebih lanjut tentang AI yang Bertanggung Jawab dengan mengikuti [Jalur Belajar](https://docs.microsoft.com/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa) ini
+
+[![Pendekatan Microsoft untuk AI yang Bertanggung Jawab](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Pendekatan Microsoft untuk AI yang Bertanggung Jawab")
+
+> 🎥 Klik gambar diatas untuk menonton video: Pendekatan Microsoft untuk AI yang Bertanggung Jawab
+
+## Ketidakadilan dalam data dan algoritma
+
+> "Jika Anda menyiksa data cukup lama, data itu akan mengakui apa pun " - Ronald Coase
+
+Pernyataan ini terdengar ekstrem, tetapi memang benar bahwa data dapat dimanipulasi untuk mendukung kesimpulan apa pun. Manipulasi semacam itu terkadang bisa terjadi secara tidak sengaja. Sebagai manusia, kita semua memiliki bias, dan seringkali sulit untuk secara sadar mengetahui kapan kamu memperkenalkan bias dalam data.
+
+Menjamin keadilan dalam AI dan machine learning tetap menjadi tantangan sosioteknik yang kompleks. Artinya, hal itu tidak bisa ditangani baik dari perspektif sosial atau teknis semata. 
+
+### Kerugian Terkait Keadilan  
+
+Apa yang kamu maksud dengan ketidakadilan? "Ketidakadilan" mencakup dampak negatif, atau "bahaya", bagi sekelompok orang, seperti yang didefinisikan dalam hal ras, jenis kelamin, usia, atau status disabilitas. 
+
+Kerugian utama yang terkait dengan keadilan dapat diklasifikasikan sebagai: 
+
+- **Alokasi**, jika suatu jenis kelamin atau etnisitas misalkan lebih disukai daripada yang lain.
+- **Kualitas layanan**. Jika kamu melatih data untuk satu skenario tertentu tetapi kenyataannya jauh lebih kompleks, itu mengarah ke layanan yang berkinerja buruk.
+- **Stereotip**. Mengaitkan grup tertentu dengan atribut yang ditentukan sebelumnya.
+- **Fitnah**. Untuk mengkritik dan melabeli sesuatu atau seseorang secara tidak adil.
+- **Representasi yang kurang atau berlebihan**. Idenya adalah bahwa kelompok tertentu tidak terlihat dalam profesi tertentu, dan layanan atau fungsi apa pun yang terus dipromosikan yang berkontribusi pada kerugian.
+
+Mari kita lihat contoh-contohnya.
+
+### Alokasi
+
+Pertimbangkan sistem hipotetis untuk menyaring aplikasi pinjaman. Sistem cenderung memilih pria kulit putih sebagai kandidat yang lebih baik daripada kelompok lain. Akibatnya, pinjaman ditahan dari pemohon tertentu. 
+
+Contoh lain adalah alat perekrutan eksperimental yang dikembangkan oleh perusahaan besar untuk menyaring kandidat. Alat tersebut secara sistematis mendiskriminasi satu gender dengan menggunakan model yang dilatih untuk lebih memilih kata-kata yang terkait dengan yang lain. Hal ini mengakibatkan kandidat yang resumenya berisi kata-kata seperti "tim rugby wanita". 
+
+✅ Lakukan sedikit riset untuk menemukan contoh dunia nyata dari sesuatu seperti ini
+
+### Kualitas Layanan
+
+Para peneliti menemukan bahwa beberapa pengklasifikasi gender komersial memiliki tingkat kesalahan yang lebih tinggi di sekitar gambar wanita dengan warna kulit lebih gelap dibandingkan dengan gambar pria dengan warna kulit lebih terang. [Referensi](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/) 
+
+Contoh terkenal lainnya adalah dispenser sabun tangan yang sepertinya tidak bisa merasakan orang dengan kulit gelap. [Referensi](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+
+### Stereotip
+
+Stereotypical gender view was found in machine translation. When translating “he is a nurse and she is a doctor� into Turkish, problems were encountered. Turkish is a genderless language which has one pronoun, “o� to convey a singular third person, but translating the sentence back from Turkish to English yields the stereotypical and incorrect as “she is a nurse and he is a doctor�.
+
+Pandangan gender stereotip ditemukan dalam terjemahan mesin. Ketika menerjemahkan "dia (laki-laki) adalah seorang perawat dan dia (perempuan) adalah seorang dokter" ke dalam bahasa Turki, masalah muncul. Turki adalah bahasa tanpa gender yang memiliki satu kata ganti, "o" untuk menyampaikan orang ketiga tunggal, tetapi menerjemahkan kalimat kembali dari Turki ke Inggris menghasilkan stereotip dan salah sebagai "dia (perempuan) adalah seorang perawat dan dia (laki-laki) adalah seorang dokter". 
+
+![terjemahan ke bahasa Turki](images/gender-bias-translate-en-tr.png)
+
+![terjemahan kembali ke bahasa Inggris](images/gender-bias-translate-tr-en.png)
+
+### Fitnah
+
+Sebuah teknologi pelabelan gambar yang terkenal salah memberi label gambar orang berkulit gelap sebagai gorila. Pelabelan yang salah berbahaya bukan hanya karena sistem membuat kesalahan karena secara khusus menerapkan label yang memiliki sejarah panjang yang sengaja digunakan untuk merendahkan orang kulit hitam. 
+
+[![AI: Bukankah Aku Seorang Wanita?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "Bukankah Aku Seorang Wanita?")
+> 🎥 Klik gambar diatas untuk sebuah video: AI, Bukankah Aku Seorang Wanita? - menunjukkan kerugian yang disebabkan oleh pencemaran nama baik yang menyinggung ras oleh AI
+
+### Representasi yang kurang atau berlebihan
+
+Hasil pencarian gambar yang condong ke hal tertentu (skewed) dapat menjadi contoh yang bagus dari bahaya ini. Saat menelusuri gambar profesi dengan persentase pria yang sama atau lebih tinggi daripada wanita, seperti teknik, atau CEO, perhatikan hasil yang lebih condong ke jenis kelamin tertentu. 
+
+![Pencarian CEO di Bing](images/ceos.png)
+> Pencarian di Bing untuk 'CEO' ini menghasilkan hasil yang cukup inklusif
+
+Lima jenis bahaya utama ini tidak saling eksklusif, dan satu sistem dapat menunjukkan lebih dari satu jenis bahaya. Selain itu, setiap kasus bervariasi dalam tingkat keparahannya. Misalnya, memberi label yang tidak adil kepada seseorang sebagai penjahat adalah bahaya yang jauh lebih parah daripada memberi label yang salah pada gambar. Namun, penting untuk diingat bahwa bahkan kerugian yang relatif tidak parah dapat membuat orang merasa terasing atau diasingkan dan dampak kumulatifnya bisa sangat menekan. 
+ 
+✅ **Diskusi**: Tinjau kembali beberapa contoh dan lihat apakah mereka menunjukkan bahaya yang berbeda.  
+
+|                         | Alokasi | Kualitas Layanan | Stereotip | Fitnah | Representasi yang kurang atau berlebihan |
+| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
+| Sistem perekrutan otomatis |     x      |         x          |      x       |             |               x                |
+| Terjemahan mesin     |            |                    |              |             |                                |
+| Melabeli foto          |            |                    |              |             |                                |
+
+
+## Mendeteksi Ketidakadilan 
+
+Ada banyak alasan mengapa sistem tertentu berperilaku tidak adil. Bias sosial, misalnya, mungkin tercermin dalam kumpulan data yang digunakan untuk melatih mereka. Misalnya, ketidakadilan perekrutan mungkin telah diperburuk oleh ketergantungan yang berlebihan pada data historis. Dengan menggunakan pola dalam resume yang dikirimkan ke perusahaan selama periode 10 tahun, model tersebut menentukan bahwa pria lebih berkualitas karena mayoritas resume berasal dari pria, yang mencerminkan dominasi pria di masa lalu di industri teknologi. 
+
+Data yang tidak memadai tentang sekelompok orang tertentu dapat menjadi alasan ketidakadilan. Misalnya, pengklasifikasi gambar memiliki tingkat kesalahan yang lebih tinggi untuk gambar orang berkulit gelap karena warna kulit yang lebih gelap kurang terwakili dalam data. 
+
+Asumsi yang salah yang dibuat selama pengembangan menyebabkan ketidakadilan juga. Misalnya, sistem analisis wajah yang dimaksudkan untuk memprediksi siapa yang akan melakukan kejahatan berdasarkan gambar wajah orang dapat menyebabkan asumsi yang merusak. Hal ini dapat menyebabkan kerugian besar bagi orang-orang yang salah diklasifikasikan. 
+
+## Pahami model kamu dan bangun dalam keadilan
+
+Meskipun banyak aspek keadilan tidak tercakup dalam metrik keadilan kuantitatif, dan tidak mungkin menghilangkan bias sepenuhnya dari sistem untuk menjamin keadilan, Kamu tetap bertanggung jawab untuk mendeteksi dan mengurangi masalah keadilan sebanyak mungkin. 
+
+Saat Kamu bekerja dengan model pembelajaran mesin, penting untuk memahami model Kamu dengan cara memastikan interpretasinya dan dengan menilai serta mengurangi ketidakadilan. 
+
+Mari kita gunakan contoh pemilihan pinjaman untuk mengisolasi kasus untuk mengetahui tingkat dampak setiap faktor pada prediksi. 
+
+## Metode Penilaian
+
+1. **Identifikasi bahaya (dan manfaat)**. Langkah pertama adalah mengidentifikasi bahaya dan manfaat. Pikirkan tentang bagaimana tindakan dan keputusan dapat memengaruhi calon pelanggan dan bisnis itu sendiri.
+  
+1. **Identifikasi kelompok yang terkena dampak**. Setelah Kamu memahami jenis kerugian atau manfaat apa yang dapat terjadi, identifikasi kelompok-kelompok yang mungkin terpengaruh. Apakah kelompok-kelompok ini ditentukan oleh jenis kelamin, etnis, atau kelompok sosial? 
+
+1. **Tentukan metrik keadilan**. Terakhir, tentukan metrik sehingga Kamu memiliki sesuatu untuk diukur dalam pekerjaan Kamu untuk memperbaiki situasi. 
+
+### Identifikasi bahaya (dan manfaat)
+
+Apa bahaya dan manfaat yang terkait dengan pinjaman? Pikirkan tentang skenario negatif palsu dan positif palsu: 
+
+**False negatives** (ditolak, tapi Y=1) - dalam hal ini, pemohon yang akan mampu membayar kembali pinjaman ditolak. Ini adalah peristiwa yang merugikan karena sumber pinjaman ditahan dari pemohon yang memenuhi syarat.
+
+**False positives** (diterima, tapi Y=0) - dalam hal ini, pemohon memang mendapatkan pinjaman tetapi akhirnya wanprestasi. Akibatnya, kasus pemohon akan dikirim ke agen penagihan utang yang dapat mempengaruhi permohonan pinjaman mereka di masa depan. 
+
+### Identifikasi kelompok yang terkena dampak
+
+Langkah selanjutnya adalah menentukan kelompok mana yang kemungkinan akan terpengaruh. Misalnya, dalam kasus permohonan kartu kredit, seorang model mungkin menentukan bahwa perempuan harus menerima batas kredit yang jauh lebih rendah dibandingkan dengan pasangan mereka yang berbagi aset rumah tangga. Dengan demikian, seluruh demografi, yang ditentukan berdasarkan jenis kelamin, terpengaruh. 
+
+### Tentukan metrik keadilan
+
+Kamu telah mengidentifikasi bahaya dan kelompok yang terpengaruh, dalam hal ini, digambarkan berdasarkan jenis kelamin. Sekarang, gunakan faktor terukur untuk memisahkan metriknya. Misalnya, dengan menggunakan data di bawah ini, Kamu dapat melihat bahwa wanita memiliki tingkat *false positive* terbesar dan pria memiliki yang terkecil, dan kebalikannya berlaku untuk *false negative*. 
+
+✅ Dalam pelajaran selanjutnya tentang Pengelompokan, Kamu akan melihat bagaimana membangun 'matriks kebingungan' ini dalam kode 
+
+|            | False positive rate | False negative rate | count |
+| ---------- | ------------------- | ------------------- | ----- |
+| Women      | 0.37                | 0.27                | 54032 |
+| Men        | 0.31                | 0.35                | 28620 |
+| Non-binary | 0.33                | 0.31                | 1266  |
+
+ 
+Tabel ini memberitahu kita beberapa hal. Pertama, kami mencatat bahwa ada sedikit orang non-biner dalam data. Datanya condong, jadi Kamu harus berhati-hati dalam menafsirkan angka-angka ini. 
+
+Dalam hal ini, kita memiliki 3 grup dan 2 metrik. Ketika kita memikirkan tentang bagaimana sistem kita memengaruhi kelompok pelanggan dengan permohonan pinjaman mereka, ini mungkin cukup, tetapi ketika Kamu ingin menentukan jumlah grup yang lebih besar, Kamu mungkin ingin menyaringnya menjadi kumpulan ringkasan yang lebih kecil. Untuk melakukannya, Kamu dapat menambahkan lebih banyak metrik, seperti perbedaan terbesar atau rasio terkecil dari setiap *false negative* dan *false positive*. 
+
+✅ Berhenti dan Pikirkan: Kelompok lain yang apa lagi yang mungkin terpengaruh untuk pengajuan pinjaman? 
+ 
+## Mengurangi ketidakadilan 
+
+Untuk mengurangi ketidakadilan, jelajahi model untuk menghasilkan berbagai model yang dimitigasi dan bandingkan pengorbanan yang dibuat antara akurasi dan keadilan untuk memilih model yang paling adil. 
+
+Pelajaran pengantar ini tidak membahas secara mendalam mengenai detail mitigasi ketidakadilan algoritmik, seperti pendekatan pasca-pemrosesan dan pengurangan (*post-processing and reductions approach*), tetapi berikut adalah *tool* yang mungkin ingin Kamu coba. 
+
+### Fairlearn 
+ 
+[Fairlearn](https://fairlearn.github.io/) adalah sebuah *package* Python open-source yang memungkinkan Kamu untuk menilai keadilan sistem Kamu dan mengurangi ketidakadilan.   
+
+*Tool* ini membantu Kamu menilai bagaimana prediksi model memengaruhi kelompok yang berbeda, memungkinkan Kamu untuk membandingkan beberapa model dengan menggunakan metrik keadilan dan kinerja, dan menyediakan serangkaian algoritma untuk mengurangi ketidakadilan dalam klasifikasi dan regresi biner. 
+
+- Pelajari bagaimana cara menggunakan komponen-komponen yang berbeda dengan mengunjungi [GitHub](https://github.com/fairlearn/fairlearn/) Fairlearn
+
+- Jelajahi [panduan pengguna](https://fairlearn.github.io/main/user_guide/index.html), [contoh-contoh](https://fairlearn.github.io/main/auto_examples/index.html)
+
+- Coba beberapa [sampel notebook](https://github.com/fairlearn/fairlearn/tree/master/notebooks). 
+
+- Pelajari [bagaimana cara mengaktifkan penilaian keadilan](https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-fairness-aml?WT.mc_id=academic-15963-cxa) dari model machine learning di Azure Machine Learning.
+
+- Lihat [sampel notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) ini untuk skenario penilaian keadilan yang lebih banyak di Azure Machine Learning. 
+
+---
+## 🚀 Tantangan 
+
+Untuk mencegah kemunculan bias pada awalnya, kita harus: 
+
+- memiliki keragaman latar belakang dan perspektif di antara orang-orang yang bekerja pada sistem 
+- berinvestasi dalam dataset yang mencerminkan keragaman masyarakat kita 
+- mengembangkan metode yang lebih baik untuk mendeteksi dan mengoreksi bias ketika itu terjadi 
+
+Pikirkan tentang skenario kehidupan nyata di mana ketidakadilan terbukti dalam pembuatan dan penggunaan model. Apa lagi yang harus kita pertimbangkan? 
+
+## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## Ulasan & Belajar Mandiri 
+ 
+Dalam pelajaran ini, Kamu telah mempelajari beberapa dasar konsep keadilan dan ketidakadilan dalam pembelajaran mesin. 
+ 
+Tonton workshop ini untuk menyelami lebih dalam kedalam topik: 
+
+- YouTube: Kerugian terkait keadilan dalam sistem AI: Contoh, penilaian, dan mitigasi oleh Hanna Wallach dan Miro Dudik [Kerugian terkait keadilan dalam sistem AI: Contoh, penilaian, dan mitigasi - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k) 
+
+Kamu juga dapat membaca:
+
+- Pusat sumber daya RAI Microsoft: [Sumber daya Responsible AI – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
+
+- Grup riset FATE Microsoft: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
+
+Jelajahi *toolkit* Fairlearn
+
+[Fairlearn](https://fairlearn.org/)
+
+Baca mengenai *tools* Azure Machine Learning untuk memastikan keadilan
+
+- [Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/concept-fairness-ml?WT.mc_id=academic-15963-cxa) 
+
+## Tugas
+
+[Jelajahi Fairlearn](assignment.md) 

From f8f4ceacf499aae9cadd26d9e6b8bba788c41d2f Mon Sep 17 00:00:00 2001
From: fribble186 <psj124@qq.com>
Date: Mon, 19 Jul 2021 16:12:43 +0800
Subject: [PATCH 213/368] Translated 2-Regression assignment.md into Simplified
 Chinese

---
 .../1-Tools/translations/assignment.zh-cn.md       | 14 ++++++++++++++
 .../2-Data/translations/assignment.zh-cn.md        |  9 +++++++++
 .../3-Linear/translations/assignment.zh-cn.md      | 12 ++++++++++++
 .../4-Logistic/translations/assignment.zh-cn.md    | 11 +++++++++++
 4 files changed, 46 insertions(+)
 create mode 100644 2-Regression/1-Tools/translations/assignment.zh-cn.md
 create mode 100644 2-Regression/2-Data/translations/assignment.zh-cn.md
 create mode 100644 2-Regression/3-Linear/translations/assignment.zh-cn.md
 create mode 100644 2-Regression/4-Logistic/translations/assignment.zh-cn.md

diff --git a/2-Regression/1-Tools/translations/assignment.zh-cn.md b/2-Regression/1-Tools/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..c296c8cabf
--- /dev/null
+++ b/2-Regression/1-Tools/translations/assignment.zh-cn.md
@@ -0,0 +1,14 @@
+# 用 Scikit-learn 实现一次回归算法
+
+## 说明
+
+先看看 Scikit-learn 中的 [Linnerud 数�集](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud)
+这个数�集中有多个[目标��（target）](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset)，其中包�了三��动（训练数�）和三个生�指标（目标��）组�，这些数�都是从一个�身俱�部中的20�中年男�收集到的。
+
+之�用自己的方�，创建一个�以�述腰围和完�仰�起�个数关系的回归模型。用�样的方�对这个数�集中的其它数�也建立一下模型探究一下其中的关系。
+
+## 评判标准
+
+| 标准                       | 优秀                           | 中规中矩                      | �需努力          |
+| ------------------------------ | ----------------------------------- | ----------------------------- | -------------------------- |
+| 需��交一段能�述数�集中关系的文字 | 很好的�述了数�集中的关系 | �能�述少部分的关系 | 啥都没有�交 |
diff --git a/2-Regression/2-Data/translations/assignment.zh-cn.md b/2-Regression/2-Data/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..e9c0f1c275
--- /dev/null
+++ b/2-Regression/2-Data/translations/assignment.zh-cn.md
@@ -0,0 +1,9 @@
+# 探索数��视化
+
+有好几个库都�以进行数��视化。用 matplotlib 和 seaborn 对本课中涉�的 Pumpkin 数�集创建一些数��视化的图标。并�考哪个库更容易使用？
+
+## 评判标准
+
+| 标准 | 优秀 | 中规中矩 | �需努力 |
+| -------- | --------- | -------- | ----------------- |
+|          | �交了�有两�探索�视化方法的notebook工程文件         |   �交了�包�有一�探索�视化方法的notebook工程文件       |  没�交  notebook 工程文件                 |
diff --git a/2-Regression/3-Linear/translations/assignment.zh-cn.md b/2-Regression/3-Linear/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..e9c476c361
--- /dev/null
+++ b/2-Regression/3-Linear/translations/assignment.zh-cn.md
@@ -0,0 +1,12 @@
+# 创建自己的回归模型
+
+## 说明
+
+在这节课中你学到了如何用线性回归和多项�回归建立一个模型。利用这些�是，找到一个你感兴趣的数�集或者是 Scikit-learn 内置的数�集�建立一个全新的模型。用你的 notebook �解释为什么用了这�技术�对这个数�集进行建模，并且�明出你的模型的准确度。如果它没你想象中准确，请�考一下并解释一下原因。
+
+## 评判标准
+
+| 标准 | 优秀                                                    | 中规中矩                   | �需努力               |
+| -------- | ------------------------------------------------------------ | -------------------------- | ------------------------------- |
+|          | �交了一个完整的 notebook 工程文件，其中包�了解集，并且�读性良好 | �完整的解集 | 解集是有缺陷或者有错误的 |
+
diff --git a/2-Regression/4-Logistic/translations/assignment.zh-cn.md b/2-Regression/4-Logistic/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..8dc55af3d3
--- /dev/null
+++ b/2-Regression/4-Logistic/translations/assignment.zh-cn.md
@@ -0,0 +1,11 @@
+# �探回归模型
+
+## 说明
+
+在这节课中，你使用了 pumpkin 数�集的�集。现在，让我们回到原始数�，并�试使用所有数�。�过了数�清�和标准化，建立一个逻辑回归模型。
+
+## 评判标准
+
+| 标准 | 优秀                                                               | 中规中矩                                                     | �需努力                                           |
+| -------- | ----------------------------------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------- |
+|          | 用notebook呈现了一个解释性和性能良好的模型 | 用notebook呈现了一个性能一般的模型 | 用notebook呈现了一个性能差的模型或根本没有模型 |

From fafbd6b2b44d35dac162390ed37aed84e433303b Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Mon, 19 Jul 2021 18:39:02 +0900
Subject: [PATCH 214/368] fix: typo

---
 1-Introduction/1-intro-to-ML/translations/README.ja.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md
index aded0f7e65..1107739c62 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ja.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md
@@ -98,7 +98,7 @@ AI�ML�深層学習�データサイエンス��������解�
 
 ## 振り返り�自習
 
-クラウド上�MLアルゴリズムを��よ��扱������る���������[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)�従������。.
+クラウド上�MLアルゴリズムを��よ��扱������る���������[ラーニングパス](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa)�従������。
 
 ## 課題
 

From 48590a589ecae9cb929bfe5a6d52740eec40b79b Mon Sep 17 00:00:00 2001
From: manusquall <manusquall@gmail.com>
Date: Mon, 19 Jul 2021 10:35:08 +0000
Subject: [PATCH 215/368] finish the fr.json translation proposition

---
 quiz-app/src/assets/translations/fr.json | 156 +++++++++++------------
 1 file changed, 78 insertions(+), 78 deletions(-)

diff --git a/quiz-app/src/assets/translations/fr.json b/quiz-app/src/assets/translations/fr.json
index 3ec4ec9e7a..80b5574970 100644
--- a/quiz-app/src/assets/translations/fr.json
+++ b/quiz-app/src/assets/translations/fr.json
@@ -415,7 +415,7 @@
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "tous les deux",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -1033,7 +1033,7 @@
                     "questionText": "La classification est une forme d'apprentissage supervisé qui a beaucoup en commun avec",
                     "answerOptions": [
 							{
-							"answerText": "Série temporelle",
+							"answerText": "Série chronologique",
                             "isCorrect": "false"
                         },
                         {
@@ -1164,7 +1164,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -1213,7 +1213,7 @@
                     "questionText": "Quel classificateur avons-nous utilisé dans cette leçon?",
                     "answerOptions": [
 							{
-							"answerText": "régression logistique",
+							"answerText": "Régression logistique",
                             "isCorrect": "true"
                         },
                         {
@@ -1221,7 +1221,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "MultiClass one-vs-tout",
+							"answerText": "Multiclasse un-contre-tous",
                             "isCorrect": "false"
                         }
                     ]
@@ -1257,7 +1257,7 @@
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "k-signifie",
+							"answerText": "K-Means",
                             "isCorrect": "false"
                         },
                         {
@@ -1295,7 +1295,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "tous les deux",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -1310,15 +1310,15 @@
                     "questionText": "Les classificateurs de support-vectoriel peuvent être utilisés pour",
                     "answerOptions": [
 							{
-							"answerText": "Classification",
+							"answerText": "La classification",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "régression",
+							"answerText": "La régression",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "tous les deux",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -1327,15 +1327,15 @@
                     "questionText": "Forêt aléatoire est un type de classificateur ___",
                     "answerOptions": [
 							{
-							"answerText": "Ensemble",
+							"answerText": "Ensembliste",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Dissembliste",
+							"answerText": "Disensembliste",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Assemblez",
+							"answerText": "Assembliste",
                             "isCorrect": "false"
                         }
                     ]
@@ -1371,17 +1371,17 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "recommander des modes à essayer",
+							"answerText": "Recommander des modes à essayer",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "tous les deux",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
                 },
                 {
-                    "questionText": "L'intégration d'un modèle dans une application Web l'aide à être hors ligne",
+                    "questionText": "L'intégration d'un modèle dans une application Web l'aide à être compatible hors ligne",
                     "answerOptions": [
 							{
 							"answerText": "Vrai",
@@ -1394,7 +1394,7 @@
                     ]
                 },
                 {
-                    "questionText": "OnNX Runtime peut être utilisé pour",
+                    "questionText": "Onnx Runtime peut être utilisé pour",
                     "answerOptions": [
 							{
 							"answerText": "Exécution de modèles dans une application Web",
@@ -1405,7 +1405,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Hyperparameter Tuning",
+							"answerText": "Réglage des hyperparamètres",
                             "isCorrect": "false"
                         }
                     ]
@@ -1417,7 +1417,7 @@
             "title": "Classification 4: Quiz de validation des connaissances",
             "quiz": [
 							{
-                    "questionText": "L'application Nettron vous aide:",
+                    "questionText": "L'application Netron vous aide:",
                     "answerOptions": [
 							{
 							"answerText": "Visualiser les données",
@@ -1434,10 +1434,10 @@
                     ]
                 },
                 {
-                    "questionText": "Convertissez votre modèle SCIKIT-HALL pour une utilisation avec OnNX en utilisant:",
+                    "questionText": "Convertissez votre modèle Scikit-learnL pour une utilisation avec Onnx en utilisant:",
                     "answerOptions": [
 							{
-							"answerText": "Sklearn-App",
+							"answerText": "Sklearn-app",
                             "isCorrect": "false"
                         },
                         {
@@ -1445,7 +1445,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Sklearn-ONNX",
+							"answerText": "Sklearn-onnX",
                             "isCorrect": "true"
                         }
                     ]
@@ -1471,7 +1471,7 @@
         },
         {
             "id": 27,
-            "title": "Introduction au Clustering: Quiz préalable",
+            "title": "Introduction au Clustering (regroupement): Quiz préalable",
             "quiz": [
 							{
                     "questionText": "Un exemple de vie réel de regroupement serait",
@@ -1502,7 +1502,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "tous les deux",
+							"answerText": "Les deux",
                             "isCorrect": "false"
                         }
                     ]
@@ -1528,10 +1528,10 @@
         },
         {
             "id": 28,
-            "title": "Introduction au Clustering: Quiz de validation des connaissances",
+            "title": "Introduction au Clustering (regroupement): Quiz de validation des connaissances",
             "quiz": [
 							{
-                    "questionText": "La géométrie euclidienne est disposée le long",
+                    "questionText": "La géométrie Euclidienne est disposée le long",
                     "answerOptions": [
 							{
 							"answerText": "De plans",
@@ -1633,7 +1633,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "tous les deux",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -1701,15 +1701,15 @@
                     "questionText": "Que signifie NLP pour ces leçons?",
                     "answerOptions": [
 							{
-							"answerText": "Traitement des langues neurales",
+							"answerText": "Neural Language Processing (Traitement des langues neurales)",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Traitement des langues naturelles",
+							"answerText": "Natural Language Processing (Traitement des langues naturelles)",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Traitement linguistique naturel",
+							"answerText": "Natural Linguistic Processing (Traitement linguistique naturel)",
                             "isCorrect": "false"
                         }
                     ]
@@ -1783,7 +1783,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "tous les deux",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -1812,7 +1812,7 @@
             "title": "Tâches NLP: Quiz préalable",
             "quiz": [
 							{
-                    "questionText": "Tokenization",
+                    "questionText": "La tokenization",
                     "answerOptions": [
 							{
 							"answerText": "Divise le texte au moyen de la ponctuation",
@@ -1829,7 +1829,7 @@
                     ]
                 },
                 {
-                    "questionText": "Embeddings",
+                    "questionText": "L'Embeddings",
                     "answerOptions": [
 							{
 							"answerText": "Convertit numériquement les données de texte afin que les mots puissent se classer",
@@ -1846,18 +1846,18 @@
                     ]
                 },
                 {
-                    "questionText": "Marquage des parties de la parole",
+                    "questionText": "Le balisage des parties du discours (Parts-of-Speech Tagging)",
                     "answerOptions": [
 							{
-							"answerText": "Divise des phrases par leurs parties de la parole",
+							"answerText": "Divise les phrases en fonction de leurs parties du discours",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "prend des mots togmentés et les étiquettes de leur part de la parole",
+							"answerText": "prend les mots tokenisés et les marque selon leur partie du discours",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Phrases de diagrammes",
+							"answerText": "schématise des phrases",
                             "isCorrect": "false"
                         }
                     ]
@@ -1886,18 +1886,18 @@
                     ]
                 },
                 {
-                    "questionText": "N-grammes se réfèrent à",
+                    "questionText": "N-grams fait référence à",
                     "answerOptions": [
 							{
-							"answerText": "Un texte peut être divisé en séquences de mots d'une longueur définie",
+							"answerText": "Un texte pouvant être divisé en séquences de mots d'une longueur définie",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Un mot peut être divisé en séquences de caractères d'une longueur de jeu",
+							"answerText": "Un mot pouvant être divisé en séquences de caractères d'une longueur de jeu",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Un texte peut être divisé en paragraphes d'une longueur définie",
+							"answerText": "Un texte pouvant être divisé en paragraphes d'une longueur définie",
                             "isCorrect": "false"
                         }
                     ]
@@ -2000,7 +2000,7 @@
                     ]
                 },
                 {
-                    "questionText": "Pour utiliser `blob.translate` vous avez besoin:",
+                    "questionText": "Pour utiliser `blob.translate` vous avez besoin de:",
                     "answerOptions": [
 							{
 							"answerText": "Une connexion Internet",
@@ -2017,18 +2017,18 @@
                     ]
                 },
                 {
-                    "questionText": "Pour déterminer un sentiment, une approche ML serait de:",
+                    "questionText": "Pour déterminer un sentiment, une approche ML serait d':",
                     "answerOptions": [
 							{
-							"answerText": "Appliquez des techniques de régression pour générer manuellement des opinions et des scores et rechercher des modèles",
+							"answerText": "Appliquer des techniques de régression pour générer manuellement des opinions et des scores et rechercher des modèles",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Appliquez des techniques de PNL pour générer manuellement des opinions et des scores et rechercher des modèles",
+							"answerText": "Appliquer des techniques de PNL pour générer manuellement des opinions et des scores et rechercher des modèles",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Appliquez des techniques de regroupement pour des opinions et des scores générés manuellement et rechercher des modèles",
+							"answerText": "Appliquer des techniques de regroupement pour des opinions et des scores générés manuellement et rechercher des modèles",
                             "isCorrect": "false"
                         }
                     ]
@@ -2253,10 +2253,10 @@
         },
         {
             "id": 41,
-            "title": "Introduction aux Time Series (séries temporelles): Quiz préalable",
+            "title": "Introduction aux Séries chronologiques (Time Series) : Quiz préalable",
             "quiz": [
 							{
-                    "questionText": "La prévision de Time Series est utile pour",
+                    "questionText": "La prévision de série chronologique est utile pour",
                     "answerOptions": [
 							{
 							"answerText": "Déterminer les coûts futurs",
@@ -2273,7 +2273,7 @@
                     ]
                 },
                 {
-                    "questionText": "Une série temporelles est une séquence prise à:",
+                    "questionText": "Une série chronologique est une séquence prise à:",
                     "answerOptions": [
 							{
 							"answerText": "points successifs également espacés dans l'espace",
@@ -2290,7 +2290,7 @@
                     ]
                 },
                 {
-                    "questionText": "La série temporelles peut être utilisée dans les cas de:",
+                    "questionText": "La série chronologique peut être utilisée dans les cas de:",
                     "answerOptions": [
 							{
 							"answerText": "Prévision de tremblement de terre",
@@ -2310,10 +2310,10 @@
         },
         {
             "id": 42,
-            "title": "Introduction aux Time Series (séries temporelles): Quiz de validation des connaissances",
+            "title": "Introduction aux séries chronologiques : Quiz de validation des connaissances",
             "quiz": [
 							{
-                    "questionText": "Les tendances de série temporelles sont",
+                    "questionText": "Les tendances de série chronologique sont",
                     "answerOptions": [
 							{
 							"answerText": "des augmentations et des diminutions mesurables au fil du temps",
@@ -2347,10 +2347,10 @@
                     ]
                 },
                 {
-                    "questionText": "La prévision de la série temporelle est la plus utile pour",
+                    "questionText": "La prévision de séries chronologiques est utile pour",
                     "answerOptions": [
 							{
-							"answerText": "L'conométrics",
+							"answerText": "L'économétrie",
                             "isCorrect": "true"
                         },
                         {
@@ -2367,27 +2367,27 @@
         },
         {
             "id": 43,
-            "title": "Série TIME ARIMA: Quiz préalable",
+            "title": "Les séries chronologiques ARIMA: Quiz préalable",
             "quiz": [
 							{
-                    "questionText": "Arima signifie",
+                    "questionText": "ARIMA signifie",
                     "answerOptions": [
 							{
-							"answerText": "Moyenne mobile intégrale autonome",
+							"answerText": "AutoRegressive Integral Moving Average",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Action mobile intégrée autorégressive",
+							"answerText": "AutoRegressive Integrated Moving Action",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Moyenne mobile intégrée autorégresive",
+							"answerText": "AutoRegressive Integrated Moving Average",
                             "isCorrect": "true"
                         }
                     ]
                 },
                 {
-                    "questionText": "Stationarité fait référence à",
+                    "questionText": "La stationnarité fait référence à",
                     "answerOptions": [
 							{
 							"answerText": "Les données dont les attributs ne changent pas lors de la décalage",
@@ -2404,7 +2404,7 @@
                     ]
                 },
                 {
-                    "questionText": "différenciation",
+                    "questionText": "La différenciation",
                     "answerOptions": [
 							{
 							"answerText": "Stabilise la tendance et la saisonnalité",
@@ -2424,7 +2424,7 @@
         },
         {
             "id": 44,
-            "title": "Série TIME ARIMA: Quiz de validation des connaissances",
+            "title": "Les séries chronologiques ARIMA: Quiz de validation des connaissances",
             "quiz": [
 							{
                     "questionText": "Arima est utilisé pour créer un modèle adapté à la forme spéciale des données de la série chronologique",
@@ -2444,7 +2444,7 @@
                     ]
                 },
                 {
-                    "questionText": "Utilisez Sarimax à",
+                    "questionText": "Utilisez Sarimax pour",
                     "answerOptions": [
 							{
 							"answerText": "Gérer les modèles d'ARIMA saisonniers",
@@ -2461,18 +2461,18 @@
                     ]
                 },
                 {
-                    "questionText": " `La validation` de la promenade implique ",
+                    "questionText": " La validation « Walk-Forward » implique de",
                     "answerOptions": [
 							{
 							"answerText": "Réévaluer un modèle progressivement tel qu'il est validé",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Ré-entraînant un modèle progressivement tel qu'il est validé",
+							"answerText": "Re-entraîner un modèle progressivement tel qu'il est validé",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Ré-configurez un modèle progressivement tel qu'il est validé",
+							"answerText": "Re-configurer un modèle progressivement tel qu'il est validé",
                             "isCorrect": "false"
                         }
                     ]
@@ -2535,7 +2535,7 @@
             "title": "Renforcement 1: Quiz de validation des connaissances",
             "quiz": [
 							{
-                    "questionText": "Qu'est-ce que q-apprentissage?",
+                    "questionText": "Qu'est-ce que le Q-Learning?",
                     "answerOptions": [
 							{
 							"answerText": "Un mécanisme d'enregistrement de la \"bonté\" de chaque État",
@@ -2552,7 +2552,7 @@
                     ]
                 },
                 {
-                    "questionText": "Pour quelles valeurs une table Q correspond à la stratégie de marche aléatoire?",
+                    "questionText": "Pour quelles valeurs une Q-Table correspond à la stratégie de marche aléatoire?",
                     "answerOptions": [
 							{
 							"answerText": "toutes les valeurs égales",
@@ -2588,7 +2588,7 @@
             "title": "Renforcement 2: Quiz préalable",
             "quiz": [
 							{
-                    "questionText": "Les échecs et les jeux sont des jeux avec des états continus.",
+                    "questionText": "Les échecs et le go sont des jeux avec des états continus",
                     "answerOptions": [
 							{
 							"answerText": "Vrai",
@@ -2601,7 +2601,7 @@
                     ]
                 },
                 {
-                    "questionText": "Quel est le problème de la cartpole?",
+                    "questionText": "Quel est le problème CartPole ?",
                     "answerOptions": [
 							{
 							"answerText": "Un processus d'élimination des valeurs aberrantes",
@@ -2658,14 +2658,14 @@
                     ]
                 },
                 {
-                    "questionText": "Quelle paire avez-nous utilisée comme valeur de la clé de dictionnaire?",
+                    "questionText": "Quelle paire avons-nous utilisée comme valeur de la clé de dictionnaire?",
                     "answerOptions": [
 							{
-							"answerText": "(état, action) comme clé de la table Q-Table comme valeur",
+							"answerText": "(état, action) comme clé, l'entrée Q-Table comme valeur",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "State comme clé, action en tant que valeur",
+							"answerText": "L'état comme clé, action en tant que valeur",
                             "isCorrect": "false"
                         },
                         {
@@ -2675,7 +2675,7 @@
                     ]
                 },
                 {
-                    "questionText": "Quels sont les hyperparamètres que nous avons utilisés pendant q-apprentissage?",
+                    "questionText": "Quels sont les hyperparamètres que nous avons utilisés pendant le Q-Learning?",
                     "answerOptions": [
 							{
 							"answerText": "Valeur de la table Q, récompense actuelle, action aléatoire",
@@ -2718,15 +2718,15 @@
                     "questionText": "Quelle technique ML peut utiliser les hôpitaux pour gérer la réadmission?",
                     "answerOptions": [
 							{
-							"answerText": "Clustering",
+							"answerText": "Le Clustering (Regroupement)",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Série temporelle",
+							"answerText": "Les séries chronologiques",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "NLP",
+							"answerText": "Le NLP",
                             "isCorrect": "false"
                         }
                     ]
@@ -2779,7 +2779,7 @@
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Série temporelle",
+							"answerText": "Série chronologique",
                             "isCorrect": "false"
                         },
                         {

From d741c3f67ea6e4d2d79749fb94d425c4c4f9d58a Mon Sep 17 00:00:00 2001
From: manusquall <manusquall@gmail.com>
Date: Mon, 19 Jul 2021 10:41:33 +0000
Subject: [PATCH 216/368] review and correct some meaning

---
 quiz-app/src/assets/translations/fr.json | 40 ++++++++++++------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/quiz-app/src/assets/translations/fr.json b/quiz-app/src/assets/translations/fr.json
index 80b5574970..9908c3eae9 100644
--- a/quiz-app/src/assets/translations/fr.json
+++ b/quiz-app/src/assets/translations/fr.json
@@ -820,7 +820,7 @@
                     ]
                 },
                 {
-                    "questionText": "Types de régression logistique incluent",
+                    "questionText": "Les types de régression logistique incluent",
                     "answerOptions": [
 							{
 							"answerText": "multinomial et cardinal",
@@ -848,7 +848,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "cardinal",
+							"answerText": "cardinale",
                             "isCorrect": "false"
                         }
                     ]
@@ -860,7 +860,7 @@
             "title": "Régression logistique: Quiz de validation des connaissances",
             "quiz": [
 							{
-                    "questionText": "Sea-né est un type de",
+                    "questionText": "Seaborn est un type de",
                     "answerOptions": [
 							{
 							"answerText": "Bibliothèque de visualisation de données",
@@ -880,15 +880,15 @@
                     "questionText": "Une matrice de confusion est également connue sous le nom de:",
                     "answerOptions": [
 							{
-							"answerText": "matrice d'erreur",
+							"answerText": "Matrice d'erreur",
                             "isCorrect": "true"
                         },
                         {
-                            "answerText": "Matrix de vérité",
+                            "answerText": "Matrice de vérité",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "matrice de précision",
+							"answerText": "Matrice de précision",
                             "isCorrect": "false"
                         }
                     ]
@@ -916,35 +916,35 @@
             "title": "Construire une application Web: Quiz préalable",
             "quiz": [
 							{
-                    "questionText": "Qu'est-ce que OnNX signifie?",
+                    "questionText": "Qu'est-ce que ONNX signifie?",
                     "answerOptions": [
 							{
-							"answerText": "Exchange de réseau de neurones",
+							"answerText": "Over Neural Network Exchange",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Exchange de réseau de neurones ouverts",
+							"answerText": "Open Neural Network Exchange",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Exchange de réseau neural de sortie",
+							"answerText": "Output Neural Network Exchange",
                             "isCorrect": "false"
                         }
                     ]
                 },
                 {
-                    "questionText": "Comment le ballon est-il défini par ses créateurs?",
+                    "questionText": "Comment Flask est-il défini par ses créateurs?",
                     "answerOptions": [
 							{
-							"answerText": "mini-cadre",
+							"answerText": "mini-framework",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Grand-cadre",
+							"answerText": "grand-framework",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "micro-cadre",
+							"answerText": "micro-framework",
                             "isCorrect": "true"
                         }
                     ]
@@ -953,15 +953,15 @@
                     "questionText": "Que fait le module de cornichon de Python",
                     "answerOptions": [
 							{
-							"answerText": "Serialise un objet Python",
+							"answerText": "Serialiser un objet Python",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "désagréalise un objet Python",
+							"answerText": "Dé-sérialiser un objet Python",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Serialise et désémarifier un objet Python",
+							"answerText": "Sérialiser et Dé-sérialiser un objet Python",
                             "isCorrect": "true"
                         }
                     ]
@@ -976,7 +976,7 @@
                     "questionText": "Quels sont les outils que nous pouvons utiliser pour héberger un modèle pré-formé sur le Web à l'aide de Python?",
                     "answerOptions": [
 							{
-							"answerText": "Flacon",
+							"answerText": "Flask",
                             "isCorrect": "true"
                         },
                         {
@@ -984,7 +984,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "ONNX.JS",
+							"answerText": "onnx.JS",
                             "isCorrect": "false"
                         }
                     ]
@@ -1018,7 +1018,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Code des données en série",
+							"answerText": "Encode des données en série",
                             "isCorrect": "false"
                         }
                     ]

From 7dea56de001724f4df76d2e32e998e904d5ba189 Mon Sep 17 00:00:00 2001
From: manusquall <manusquall@gmail.com>
Date: Mon, 19 Jul 2021 10:58:34 +0000
Subject: [PATCH 217/368] fix mistranslation

---
 quiz-app/src/assets/translations/fr.json | 104 +++++++++++------------
 1 file changed, 52 insertions(+), 52 deletions(-)

diff --git a/quiz-app/src/assets/translations/fr.json b/quiz-app/src/assets/translations/fr.json
index 9908c3eae9..da0ee317e4 100644
--- a/quiz-app/src/assets/translations/fr.json
+++ b/quiz-app/src/assets/translations/fr.json
@@ -50,7 +50,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -107,7 +107,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -236,15 +236,15 @@
                     "questionText": "L'injustice dans le machine learning peut arriver",
                     "answerOptions": [
 							{
-							"answerText": "intentionnellement",
+							"answerText": "Intentionnellement",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Indormalement",
+							"answerText": "Involontairement",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -253,15 +253,15 @@
                     "questionText": "Le terme \" injustice \" en ml connotes:",
                     "answerOptions": [
 							{
-							"answerText": "nuit à un groupe de personnes",
+							"answerText": "Préjudices pour un groupe de personnees",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Dommage à une personne",
+							"answerText": "préjudice à une personne",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "nuit à la majorité des gens",
+							"answerText": "Préjudices pour la majorité des gens",
                             "isCorrect": "false"
                         }
                     ]
@@ -293,21 +293,21 @@
                     "questionText": "L'injustice dans un modèle peut être causée par",
                     "answerOptions": [
 							{
-							"answerText": "dépassement de données historiques",
+							"answerText": "Dépendance excessive de données historiques",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Sous-solliance sur les données historiques",
+							"answerText": "sous-dépendance sur les données historiques",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Trop d'alignement sur les données historiques",
+							"answerText": "Alignement trop étroit sur les données historiques",
                             "isCorrect": "false"
                         }
                     ]
                 },
                 {
-                    "questionText": "Pour atténuer l'injustice, tu peux",
+                    "questionText": "Pour atténuer l'injustice, vous pouvez",
                     "answerOptions": [
 							{
 							"answerText": "Identifier les préjudices et les groupes affectés",
@@ -318,7 +318,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "à la fois ce qui précède",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -375,7 +375,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -521,11 +521,11 @@
                     "questionText": "Si la précision de la formation du modèle d'apprentissage de votre machine est de 95% et que la précision des tests est de 30%, quel type de condition est appelé?",
                     "answerOptions": [
 							{
-							"answerText": "Surface",
+							"answerText": "Surapprentissage",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "sous-facture",
+							"answerText": "Insuffisance",
                             "isCorrect": "false"
                         },
                         {
@@ -552,18 +552,18 @@
                     ]
                 },
                 {
-                    "questionText": "Le processus de division d'un jeu de données dans un certain rapport d'entraînement et de test de jeu de données à l'aide de la méthode / la fonction Train_Test_split () '' Train_Test_Split () 'est appelée:",
+                    "questionText": "Le processus de division d'un ensemble de données en un certain rapport d'ensemble de données d'entraînement et de test à l'aide de la méthode/fonction 'train_test_split ()' de Scikit Learn est appelé une:",
                     "answerOptions": [
 							{
 							"answerText": "Validation croisée",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "validation de maintien",
+							"answerText": "Validation d'attentn",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "laissez une validation d'une sortie",
+							"answerText": "Validation \"Oubliez-en un\" ",
                             "isCorrect": "false"
                         }
                     ]
@@ -582,7 +582,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Scikit-apprendre",
+							"answerText": "Scikit-learn",
                             "isCorrect": "false"
                         },
                         {
@@ -592,18 +592,18 @@
                     ]
                 },
                 {
-                    "questionText": "Si vous souhaitez comprendre la propagation ou les autres caractéristiques des points de données de votre ensemble de données, puis effectuez:",
+                    "questionText": "Si vous souhaitez comprendre la propagation ou les autres caractéristiques des points de données de votre ensemble de données, alors effectuez:",
                     "answerOptions": [
 							{
-							"answerText": "Visualisation des données",
+							"answerText": "Une visualisation des données",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Pré-traitement des données",
+							"answerText": "Un pré-traitement des données",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Split test de train",
+							"answerText": "Un Train Test Splitn",
                             "isCorrect": "false"
                         }
                     ]
@@ -612,7 +612,7 @@
                     "questionText": "Lequel d'entre eux fait partie de l'étape de visualisation des données dans un projet de machine learning?",
                     "answerOptions": [
 							{
-							"answerText": "Intégrant un algorithme d'apprentissage de certains machines",
+							"answerText": "Intégrer un algorithme d'apprentissage de certains machines",
                             "isCorrect": "false"
                         },
                         {
@@ -634,16 +634,16 @@
 							{
                     "questionText": "Lequel de ces extraits de code est correct d'après cette leçon, si vous souhaitez vérifier la présence de valeurs manquantes dans votre ensemble de données ? Supposons que l'ensemble de données soit stocké dans une variable nommée \"ensemble de données\", qui est un objet Pandas DataFrame.",
                     "answerOptions": [
-							{
-							"answerText": "DataSet.isnull (). Somme ()",
+                        {
+                            "answerText": "dataset.isnull().sum()",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "FindMissing (DataSet)",
+                            "answerText": "findMissing(dataset)",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Somme (NULL (DataSet))",
+                            "answerText": "sum(null(dataset))",
                             "isCorrect": "false"
                         }
                     ]
@@ -652,15 +652,15 @@
                     "questionText": "Laquelle de ces méthodes de traçage est utile lorsque vous souhaitez comprendre la propagation de différents groupes de fichiers de données de votre jeu de données?",
                     "answerOptions": [
 							{
-							"answerText": "Terrain de dispersion",
+							"answerText": "Nuage de pointsn",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Terrain de ligne",
+							"answerText": "Graphique linéaire",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "barre de bar",
+							"answerText": "Graphique à barres",
                             "isCorrect": "true"
                         }
                     ]
@@ -689,7 +689,7 @@
             "title": "Régression linéaire et polynomiale: Quiz préalable",
             "quiz": [
 							{
-                    "questionText": "Matplotlib est un",
+                    "questionText": "Matplotlib est une",
                     "answerOptions": [
 							{
 							"answerText": "Bibliothèque de dessin",
@@ -700,7 +700,7 @@
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Library Lanchage",
+							"answerText": "Bibliothèque de prêt",
                             "isCorrect": "false"
                         }
                     ]
@@ -717,7 +717,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "une courbe",
+							"answerText": "Une courbe",
                             "isCorrect": "false"
                         }
                     ]
@@ -726,15 +726,15 @@
                     "questionText": "Un bon modèle de régression linéaire a un coefficient de corrélation ___",
                     "answerOptions": [
 							{
-							"answerText": "Low",
+							"answerText": "Bas",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "High",
+							"answerText": "Elevé",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "flat",
+							"answerText": "Plat",
                             "isCorrect": "false"
                         }
                     ]
@@ -757,7 +757,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "polynôme",
+							"answerText": "polynômial",
                             "isCorrect": "true"
                         }
                     ]
@@ -766,15 +766,15 @@
                     "questionText": "Ce sont tous types de méthodes de régression",
                     "answerOptions": [
 							{
-							"answerText": "Falsestetep, crête, lasso et élastique",
+							"answerText": "Falsestep, Ridge, Lasso et Elasticnet",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Stealwise, Ridge, Lasso et Elasticnet",
+							"answerText": "Stepwise, Ridge, Lasso et Elasticnet",
                             "isCorrect": "true"
                         },
                         {
-							"answerText": "Stealwise, Ridge, Lariat et Elasticnet",
+							"answerText": "Stepwise, Ridge, Lariat et Elasticnet",
                             "isCorrect": "false"
                         }
                     ]
@@ -1735,7 +1735,7 @@
                     "questionText": "Le test Turing d'Alan Turing a essayé de déterminer si un ordinateur était",
                     "answerOptions": [
 							{
-							"answerText": "indiscernable d'un humain",
+							"answerText": "Indiscernable d'un humain",
                             "isCorrect": "false"
                         },
                         {
@@ -1743,7 +1743,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -2047,11 +2047,11 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "sentiment et signification",
+							"answerText": "Sentiment et signification",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -2121,11 +2121,11 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "lignes vierges ou colonnes",
+							"answerText": "Lignes vierges ou colonnes",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -2161,7 +2161,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -2227,11 +2227,11 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "mots que vous pouvez supprimer pour accélérer l'analyse du sentiment",
+							"answerText": "Mots que vous pouvez supprimer pour accélérer l'analyse du sentiment",
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]
@@ -2546,7 +2546,7 @@
                             "isCorrect": "false"
                         },
                         {
-							"answerText": "Les deux ci-dessus",
+							"answerText": "Les deux",
                             "isCorrect": "true"
                         }
                     ]

From cdd0dfb8baddaebb2dcdfe987bda759ae5374245 Mon Sep 17 00:00:00 2001
From: "Charles Emmanuel S. Ndiaye" <manusquall@outlook.com>
Date: Mon, 19 Jul 2021 12:32:28 +0000
Subject: [PATCH 218/368] Update the links to redirect to translated pages

---
 1-Introduction/translations/README.fr.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/1-Introduction/translations/README.fr.md b/1-Introduction/translations/README.fr.md
index c27f9bef1b..462dea70e8 100644
--- a/1-Introduction/translations/README.fr.md
+++ b/1-Introduction/translations/README.fr.md
@@ -7,10 +7,10 @@ Dans cette section du programme, vous découvrirez les concepts de base sous-jac
   
 ### Leçons
 
-1. [Introduction au machine learning](1-intro-to-ML/README.md)
-1. [L’histoire du machine learning et de l’IA](2-history-of-ML/README.md)
-1. [Équité et machine learning](3-équité/README.md)
-1. [Techniques de machine learning](4-techniques-of-ML/README.md)
+1. [Introduction au machine learning](../1-intro-to-ML/translations/README.fr.md)
+1. [L’histoire du machine learning et de l’IA](../2-history-of-ML/translations/README.fr.md)
+1. [Équité et machine learning](../3-fairness/translations/README.fr.md)
+1. [Techniques de machine learning](../4-techniques-of-ML/translations/README.fr.md)
 ### Crédits
 
 "Introduction au machine learning" a été écrit avec ♥� par une équipe de personnes comprenant [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) et [Jen Looper](https://twitter.com/jenlooper)

From 7bb38cd9d98a11e97bb74ee58927f24c2d928a32 Mon Sep 17 00:00:00 2001
From: "Charles Emmanuel S. Ndiaye" <manusquall@outlook.com>
Date: Mon, 19 Jul 2021 12:38:53 +0000
Subject: [PATCH 219/368] update the quiz links to the french translation

---
 1-Introduction/1-intro-to-ML/translations/README.fr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index 0d07a5e806..d8915f581c 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
 
 > 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
 
-## [Quiz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=fr)
 
 ### Introduction
 
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
 
 Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
 
-## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=fr)
 
 ## Révision et auto-apprentissage
 

From 2cbe730dd959bc6ce906c0ea0dd5b7b42dacfc40 Mon Sep 17 00:00:00 2001
From: "Charles Emmanuel S. Ndiaye" <manusquall@outlook.com>
Date: Mon, 19 Jul 2021 12:40:20 +0000
Subject: [PATCH 220/368] update the quiz links to the french translation

---
 1-Introduction/2-history-of-ML/translations/README.fr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
index 6613216277..9c59eb6fbe 100644
--- a/1-Introduction/2-history-of-ML/translations/README.fr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -3,7 +3,7 @@
 ![Résumé de l'histoire du machine learning dans un sketchnote](../../../sketchnotes/ml-history.png)
 > Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Quizz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Quizz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=fr)
 
 Dans cette leçon, nous allons parcourir les principales étapes de l'histoire du machine learning et de l'intelligence artificielle.
 
@@ -102,7 +102,7 @@ Reste à savoir ce que l'avenir nous réserve, mais il est important de comprend
 
 Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ?
 
-## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=fr)
 
 ## Révision et auto-apprentissage
 

From 922413c563afdda446f99d582c08db3da38d253b Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Mon, 19 Jul 2021 21:44:29 +0900
Subject: [PATCH 221/368] feat: add ja 1.1 assignment

Add Japanese translation of the 1.1 assignment.

Refs #149
---
 1-Introduction/1-intro-to-ML/translations/README.ja.md   | 2 +-
 .../1-intro-to-ML/translations/assignment.ja.md          | 9 +++++++++
 2 files changed, 10 insertions(+), 1 deletion(-)
 create mode 100644 1-Introduction/1-intro-to-ML/translations/assignment.ja.md

diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md
index 1107739c62..ada0055008 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ja.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md
@@ -102,4 +102,4 @@ AI�ML�深層学習�データサイエンス��������解�
 
 ## 課題
 
-[起動��実行������。](assignment.md)
+[稼���る](assignment.ja.md)
diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.ja.md b/1-Introduction/1-intro-to-ML/translations/assignment.ja.md
new file mode 100644
index 0000000000..9c86969cd9
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.ja.md
@@ -0,0 +1,9 @@
+# 稼���る
+
+## 指示
+
+��評価���課題���Python����復習��環境を稼����ノートブックを実行��るよ���る必���り��。
+
+��[Pythonラーニングパス](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa)を�講��次�入門用ビデオ�従��システムをセットアップ������。
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6

From e1e18f2a3cad47b9e9c1199f36dfdc80bb9f1a3e Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Mon, 19 Jul 2021 14:50:54 +0200
Subject: [PATCH 222/368] Add missing comma

Add the missing comma and fix #208
---
 4-Classification/1-Introduction/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index 4490131c71..09a3515446 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -163,7 +163,7 @@ Now you can dig deeper into the data and learn what are the typical ingredients
     def create_ingredient_df(df):
         ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
         ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
-        ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+        ingredient_df = ingredient_df.sort_values(by='value', ascending=False,
         inplace=False)
         return ingredient_df
     ```

From 34807d24a36319b21a93049bee2fff52fa1769ef Mon Sep 17 00:00:00 2001
From: simplg <81249731+simplg@users.noreply.github.com>
Date: Mon, 19 Jul 2021 14:52:54 +0200
Subject: [PATCH 223/368] Fix path and table format

Fix #209 with the correct path to cleaned_cuisine.csv

Fix also a formating error with the table where almond was the index column of the dataframe
---
 4-Classification/2-Classifiers-1/README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index 0db1aebaad..213179bbfb 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -15,7 +15,7 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     ```python
     import pandas as pd
-    cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+    cuisines_df = pd.read_csv("../data/cleaned_cuisine.csv")
     cuisines_df.head()
     ```
 
@@ -67,13 +67,13 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     Your features look like this:
 
-| almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
-| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | 
-|      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-|      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-|      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-|      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-|      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+| | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | -----: |
+|      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   | 0 |
+|      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   | 0 |
+|      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   | 0 |
+|      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   | 0 |
+|      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   | 0 |
 
 Now you are ready to train your model!
 

From 60526b022f0d2d098693448d361972613f20ed4d Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Mon, 19 Jul 2021 10:20:02 -0400
Subject: [PATCH 224/368] changing classification file name to cleaned_cuisines

---
 4-Classification/1-Introduction/README.md                       | 2 +-
 4-Classification/1-Introduction/solution/notebook.ipynb         | 2 +-
 4-Classification/1-Introduction/translations/README.tr.md       | 2 +-
 4-Classification/1-Introduction/translations/README.zh-cn.md    | 2 +-
 4-Classification/2-Classifiers-1/README.md                      | 2 +-
 4-Classification/2-Classifiers-1/solution/notebook.ipynb        | 2 +-
 4-Classification/3-Classifiers-2/README.md                      | 2 +-
 4-Classification/3-Classifiers-2/notebook.ipynb                 | 2 +-
 4-Classification/3-Classifiers-2/solution/notebook.ipynb        | 2 +-
 4-Classification/4-Applied/README.md                            | 2 +-
 4-Classification/4-Applied/solution/notebook.ipynb              | 2 +-
 .../data/{cleaned_cuisine.csv => cleaned_cuisines.csv}          | 0
 12 files changed, 11 insertions(+), 11 deletions(-)
 rename 4-Classification/data/{cleaned_cuisine.csv => cleaned_cuisines.csv} (100%)

diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index 4490131c71..8e075f0e0b 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -275,7 +275,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
     ```python
     transformed_df.head()
     transformed_df.info()
-    transformed_df.to_csv("../data/cleaned_cuisine.csv")
+    transformed_df.to_csv("../data/cleaned_cuisines.csv")
     ```
 
     This fresh CSV can now be found in the root data folder.
diff --git a/4-Classification/1-Introduction/solution/notebook.ipynb b/4-Classification/1-Introduction/solution/notebook.ipynb
index c5b8c6299c..5abb9693de 100644
--- a/4-Classification/1-Introduction/solution/notebook.ipynb
+++ b/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -622,7 +622,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "transformed_df.to_csv(\"../../data/cleaned_cuisine.csv\")"
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
    ]
   },
   {
diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
index e4cc372aa4..fd0e21848c 100644
--- a/4-Classification/1-Introduction/translations/README.tr.md
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -275,7 +275,7 @@ Veriyi temizlediniz, ÅŸimdi [SMOTE](https://imbalanced-learn.org/dev/references/
     ```python
     transformed_df.head()
     transformed_df.info()
-    transformed_df.to_csv("../../data/cleaned_cuisine.csv")
+    transformed_df.to_csv("../../data/cleaned_cuisines.csv")
     ```
 
     Bu yeni CSV şimdi kök data (veri) klasöründe görülebilir.
diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
index 2e258f3f1b..adef7c8adc 100644
--- a/4-Classification/1-Introduction/translations/README.zh-cn.md
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -269,7 +269,7 @@ Scikit-learn项目�供多�对数�进行分类的算法，你需�根�
     ```python
     transformed_df.head()
     transformed_df.info()
-    transformed_df.to_csv("../data/cleaned_cuisine.csv")
+    transformed_df.to_csv("../data/cleaned_cuisines.csv")
     ```
 
     这个全新的CSV文件�以在数�根目录中被找到。
diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index 0db1aebaad..c5588f1be5 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -15,7 +15,7 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     ```python
     import pandas as pd
-    cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+    cuisines_df = pd.read_csv("../../data/cleaned_cuisines.csv")
     cuisines_df.head()
     ```
 
diff --git a/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/4-Classification/2-Classifiers-1/solution/notebook.ipynb
index a819dbe5b7..770ac85c7e 100644
--- a/4-Classification/2-Classifiers-1/solution/notebook.ipynb
+++ b/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },
diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md
index dd25926e12..9720c76379 100644
--- a/4-Classification/3-Classifiers-2/README.md
+++ b/4-Classification/3-Classifiers-2/README.md
@@ -6,7 +6,7 @@ In this second classification lesson, you will explore more ways to classify num
 
 ### Prerequisite
 
-We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisine.csv_ in the root of this 4-lesson folder.
+We assume that you have completed the previous lessons and have a cleaned dataset in your `data` folder called _cleaned_cuisines.csv_ in the root of this 4-lesson folder.
 
 ### Preparation
 
diff --git a/4-Classification/3-Classifiers-2/notebook.ipynb b/4-Classification/3-Classifiers-2/notebook.ipynb
index f4dec474dd..4659a7b623 100644
--- a/4-Classification/3-Classifiers-2/notebook.ipynb
+++ b/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },
diff --git a/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/4-Classification/3-Classifiers-2/solution/notebook.ipynb
index d953c603d4..a089b21fa6 100644
--- a/4-Classification/3-Classifiers-2/solution/notebook.ipynb
+++ b/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -47,7 +47,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
     "cuisines_df.head()"
    ]
   },
diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index 773271a1e4..1f3573c2e1 100644
--- a/4-Classification/4-Applied/README.md
+++ b/4-Classification/4-Applied/README.md
@@ -40,7 +40,7 @@ First, train a classification model using the cleaned cuisines dataset we used.
 1. Then, work with your data in the same way you did in previous lessons, by reading a CSV file using `read_csv()`:
 
     ```python
-    data = pd.read_csv('../data/cleaned_cuisine.csv')
+    data = pd.read_csv('../data/cleaned_cuisines.csv')
     data.head()
     ```
 
diff --git a/4-Classification/4-Applied/solution/notebook.ipynb b/4-Classification/4-Applied/solution/notebook.ipynb
index 5ed9da5203..b388d2ca87 100644
--- a/4-Classification/4-Applied/solution/notebook.ipynb
+++ b/4-Classification/4-Applied/solution/notebook.ipynb
@@ -115,7 +115,7 @@
     }
    ],
    "source": [
-    "data = pd.read_csv('../../data/cleaned_cuisine.csv')\n",
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
     "data.head()"
    ]
   },
diff --git a/4-Classification/data/cleaned_cuisine.csv b/4-Classification/data/cleaned_cuisines.csv
similarity index 100%
rename from 4-Classification/data/cleaned_cuisine.csv
rename to 4-Classification/data/cleaned_cuisines.csv

From 0c108193ccae130f6dfa7e326db29e835f206219 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Mon, 19 Jul 2021 19:44:41 +0300
Subject: [PATCH 225/368] Add Turkish translation of Classifiers-1

---
 .../2-Classifiers-1/translations/README.tr.md | 241 ++++++++++++++++++
 .../translations/assignment.tr.md             |   8 +
 2 files changed, 249 insertions(+)
 create mode 100644 4-Classification/2-Classifiers-1/translations/README.tr.md
 create mode 100644 4-Classification/2-Classifiers-1/translations/assignment.tr.md

diff --git a/4-Classification/2-Classifiers-1/translations/README.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md
new file mode 100644
index 0000000000..f02bd759a9
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/README.tr.md
@@ -0,0 +1,241 @@
+# Mutfak sınıflandırıcıları 1
+
+Bu derste, mutfaklarla ilgili dengeli ve temiz veriyle dolu, geçen dersten kaydettiğiniz veri setini kullanacaksınız.
+
+Bu veri setini çeşitli sınıflandırıcılarla _bir grup malzemeyi baz alarak verilen bir ulusal mutfağı öngörmek_ için kullanacaksınız. Bunu yaparken, sınıflandırma görevleri için algoritmaların leveraj edilebileceği yollardan bazıları hakkında daha fazla bilgi edineceksiniz.
+
+## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/?loc=tr)
+# Hazırlık
+
+[Birinci dersi](../../1-Introduction/README.md) tamamladığınızı varsayıyoruz, dolayısıyla bu dört ders için _cleaned_cuisines.csv_ dosyasının kök `/data` klasöründe var olduğundan emin olun.
+
+## Alıştırma - ulusal bir mutfağı öngörün
+
+1. Bu dersin _notebook.ipynb_ dosyasında çalışarak, Pandas kütüphanesiyle beraber o dosyayı da alın:
+
+    ```python
+    import pandas as pd
+    cuisines_df = pd.read_csv("../data/cleaned_cuisines.csv")
+    cuisines_df.head()
+    ```
+
+    Veri şöyle görünüyor:
+
+|     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+| --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+| 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+  
+
+1. Şimdi, birkaç kütüphane daha alın:
+
+    ```python
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.model_selection import train_test_split, cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+    from sklearn.svm import SVC
+    import numpy as np
+    ```
+
+1. X ve y koordinatlarını eğitme için iki veri iskeletine bölün. `cuisine` etiket veri iskeleti olabilir:
+
+    ```python
+    cuisines_label_df = cuisines_df['cuisine']
+    cuisines_label_df.head()
+    ```
+
+    Şöyle görünecek:
+
+    ```output
+    0    indian
+    1    indian
+    2    indian
+    3    indian
+    4    indian
+    Name: cuisine, dtype: object
+    ```
+
+1. `Unnamed: 0` ve `cuisine` sütunlarını, `drop()` fonksiyonunu çağırarak temizleyin. Kalan veriyi eğitilebilir öznitelikler olarak kaydedin:
+
+    ```python
+    cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
+    cuisines_feature_df.head()
+    ```
+
+    Öznitelikleriniz şöyle görünüyor:
+
+| almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | 
+|      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+|      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+|      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+|      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+|      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+
+Şimdi modelinizi eğitmek için hazırsınız!
+
+## Sınıflandırıcınızı seçme
+
+Veriniz temiz ve eğitme için hazır, şimdi bu iş için hangi algoritmanın kullanılması gerektiğine karar vermelisiniz.
+
+Scikit-learn, sınıflandırmayı gözetimli öğrenme altında grupluyor. Bu kategoride sınıflandırma için birçok yöntem görebilirsiniz. [Çeşitlilik](https://scikit-learn.org/stable/supervised_learning.html) ilk bakışta oldukça şaşırtıcı. Aşağıdaki yöntemlerin hepsi sınıflandırma yöntemlerini içermektedir:
+
+- DoÄŸrusal Modeller
+- Destek Vektör Makineleri
+- Stokastik Gradyan Ä°niÅŸi
+- En Yakın Komşu
+- Gauss Süreçleri
+- Karar Ağaçları
+- Topluluk Metotları (Oylama Sınıflandırıcısı)
+- Çok sınıflı ve çok çıktılı algoritmalar (çok sınıflı ve çok etiketli sınıflandırma, çok sınıflı-çok çıktılı sınıflandırma)
+
+> [Verileri sınıflandırmak için sinir ağlarını](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification) da kullanabilirsiniz, ancak bu, bu dersin kapsamı dışındadır.
+
+### Hangi sınıflandırıcıyı kullanmalı?
+
+Şimdi, hangi sınıflandırıcıyı seçmelisiniz? Genellikle, birçoğunu gözden geçirmek ve iyi bir sonuç aramak deneme yollarından biridir. Scikit-learn, oluşturulmuş bir veri seti üzerinde KNeighbors, iki yolla SVC, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB ve QuadraticDiscrinationAnalysis karşılaştırmaları yapan ve sonuçları görsel olarak gösteren bir [yan yana karşılaştırma](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) sunar:
+
+![sınıflandırıcıların karşılaştırılması](../images/comparison.png)
+> Grafikler Scikit-learn dokümantasyonlarında oluşturulmuştur.
+
+> AutoML, bu karşılaştırmaları bulutta çalıştırarak bu problemi muntazam bir şekilde çözer ve veriniz için en iyi algoritmayı seçmenizi sağlar. [Buradan](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa) deneyin.
+
+### Daha iyi bir yaklaşım
+
+Böyle tahminlerle çözmekten daha iyi bir yol ise, indirilebilir [ML Kopya kağıdı](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa) içindeki fikirlere bakmaktır. Burada, bizim çok sınıflı problemimiz için bazı seçenekler olduğunu görüyoruz:
+
+![çok sınıflı problemler için kopya kağıdı](../images/cheatsheet.png)
+> Microsoft'un Algoritma Kopya Kağıdı'ndan, çok sınıflı sınıflandırma seçeneklerini detaylandıran bir bölüm
+
+:white_check_mark: Bu kopya kağıdını indirin, yazdırın ve duvarınıza asın!
+
+### Akıl yürütme
+
+Elimizdeki kısıtlamalarla farklı yaklaşımlar üzerine akıl yürütelim:
+
+- **Sinir ağları çok ağır**. Temiz ama minimal veri setimizi ve eğitimi not defterleriyle yerel makinelerde çalıştırdığımızı göz önünde bulundurursak, sinir ağları bu görev için çok ağır oluyor.
+- **İki sınıflı sınıflandırıcısı yok**. İki sınıflı sınıflandırıcı kullanmıyoruz, dolayısıyla bire karşı hepsi (one-vs-all) yöntemi eleniyor.
+- **Karar ağacı veya lojistik regresyon işe yarayabilirdi**. Bir karar ağacı veya çok sınıflı veri için lojistik regresyon işe yarayabilir.
+- **Çok Sınıf Artırmalı Karar Ağaçları farklı bir problemi çözüyor**. Çok sınıf artırmalı karar ağacı, parametrik olmayan görevler için en uygunu, mesela sıralama (ranking) oluşturmak için tasarlanan görevler. Yani, bizim için kullanışlı değil.
+
+### Scikit-learn kullanımı
+
+Verimizi analiz etmek için Scikit-learn kullanacağız. Ancak, Scikit-learn içerisinde lojistik regresyonu kullanmanın birçok yolu var. [Geçirilecek parametreler](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression) göz atın.
+
+Aslında, Scikit-learn'den lojistik regresyon yapmasını beklediğimizde belirtmemiz gereken `multi_class` ve `solver` diye iki önemli parametre var. `multi_class` değeri belli bir davranış uygular. Çözücünün değeri, hangi algoritmanın kullanılacağını gösterir. Her çözücü her `multi_class` değeriyle eşleştirilemez.
+
+Dokümanlara göre, çok sınıflı durumunda eğitme algoritması:
+
+- Eğer `multi_class` seçeneği `ovr` olarak ayarlanmışsa, **bire karşı diğerleri (one-vs-rest, OvR) şemasını kullanır**
+- Eğer `multi_class` seçeneği `multinomial` olarak ayarlanmışsa, **çapraz düzensizlik yitimini/kaybını kullanır**. (Güncel olarak `multinomial` seçeneği yalnızca ‘lbfgs’, ‘sag’, ‘saga’ ve ‘newton-cg’ çözücüleriyle destekleniyor.)
+
+> :mortar_board: Buradaki 'şema' ya 'ovr' (one-vs-rest, yani bire karşı diğerleri) ya da 'multinomial' olabilir. Lojistik regresyon aslında ikili sınıflandırmayı desteklemek için tasarlandığından, bu şemalar onun çok sınıflı sınıflandırma görevlerini daha iyi ele alabilmesini sağlıyor. [kaynak](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+
+> :mortar_board: 'Çözücü', "eniyileştirme probleminde kullanılacak algoritma" olarak tanımlanır. [kaynak](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)
+
+Scikit-learn, çözücülerin, farklı tür veri yapıları tarafından sunulan farklı meydan okumaları nasıl ele aldığını açıklamak için bu tabloyu sunar:
+
+![çözücüler](../images/solvers.png)
+
+## Alıştırma - veriyi bölün
+
+İkincisini önceki derte öğrendiğinizden, ilk eğitme denememiz için lojistik regresyona odaklanabiliriz.
+`train_test_split()` fonksiyonunu çağırarak verilerinizi eğitme ve sınama gruplarına bölün:
+
+```python
+X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+```
+
+## Alıştırma - lojistik regresyon uygulayın
+
+Çok sınıflı durumu kullandığınız için, hangi _şemayı_ kullanacağınızı ve hangi _çözücüyü_ ayarlayacağınızı seçmeniz gerekiyor. Eğitme için, bir çok sınıflı ayarında LogisticRegression ve **liblinear** çözücüsünü kullanın.
+
+1. multi_class'ı `ovr` ve solver'ı `liblinear` olarak ayarlayarak bir lojistik regresyon oluşturun:
+
+    ```python
+    lr = LogisticRegression(multi_class='ovr',solver='liblinear')
+    model = lr.fit(X_train, np.ravel(y_train))
+    
+    accuracy = model.score(X_test, y_test)
+    print ("Accuracy is {}".format(accuracy))
+    ```
+
+    :white_check_mark: Genelde varsayılan olarak ayarlanan `lbfgs` gibi farklı bir çözücü deneyin.
+
+    > Not olarak, gerektiğinde verinizi düzleştirmek için Pandas [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) fonksiyonunu kullanın.
+
+    Doğruluk **%80** üzerinde iyidir!
+
+1. Bir satır veriyi (#50) sınayarak bu modeli eylem halinde görebilirsiniz:
+
+    ```python
+    print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
+    print(f'cuisine: {y_test.iloc[50]}')
+    ```
+
+    Sonuç bastırılır:
+
+   ```output
+   ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
+   cuisine: indian
+   ```
+
+   :white_check_mark: Farklı bir satır sayısı deneyin ve sonuçları kontrol edin
+
+1. Daha derinlemesine inceleyerek, bu öngörünün doğruluğunu kontrol edebilirsiniz:
+
+    ```python
+    test= X_test.iloc[50].values.reshape(-1, 1).T
+    proba = model.predict_proba(test)
+    classes = model.classes_
+    resultdf = pd.DataFrame(data=proba, columns=classes)
+    
+    topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
+    topPrediction.head()
+    ```
+
+    Sonuç bastırılır - Hint mutfağı iyi olasılıkla en iyi öngörü:
+
+    |          |        0 | 
+    | -------: | -------: | 
+    |   indian | 0.715851 |
+    |  chinese | 0.229475 |
+    | japanese | 0.029763 |
+    |   korean | 0.017277 |
+    |     thai | 0.007634 |
+
+    :while_check_mark: Modelin, bunun bir Hint mutfağı olduğundan nasıl emin olduğunu açıklayabilir misiniz?
+
+1. Regresyon derslerinde yaptığınız gibi, bir sınıflandırma raporu bastırarak daha fazla detay elde edin:
+
+    ```python
+    y_pred = model.predict(X_test)
+    print(classification_report(y_test,y_pred))
+    ```
+
+    |              | precision | recall | f1-score | support |
+    | ------------ | ------ | -------- | ------- | ---- |
+    | chinese      | 0.73   | 0.71     | 0.72    | 229  |
+    | indian       | 0.91   | 0.93     | 0.92    | 254  |
+    | japanese     | 0.70   | 0.75     | 0.72    | 220  |
+    | korean       | 0.86   | 0.76     | 0.81    | 242  |
+    | thai         | 0.79   | 0.85     | 0.82    | 254  |
+    | accuracy     | 0.80   | 1199     |         |      |
+    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |
+    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |
+
+## :rocket: Meydan Okuma
+
+Bu derste, bir grup malzemeyi baz alarak bir ulusal mutfağı öngörebilen bir makine öğrenimi modeli oluşturmak için temiz verinizi kullandınız. Scikit-learn'ün veri sınıflandırmak için sağladığı birçok yöntemi okumak için biraz vakit ayırın. Arka tarafta neler olduğunu anlamak için 'çözücü' kavramını derinlemesine inceleyin.
+
+## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/?loc=tr)
+
+## Gözden geçirme & kendi kendine çalışma
+
+[Bu deste](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf) lojistik regresyonun arkasındaki matematiği derinlemesine inceleyin.
+## Ödev
+
+[Çözücüleri çalışın](assignment.tr.md)
diff --git a/4-Classification/2-Classifiers-1/translations/assignment.tr.md b/4-Classification/2-Classifiers-1/translations/assignment.tr.md
new file mode 100644
index 0000000000..9b545f1ae3
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/assignment.tr.md
@@ -0,0 +1,8 @@
+# Çözücüleri çalışın
+## Yönergeler
+
+Bu derste, doğru bir model yaratmak için algoritmaları bir makine öğrenimi süreciyle eşleştiren çeşitli çözücüleri öğrendiniz. Derste sıralanan çözücüleri inceleyin ve iki tanesini seçin. Kendi cümlelerinizle, bu iki çözücünün benzerliklerini ve farklılıklarını bulup yazın. Ne tür problemleri ele alıyorlar? Çeşitli veri yapılarıyla nasıl çalışıyorlar? Birini diğerine neden tercih ederdiniz?
+
+|  Ölçüt   | Örnek Alınacak Nitelikte                                                                                             | Yeterli                                          | Geliştirme Gerekli           |
+| -------- | -------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------ | ---------------------------- |
+|          | Her biri bir çözücü üzerine yazılmış, onları dikkatle karşılaştıran ve iki paragraf içeren bir .doc dosyası sunulmuş | Bir paragraf içeren bir .doc dosyası sunulmuş    | Görev tamamlanmamış          |

From 3707c00ab3355b357e91e0471e081a32c0a7e5f2 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Mon, 19 Jul 2021 19:48:02 +0300
Subject: [PATCH 226/368] Add Turkish translation of Classifiers-1

---
 4-Classification/2-Classifiers-1/translations/assignment.tr.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/4-Classification/2-Classifiers-1/translations/assignment.tr.md b/4-Classification/2-Classifiers-1/translations/assignment.tr.md
index 9b545f1ae3..10d4c64f3f 100644
--- a/4-Classification/2-Classifiers-1/translations/assignment.tr.md
+++ b/4-Classification/2-Classifiers-1/translations/assignment.tr.md
@@ -2,6 +2,7 @@
 ## Yönergeler
 
 Bu derste, doğru bir model yaratmak için algoritmaları bir makine öğrenimi süreciyle eşleştiren çeşitli çözücüleri öğrendiniz. Derste sıralanan çözücüleri inceleyin ve iki tanesini seçin. Kendi cümlelerinizle, bu iki çözücünün benzerliklerini ve farklılıklarını bulup yazın. Ne tür problemleri ele alıyorlar? Çeşitli veri yapılarıyla nasıl çalışıyorlar? Birini diğerine neden tercih ederdiniz?
+## Rubrik
 
 |  Ölçüt   | Örnek Alınacak Nitelikte                                                                                             | Yeterli                                          | Geliştirme Gerekli           |
 | -------- | -------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------ | ---------------------------- |

From e2a499310553f917b9a32eacdcf9c2e30330047a Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Mon, 19 Jul 2021 19:52:29 +0300
Subject: [PATCH 227/368] Fix typo

---
 translations/README.tr.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/translations/README.tr.md b/translations/README.tr.md
index cc517004ed..f4dab353a7 100644
--- a/translations/README.tr.md
+++ b/translations/README.tr.md
@@ -100,7 +100,7 @@ Bu eğitim programını oluştururken iki pedagojik ilke seçtik: uygulamalı **
 |   Ek Yazı     |       Gerçek Hayattan ML Senaryoları ve Uygulamaları       |     [Vahşi Doğada ML](../9-Real-World/README.md)       | Klasik makine öğreniminin ilginç ve açıklayıcı gerçek hayat uygulamaları                                                        |     [ders](../9-Real-World/1-Applications/README.md)     |     Takım      |
 ## Çevrimdışı erişim
 
-Bu dokümantasyonu [Docsify](https://docsify.js.org/#/) kullanarak çevrimdışı çalıştırabilirsiniz. Bu yazılım havuzunu çatallayın, yerel makinenizde [Docsify'ı kurum](https://docsify.js.org/#/quickstart) ve sonra bu yazılım havuzunun kök dizininde `docsify serve` yazın. İnternet sitesi, 3000 portunda `localhost:3000` yerel ana makinenizde sunulacaktır.
+Bu dokümantasyonu [Docsify](https://docsify.js.org/#/) kullanarak çevrimdışı çalıştırabilirsiniz. Bu yazılım havuzunu çatallayın, yerel makinenizde [Docsify'ı kurun](https://docsify.js.org/#/quickstart) ve sonra bu yazılım havuzunun kök dizininde `docsify serve` yazın. İnternet sitesi, 3000 portunda `localhost:3000` yerel ana makinenizde sunulacaktır.
 
 ## PDF'ler
 

From 534f059fef10a984cf17debb46800efcd4a131bf Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Mon, 19 Jul 2021 19:54:31 +0300
Subject: [PATCH 228/368] Fix file paths

---
 4-Classification/1-Introduction/translations/README.tr.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
index e4cc372aa4..4b8c2c9048 100644
--- a/4-Classification/1-Introduction/translations/README.tr.md
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -49,7 +49,7 @@ Scikit-learn, veriyi sınıflandırmak için kullanmak üzere, çözmek istediğ
 
 ## Alıştırma - verinizi temizleyip dengeleyin
 
-Bu projeye başlamadan önce elinizdeki ilk görev, daha iyi sonuçlar almak için, verinizi temizlemek ve **dengelemek**. Bu klasördeki boş _notebook.ipynb_ dosyasıyla başlayın.
+Bu projeye başlamadan önce elinizdeki ilk görev, daha iyi sonuçlar almak için, verinizi temizlemek ve **dengelemek**. Üst klasördeki boş _notebook.ipynb_ dosyasıyla başlayın.
 
 Kurmanız gereken ilk şey [imblearn](https://imbalanced-learn.org/stable/). Bu, veriyi daha iyi dengelemenizi sağlayacak bir Scikit-learn paketidir. (Bu görev hakkında birazdan daha fazla bilgi göreceksiniz.)
 
@@ -74,7 +74,7 @@ Kurmanız gereken ilk şey [imblearn](https://imbalanced-learn.org/stable/). Bu,
 1. Sonraki görev veriyi almak olacak:
 
     ```python
-    df  = pd.read_csv('../../data/cuisines.csv')
+    df  = pd.read_csv('../data/cuisines.csv')
     ```
 
    `read_csv()` kullanmak _cusines.csv_ csv dosyasının içeriğini okuyacak ve `df` değişkenine yerleştirecek.
@@ -275,7 +275,7 @@ Veriyi temizlediniz, ÅŸimdi [SMOTE](https://imbalanced-learn.org/dev/references/
     ```python
     transformed_df.head()
     transformed_df.info()
-    transformed_df.to_csv("../../data/cleaned_cuisine.csv")
+    transformed_df.to_csv("../data/cleaned_cuisine.csv")
     ```
 
     Bu yeni CSV şimdi kök data (veri) klasöründe görülebilir.

From e3d630e499752e47b9e060f3b98535a574260382 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Mon, 19 Jul 2021 19:56:13 +0300
Subject: [PATCH 229/368] Link to translated lesson and fix typo

---
 4-Classification/translations/README.tr.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/4-Classification/translations/README.tr.md b/4-Classification/translations/README.tr.md
index 91b7425596..347999d875 100644
--- a/4-Classification/translations/README.tr.md
+++ b/4-Classification/translations/README.tr.md
@@ -1,21 +1,21 @@
 # Sınıflandırmaya başlarken
 ## Bölgesel konu: Leziz Asya ve Hint Mutfağı :ramen:
 
-Asya ve Hindistan'da yemek gelenekleri fazlaca çeşitlilik gösterir ve çok lezzetlidir! Malzemelerini anlamaya çalışmak için bölgesel mutfak hakkındaki verilere bakalım.
+Asya ve Hindistan'da yemek gelenekleri fazlaca çeşitlilik gösterir ve çok lezzetlidir! Malzemelerini anlamaya çalışmak için bölgesel mutfaklar hakkındaki veriye bakalım.
 
 ![Taylandlı yemek satıcısı](../images/thai-food.jpg)
 > Fotoğraf <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> tarafından çekilmiştir ve <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>'tadır.
 
 ## Öğrenecekleriniz
 
-Bu bölümde, bu eğitim programının tamamen regresyon üzerine olan ilk bölümünde öğrendiğiniz becerilere dayanıp onların üstüne beceriler ekleyeceksiniz ve verileriniz hakkında bilgi sahibi olmanızı sağlayacak diğer sınıflandırıcıları öğreneceksiniz.
+Bu bölümde, bu eğitim programının tamamen regresyon üzerine olan ilk bölümünde öğrendiğiniz becerilere dayanıp onların üstüne beceriler ekleyeceksiniz ve veriniz hakkında bilgi sahibi olmanızı sağlayacak diğer sınıflandırıcıları öğreneceksiniz.
 
 > Sınıflandırma modelleriyle çalışmayı öğrenmenizi sağlayacak faydalı düşük kodlu araçlar vardır. [Bu görev için Azure ML](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)'i deneyin.
 
 ## Dersler
 
 1. [Sınıflandırmaya giriş](../1-Introduction/translations/README.tr.md)
-2. [Daha fazla sınıflandırıcı](../2-Classifiers-1/README.md)
+2. [Daha fazla sınıflandırıcı](../2-Classifiers-1/translations/README.tr.md)
 3. [Hatta daha fazla sınıflandırıcı](../3-Classifiers-2/README.md)
 4. [Uygulamalı Makine Öğrenimi: bir web uygulaması oluşturun](../4-Applied/README.md)
 ## Katkıda bulunanlar

From 6a245acfc044382d9965442f93f99c5d2dbb152c Mon Sep 17 00:00:00 2001
From: feiyun0112 <feiyun0112@gmail.com>
Date: Tue, 20 Jul 2021 11:57:52 +0800
Subject: [PATCH 230/368] Update README.zh-cn.md

---
 2-Regression/2-Data/translations/README.zh-cn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md
index c3fbf34835..bc273ab1e3 100644
--- a/2-Regression/2-Data/translations/README.zh-cn.md
+++ b/2-Regression/2-Data/translations/README.zh-cn.md
@@ -174,14 +174,14 @@
 
 为了让图表显示有用的数�，你通常需�以��方�对数�进行分组。让我们�试创建一个图，其中y轴显示月份，数�显示数�的分布。 
 
-1. 添加�元格以创建分组�形图：
+1. 添加�元格以创建分组柱状图：
 
     ```python
     new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')
     plt.ylabel("Pumpkin Price")
     ```
 
-    ![显示价格与月份关系的�形图](../images/barchart.png)
+    ![显示价格与月份关系的柱状图](../images/barchart.png)
 
     这是一个更有用的数��视化�似乎表明�瓜的最高价格出现在9月和10月。这符�你的期望�？为什么？为什么�？
 

From 0ef25fe4c2e830211011f6e6b086b49a79ff5175 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Tue, 20 Jul 2021 17:16:11 +0200
Subject: [PATCH 231/368] Italian Translation - Chapter 2 fixed links

---
 2-Regression/translations/README.it.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2-Regression/translations/README.it.md b/2-Regression/translations/README.it.md
index 19e0100bd5..c6e957f9e2 100644
--- a/2-Regression/translations/README.it.md
+++ b/2-Regression/translations/README.it.md
@@ -19,10 +19,10 @@ In questo gruppo di lezioni si imposterà una configurazione per iniziare le att
 
 ### Lezioni
 
-1. [Gli Attrezzi Necessari](1-Tools/README.md)
-2. [Gestione dati](2-Data/README.md)
-3. [Regressione lineare e polinomiale](3-Linear/README.md)
-4. [Regressione logistica](4-Logistic/README.md)
+1. [Gli Attrezzi Necessari](../1-Tools/translations/README.it.md)
+2. [Gestione dati](../2-Data/translations/README.it.md)
+3. [Regressione lineare e polinomiale](../3-Linear/translations/README.it.md)
+4. [Regressione logistica](../4-Logistic/translations/README.it.md)
 
 ---
 ### Crediti

From 9aebad19bb38f424e2f72e0db4a45d900fe5ad47 Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Wed, 21 Jul 2021 01:11:37 +0900
Subject: [PATCH 232/368] feat: add ja Base README

Refs #149
---
 translations/README.ja.md | 118 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 118 insertions(+)
 create mode 100644 translations/README.ja.md

diff --git a/translations/README.ja.md b/translations/README.ja.md
new file mode 100644
index 0000000000..bf0604b654
--- /dev/null
+++ b/translations/README.ja.md
@@ -0,0 +1,118 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/ML-For-Beginners.svg)](https://github.com/microsoft/ML-For-Beginners/blob/master/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/ML-For-Beginners.svg)](https://GitHub.com/microsoft/ML-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/ML-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/ML-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/ML-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/ML-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/ML-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/ML-For-Beginners/stargazers/)
+
+# �心者����機械学習 - カリキュラム
+
+> � 世界�文化�触れ��ら機械学習を探求�る旅 �
+
+マイクロソフト� Azure Cloud Advocates ���12週間�24レッスン�**機械学習**�関�るカリキュラムを�供�����。��カリキュラム���今後公開�る予定�「�心者����AI��扱�深層学習を���主� Scikit-learn ライブラリを使用��**�典的機械学習**�呼�れるも�����学���。�様�公開予定�「�心者����データサイエンス�������活用�����
+
+世界�地�データ��典的�手法を�用���ら�一緒�世界を旅�����ょ�。�レッスン���レッスン�後��テストや�レッスンを完了�る���指示・解答・課題�����れ����。新��スキルを「定����るも����実証�れ��るプロジェクトベース�教育法�よ���構築���ら学��������。
+
+**�� 著者�皆様�心�ら感������** Jen Looper �ん�Stephen Howell �ん�Francesca Lazzeri �ん�Tomomi Imura �ん�Cassie Breviu �ん�Dmitry Soshnikov �ん�Chris Noring �ん�Ornella Altunyan �ん���� Amy Boyd �ん
+
+**🎨 イラストレーター�皆様�も心�ら感������** Tomomi Imura �ん�Dasani Madipalli �ん���� Jen Looper �ん
+
+**� Microsoft Student Ambassador �著者・査読者・コンテンツ�供者�皆様�特�感������ �** 特��Rishit Dagli �ん�Muhammad Sakib Khan Inan �ん�Rohan Raj �ん�Alexandru Petrescu �ん�Abhishek Jaiswal �ん�Nawrin Tabassum �ん�Ioan Samuila �ん���� Snigdha Agarwal �ん
+
+---
+# ����
+
+**学生�皆�ん**���カリキュラムを利用�る���自分�GitHubアカウント�リ�ジトリ全体をフォーク���一人も���グループ�演習を完了�������。
+
+- 講義���テスト�ら始������。
+- 知識を確��る���立�止���り振り返��り���ら�講義を読ん��アクティビティを完了�������。
+- 解答�コードを��実行�る������レッスンを�解��プロジェクトを作��るよ��������。���解答�コード��プロジェクト�����レッスン� `/solution` フォルダ��り��。
+- 講義後��テストを�������。
+- �ャレンジを完了�������。
+- 課題を完了�������。
+- レッスングループ�完了後� [Discussionボード](https://github.com/microsoft/ML-For-Beginners/discussions) �アクセス���切�PAT表�記入�る���「声�出��学習�������。"PAT" �� Progress Assessment Tool（進�評価ツール）�略��学習を促進�る���記入�る表�����。他�PAT�リアクション�る��も��る���共�学�����能��。
+
+> �ら�学習を進�る場���[Microsoft Learn](https://docs.microsoft.com/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) �ラーニングパス�従���を�勧����。
+
+**先生方**���カリキュラムを��よ��使用�る��[������案](/for-teachers.md) ��り��。
+
+---
+
+## �ーム�紹介
+
+[![プロモーションビデオ](/ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "プロモーションビデオ")
+
+> 🎥 上�画�をクリック�る����プロジェクト��プロジェクトを作��人�������ビデオを観る��������
+
+---
+## 教育法
+
+��カリキュラムを構築�る���り�����2��教育方�を�����。**プロジェクトベース**�体験��**頻���テスト**を�む����。�ら���カリキュラム������りを���る���共通�**テーマ**��り��。
+
+内容�プロジェクト��整�性を������学生����学習プロセス�より魅力的��り�概念�定�度�高�り��。�ら��授業��軽��テスト�学生�学習�欲を高��授業後�2回目��テスト�より一層�定�����り��。��カリキュラム�柔軟��楽��も���るよ�デザイン�れ��り�����も���一部を�講����。プロジェクト����も��ら始�り�12週間�間�少���複雑�も���������。�����カリキュラム��機械学習�実世界��応用�関�る����も�ん��り�追加����る��議論�題����使用����。
+
+> [行動�範](/CODE_OF_CONDUCT.md)�[貢献](/CONTRIBUTING.md)�[翻訳](/TRANSLATIONS.md) �ガイドラインを�覧����。建設的���見を�待����り���
+## �レッスン�内容
+
+- オプション�スケッ�ノート
+- オプション�補足ビデオ
+- 講義���テスト
+- �文�レッスン
+- プロジェクトベース�レッスンを行����プロジェクト�構築方法�関�る段階的�ガイド
+- 知識�確�
+- �ャレンジ
+- 副読本
+- 課題
+- 講義後��テスト
+
+> **�テスト�関�る注�**: �����テスト� [��アプリ](https://jolly-sea-0a877260f.azurestaticapps.net) ���れ��り��3��ら�る50個��テスト��り��。�れら�レッスン内�らリンク�れ������アプリをローカル�実行�る��も����。`quiz-app` フォルダ内�指示�従������。
+
+
+| レッスン番� |                           トピック                            |                       レッスングループ                       | 学習�目的                                                                                                             |                     関連�るレッスン                     |     著者     |
+| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
+|      01       |              機械学習��導入              |      [導入](/1-Introduction/translations/README.ja.md)       | 機械学習�基本的�概念を学�                                                                                |   [レッスン](/1-Introduction/1-intro-to-ML/translations/README.ja.md)    |    Muhammad    |
+|      02       |              機械学習�歴�               |      [導入](/1-Introduction/translations/README.ja.md)       | ��分野�背景��る歴�を学�                                                                                         |  [レッスン](/1-Introduction/2-history-of-ML/translations/README.ja.md)   |  Jen and Amy   |
+|      03       |               公平性�機械学習                |      [導入](/1-Introduction/translations/README.ja.md)       | 機械学習モデルを構築・�用�る際�学生�考慮����公平性�関�る���哲学的�題�何�？ |     [レッスン](/1-Introduction/3-fairness/translations/README.ja.md)     |     Tomomi     |
+|      04       |              機械学習�手法               |      [導入](/1-Introduction/translations/README.ja.md)       | 機械学習�研究者���よ��手法�モデルを構築���る�？                                                                       | [レッスン](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
+|      05       |                 回帰��導入                 |        [回帰](/2-Regression/README.md)         | 回帰モデルをPython� Scikit-learn �始�る                                                                  |       [レッスン](/2-Regression/1-Tools/translations/README.ja.md)        |      Jen       |
+|      06       |              北米�カボ�ャ�価格 🎃               |        [回帰](/2-Regression/README.md)         | 機械学習����データを�視化��クリーニング�る                                                                                  |        [レッスン](/2-Regression/2-Data/translations/README.ja.md)        |      Jen       |
+|      07       |              北米�カボ�ャ�価格 🎃               |        [回帰](/2-Regression/README.md)         | 線形�よ�多項�回帰モデルを構築�る                                                                                   |       [レッスン](2-Regression/3-Linear/README.md)       |      Jen       |
+|      08       |              北米�カボ�ャ�価格 🎃               |        [回帰](/2-Regression/README.md)         | ロジスティック回帰モデルを構築�る                                                                                               |      [レッスン](/2-Regression/4-Logistic/README.md)      |      Jen       |
+|      09       |                        Webアプリ 🔌                         |           [Webアプリ](/3-Web-App/README.md)            | 学習��モデルを使用�るWebアプリを構築�る                                                                                       |        [レッスン](/3-Web-App/1-Web-App/README.md)        |      Jen       |
+|      10       |               分類��導入               |    [分類](/4-Classification/README.md)     | データをクリーニング・�処�・�視化�る。分類��導入                                                            |  [レッスン](/4-Classification/1-Introduction/README.md)  | Jen and Cassie |
+|      11       |           美味��アジア料��インド料� �            |    [分類](/4-Classification/README.md)     | 分類器��導入                                                                                                     | [レッスン](/4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
+|      12       |           美味��アジア料��インド料� �            |    [分類](/4-Classification/README.md)     | ��他�分類器                                                                                                                | [レッスン](/4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
+|      13       |           美味��アジア料��インド料� �            |    [分類](/4-Classification/README.md)     | モデルを使用��推薦Webアプリを構築�る                                                                                    |    [レッスン](/4-Classification/4-Applied/README.md)     |      Jen       |
+|      14       |                 クラスタリング��導入                 |        [クラスタリング](/5-Clustering/README.md)         | データをクリーニング・�処�・�視化�る。クラスタリング��導入                                                                |     [レッスン](/5-Clustering/1-Visualize/README.md)      |      Jen       |
+|      15       |            ナイジェリア�音楽的嗜好を探る 🎧             |        [クラスタリング](/5-Clustering/README.md)         | K-Means法を探る                                                                                           |      [レッスン](/5-Clustering/2-K-Means/README.md)       |      Jen       |
+|      16       |       自然言語処���導入 ☕�       |   [自然言語処�](/6-NLP/README.md)    | �純�ボットを構築��自然言語処��基礎を学�                                                                             |    [レッスン](/6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
+|      17       |                     自然言語処��一般的�タスク ☕�                     |   [自然言語処�](/6-NLP/README.md)    | 言語構造を扱�際�必���る一般的�タスクを�解�る����自然言語処��知識を深�る                          |           [レッスン](/6-NLP/2-Tasks/README.md)           |    Stephen     |
+|      18       |            翻訳�感情分� ♥�            |   [自然言語処�](/6-NLP/README.md)    | ジェーン・オースティン�翻訳�感情分�                                                                             |   [レッスン](/6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
+|      19       |                ヨーロッパ�ロマン�ック�ホテル ♥�                 |   [自然言語処�](/6-NLP/README.md)    | ホテル�レビュー�感情分� 1                                                                                        |      [レッスン](/6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
+|      20       |                ヨーロッパ�ロマン�ック�ホテル ♥�                 |   [自然言語処�](/6-NLP/README.md)    | ホテル�レビュー�感情分� 2                                                                                         |      [レッスン](/6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
+|      21       |          時系列予測��導入           |        [Time series](/7-TimeSeries/README.md)        | 時系列予測��導入                                                                                         |    [レッスン](/7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
+|      22       | ⚡� 世界�電力使用� ⚡� - ARIMA�よる時系列予測 |        [Time series](/7-TimeSeries/README.md)        | ARIMA�よる時系列予測                                                                                              |       [レッスン](/7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
+|      23       |           強化学習��導入           | [Reinforcement learning](/8-Reinforcement/README.md) | Q学習を使��強化学習��導入                                                                          |    [レッスン](/8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
+|      24       |                ピーター�狼を��る�を手�������� �                | [Reinforcement learning](/8-Reinforcement/README.md) | 強化学習ジム                                                                                                      |       [レッスン](/8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
+|  Postscript   |          実世界�機械学習シナリオ�応用          |      [ML in the Wild](/9-Real-World/README.md)       | 興味深���義��る��典的機械学習�実世界��応用                                                               |    [レッスン](/9-Real-World/1-Applications/README.md)    |      Team      |
+## オフラインアクセス
+
+[Docsify](https://docsify.js.org/#/) を使�����ドキュメントをオフライン�実行����。��リ�ジトリをフォーク���ローカルマシン� [Docsify をインストール](https://docsify.js.org/#/quickstart) ����リ�ジトリ�ルートフォルダ� `docsify serve` �入力������。ローカルホスト�3000番�ート���り `localhost:3000` �Webサイト�起動���。
+
+## PDF
+
+カリキュラム�PDF��リンク� [��ら](/pdf/readme.pdf)。
+
+## ヘルプ募集�
+
+翻訳を�����ん�？[翻訳ガイドライン](/TRANSLATIONS.md) を�覧�上�[��ら](https://github.com/microsoft/ML-For-Beginners/issues/71) ��知ら�����。
+
+## ��他�カリキュラム
+
+������他�カリキュラムも�供���������ェック��������。
+
+- [�心者����Web開発](https://aka.ms/webdev-beginners)
+- [�心者����IoT](https://aka.ms/iot-beginners)

From ab48ec879a94d829d728b4a23522748fe1300840 Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Wed, 21 Jul 2021 02:19:08 +0900
Subject: [PATCH 233/368] fix: relative link path

---
 translations/README.ja.md | 60 +++++++++++++++++++--------------------
 1 file changed, 30 insertions(+), 30 deletions(-)

diff --git a/translations/README.ja.md b/translations/README.ja.md
index bf0604b654..a719c7464d 100644
--- a/translations/README.ja.md
+++ b/translations/README.ja.md
@@ -37,13 +37,13 @@
 
 > �ら�学習を進�る場���[Microsoft Learn](https://docs.microsoft.com/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) �ラーニングパス�従���を�勧����。
 
-**先生方**���カリキュラムを��よ��使用�る��[������案](/for-teachers.md) ��り��。
+**先生方**���カリキュラムを��よ��使用�る��[������案](../for-teachers.md) ��り��。
 
 ---
 
 ## �ーム�紹介
 
-[![プロモーションビデオ](/ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "プロモーションビデオ")
+[![プロモーションビデオ](../ml-for-beginners.png)](https://youtu.be/Tj1XWrDSYJU "プロモーションビデオ")
 
 > 🎥 上�画�をクリック�る����プロジェクト��プロジェクトを作��人�������ビデオを観る��������
 
@@ -54,7 +54,7 @@
 
 内容�プロジェクト��整�性を������学生����学習プロセス�より魅力的��り�概念�定�度�高�り��。�ら��授業��軽��テスト�学生�学習�欲を高��授業後�2回目��テスト�より一層�定�����り��。��カリキュラム�柔軟��楽��も���るよ�デザイン�れ��り�����も���一部を�講����。プロジェクト����も��ら始�り�12週間�間�少���複雑�も���������。�����カリキュラム��機械学習�実世界��応用�関�る����も�ん��り�追加����る��議論�題����使用����。
 
-> [行動�範](/CODE_OF_CONDUCT.md)�[貢献](/CONTRIBUTING.md)�[翻訳](/TRANSLATIONS.md) �ガイドラインを�覧����。建設的���見を�待����り���
+> [行動�範](../CODE_OF_CONDUCT.md)�[貢献](../CONTRIBUTING.md)�[翻訳](../TRANSLATIONS.md) �ガイドラインを�覧����。建設的���見を�待����り���
 ## �レッスン�内容
 
 - オプション�スケッ�ノート
@@ -73,42 +73,42 @@
 
 | レッスン番� |                           トピック                            |                       レッスングループ                       | 学習�目的                                                                                                             |                     関連�るレッスン                     |     著者     |
 | :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              機械学習��導入              |      [導入](/1-Introduction/translations/README.ja.md)       | 機械学習�基本的�概念を学�                                                                                |   [レッスン](/1-Introduction/1-intro-to-ML/translations/README.ja.md)    |    Muhammad    |
-|      02       |              機械学習�歴�               |      [導入](/1-Introduction/translations/README.ja.md)       | ��分野�背景��る歴�を学�                                                                                         |  [レッスン](/1-Introduction/2-history-of-ML/translations/README.ja.md)   |  Jen and Amy   |
-|      03       |               公平性�機械学習                |      [導入](/1-Introduction/translations/README.ja.md)       | 機械学習モデルを構築・�用�る際�学生�考慮����公平性�関�る���哲学的�題�何�？ |     [レッスン](/1-Introduction/3-fairness/translations/README.ja.md)     |     Tomomi     |
-|      04       |              機械学習�手法               |      [導入](/1-Introduction/translations/README.ja.md)       | 機械学習�研究者���よ��手法�モデルを構築���る�？                                                                       | [レッスン](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                 回帰��導入                 |        [回帰](/2-Regression/README.md)         | 回帰モデルをPython� Scikit-learn �始�る                                                                  |       [レッスン](/2-Regression/1-Tools/translations/README.ja.md)        |      Jen       |
-|      06       |              北米�カボ�ャ�価格 🎃               |        [回帰](/2-Regression/README.md)         | 機械学習����データを�視化��クリーニング�る                                                                                  |        [レッスン](/2-Regression/2-Data/translations/README.ja.md)        |      Jen       |
-|      07       |              北米�カボ�ャ�価格 🎃               |        [回帰](/2-Regression/README.md)         | 線形�よ�多項�回帰モデルを構築�る                                                                                   |       [レッスン](2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              北米�カボ�ャ�価格 🎃               |        [回帰](/2-Regression/README.md)         | ロジスティック回帰モデルを構築�る                                                                                               |      [レッスン](/2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        Webアプリ 🔌                         |           [Webアプリ](/3-Web-App/README.md)            | 学習��モデルを使用�るWebアプリを構築�る                                                                                       |        [レッスン](/3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               分類��導入               |    [分類](/4-Classification/README.md)     | データをクリーニング・�処�・�視化�る。分類��導入                                                            |  [レッスン](/4-Classification/1-Introduction/README.md)  | Jen and Cassie |
-|      11       |           美味��アジア料��インド料� �            |    [分類](/4-Classification/README.md)     | 分類器��導入                                                                                                     | [レッスン](/4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
-|      12       |           美味��アジア料��インド料� �            |    [分類](/4-Classification/README.md)     | ��他�分類器                                                                                                                | [レッスン](/4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
-|      13       |           美味��アジア料��インド料� �            |    [分類](/4-Classification/README.md)     | モデルを使用��推薦Webアプリを構築�る                                                                                    |    [レッスン](/4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 クラスタリング��導入                 |        [クラスタリング](/5-Clustering/README.md)         | データをクリーニング・�処�・�視化�る。クラスタリング��導入                                                                |     [レッスン](/5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            ナイジェリア�音楽的嗜好を探る 🎧             |        [クラスタリング](/5-Clustering/README.md)         | K-Means法を探る                                                                                           |      [レッスン](/5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       自然言語処���導入 ☕�       |   [自然言語処�](/6-NLP/README.md)    | �純�ボットを構築��自然言語処��基礎を学�                                                                             |    [レッスン](/6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     自然言語処��一般的�タスク ☕�                     |   [自然言語処�](/6-NLP/README.md)    | 言語構造を扱�際�必���る一般的�タスクを�解�る����自然言語処��知識を深�る                          |           [レッスン](/6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            翻訳�感情分� ♥�            |   [自然言語処�](/6-NLP/README.md)    | ジェーン・オースティン�翻訳�感情分�                                                                             |   [レッスン](/6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                ヨーロッパ�ロマン�ック�ホテル ♥�                 |   [自然言語処�](/6-NLP/README.md)    | ホテル�レビュー�感情分� 1                                                                                        |      [レッスン](/6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                ヨーロッパ�ロマン�ック�ホテル ♥�                 |   [自然言語処�](/6-NLP/README.md)    | ホテル�レビュー�感情分� 2                                                                                         |      [レッスン](/6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          時系列予測��導入           |        [Time series](/7-TimeSeries/README.md)        | 時系列予測��導入                                                                                         |    [レッスン](/7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
-|      22       | ⚡� 世界�電力使用� ⚡� - ARIMA�よる時系列予測 |        [Time series](/7-TimeSeries/README.md)        | ARIMA�よる時系列予測                                                                                              |       [レッスン](/7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           強化学習��導入           | [Reinforcement learning](/8-Reinforcement/README.md) | Q学習を使��強化学習��導入                                                                          |    [レッスン](/8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                ピーター�狼を��る�を手�������� �                | [Reinforcement learning](/8-Reinforcement/README.md) | 強化学習ジム                                                                                                      |       [レッスン](/8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  Postscript   |          実世界�機械学習シナリオ�応用          |      [ML in the Wild](/9-Real-World/README.md)       | 興味深���義��る��典的機械学習�実世界��応用                                                               |    [レッスン](/9-Real-World/1-Applications/README.md)    |      Team      |
+|      01       |              機械学習��導入              |      [導入](../1-Introduction/translations/README.ja.md)       | 機械学習�基本的�概念を学�                                                                                |   [レッスン](../1-Introduction/1-intro-to-ML/translations/README.ja.md)    |    Muhammad    |
+|      02       |              機械学習�歴�               |      [導入](../1-Introduction/translations/README.ja.md)       | ��分野�背景��る歴�を学�                                                                                         |  [レッスン](../1-Introduction/2-history-of-ML/translations/README.ja.md)   |  Jen and Amy   |
+|      03       |               公平性�機械学習                |      [導入](../1-Introduction/translations/README.ja.md)       | 機械学習モデルを構築・�用�る際�学生�考慮����公平性�関�る���哲学的�題�何�？ |     [レッスン](../1-Introduction/3-fairness/translations/README.ja.md)     |     Tomomi     |
+|      04       |              機械学習�手法               |      [導入](../1-Introduction/translations/README.ja.md)       | 機械学習�研究者���よ��手法�モデルを構築���る�？                                                                       | [レッスン](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
+|      05       |                 回帰��導入                 |        [回帰](../2-Regression/README.md)         | 回帰モデルをPython� Scikit-learn �始�る                                                                  |       [レッスン](../2-Regression/1-Tools/translations/README.ja.md)        |      Jen       |
+|      06       |              北米�カボ�ャ�価格 🎃               |        [回帰](../2-Regression/README.md)         | 機械学習����データを�視化��クリーニング�る                                                                                  |        [レッスン](../2-Regression/2-Data/translations/README.ja.md)        |      Jen       |
+|      07       |              北米�カボ�ャ�価格 🎃               |        [回帰](../2-Regression/README.md)         | 線形�よ�多項�回帰モデルを構築�る                                                                                   |       [レッスン](2-Regression/3-Linear/README.md)       |      Jen       |
+|      08       |              北米�カボ�ャ�価格 🎃               |        [回帰](../2-Regression/README.md)         | ロジスティック回帰モデルを構築�る                                                                                               |      [レッスン](../2-Regression/4-Logistic/README.md)      |      Jen       |
+|      09       |                        Webアプリ 🔌                         |           [Webアプリ](../3-Web-App/README.md)            | 学習��モデルを使用�るWebアプリを構築�る                                                                                       |        [レッスン](../3-Web-App/1-Web-App/README.md)        |      Jen       |
+|      10       |               分類��導入               |    [分類](../4-Classification/README.md)     | データをクリーニング・�処�・�視化�る。分類��導入                                                            |  [レッスン](../4-Classification/1-Introduction/README.md)  | Jen and Cassie |
+|      11       |           美味��アジア料��インド料� �            |    [分類](../4-Classification/README.md)     | 分類器��導入                                                                                                     | [レッスン](../4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
+|      12       |           美味��アジア料��インド料� �            |    [分類](../4-Classification/README.md)     | ��他�分類器                                                                                                                | [レッスン](../4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
+|      13       |           美味��アジア料��インド料� �            |    [分類](../4-Classification/README.md)     | モデルを使用��推薦Webアプリを構築�る                                                                                    |    [レッスン](../4-Classification/4-Applied/README.md)     |      Jen       |
+|      14       |                 クラスタリング��導入                 |        [クラスタリング](../5-Clustering/README.md)         | データをクリーニング・�処�・�視化�る。クラスタリング��導入                                                                |     [レッスン](../5-Clustering/1-Visualize/README.md)      |      Jen       |
+|      15       |            ナイジェリア�音楽的嗜好を探る 🎧             |        [クラスタリング](../5-Clustering/README.md)         | K-Means法を探る                                                                                           |      [レッスン](../5-Clustering/2-K-Means/README.md)       |      Jen       |
+|      16       |       自然言語処���導入 ☕�       |   [自然言語処�](../6-NLP/README.md)    | �純�ボットを構築��自然言語処��基礎を学�                                                                             |    [レッスン](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
+|      17       |                     自然言語処��一般的�タスク ☕�                     |   [自然言語処�](../6-NLP/README.md)    | 言語構造を扱�際�必���る一般的�タスクを�解�る����自然言語処��知識を深�る                          |           [レッスン](../6-NLP/2-Tasks/README.md)           |    Stephen     |
+|      18       |            翻訳�感情分� ♥�            |   [自然言語処�](../6-NLP/README.md)    | ジェーン・オースティン�翻訳�感情分�                                                                             |   [レッスン](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
+|      19       |                ヨーロッパ�ロマン�ック�ホテル ♥�                 |   [自然言語処�](../6-NLP/README.md)    | ホテル�レビュー�感情分� 1                                                                                        |      [レッスン](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
+|      20       |                ヨーロッパ�ロマン�ック�ホテル ♥�                 |   [自然言語処�](../6-NLP/README.md)    | ホテル�レビュー�感情分� 2                                                                                         |      [レッスン](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
+|      21       |          時系列予測��導入           |        [Time series](../7-TimeSeries/README.md)        | 時系列予測��導入                                                                                         |    [レッスン](../7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
+|      22       | ⚡� 世界�電力使用� ⚡� - ARIMA�よる時系列予測 |        [Time series](../7-TimeSeries/README.md)        | ARIMA�よる時系列予測                                                                                              |       [レッスン](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
+|      23       |           強化学習��導入           | [Reinforcement learning](../8-Reinforcement/README.md) | Q学習を使��強化学習��導入                                                                          |    [レッスン](../8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
+|      24       |                ピーター�狼を��る�を手�������� �                | [Reinforcement learning](../8-Reinforcement/README.md) | 強化学習ジム                                                                                                      |       [レッスン](../8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
+|  Postscript   |          実世界�機械学習シナリオ�応用          |      [ML in the Wild](../9-Real-World/README.md)       | 興味深���義��る��典的機械学習�実世界��応用                                                               |    [レッスン](../9-Real-World/1-Applications/README.md)    |      Team      |
 ## オフラインアクセス
 
 [Docsify](https://docsify.js.org/#/) を使�����ドキュメントをオフライン�実行����。��リ�ジトリをフォーク���ローカルマシン� [Docsify をインストール](https://docsify.js.org/#/quickstart) ����リ�ジトリ�ルートフォルダ� `docsify serve` �入力������。ローカルホスト�3000番�ート���り `localhost:3000` �Webサイト�起動���。
 
 ## PDF
 
-カリキュラム�PDF��リンク� [��ら](/pdf/readme.pdf)。
+カリキュラム�PDF��リンク� [��ら](../pdf/readme.pdf)。
 
 ## ヘルプ募集�
 
-翻訳を�����ん�？[翻訳ガイドライン](/TRANSLATIONS.md) を�覧�上�[��ら](https://github.com/microsoft/ML-For-Beginners/issues/71) ��知ら�����。
+翻訳を�����ん�？[翻訳ガイドライン](../TRANSLATIONS.md) を�覧�上�[��ら](https://github.com/microsoft/ML-For-Beginners/issues/71) ��知ら�����。
 
 ## ��他�カリキュラム
 

From 7248e533e6284a83fc65ca9a976a5cf09e16b58f Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Tue, 20 Jul 2021 20:07:31 -0400
Subject: [PATCH 234/368] quiz fix

---
 quiz-app/src/assets/translations/en.json | 5626 +++++++++++-----------
 quiz-app/src/assets/translations/fr.json |    2 +-
 2 files changed, 2814 insertions(+), 2814 deletions(-)

diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index ae358aef20..21b8375e6d 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -1,2815 +1,2815 @@
 [
-	{
-		"title": "Machine Learning for Beginners: Quizzes",
-		"complete": "Congratulations, you completed the quiz!",
-		"error": "Sorry, try again",
-		"quizzes": [
-			{
-				"id": 1,
-				"title": "Introduction to Machine Learning: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Applications of machine learning are all around us",
-						"answerOptions": [
-							{
-								"answerText": "True",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "False",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is the technical difference between classical ML and deep learning?",
-						"answerOptions": [
-							{
-								"answerText": "classical ML was invented first",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the use of neural networks",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "deep learning is used in robots",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Why might a business want to use ML strategies?",
-						"answerOptions": [
-							{
-								"answerText": "to automate the solving of multi-dimensional problems",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "to customize a shopping experience based on the type of customer",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 2,
-				"title": "Introduction to Machine Learning: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Machine learning algorithms are meant to simulate",
-						"answerOptions": [
-							{
-								"answerText": "intelligent machines",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the human brain",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "orangutans",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is an example of a classical ML technique?",
-						"answerOptions": [
-							{
-								"answerText": "natural language processing",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "deep learning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Neural Networks",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Why should everyone learn the basics of ML?",
-						"answerOptions": [
-							{
-								"answerText": "learning ML is fun and accessible to everyone",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "ML strategies are being used in many industries and domains",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 3,
-				"title": "History of Machine Learning: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Approximately when was the term 'artificial intelligence' coined?",
-						"answerOptions": [
-							{
-								"answerText": "1980s",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "1950s",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "1930s",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Who was one of the early pioneers of machine learning?",
-						"answerOptions": [
-							{
-								"answerText": "Alan Turing",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Bill Gates",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Shakey the robot",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is one of the reasons that advancement in AI slowed in the 1970s?",
-						"answerOptions": [
-							{
-								"answerText": "Limited compute power",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Not enough skilled engineers",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Conflicts between countries",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 4,
-				"title": "History of Machine Learning: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What's an example of a 'scruffy' AI system?",
-						"answerOptions": [
-							{
-								"answerText": "ELIZA",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "HACKML",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "SSYSTEM",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is an example of a technology that was developed during 'The Golden Years'?",
-						"answerOptions": [
-							{
-								"answerText": "Blocks world",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Jibo",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Robot dogs",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which event was foundational in the creation and expansion of the field of artificial intelligence?",
-						"answerOptions": [
-							{
-								"answerText": "Turing Test",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Dartmouth Summer Research Project",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "AI Winter",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 5,
-				"title": "Fairness and Machine Learning: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Unfairness in Machine Learning can happen",
-						"answerOptions": [
-							{
-								"answerText": "intentionally",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "unintentionally",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "The term 'unfairness' in ML connotes:",
-						"answerOptions": [
-							{
-								"answerText": "harms for a group of people",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "harm to one person",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "harms for the majority of people",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "The five main types of harms include",
-						"answerOptions": [
-							{
-								"answerText": "allocation, quality of service, stereotyping, denigration, and over- or under- representation",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "elocation, quality of service, stereotyping, denigration, and over- or under- representation ",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "allocation, quality of service, stereophonics, denigration, and over- or under- representation ",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 6,
-				"title": "Fairness and Machine Learning: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Unfairness in a model can be caused by",
-						"answerOptions": [
-							{
-								"answerText": "overrreliance on historical data",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "underreliance on historical data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "too closely aligning to historical data",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "To mitigate unfairness, you can",
-						"answerOptions": [
-							{
-								"answerText": "identify harms and affected groups",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "define fairness metrics",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Fairlearn is a package that can",
-						"answerOptions": [
-							{
-								"answerText": "compare multiple models by using fairness and performance metrics",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "choose the best model for your needs",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "help you decide what is fair and what is not",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 7,
-				"title": "Tools and Techniques: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "When building a model, you should:",
-						"answerOptions": [
-							{
-								"answerText": "prepare your data, then train your model",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "choose a training method, then prepare your data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "tune parameters, then train your model",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Your data's ___ will impact the quality of your ML model",
-						"answerOptions": [
-							{
-								"answerText": "quantity",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "shape",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "A feature variable is:",
-						"answerOptions": [
-							{
-								"answerText": "a quality of your data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a measurable property of your data",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a row of your data",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 8,
-				"title": "Tools and Techniques: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "You should visualize your data because",
-						"answerOptions": [
-							{
-								"answerText": "you can discover outliers",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "you can discover potential cause for bias",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Split your data into:",
-						"answerOptions": [
-							{
-								"answerText": "training and turing sets",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "training and test sets",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "validation and evaluation sets",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A common command to start the training process in various ML libraries is:",
-						"answerOptions": [
-							{
-								"answerText": "model.travel",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "model.train",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "model.fit",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 9,
-				"title": "Introduction to Regression: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Which of these variables is a numeric variable?",
-						"answerOptions": [
-							{
-								"answerText": "Height",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Gender",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Hair Color",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which of these variables is a categorical variable?",
-						"answerOptions": [
-							{
-								"answerText": "Heart Rate",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Blood Type",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Weight",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which of these problems is a Regression analysis-based problem?",
-						"answerOptions": [
-							{
-								"answerText": "Predicting the final exam marks of a student",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Predicting the blood type of a person",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Predicting whether an email is spam or not",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 10,
-				"title": "Introduction to Regression: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "If your Machine Learning model's training accuracy is 95 % and the testing accuracy is 30 %, then what type of condition it is called?",
-						"answerOptions": [
-							{
-								"answerText": "Overfitting",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Underfitting",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Double Fitting",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "The process of identifying significant features from a set of features is called:",
-						"answerOptions": [
-							{
-								"answerText": "Feature Extraction",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Feature Dimensionality Reduction",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Feature Selection",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "The process of splitting a dataset into a certain ratio of training and testing dataset using Scikit Learn's 'train_test_split()' method/function is called:",
-						"answerOptions": [
-							{
-								"answerText": "Cross-Validation",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Hold-Out Validation",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Leave one out Validation",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 11,
-				"title": "Prepare and Visualize Data for Regression: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Which of these Python modules is used to plot the visualization of data?",
-						"answerOptions": [
-							{
-								"answerText": "Numpy",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Scikit-learn",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Matplotlib",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "If you want to understand the spread or the other characteristics of data points of your dataset, then perform:",
-						"answerOptions": [
-							{
-								"answerText": "Data Visualization",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Data Preprocessing",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Train Test Split",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which of these is a part of the Data Visualization step in a Machine Learning project?",
-						"answerOptions": [
-							{
-								"answerText": "Incorporating a certain Machine Learning algorithm",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Creating a pictorial representation of data using different plotting methods",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Normalizing the values of a dataset",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 12,
-				"title": "Prepare and Visualize Data for Regression: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Which of these code snippets is correct based on this lesson, if you want to check for the presence of missing values in your dataset? Suppose the dataset is stored in a variable named 'dataset' which is a Pandas DataFrame object.",
-						"answerOptions": [
-							{
-								"answerText": "dataset.isnull().sum()",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "findMissing(dataset)",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "sum(null(dataset))",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which of these plotting methods is useful when you would like to understand the spread of different groups of datapoints from your dataset?",
-						"answerOptions": [
-							{
-								"answerText": "Scatter Plot",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Line Plot",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Bar Plot",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What can Data Visualization NOT tell you?",
-						"answerOptions": [
-							{
-								"answerText": "Relationships among datapoints",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "The source from where the dataset is collected",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Finding the presence of outliers in the dataset",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 13,
-				"title": "Linear and Polynomial Regression: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Matplotlib is a ",
-						"answerOptions": [
-							{
-								"answerText": "drawing library",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "data visualization library",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "lending library",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Linear Regression uses the following to plot relationships between variables",
-						"answerOptions": [
-							{
-								"answerText": "a straight line",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a circle",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a curve",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A good Linear Regression model has a ___ Correlation Coefficient",
-						"answerOptions": [
-							{
-								"answerText": "low",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "high",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "flat",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 14,
-				"title": "Linear and Polynomial Regression: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "If your data is nonlinear, try a ___ type of Regression",
-						"answerOptions": [
-							{
-								"answerText": "linear",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "spherical",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "polynomial",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "These are all types of Regression methods",
-						"answerOptions": [
-							{
-								"answerText": "Falsestep, Ridge, Lasso and Elasticnet",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Stepwise, Ridge, Lasso and Elasticnet",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Stepwise, Ridge, Lariat and Elasticnet",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Least-Squares Regression means that all the datapoints surrounding the regression line are:",
-						"answerOptions": [
-							{
-								"answerText": "squared and then subtracted",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "multiplied",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "squared and then added up",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 15,
-				"title": "Logistic Regression: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Use Logistic Regression to predict",
-						"answerOptions": [
-							{
-								"answerText": "whether an apple is ripe or not",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "how many tickets can be sold in a month",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "what color the sky will turn tomorrow at 6 PM",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Types of Logistic Regression include",
-						"answerOptions": [
-							{
-								"answerText": "multinomial and cardinal",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "multinomial and ordinal",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "principal and ordinal",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Your data has weak correlations. The best type of Regression to use is:",
-						"answerOptions": [
-							{
-								"answerText": "Logistic",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Linear",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Cardinal",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 16,
-				"title": "Logistic Regression: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Seaborn is a type of",
-						"answerOptions": [
-							{
-								"answerText": "data visualization library",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "mapping library",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "mathematical library",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A confusion matrix is also known as a:",
-						"answerOptions": [
-							{
-								"answerText": "error matrix",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "truth matrix",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "accuracy matrix",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A good model will have:",
-						"answerOptions": [
-							{
-								"answerText": "a large number of false positives and true negatives in its confusion matrix",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a large number of true positives and true negatives in its confusion matrix",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a large number of true positives and false negatives in its confusion matrix",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 17,
-				"title": "Build a Web App: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What does ONNX stand for?",
-						"answerOptions": [
-							{
-								"answerText": "Over Neural Network Exchange",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Open Neural Network Exchange",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Output Neural Network Exchange",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "How is Flask defined by its creators?",
-						"answerOptions": [
-							{
-								"answerText": "mini-framework",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "large-framework",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "micro-framework",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What does the Pickle module of Python do",
-						"answerOptions": [
-							{
-								"answerText": "Serializes a Python Object",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "De-serializes a Python Object",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Serializes and De-serializes a Python Object",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 18,
-				"title": "Build a Web App: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What are the tools we can use to host a pre-trained model on the web using Python?",
-						"answerOptions": [
-							{
-								"answerText": "Flask",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "TensorFlow.js",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "onnx.js",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What does SaaS stand for?",
-						"answerOptions": [
-							{
-								"answerText": "System as a Service",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Software as a Service",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Security as a Service",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What does Scikit-learn's LabelEncoder library do?",
-						"answerOptions": [
-							{
-								"answerText": "Encodes data alphabetically",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Encodes data numerically",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Encodes data serially",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 19,
-				"title": "Classification 1: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Classification is a form of supervised learning that has a lot in common with",
-						"answerOptions": [
-							{
-								"answerText": "Time Series",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Regression techniques",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What question can classification help answer?",
-						"answerOptions": [
-							{
-								"answerText": "Is this email spam or not?",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Can pigs fly?",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "What is the meaning of life?",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is the first step to using Classification techniques?",
-						"answerOptions": [
-							{
-								"answerText": "creating classes of a dataset",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "cleaning and balancing your data",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "assigning a data point to a group or outcome",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 20,
-				"title": "Classification 1: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is a multiclass question?",
-						"answerOptions": [
-							{
-								"answerText": "the task of classifying data points into multiple classes",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "the task of classifying data points into one of several classes",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "the task of cleaning data points in multiple ways",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "It's important to clean out recurrent or unhelpful data to help your classifiers solve your problem.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What's the best reason to balance your data?",
-						"answerOptions": [
-							{
-								"answerText": "Imbalanced data looks bad in visualizations",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Balancing your data yields better results because an ML model won't skew towards one class",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Balancing your data gives you more data points",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 21,
-				"title": "Classification 2: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Balanced, clean data yields the best classification results",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "How do you choose the right classifier?",
-						"answerOptions": [
-							{
-								"answerText": "Understand which classifiers work best for which scenarios",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Educated guess and check",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Classification is a type of",
-						"answerOptions": [
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Supervised Learning",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Programming language",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 22,
-				"title": "Classification 2: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is a 'solver'?",
-						"answerOptions": [
-							{
-								"answerText": "the person who double-checks your work",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the algorithm to use in the optimization problem",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a machine learning technique",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which classifier did we use in this lesson?",
-						"answerOptions": [
-							{
-								"answerText": "Logistic Regression",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Decision Trees",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "One-vs-All Multiclass",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "How do you know if the classification algorithm is working as expected?",
-						"answerOptions": [
-							{
-								"answerText": "By checking the accuracy of its predictions",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "By checking it against other algorithms",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "By looking at historical data for how good this algorithm is at solving similar problems",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 23,
-				"title": "Classification 3: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "A good initial classifier to try is:",
-						"answerOptions": [
-							{
-								"answerText": "Linear SVC",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "K-Means",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Logical SVC",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Regularization controls:",
-						"answerOptions": [
-							{
-								"answerText": "the influence of parameters",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "the influence of training speed",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the influence of outliers",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "K-Neighbors classifier can be used for:",
-						"answerOptions": [
-							{
-								"answerText": "supervised learning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "unsupervised learning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 24,
-				"title": "Classification 3: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Support-Vector classifiers can be used for",
-						"answerOptions": [
-							{
-								"answerText": "classification",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "regression",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Random Forest is a ___ type of classifier",
-						"answerOptions": [
-							{
-								"answerText": "Ensemble",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Dissemble",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Assemble",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Adaboost is known for:",
-						"answerOptions": [
-							{
-								"answerText": "focusing on the weights of incorrectly classified items",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "focusing on outliers",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "focusing on incorrect data",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 25,
-				"title": "Classification 4: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Recommendation systems might be used for",
-						"answerOptions": [
-							{
-								"answerText": "Recommending a good restaurant",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Recommending fashions to try",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Both of these",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Embedding a model in a web app helps it to be offline-capable",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Onnx Runtime can be used for",
-						"answerOptions": [
-							{
-								"answerText": "Running models in a web app",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Training models",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Hyperparameter tuning",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 26,
-				"title": "Classification 4: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Netron app helps you:",
-						"answerOptions": [
-							{
-								"answerText": "Visualize data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Visualize your model's structure",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Test your web app",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Convert your Scikit-learn model for use with Onnx using:",
-						"answerOptions": [
-							{
-								"answerText": "sklearn-app",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "sklearn-web",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "sklearn-onnx",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Using your model in a web app is called:",
-						"answerOptions": [
-							{
-								"answerText": "inference",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "interference",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "insurance",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 27,
-				"title": "Introduction to Clustering: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "A real-life example of clustering would be",
-						"answerOptions": [
-							{
-								"answerText": "Setting the dinner table",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Sorting the laundry",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Grocery shopping",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Clustering techniques can be used in these industries",
-						"answerOptions": [
-							{
-								"answerText": "banking",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "e-commerce",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Clustering is a type of:",
-						"answerOptions": [
-							{
-								"answerText": "supervised learning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "unsupervised learning",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "reinforcement learning",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 28,
-				"title": "Introduction to Clustering: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Euclidean geometry is arranged along",
-						"answerOptions": [
-							{
-								"answerText": "planes",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "curves",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "spheres",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "The density of your clustering data is related to its",
-						"answerOptions": [
-							{
-								"answerText": "noise",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "depth",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "validity",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "The best-known clustering algorithm is",
-						"answerOptions": [
-							{
-								"answerText": "k-means",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "k-middle",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "k-mart",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 29,
-				"title": "K-Means Clustering: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "K-Means is derived from:",
-						"answerOptions": [
-							{
-								"answerText": "electrical engineering",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "signal processing",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "computational linguistics",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A good Silhouette score means:",
-						"answerOptions": [
-							{
-								"answerText": "clusters are well-separated and well-defined",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "there are few clusters",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "there are many clusters",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Variance is:",
-						"answerOptions": [
-							{
-								"answerText": "the average of the squared differences from the mean",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a problem for clustering if it becomes too high",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of these",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 30,
-				"title": "K-Means Clustering: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "A Voronoi diagram shows:",
-						"answerOptions": [
-							{
-								"answerText": "a cluster's variance",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a cluster's seed and its region",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a cluster's inertia",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Inertia is",
-						"answerOptions": [
-							{
-								"answerText": "a measure of how internally coherent clusters are",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a measure of how much clusters move",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a measure of cluster quality",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Using K-Means, you must first determine the value of 'k'",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 31,
-				"title": "Intro to NLP: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What does NLP stand for in these lessons?",
-						"answerOptions": [
-							{
-								"answerText": "Neural Language Processing",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "natural language processing",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Natural Linguistic Processing",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Eliza was an early bot that acted as a computer",
-						"answerOptions": [
-							{
-								"answerText": "therapist",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "doctor",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "nurse",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Alan Turing's 'Turing Test' tried to determine if a computer was",
-						"answerOptions": [
-							{
-								"answerText": "indistinguishable from a human",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "thinking",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 32,
-				"title": "Intro to NLP: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Joseph Weizenbaum invented the bot",
-						"answerOptions": [
-							{
-								"answerText": "Elisha",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Eliza",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Eloise",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A conversational bot gives output based on",
-						"answerOptions": [
-							{
-								"answerText": "Randomly choosing predefined choices",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Analyzing the input and using machine intelligence",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Both of these",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "How would you make the bot more effective?",
-						"answerOptions": [
-							{
-								"answerText": "By asking it more questions.",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "By feeding it more data and training it accordingly",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "The bot is dumb, it cannot learn :(",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 33,
-				"title": "NLP Tasks: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Tokenization",
-						"answerOptions": [
-							{
-								"answerText": "Splits text by means of punctuation",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Splits text into separate tokens (words)",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Splits text into phrases",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Embeddings",
-						"answerOptions": [
-							{
-								"answerText": "converts text data numerically so words can cluster",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "embeds words into phrases",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "embeds sentences into paragraphs",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Parts-of-Speech Tagging",
-						"answerOptions": [
-							{
-								"answerText": "divides sentences by their parts of speech",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "takes tokenized words and tags them by their part of speech",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "diagrams sentences",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 34,
-				"title": "NLP Tasks: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Build a dictionary of how often words reoccur using:",
-						"answerOptions": [
-							{
-								"answerText": "Word and Phrase Dictionary",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Word and Phrase Frequencies",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Word and Phrase Library",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "N-grams refer to",
-						"answerOptions": [
-							{
-								"answerText": "A text can be split into sequences of words of a set length",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "A word can be split into sequences of characters of a set length",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "A text can be split into paragraphs of a set length",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Sentiment analysis",
-						"answerOptions": [
-							{
-								"answerText": "analyzes a phrase for positivity or negativity",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "analyzes a phrase for sentimentality",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "analyzes a phrase for sadness",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 35,
-				"title": "NLP and Translation: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Naive translation",
-						"answerOptions": [
-							{
-								"answerText": "translates words only",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "translates sentence structure",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "translates sentiment",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A *corpus* of texts refers to",
-						"answerOptions": [
-							{
-								"answerText": "A small number of texts",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "A large number of texts",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "One standard text",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "If a ML model has enough human translations to build a model on, it can",
-						"answerOptions": [
-							{
-								"answerText": "abbreviate translations",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "standardize translations",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "improve the accuracy of translations",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 36,
-				"title": "NLP and Translation: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Underlying TextBlob's translation library is:",
-						"answerOptions": [
-							{
-								"answerText": "Google Translate",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Bing",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "A custom ML model",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "To use `blob.translate` you need:",
-						"answerOptions": [
-							{
-								"answerText": "an internet connection",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a dictionary",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "JavaScript",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "To determine sentiment, an ML approach would be to:",
-						"answerOptions": [
-							{
-								"answerText": "apply Regression techniques to manually generated opinions and scores and look for patterns",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "apply NLP techniques to manually generated opinions and scores and look for patterns",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "apply Clustering techniques to manually generated opinions and scores and look for patterns",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 37,
-				"title": "NLP 4: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What information can we get from text that was written or spoken by a human?",
-						"answerOptions": [
-							{
-								"answerText": "patterns and frequencies",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "sentiment and meaning",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What is sentiment analysis?",
-						"answerOptions": [
-							{
-								"answerText": "a study of whether a family heirloom has sentimental value",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a method of systematically identifying, extracting, quantifying, and studying affective states and subjective information",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "the ability to tell whether someone is sad or happy",
-								"isCorrect": "false"
-							}
-						]
-					},					
-					{
-						"questionText": "What question could be answered using a dataset of hotel reviews, Python, and sentiment analysis?",
-						"answerOptions": [
-							{
-								"answerText": "What are the most frequently used words and phrases in reviews?",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Which resort has the best pool?",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Is there valet parking at this hotel?",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 38,
-				"title": "NLP 4: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is the essence of NLP?",
-						"answerOptions": [
-							{
-								"answerText": "categorizing human language into happy or sad",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "interpreting meaning or sentiment without having to have a human do it",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "finding outliers in sentiment and examining them",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What are some things you might look for while cleaning data?",
-						"answerOptions": [
-							{
-								"answerText": "characters in other languages",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "blank rows or columns",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "It is important to understand your data and its foibles before performing operations on it.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 39,
-				"title": "NLP 5: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Why is it important to clean data before analyzing it?",
-						"answerOptions": [
-							{
-								"answerText": "Some columns might have missing or incorrect data",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Messy data can lead to false conclusions about the dataset",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What is one example of a strategy for cleaning data?",
-						"answerOptions": [
-							{
-								"answerText": "removing columns/rows that aren't useful for answering a specific question",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "getting rid of verified values that don't fit your hypothesis",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "moving the outliers to a separate table and running the calculations for that table to see if they match",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "It can be useful to categorize data using a Tag column.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 40,
-				"title": "NLP 5: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is the goal of the dataset?",
-						"answerOptions": [
-							{
-								"answerText": "to see how many negative and positive reviews there are for hotels across the world",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "to add sentiment and columns that will help you choose the best hotel",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "to analyze why people leave specific reviews",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What are stop words?",
-						"answerOptions": [
-							{
-								"answerText": "common English words that do not change the sentiment of a sentence",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "words that you can remove to speed up sentiment analysis",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "To test the sentiment analysis, make sure it matches the reviewer's score for the same review.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 41,
-				"title": "Intro to Time Series: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Time Series Forecasting is useful in",
-						"answerOptions": [
-							{
-								"answerText": "determining future costs",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "predicting future pricing",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "A time series is a sequence taken at:",
-						"answerOptions": [
-							{
-								"answerText": "successive equally spaced points in space",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "successive equally spaced points in time",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "successive equally spaced points in space and time",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Time series can be used in:",
-						"answerOptions": [
-							{
-								"answerText": "earthquake prediction",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "computer vision",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "color analysis",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 42,
-				"title": "Intro to Time Series: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Time series trends are",
-						"answerOptions": [
-							{
-								"answerText": "Measurable increases and decreases over time",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Quantifying decreases over time",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Gaps between increases and decreases over time",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Outliers are",
-						"answerOptions": [
-							{
-								"answerText": "points close to standard data variance",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "points far away from standard data variance",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "points within standard data variance",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Time Series Forecasting is most useful for",
-						"answerOptions": [
-							{
-								"answerText": "Econometrics",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "History",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Libraries",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 43,
-				"title": "Time Series ARIMA: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "ARIMA stands for",
-						"answerOptions": [
-							{
-								"answerText": "AutoRegressive Integral Moving Average",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "AutoRegressive Integrated Moving Action",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "AutoRegressive Integrated Moving Average",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "Stationarity refers to",
-						"answerOptions": [
-							{
-								"answerText": "data whose attributes does not change when shifted in time",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "data whose distribution does not change when shifted in time",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "data whose distribution changes when shifted in time",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Differencing",
-						"answerOptions": [
-							{
-								"answerText": "stabilizes trend and seasonality",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "exacerbates trend and seasonality",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "eliminates trend and seasonality",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 44,
-				"title": "Time Series ARIMA: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "ARIMA is used to make a model fit the special form of time series data",
-						"answerOptions": [
-							{
-								"answerText": "as flat as possible",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "as closely as possible",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "via scatterplots",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Use SARIMAX to",
-						"answerOptions": [
-							{
-								"answerText": "manage seasonal ARIMA models",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "manage special ARIMA models",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "manage statistical ARIMA models",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "'Walk-Forward' validation involves",
-						"answerOptions": [
-							{
-								"answerText": "re-evaluating a model progressively as it is validated",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "re-training a model progressively as it is validated",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "re-configuring a model progressively as it is validated",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 45,
-				"title": "Reinforcement 1: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is reinforcement learning?",
-						"answerOptions": [
-							{
-								"answerText": "teaching someone something over and over again until they understand",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a learning technique that deciphers the optimal behavior of an agent in some environment by running many experiments",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "understanding how to run multiple experiments at once",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is a policy?",
-						"answerOptions": [
-							{
-								"answerText": "a function that returns the action at any given state",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "a document that tells you whether or not you can return an item",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a function that is used for a random purpose",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "A reward function returns a score for each state of an environment.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 46,
-				"title": "Reinforcement 1: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What is Q-Learning?",
-						"answerOptions": [
-							{
-								"answerText": "a mechanism for recording the 'goodness' of each state",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "an algorithm where the policy is defined by a Q-Table",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "both of the above",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "For what values does a Q-Table correspond to the random walk policy?",
-						"answerOptions": [
-							{
-								"answerText": "all equal values",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "-0.25",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "all different values",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "It was better to use exploration than exploitation during the learning process in our lesson.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "true"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 47,
-				"title": "Reinforcement 2: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Chess and Go are games with continuous states.",
-						"answerOptions": [
-							{
-								"answerText": "true",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "false",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What is the CartPole problem?",
-						"answerOptions": [
-							{
-								"answerText": "a process for eliminating outliers",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a method for optimizing your shopping cart",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "a simplified version of balancing",
-								"isCorrect": "true"
-							}
-						]
-					},
-					{
-						"questionText": "What tool can we use to play out different scenarios of potential states in a game?",
-						"answerOptions": [
-							{
-								"answerText": "guess and check",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "simulation environments",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "state transition testing",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 48,
-				"title": "Reinforcement 2: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Where do we define all possible actions in an environment?",
-						"answerOptions": [
-							{
-								"answerText": "methods",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "action space",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "action list",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What pair did we use as the dictionary key-value?",
-						"answerOptions": [
-							{
-								"answerText": "(state, action) as the key, Q-Table entry as the value",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "state as the key, action as the value",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "the value of the qvalues function as the key, action as the value",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What are the hyperparameters we used during Q-Learning?",
-						"answerOptions": [
-							{
-								"answerText": "q-table value, current reward, random action",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "learning rate, discount factor, exploration/exploitation factor",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "cumulative rewards, learning rate, exploration factor",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 49,
-				"title": "Real World Applications: Pre-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "What's an example of an ML application in the Finance industry?",
-						"answerOptions": [
-							{
-								"answerText": "Personalizing the customer journey using NLP",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Wealth management using linear regression",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Energy management using Time Series",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What ML technique can hospitals use to manage readmission?",
-						"answerOptions": [
-							{
-								"answerText": "Clustering",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Time Series",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What is an example of using Time Series for energy management?",
-						"answerOptions": [
-							{
-								"answerText": "Motion sensing animals",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Smart parking meters",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Tracking forest fires",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			},
-			{
-				"id": 50,
-				"title": "Real World Applications: Post-Lecture Quiz",
-				"quiz": [
-					{
-						"questionText": "Which ML technique can be used to detect credit card fraud?",
-						"answerOptions": [
-							{
-								"answerText": "Regression",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Clustering",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "Which ML technique is exemplified in forest management?",
-						"answerOptions": [
-							{
-								"answerText": "Reinforcement Learning",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Time Series",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "NLP",
-								"isCorrect": "false"
-							}
-						]
-					},
-					{
-						"questionText": "What's an example of an ML application in the Health Care industry?",
-						"answerOptions": [
-							{
-								"answerText": "Predicting student behavior using regression",
-								"isCorrect": "false"
-							},
-							{
-								"answerText": "Managing clinical trials using classifiers",
-								"isCorrect": "true"
-							},
-							{
-								"answerText": "Motion sensing of animals using classifiers",
-								"isCorrect": "false"
-							}
-						]
-					}
-				]
-			}
-		]
-	}
+  {
+    "title": "Machine Learning for Beginners: Quizzes",
+    "complete": "Congratulations, you completed the quiz!",
+    "error": "Sorry, try again",
+    "quizzes": [
+      {
+        "id": 1,
+        "title": "Introduction to Machine Learning: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Applications of machine learning are all around us",
+            "answerOptions": [
+              {
+                "answerText": "True",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "False",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is the technical difference between classical ML and deep learning?",
+            "answerOptions": [
+              {
+                "answerText": "classical ML was invented first",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the use of neural networks",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "deep learning is used in robots",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Why might a business want to use ML strategies?",
+            "answerOptions": [
+              {
+                "answerText": "to automate the solving of multi-dimensional problems",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "to customize a shopping experience based on the type of customer",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 2,
+        "title": "Introduction to Machine Learning: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Machine learning algorithms are meant to simulate",
+            "answerOptions": [
+              {
+                "answerText": "intelligent machines",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the human brain",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "orangutans",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is an example of a classical ML technique?",
+            "answerOptions": [
+              {
+                "answerText": "natural language processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "deep learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Neural Networks",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Why should everyone learn the basics of ML?",
+            "answerOptions": [
+              {
+                "answerText": "learning ML is fun and accessible to everyone",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ML strategies are being used in many industries and domains",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 3,
+        "title": "History of Machine Learning: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Approximately when was the term 'artificial intelligence' coined?",
+            "answerOptions": [
+              {
+                "answerText": "1980s",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "1950s",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "1930s",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Who was one of the early pioneers of machine learning?",
+            "answerOptions": [
+              {
+                "answerText": "Alan Turing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bill Gates",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Shakey the robot",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is one of the reasons that advancement in AI slowed in the 1970s?",
+            "answerOptions": [
+              {
+                "answerText": "Limited compute power",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Not enough skilled engineers",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Conflicts between countries",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 4,
+        "title": "History of Machine Learning: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What's an example of a 'scruffy' AI system?",
+            "answerOptions": [
+              {
+                "answerText": "ELIZA",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "HACKML",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "SSYSTEM",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is an example of a technology that was developed during 'The Golden Years'?",
+            "answerOptions": [
+              {
+                "answerText": "Blocks world",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Jibo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Robot dogs",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which event was foundational in the creation and expansion of the field of artificial intelligence?",
+            "answerOptions": [
+              {
+                "answerText": "Turing Test",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Dartmouth Summer Research Project",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "AI Winter",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 5,
+        "title": "Fairness and Machine Learning: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Unfairness in Machine Learning can happen",
+            "answerOptions": [
+              {
+                "answerText": "intentionally",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "unintentionally",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "The term 'unfairness' in ML connotes:",
+            "answerOptions": [
+              {
+                "answerText": "harms for a group of people",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "harm to one person",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "harms for the majority of people",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "The five main types of harms include",
+            "answerOptions": [
+              {
+                "answerText": "allocation, quality of service, stereotyping, denigration, and over- or under- representation",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "elocation, quality of service, stereotyping, denigration, and over- or under- representation ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "allocation, quality of service, stereophonics, denigration, and over- or under- representation ",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 6,
+        "title": "Fairness and Machine Learning: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Unfairness in a model can be caused by",
+            "answerOptions": [
+              {
+                "answerText": "overrreliance on historical data",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "underreliance on historical data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "too closely aligning to historical data",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "To mitigate unfairness, you can",
+            "answerOptions": [
+              {
+                "answerText": "identify harms and affected groups",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "define fairness metrics",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Fairlearn is a package that can",
+            "answerOptions": [
+              {
+                "answerText": "compare multiple models by using fairness and performance metrics",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "choose the best model for your needs",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "help you decide what is fair and what is not",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 7,
+        "title": "Tools and Techniques: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "When building a model, you should:",
+            "answerOptions": [
+              {
+                "answerText": "prepare your data, then train your model",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "choose a training method, then prepare your data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "tune parameters, then train your model",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Your data's ___ will impact the quality of your ML model",
+            "answerOptions": [
+              {
+                "answerText": "quantity",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "shape",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "A feature variable is:",
+            "answerOptions": [
+              {
+                "answerText": "a quality of your data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a measurable property of your data",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a row of your data",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 8,
+        "title": "Tools and Techniques: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "You should visualize your data because",
+            "answerOptions": [
+              {
+                "answerText": "you can discover outliers",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "you can discover potential cause for bias",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Split your data into:",
+            "answerOptions": [
+              {
+                "answerText": "training and turing sets",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "training and test sets",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "validation and evaluation sets",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A common command to start the training process in various ML libraries is:",
+            "answerOptions": [
+              {
+                "answerText": "model.travel",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.train",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.fit",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 9,
+        "title": "Introduction to Regression: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Which of these variables is a numeric variable?",
+            "answerOptions": [
+              {
+                "answerText": "Height",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Gender",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Hair Color",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these variables is a categorical variable?",
+            "answerOptions": [
+              {
+                "answerText": "Heart Rate",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Blood Type",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Weight",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these problems is a Regression analysis-based problem?",
+            "answerOptions": [
+              {
+                "answerText": "Predicting the final exam marks of a student",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Predicting the blood type of a person",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Predicting whether an email is spam or not",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 10,
+        "title": "Introduction to Regression: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "If your Machine Learning model's training accuracy is 95 % and the testing accuracy is 30 %, then what type of condition it is called?",
+            "answerOptions": [
+              {
+                "answerText": "Overfitting",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Underfitting",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Double Fitting",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "The process of identifying significant features from a set of features is called:",
+            "answerOptions": [
+              {
+                "answerText": "Feature Extraction",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Feature Dimensionality Reduction",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Feature Selection",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "The process of splitting a dataset into a certain ratio of training and testing dataset using Scikit Learn's 'train_test_split()' method/function is called:",
+            "answerOptions": [
+              {
+                "answerText": "Cross-Validation",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Hold-Out Validation",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Leave one out Validation",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 11,
+        "title": "Prepare and Visualize Data for Regression: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Which of these Python modules is used to plot the visualization of data?",
+            "answerOptions": [
+              {
+                "answerText": "Numpy",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Scikit-learn",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Matplotlib",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "If you want to understand the spread or the other characteristics of data points of your dataset, then perform:",
+            "answerOptions": [
+              {
+                "answerText": "Data Visualization",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Data Preprocessing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Train Test Split",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these is a part of the Data Visualization step in a Machine Learning project?",
+            "answerOptions": [
+              {
+                "answerText": "Incorporating a certain Machine Learning algorithm",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Creating a pictorial representation of data using different plotting methods",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Normalizing the values of a dataset",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 12,
+        "title": "Prepare and Visualize Data for Regression: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Which of these code snippets is correct based on this lesson, if you want to check for the presence of missing values in your dataset? Suppose the dataset is stored in a variable named 'dataset' which is a Pandas DataFrame object.",
+            "answerOptions": [
+              {
+                "answerText": "dataset.isnull().sum()",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "findMissing(dataset)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sum(null(dataset))",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which of these plotting methods is useful when you would like to understand the spread of different groups of datapoints from your dataset?",
+            "answerOptions": [
+              {
+                "answerText": "Scatter Plot",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Line Plot",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Bar Plot",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What can Data Visualization NOT tell you?",
+            "answerOptions": [
+              {
+                "answerText": "Relationships among datapoints",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "The source from where the dataset is collected",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Finding the presence of outliers in the dataset",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 13,
+        "title": "Linear and Polynomial Regression: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Matplotlib is a ",
+            "answerOptions": [
+              {
+                "answerText": "drawing library",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "data visualization library",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "lending library",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Linear Regression uses the following to plot relationships between variables",
+            "answerOptions": [
+              {
+                "answerText": "a straight line",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a circle",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a curve",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A good Linear Regression model has a ___ Correlation Coefficient",
+            "answerOptions": [
+              {
+                "answerText": "low",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "high",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "flat",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 14,
+        "title": "Linear and Polynomial Regression: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "If your data is nonlinear, try a ___ type of Regression",
+            "answerOptions": [
+              {
+                "answerText": "linear",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "spherical",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "polynomial",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "These are all types of Regression methods",
+            "answerOptions": [
+              {
+                "answerText": "Falsestep, Ridge, Lasso and Elasticnet",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Stepwise, Ridge, Lasso and Elasticnet",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Stepwise, Ridge, Lariat and Elasticnet",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Least-Squares Regression means that all the datapoints surrounding the regression line are:",
+            "answerOptions": [
+              {
+                "answerText": "squared and then subtracted",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "multiplied",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "squared and then added up",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 15,
+        "title": "Logistic Regression: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Use Logistic Regression to predict",
+            "answerOptions": [
+              {
+                "answerText": "whether an apple is ripe or not",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "how many tickets can be sold in a month",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "what color the sky will turn tomorrow at 6 PM",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Types of Logistic Regression include",
+            "answerOptions": [
+              {
+                "answerText": "multinomial and cardinal",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "multinomial and ordinal",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "principal and ordinal",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Your data has weak correlations. The best type of Regression to use is:",
+            "answerOptions": [
+              {
+                "answerText": "Logistic",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Linear",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Cardinal",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 16,
+        "title": "Logistic Regression: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Seaborn is a type of",
+            "answerOptions": [
+              {
+                "answerText": "data visualization library",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "mapping library",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "mathematical library",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A confusion matrix is also known as a:",
+            "answerOptions": [
+              {
+                "answerText": "error matrix",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "truth matrix",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "accuracy matrix",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A good model will have:",
+            "answerOptions": [
+              {
+                "answerText": "a large number of false positives and true negatives in its confusion matrix",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a large number of true positives and true negatives in its confusion matrix",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a large number of true positives and false negatives in its confusion matrix",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 17,
+        "title": "Build a Web App: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What does ONNX stand for?",
+            "answerOptions": [
+              {
+                "answerText": "Over Neural Network Exchange",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Open Neural Network Exchange",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Output Neural Network Exchange",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "How is Flask defined by its creators?",
+            "answerOptions": [
+              {
+                "answerText": "mini-framework",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "large-framework",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "micro-framework",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What does the Pickle module of Python do",
+            "answerOptions": [
+              {
+                "answerText": "Serializes a Python Object",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "De-serializes a Python Object",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Serializes and De-serializes a Python Object",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 18,
+        "title": "Build a Web App: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What are the tools we can use to host a pre-trained model on the web using Python?",
+            "answerOptions": [
+              {
+                "answerText": "Flask",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "TensorFlow.js",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "onnx.js",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What does SaaS stand for?",
+            "answerOptions": [
+              {
+                "answerText": "System as a Service",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Software as a Service",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Security as a Service",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What does Scikit-learn's LabelEncoder library do?",
+            "answerOptions": [
+              {
+                "answerText": "Encodes data alphabetically",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Encodes data numerically",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Encodes data serially",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 19,
+        "title": "Classification 1: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Classification is a form of supervised learning that has a lot in common with",
+            "answerOptions": [
+              {
+                "answerText": "Time Series",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Regression techniques",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What question can classification help answer?",
+            "answerOptions": [
+              {
+                "answerText": "Is this email spam or not?",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Can pigs fly?",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "What is the meaning of life?",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is the first step to using Classification techniques?",
+            "answerOptions": [
+              {
+                "answerText": "creating classes of a dataset",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "cleaning and balancing your data",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "assigning a data point to a group or outcome",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 20,
+        "title": "Classification 1: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is a multiclass question?",
+            "answerOptions": [
+              {
+                "answerText": "the task of classifying data points into multiple classes",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "the task of classifying data points into one of several classes",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "the task of cleaning data points in multiple ways",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "It's important to clean out recurrent or unhelpful data to help your classifiers solve your problem.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What's the best reason to balance your data?",
+            "answerOptions": [
+              {
+                "answerText": "Imbalanced data looks bad in visualizations",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Balancing your data yields better results because an ML model won't skew towards one class",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Balancing your data gives you more data points",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 21,
+        "title": "Classification 2: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Balanced, clean data yields the best classification results",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "How do you choose the right classifier?",
+            "answerOptions": [
+              {
+                "answerText": "Understand which classifiers work best for which scenarios",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Educated guess and check",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Classification is a type of",
+            "answerOptions": [
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Supervised Learning",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Programming language",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 22,
+        "title": "Classification 2: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is a 'solver'?",
+            "answerOptions": [
+              {
+                "answerText": "the person who double-checks your work",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the algorithm to use in the optimization problem",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a machine learning technique",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which classifier did we use in this lesson?",
+            "answerOptions": [
+              {
+                "answerText": "Logistic Regression",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Decision Trees",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "One-vs-All Multiclass",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "How do you know if the classification algorithm is working as expected?",
+            "answerOptions": [
+              {
+                "answerText": "By checking the accuracy of its predictions",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "By checking it against other algorithms",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "By looking at historical data for how good this algorithm is at solving similar problems",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 23,
+        "title": "Classification 3: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "A good initial classifier to try is:",
+            "answerOptions": [
+              {
+                "answerText": "Linear SVC",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "K-Means",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Logical SVC",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Regularization controls:",
+            "answerOptions": [
+              {
+                "answerText": "the influence of parameters",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "the influence of training speed",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the influence of outliers",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "K-Neighbors classifier can be used for:",
+            "answerOptions": [
+              {
+                "answerText": "supervised learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "unsupervised learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 24,
+        "title": "Classification 3: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Support-Vector classifiers can be used for",
+            "answerOptions": [
+              {
+                "answerText": "classification",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "regression",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Random Forest is a ___ type of classifier",
+            "answerOptions": [
+              {
+                "answerText": "Ensemble",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Dissemble",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Assemble",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Adaboost is known for:",
+            "answerOptions": [
+              {
+                "answerText": "focusing on the weights of incorrectly classified items",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "focusing on outliers",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "focusing on incorrect data",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 25,
+        "title": "Classification 4: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Recommendation systems might be used for",
+            "answerOptions": [
+              {
+                "answerText": "Recommending a good restaurant",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Recommending fashions to try",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Embedding a model in a web app helps it to be offline-capable",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Onnx Runtime can be used for",
+            "answerOptions": [
+              {
+                "answerText": "Running models in a web app",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Training models",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Hyperparameter tuning",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 26,
+        "title": "Classification 4: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Netron app helps you:",
+            "answerOptions": [
+              {
+                "answerText": "Visualize data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Visualize your model's structure",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Test your web app",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Convert your Scikit-learn model for use with Onnx using:",
+            "answerOptions": [
+              {
+                "answerText": "sklearn-app",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-web",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-onnx",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Using your model in a web app is called:",
+            "answerOptions": [
+              {
+                "answerText": "inference",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "interference",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "insurance",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 27,
+        "title": "Introduction to Clustering: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "A real-life example of clustering would be",
+            "answerOptions": [
+              {
+                "answerText": "Setting the dinner table",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Sorting the laundry",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Grocery shopping",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Clustering techniques can be used in these industries",
+            "answerOptions": [
+              {
+                "answerText": "banking",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "e-commerce",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Clustering is a type of:",
+            "answerOptions": [
+              {
+                "answerText": "supervised learning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "unsupervised learning",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "reinforcement learning",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 28,
+        "title": "Introduction to Clustering: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Euclidean geometry is arranged along",
+            "answerOptions": [
+              {
+                "answerText": "planes",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "curves",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "spheres",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "The density of your clustering data is related to its",
+            "answerOptions": [
+              {
+                "answerText": "noise",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "depth",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "validity",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "The best-known clustering algorithm is",
+            "answerOptions": [
+              {
+                "answerText": "k-means",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "k-middle",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "k-mart",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 29,
+        "title": "K-Means Clustering: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "K-Means is derived from:",
+            "answerOptions": [
+              {
+                "answerText": "electrical engineering",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "signal processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "computational linguistics",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A good Silhouette score means:",
+            "answerOptions": [
+              {
+                "answerText": "clusters are well-separated and well-defined",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "there are few clusters",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "there are many clusters",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Variance is:",
+            "answerOptions": [
+              {
+                "answerText": "the average of the squared differences from the mean",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a problem for clustering if it becomes too high",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of these",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 30,
+        "title": "K-Means Clustering: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "A Voronoi diagram shows:",
+            "answerOptions": [
+              {
+                "answerText": "a cluster's variance",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a cluster's seed and its region",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a cluster's inertia",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Inertia is",
+            "answerOptions": [
+              {
+                "answerText": "a measure of how internally coherent clusters are",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a measure of how much clusters move",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a measure of cluster quality",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Using K-Means, you must first determine the value of 'k'",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 31,
+        "title": "Intro to NLP: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What does NLP stand for in these lessons?",
+            "answerOptions": [
+              {
+                "answerText": "Neural Language Processing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "natural language processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Natural Linguistic Processing",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Eliza was an early bot that acted as a computer",
+            "answerOptions": [
+              {
+                "answerText": "therapist",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "doctor",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "nurse",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Alan Turing's 'Turing Test' tried to determine if a computer was",
+            "answerOptions": [
+              {
+                "answerText": "indistinguishable from a human",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "thinking",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 32,
+        "title": "Intro to NLP: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Joseph Weizenbaum invented the bot",
+            "answerOptions": [
+              {
+                "answerText": "Elisha",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Eliza",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Eloise",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A conversational bot gives output based on",
+            "answerOptions": [
+              {
+                "answerText": "Randomly choosing predefined choices",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Analyzing the input and using machine intelligence",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Both of these",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "How would you make the bot more effective?",
+            "answerOptions": [
+              {
+                "answerText": "By asking it more questions.",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "By feeding it more data and training it accordingly",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "The bot is dumb, it cannot learn :(",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 33,
+        "title": "NLP Tasks: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Tokenization",
+            "answerOptions": [
+              {
+                "answerText": "Splits text by means of punctuation",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Splits text into separate tokens (words)",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Splits text into phrases",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Embeddings",
+            "answerOptions": [
+              {
+                "answerText": "converts text data numerically so words can cluster",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "embeds words into phrases",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "embeds sentences into paragraphs",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Parts-of-Speech Tagging",
+            "answerOptions": [
+              {
+                "answerText": "divides sentences by their parts of speech",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "takes tokenized words and tags them by their part of speech",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "diagrams sentences",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 34,
+        "title": "NLP Tasks: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Build a dictionary of how often words reoccur using:",
+            "answerOptions": [
+              {
+                "answerText": "Word and Phrase Dictionary",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Word and Phrase Frequencies",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Word and Phrase Library",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "N-grams refer to",
+            "answerOptions": [
+              {
+                "answerText": "A text can be split into sequences of words of a set length",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "A word can be split into sequences of characters of a set length",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "A text can be split into paragraphs of a set length",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Sentiment analysis",
+            "answerOptions": [
+              {
+                "answerText": "analyzes a phrase for positivity or negativity",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "analyzes a phrase for sentimentality",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "analyzes a phrase for sadness",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 35,
+        "title": "NLP and Translation: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Naive translation",
+            "answerOptions": [
+              {
+                "answerText": "translates words only",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "translates sentence structure",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "translates sentiment",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A *corpus* of texts refers to",
+            "answerOptions": [
+              {
+                "answerText": "A small number of texts",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "A large number of texts",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "One standard text",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "If a ML model has enough human translations to build a model on, it can",
+            "answerOptions": [
+              {
+                "answerText": "abbreviate translations",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "standardize translations",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "improve the accuracy of translations",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 36,
+        "title": "NLP and Translation: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Underlying TextBlob's translation library is:",
+            "answerOptions": [
+              {
+                "answerText": "Google Translate",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "A custom ML model",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "To use `blob.translate` you need:",
+            "answerOptions": [
+              {
+                "answerText": "an internet connection",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a dictionary",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "JavaScript",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "To determine sentiment, an ML approach would be to:",
+            "answerOptions": [
+              {
+                "answerText": "apply Regression techniques to manually generated opinions and scores and look for patterns",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "apply NLP techniques to manually generated opinions and scores and look for patterns",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "apply Clustering techniques to manually generated opinions and scores and look for patterns",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 37,
+        "title": "NLP 4: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What information can we get from text that was written or spoken by a human?",
+            "answerOptions": [
+              {
+                "answerText": "patterns and frequencies",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sentiment and meaning",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What is sentiment analysis?",
+            "answerOptions": [
+              {
+                "answerText": "a study of whether a family heirloom has sentimental value",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a method of systematically identifying, extracting, quantifying, and studying affective states and subjective information",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "the ability to tell whether someone is sad or happy",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What question could be answered using a dataset of hotel reviews, Python, and sentiment analysis?",
+            "answerOptions": [
+              {
+                "answerText": "What are the most frequently used words and phrases in reviews?",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Which resort has the best pool?",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Is there valet parking at this hotel?",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 38,
+        "title": "NLP 4: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is the essence of NLP?",
+            "answerOptions": [
+              {
+                "answerText": "categorizing human language into happy or sad",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "interpreting meaning or sentiment without having to have a human do it",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "finding outliers in sentiment and examining them",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What are some things you might look for while cleaning data?",
+            "answerOptions": [
+              {
+                "answerText": "characters in other languages",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "blank rows or columns",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "It is important to understand your data and its foibles before performing operations on it.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 39,
+        "title": "NLP 5: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Why is it important to clean data before analyzing it?",
+            "answerOptions": [
+              {
+                "answerText": "Some columns might have missing or incorrect data",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Messy data can lead to false conclusions about the dataset",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What is one example of a strategy for cleaning data?",
+            "answerOptions": [
+              {
+                "answerText": "removing columns/rows that aren't useful for answering a specific question",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "getting rid of verified values that don't fit your hypothesis",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "moving the outliers to a separate table and running the calculations for that table to see if they match",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "It can be useful to categorize data using a Tag column.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 40,
+        "title": "NLP 5: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is the goal of the dataset?",
+            "answerOptions": [
+              {
+                "answerText": "to see how many negative and positive reviews there are for hotels across the world",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "to add sentiment and columns that will help you choose the best hotel",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "to analyze why people leave specific reviews",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What are stop words?",
+            "answerOptions": [
+              {
+                "answerText": "common English words that do not change the sentiment of a sentence",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "words that you can remove to speed up sentiment analysis",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "To test the sentiment analysis, make sure it matches the reviewer's score for the same review.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 41,
+        "title": "Intro to Time Series: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Time Series Forecasting is useful in",
+            "answerOptions": [
+              {
+                "answerText": "determining future costs",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "predicting future pricing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "A time series is a sequence taken at:",
+            "answerOptions": [
+              {
+                "answerText": "successive equally spaced points in space",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "successive equally spaced points in time",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "successive equally spaced points in space and time",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Time series can be used in:",
+            "answerOptions": [
+              {
+                "answerText": "earthquake prediction",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "computer vision",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "color analysis",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 42,
+        "title": "Intro to Time Series: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Time series trends are",
+            "answerOptions": [
+              {
+                "answerText": "Measurable increases and decreases over time",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Quantifying decreases over time",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Gaps between increases and decreases over time",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Outliers are",
+            "answerOptions": [
+              {
+                "answerText": "points close to standard data variance",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "points far away from standard data variance",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "points within standard data variance",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Time Series Forecasting is most useful for",
+            "answerOptions": [
+              {
+                "answerText": "Econometrics",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "History",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Libraries",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 43,
+        "title": "Time Series ARIMA: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "ARIMA stands for",
+            "answerOptions": [
+              {
+                "answerText": "AutoRegressive Integral Moving Average",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Action",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Average",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Stationarity refers to",
+            "answerOptions": [
+              {
+                "answerText": "data whose attributes does not change when shifted in time",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "data whose distribution does not change when shifted in time",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "data whose distribution changes when shifted in time",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Differencing",
+            "answerOptions": [
+              {
+                "answerText": "stabilizes trend and seasonality",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "exacerbates trend and seasonality",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "eliminates trend and seasonality",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 44,
+        "title": "Time Series ARIMA: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "ARIMA is used to make a model fit the special form of time series data",
+            "answerOptions": [
+              {
+                "answerText": "as flat as possible",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "as closely as possible",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "via scatterplots",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Use SARIMAX to",
+            "answerOptions": [
+              {
+                "answerText": "manage seasonal ARIMA models",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "manage special ARIMA models",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "manage statistical ARIMA models",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "'Walk-Forward' validation involves",
+            "answerOptions": [
+              {
+                "answerText": "re-evaluating a model progressively as it is validated",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "re-training a model progressively as it is validated",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "re-configuring a model progressively as it is validated",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 45,
+        "title": "Reinforcement 1: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is reinforcement learning?",
+            "answerOptions": [
+              {
+                "answerText": "teaching someone something over and over again until they understand",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a learning technique that deciphers the optimal behavior of an agent in some environment by running many experiments",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "understanding how to run multiple experiments at once",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is a policy?",
+            "answerOptions": [
+              {
+                "answerText": "a function that returns the action at any given state",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "a document that tells you whether or not you can return an item",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a function that is used for a random purpose",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "A reward function returns a score for each state of an environment.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 46,
+        "title": "Reinforcement 1: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What is Q-Learning?",
+            "answerOptions": [
+              {
+                "answerText": "a mechanism for recording the 'goodness' of each state",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "an algorithm where the policy is defined by a Q-Table",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "both of the above",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "For what values does a Q-Table correspond to the random walk policy?",
+            "answerOptions": [
+              {
+                "answerText": "all equal values",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "-0.25",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "all different values",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "It was better to use exploration than exploitation during the learning process in our lesson.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 47,
+        "title": "Reinforcement 2: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Chess and Go are games with continuous states.",
+            "answerOptions": [
+              {
+                "answerText": "true",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "false",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What is the CartPole problem?",
+            "answerOptions": [
+              {
+                "answerText": "a process for eliminating outliers",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a method for optimizing your shopping cart",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "a simplified version of balancing",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "What tool can we use to play out different scenarios of potential states in a game?",
+            "answerOptions": [
+              {
+                "answerText": "guess and check",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "simulation environments",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "state transition testing",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 48,
+        "title": "Reinforcement 2: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Where do we define all possible actions in an environment?",
+            "answerOptions": [
+              {
+                "answerText": "methods",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "action space",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "action list",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What pair did we use as the dictionary key-value?",
+            "answerOptions": [
+              {
+                "answerText": "(state, action) as the key, Q-Table entry as the value",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "state as the key, action as the value",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "the value of the qvalues function as the key, action as the value",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What are the hyperparameters we used during Q-Learning?",
+            "answerOptions": [
+              {
+                "answerText": "q-table value, current reward, random action",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "learning rate, discount factor, exploration/exploitation factor",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "cumulative rewards, learning rate, exploration factor",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 49,
+        "title": "Real World Applications: Pre-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "What's an example of an ML application in the Finance industry?",
+            "answerOptions": [
+              {
+                "answerText": "Personalizing the customer journey using NLP",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Wealth management using linear regression",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Energy management using Time Series",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What ML technique can hospitals use to manage readmission?",
+            "answerOptions": [
+              {
+                "answerText": "Clustering",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Time Series",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What is an example of using Time Series for energy management?",
+            "answerOptions": [
+              {
+                "answerText": "Motion sensing animals",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Smart parking meters",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Tracking forest fires",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 50,
+        "title": "Real World Applications: Post-Lecture Quiz",
+        "quiz": [
+          {
+            "questionText": "Which ML technique can be used to detect credit card fraud?",
+            "answerOptions": [
+              {
+                "answerText": "Regression",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Clustering",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Which ML technique is exemplified in forest management?",
+            "answerOptions": [
+              {
+                "answerText": "Reinforcement Learning",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Time Series",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "NLP",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "What's an example of an ML application in the Health Care industry?",
+            "answerOptions": [
+              {
+                "answerText": "Predicting student behavior using regression",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Managing clinical trials using classifiers",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Motion sensing of animals using classifiers",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      }
+    ]
+  }
 ]
diff --git a/quiz-app/src/assets/translations/fr.json b/quiz-app/src/assets/translations/fr.json
index da0ee317e4..f3ecffc4f7 100644
--- a/quiz-app/src/assets/translations/fr.json
+++ b/quiz-app/src/assets/translations/fr.json
@@ -1503,7 +1503,7 @@
                         },
                         {
 							"answerText": "Les deux",
-                            "isCorrect": "false"
+                            "isCorrect": "true"
                         }
                     ]
                 },

From ab1b0113307e95f2f7b39274f205292bbab1b02c Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Wed, 21 Jul 2021 16:27:14 +0700
Subject: [PATCH 235/368] Add Indonesian translation for 1-4 assignment

---
 .../4-techniques-of-ML/translations/assignment.id.md  | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/4-techniques-of-ML/translations/assignment.id.md

diff --git a/1-Introduction/4-techniques-of-ML/translations/assignment.id.md b/1-Introduction/4-techniques-of-ML/translations/assignment.id.md
new file mode 100644
index 0000000000..9f7b23be77
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/assignment.id.md
@@ -0,0 +1,11 @@
+# Wawancara seorang data scientist
+
+## Instruksi
+
+Di perusahaan Kamu, dalam user group, atau di antara teman atau sesama siswa, berbicaralah dengan seseorang yang bekerja secara profesional sebagai data scientist. Tulis makalah singkat (500 kata) tentang pekerjaan sehari-hari mereka. Apakah mereka spesialis, atau apakah mereka bekerja 'full stack'? 
+
+## Rubrik
+
+| Kriteria | Sangat Bagus                                                                            | Cukup                                                           | Perlu Peningkatan     |
+| -------- | ------------------------------------------------------------------------------------ | ------------------------------------------------------------------ | --------------------- |
+|          | Sebuah esai dengan panjang yang sesuai, dengan sumber yang dikaitkan, disajikan sebagai file .doc | Esai dikaitkan dengan buruk atau lebih pendek dari panjang yang dibutuhkan | Tidak ada esai yang disajikan |

From c7cae4e48fa3d2577a5697f3a156476834341abf Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Wed, 21 Jul 2021 18:37:18 +0900
Subject: [PATCH 236/368] style: format README

---
 README.md | 76 ++++++++++++++++++++++++++++---------------------------
 1 file changed, 39 insertions(+), 37 deletions(-)

diff --git a/README.md b/README.md
index 34c09b5706..8e7dca0039 100644
--- a/README.md
+++ b/README.md
@@ -20,19 +20,20 @@ Travel with us around the world as we apply these classic techniques to data fro
 
 **🎨 Thanks as well to our illustrators** Tomomi Imura, Dasani Madipalli, and Jen Looper
 
- **� Special thanks � to our Microsoft Student Ambassador authors, reviewers and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
+**� Special thanks � to our Microsoft Student Ambassador authors, reviewers and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
 
 ---
+
 # Getting Started
 
 **Students**, to use this curriculum, fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:
 
-- Start with a pre-lecture quiz
-- Read the lecture and complete the activities, pausing and reflecting at each knowledge check. 
-- Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the `/solution` folders in each project-oriented lesson. 
-- Take the post-lecture quiz
-- Complete the challenge
-- Complete the assignment
+- Start with a pre-lecture quiz.
+- Read the lecture and complete the activities, pausing and reflecting at each knowledge check.
+- Try to create the projects by comprehending the lessons rather than running the solution code; however that code is available in the `/solution` folders in each project-oriented lesson.
+- Take the post-lecture quiz.
+- Complete the challenge.
+- Complete the assignment.
 - After completing a lesson group, visit the [Discussion board](https://github.com/microsoft/ML-For-Beginners/discussions) and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.
 
 > For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
@@ -48,6 +49,7 @@ Travel with us around the world as we apply these classic techniques to data fro
 > 🎥 Click the image above for a video about the project and the folks who created it!
 
 ---
+
 ## Pedagogy
 
 We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on **project-based** and that it includes **frequent quizzes**. In addition, this curriculum has a common **theme** to give it cohesion.
@@ -55,6 +57,7 @@ We have chosen two pedagogical tenets while building this curriculum: ensuring t
 By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle. This curriculum also includes a postscript on real-world applications of ML, which can be used as extra credit or as a basis for discussion.
 
 > Find our [Code of Conduct](CODE_OF_CONDUCT.md), [Contributing](CONTRIBUTING.md), and [Translation](TRANSLATIONS.md) guidelines. We welcome your constructive feedback!
+
 ## Each lesson includes:
 
 - optional sketchnote
@@ -70,45 +73,45 @@ By ensuring that the content aligns with projects, the process is made more enga
 
 > **A note about quizzes**: All quizzes are contained [in this app](https://jolly-sea-0a877260f.azurestaticapps.net), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
 
-
-| Lesson Number |                           Topic                            |                       Lesson Grouping                       | Learning Objectives                                                                                                             |                     Linked Lesson                     |     Author     |
-| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              Introduction to machine learning              |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
-|      02       |              The History of machine learning               |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
-|      03       |               Fairness and machine learning                |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
-|      04       |              Techniques for machine learning               |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                 Introduction to regression                 |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
-|      06       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
-|      07       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        A Web App 🔌                         |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               Introduction to classification               |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](4-Classification/1-Introduction/README.md)  | Jen and Cassie |
-|      11       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
-|      12       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
-|      13       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 Introduction to clustering                 |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       Introduction to natural language processing  ☕�       |   [Natural language processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     Common NLP Tasks ☕�                     |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            Translation and sentiment analysis ♥�            |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews, 1                                                                                        |      [lesson](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          Introduction to time series forecasting           |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
+| Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                     Linked Lesson                     |     Author     |
+| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
+|      01       |                Introduction to machine learning                |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
+|      02       |                The History of machine learning                 |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
+|      03       |                 Fairness and machine learning                  |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
+|      04       |                Techniques for machine learning                 |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
+|      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
+|      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
+|      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
+|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |
+|      09       |                          A Web App 🔌                          |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](3-Web-App/1-Web-App/README.md)        |      Jen       |
+|      10       |                 Introduction to classification                 |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](4-Classification/1-Introduction/README.md)  | Jen and Cassie |
+|      11       |             Delicious Asian and Indian cuisines �             |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
+|      12       |             Delicious Asian and Indian cuisines �             |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
+|      13       |             Delicious Asian and Indian cuisines �             |    [Classification](4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](4-Classification/4-Applied/README.md)     |      Jen       |
+|      14       |                   Introduction to clustering                   |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](5-Clustering/1-Visualize/README.md)      |      Jen       |
+|      15       |              Exploring Nigerian Musical Tastes 🎧              |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](5-Clustering/2-K-Means/README.md)       |      Jen       |
+|      16       |        Introduction to natural language processing ☕�         |   [Natural language processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
+|      17       |                      Common NLP Tasks ☕�                      |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](6-NLP/2-Tasks/README.md)           |    Stephen     |
+|      18       |             Translation and sentiment analysis ♥�              |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
+|      19       |                  Romantic hotels of Europe ♥�                  |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 1                                                                                         |      [lesson](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
+|      20       |                  Romantic hotels of Europe ♥�                  |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
+|      21       |            Introduction to time series forecasting             |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
 |      22       | ⚡� World Power Usage ⚡� - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |       [lesson](7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           Introduction to reinforcement learning           | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                Help Peter avoid the wolf! �                | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  Postscript   |          Real-World ML scenarios and applications          |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](9-Real-World/1-Applications/README.md)    |      Team      |
+|      23       |             Introduction to reinforcement learning             | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
+|      24       |                 Help Peter avoid the wolf! �                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
+|  Postscript   |            Real-World ML scenarios and applications            |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](9-Real-World/1-Applications/README.md)    |      Team      |
+
 ## Offline access
 
 You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the root folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`.
 
 ## PDFs
 
-Find a pdf of the curriculum with links [here](pdf/readme.pdf)
+Find a pdf of the curriculum with links [here](pdf/readme.pdf).
 
 ## Help Wanted!
 
-Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71)
+Would you like to contribute a translation? Please read our [translation guidelines](TRANSLATIONS.md) and add input [here](https://github.com/microsoft/ML-For-Beginners/issues/71).
 
 ## Other Curricula
 
@@ -116,4 +119,3 @@ Our team produces other curricula! Check out:
 
 - [Web Dev for Beginners](https://aka.ms/webdev-beginners)
 - [IoT for Beginners](https://aka.ms/iot-beginners)
-

From 7509cbabcdf14c48d982d3459112a79a721ebf98 Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Wed, 21 Jul 2021 19:09:23 +0900
Subject: [PATCH 237/368] style: format whitespace and newline

---
 translations/README.ja.md | 57 ++++++++++++++++++++-------------------
 1 file changed, 30 insertions(+), 27 deletions(-)

diff --git a/translations/README.ja.md b/translations/README.ja.md
index a719c7464d..2fe13ee4cb 100644
--- a/translations/README.ja.md
+++ b/translations/README.ja.md
@@ -23,6 +23,7 @@
 **� Microsoft Student Ambassador �著者・査読者・コンテンツ�供者�皆様�特�感������ �** 特��Rishit Dagli �ん�Muhammad Sakib Khan Inan �ん�Rohan Raj �ん�Alexandru Petrescu �ん�Abhishek Jaiswal �ん�Nawrin Tabassum �ん�Ioan Samuila �ん���� Snigdha Agarwal �ん
 
 ---
+
 # ����
 
 **学生�皆�ん**���カリキュラムを利用�る���自分�GitHubアカウント�リ�ジトリ全体をフォーク���一人も���グループ�演習を完了�������。
@@ -48,6 +49,7 @@
 > 🎥 上�画�をクリック�る����プロジェクト��プロジェクトを作��人�������ビデオを観る��������
 
 ---
+
 ## 教育法
 
 ��カリキュラムを構築�る���り�����2��教育方�を�����。**プロジェクトベース**�体験��**頻���テスト**を�む����。�ら���カリキュラム������りを���る���共通�**テーマ**��り��。
@@ -55,6 +57,7 @@
 内容�プロジェクト��整�性を������学生����学習プロセス�より魅力的��り�概念�定�度�高�り��。�ら��授業��軽��テスト�学生�学習�欲を高��授業後�2回目��テスト�より一層�定�����り��。��カリキュラム�柔軟��楽��も���るよ�デザイン�れ��り�����も���一部を�講����。プロジェクト����も��ら始�り�12週間�間�少���複雑�も���������。�����カリキュラム��機械学習�実世界��応用�関�る����も�ん��り�追加����る��議論�題����使用����。
 
 > [行動�範](../CODE_OF_CONDUCT.md)�[貢献](../CONTRIBUTING.md)�[翻訳](../TRANSLATIONS.md) �ガイドラインを�覧����。建設的���見を�待����り���
+
 ## �レッスン�内容
 
 - オプション�スケッ�ノート
@@ -70,34 +73,34 @@
 
 > **�テスト�関�る注�**: �����テスト� [��アプリ](https://jolly-sea-0a877260f.azurestaticapps.net) ���れ��り��3��ら�る50個��テスト��り��。�れら�レッスン内�らリンク�れ������アプリをローカル�実行�る��も����。`quiz-app` フォルダ内�指示�従������。
 
+| レッスン番� |                   トピック                   |                    レッスングループ                    | 学習�目的                                                                                 |                            関連�るレッスン                             |      著者      |
+| :----------: | :------------------------------------------: | :----------------------------------------------------: | ------------------------------------------------------------------------------------------ | :---------------------------------------------------------------------: | :------------: |
+|      01      |               機械学習��導入               |  [導入](../1-Introduction/translations/README.ja.md)   | 機械学習�基本的�概念を学�                                                               |  [レッスン](../1-Introduction/1-intro-to-ML/translations/README.ja.md)  |    Muhammad    |
+|      02      |                機械学習�歴�                |  [導入](../1-Introduction/translations/README.ja.md)   | ��分野�背景��る歴�を学�                                                             | [レッスン](../1-Introduction/2-history-of-ML/translations/README.ja.md) |  Jen and Amy   |
+|      03      |               公平性�機械学習               |  [導入](../1-Introduction/translations/README.ja.md)   | 機械学習モデルを構築・�用�る際�学生�考慮����公平性�関�る���哲学的�題�何�？ |   [レッスン](../1-Introduction/3-fairness/translations/README.ja.md)    |     Tomomi     |
+|      04      |                機械学習�手法                |  [導入](../1-Introduction/translations/README.ja.md)   | 機械学習�研究者���よ��手法�モデルを構築���る�？                                 |         [レッスン](1-Introduction/4-techniques-of-ML/README.md)         | Chris and Jen  |
+|      05      |                 回帰��導入                 |           [回帰](../2-Regression/README.md)            | 回帰モデルをPython� Scikit-learn �始�る                                                 |      [レッスン](../2-Regression/1-Tools/translations/README.ja.md)      |      Jen       |
+|      06      |            北米�カボ�ャ�価格 🎃            |           [回帰](../2-Regression/README.md)            | 機械学習����データを�視化��クリーニング�る                                         |      [レッスン](../2-Regression/2-Data/translations/README.ja.md)       |      Jen       |
+|      07      |            北米�カボ�ャ�価格 🎃            |           [回帰](../2-Regression/README.md)            | 線形�よ�多項�回帰モデルを構築�る                                                       |               [レッスン](2-Regression/3-Linear/README.md)               |      Jen       |
+|      08      |            北米�カボ�ャ�価格 🎃            |           [回帰](../2-Regression/README.md)            | ロジスティック回帰モデルを構築�る                                                         |            [レッスン](../2-Regression/4-Logistic/README.md)             |      Jen       |
+|      09      |                 Webアプリ 🔌                  |          [Web アプリ](../3-Web-App/README.md)          | 学習��モデルを使用�るWebアプリを構築�る                                                |              [レッスン](../3-Web-App/1-Web-App/README.md)               |      Jen       |
+|      10      |                 分類��導入                 |         [分類](../4-Classification/README.md)          | データをクリーニング・�処�・�視化�る。分類��導入                                     |        [レッスン](../4-Classification/1-Introduction/README.md)         | Jen and Cassie |
+|      11      |       美味��アジア料��インド料� �       |         [分類](../4-Classification/README.md)          | 分類器��導入                                                                             |        [レッスン](../4-Classification/2-Classifiers-1/README.md)        | Jen and Cassie |
+|      12      |       美味��アジア料��インド料� �       |         [分類](../4-Classification/README.md)          | ��他�分類器                                                                             |        [レッスン](../4-Classification/3-Classifiers-2/README.md)        | Jen and Cassie |
+|      13      |       美味��アジア料��インド料� �       |         [分類](../4-Classification/README.md)          | モデルを使用��推薦Webアプリを構築�る                                                    |           [レッスン](../4-Classification/4-Applied/README.md)           |      Jen       |
+|      14      |            クラスタリング��導入            |      [クラスタリング](../5-Clustering/README.md)       | データをクリーニング・�処�・�視化�る。クラスタリング��導入                           |            [レッスン](../5-Clustering/1-Visualize/README.md)            |      Jen       |
+|      15      |       ナイジェリア�音楽的嗜好を探る 🎧       |      [クラスタリング](../5-Clustering/README.md)       | K-Means法を探る                                                                            |             [レッスン](../5-Clustering/2-K-Means/README.md)             |      Jen       |
+|      16      |            自然言語処���導入 ☕�            |           [自然言語処�](../6-NLP/README.md)           | �純�ボットを構築��自然言語処��基礎を学�                                             |          [レッスン](../6-NLP/1-Introduction-to-NLP/README.md)           |    Stephen     |
+|      17      |        自然言語処��一般的�タスク ☕�        |           [自然言語処�](../6-NLP/README.md)           | 言語構造を扱�際�必���る一般的�タスクを�解�る����自然言語処��知識を深�る     |                 [レッスン](../6-NLP/2-Tasks/README.md)                  |    Stephen     |
+|      18      |               翻訳�感情分� ♥�               |           [自然言語処�](../6-NLP/README.md)           | ジェーン・オースティン�翻訳�感情分�                                                     |         [レッスン](../6-NLP/3-Translation-Sentiment/README.md)          |    Stephen     |
+|      19      |      ヨーロッパ�ロマン�ック�ホテル ♥�      |           [自然言語処�](../6-NLP/README.md)           | ホテル�レビュー�感情分� 1                                                               |            [レッスン](../6-NLP/4-Hotel-Reviews-1/README.md)             |    Stephen     |
+|      20      |      ヨーロッパ�ロマン�ック�ホテル ♥�      |           [自然言語処�](../6-NLP/README.md)           | ホテル�レビュー�感情分� 2                                                               |            [レッスン](../6-NLP/5-Hotel-Reviews-2/README.md)             |    Stephen     |
+|      21      |              時系列予測��導入              |        [Time series](../7-TimeSeries/README.md)        | 時系列予測��導入                                                                         |          [レッスン](../7-TimeSeries/1-Introduction/README.md)           |   Francesca    |
+|      22      | ⚡� 世界�電力使用� ⚡� - ARIMA�よる時系列予測 |        [Time series](../7-TimeSeries/README.md)        | ARIMA�よる時系列予測                                                                      |              [レッスン](../7-TimeSeries/2-ARIMA/README.md)              |   Francesca    |
+|      23      |               強化学習��導入               | [Reinforcement learning](../8-Reinforcement/README.md) | Q学習を使��強化学習��導入                                                              |          [レッスン](../8-Reinforcement/1-QLearning/README.md)           |     Dmitry     |
+|      24      | ピーター�狼を��る�を手�������� � | [Reinforcement learning](../8-Reinforcement/README.md) | 強化学習ジム                                                                               |             [レッスン](../8-Reinforcement/2-Gym/README.md)              |     Dmitry     |
+|  Postscript  |        実世界�機械学習シナリオ�応用        |      [ML in the Wild](../9-Real-World/README.md)       | 興味深���義��る��典的機械学習�実世界��応用                                       |          [レッスン](../9-Real-World/1-Applications/README.md)           |      Team      |
 
-| レッスン番� |                           トピック                            |                       レッスングループ                       | 学習�目的                                                                                                             |                     関連�るレッスン                     |     著者     |
-| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              機械学習��導入              |      [導入](../1-Introduction/translations/README.ja.md)       | 機械学習�基本的�概念を学�                                                                                |   [レッスン](../1-Introduction/1-intro-to-ML/translations/README.ja.md)    |    Muhammad    |
-|      02       |              機械学習�歴�               |      [導入](../1-Introduction/translations/README.ja.md)       | ��分野�背景��る歴�を学�                                                                                         |  [レッスン](../1-Introduction/2-history-of-ML/translations/README.ja.md)   |  Jen and Amy   |
-|      03       |               公平性�機械学習                |      [導入](../1-Introduction/translations/README.ja.md)       | 機械学習モデルを構築・�用�る際�学生�考慮����公平性�関�る���哲学的�題�何�？ |     [レッスン](../1-Introduction/3-fairness/translations/README.ja.md)     |     Tomomi     |
-|      04       |              機械学習�手法               |      [導入](../1-Introduction/translations/README.ja.md)       | 機械学習�研究者���よ��手法�モデルを構築���る�？                                                                       | [レッスン](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                 回帰��導入                 |        [回帰](../2-Regression/README.md)         | 回帰モデルをPython� Scikit-learn �始�る                                                                  |       [レッスン](../2-Regression/1-Tools/translations/README.ja.md)        |      Jen       |
-|      06       |              北米�カボ�ャ�価格 🎃               |        [回帰](../2-Regression/README.md)         | 機械学習����データを�視化��クリーニング�る                                                                                  |        [レッスン](../2-Regression/2-Data/translations/README.ja.md)        |      Jen       |
-|      07       |              北米�カボ�ャ�価格 🎃               |        [回帰](../2-Regression/README.md)         | 線形�よ�多項�回帰モデルを構築�る                                                                                   |       [レッスン](2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              北米�カボ�ャ�価格 🎃               |        [回帰](../2-Regression/README.md)         | ロジスティック回帰モデルを構築�る                                                                                               |      [レッスン](../2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        Webアプリ 🔌                         |           [Webアプリ](../3-Web-App/README.md)            | 学習��モデルを使用�るWebアプリを構築�る                                                                                       |        [レッスン](../3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               分類��導入               |    [分類](../4-Classification/README.md)     | データをクリーニング・�処�・�視化�る。分類��導入                                                            |  [レッスン](../4-Classification/1-Introduction/README.md)  | Jen and Cassie |
-|      11       |           美味��アジア料��インド料� �            |    [分類](../4-Classification/README.md)     | 分類器��導入                                                                                                     | [レッスン](../4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
-|      12       |           美味��アジア料��インド料� �            |    [分類](../4-Classification/README.md)     | ��他�分類器                                                                                                                | [レッスン](../4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
-|      13       |           美味��アジア料��インド料� �            |    [分類](../4-Classification/README.md)     | モデルを使用��推薦Webアプリを構築�る                                                                                    |    [レッスン](../4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 クラスタリング��導入                 |        [クラスタリング](../5-Clustering/README.md)         | データをクリーニング・�処�・�視化�る。クラスタリング��導入                                                                |     [レッスン](../5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            ナイジェリア�音楽的嗜好を探る 🎧             |        [クラスタリング](../5-Clustering/README.md)         | K-Means法を探る                                                                                           |      [レッスン](../5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       自然言語処���導入 ☕�       |   [自然言語処�](../6-NLP/README.md)    | �純�ボットを構築��自然言語処��基礎を学�                                                                             |    [レッスン](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     自然言語処��一般的�タスク ☕�                     |   [自然言語処�](../6-NLP/README.md)    | 言語構造を扱�際�必���る一般的�タスクを�解�る����自然言語処��知識を深�る                          |           [レッスン](../6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            翻訳�感情分� ♥�            |   [自然言語処�](../6-NLP/README.md)    | ジェーン・オースティン�翻訳�感情分�                                                                             |   [レッスン](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                ヨーロッパ�ロマン�ック�ホテル ♥�                 |   [自然言語処�](../6-NLP/README.md)    | ホテル�レビュー�感情分� 1                                                                                        |      [レッスン](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                ヨーロッパ�ロマン�ック�ホテル ♥�                 |   [自然言語処�](../6-NLP/README.md)    | ホテル�レビュー�感情分� 2                                                                                         |      [レッスン](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          時系列予測��導入           |        [Time series](../7-TimeSeries/README.md)        | 時系列予測��導入                                                                                         |    [レッスン](../7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
-|      22       | ⚡� 世界�電力使用� ⚡� - ARIMA�よる時系列予測 |        [Time series](../7-TimeSeries/README.md)        | ARIMA�よる時系列予測                                                                                              |       [レッスン](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           強化学習��導入           | [Reinforcement learning](../8-Reinforcement/README.md) | Q学習を使��強化学習��導入                                                                          |    [レッスン](../8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                ピーター�狼を��る�を手�������� �                | [Reinforcement learning](../8-Reinforcement/README.md) | 強化学習ジム                                                                                                      |       [レッスン](../8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  Postscript   |          実世界�機械学習シナリオ�応用          |      [ML in the Wild](../9-Real-World/README.md)       | 興味深���義��る��典的機械学習�実世界��応用                                                               |    [レッスン](../9-Real-World/1-Applications/README.md)    |      Team      |
 ## オフラインアクセス
 
 [Docsify](https://docsify.js.org/#/) を使�����ドキュメントをオフライン�実行����。��リ�ジトリをフォーク���ローカルマシン� [Docsify をインストール](https://docsify.js.org/#/quickstart) ����リ�ジトリ�ルートフォルダ� `docsify serve` �入力������。ローカルホスト�3000番�ート���り `localhost:3000` �Webサイト�起動���。

From 1619a0888d01dd8a90412743f6ea6206a5e2d6ab Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Wed, 21 Jul 2021 19:14:16 +0900
Subject: [PATCH 238/368] fix: update end of sentence to be more natural

---
 translations/README.ja.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/translations/README.ja.md b/translations/README.ja.md
index 2fe13ee4cb..39ab3fae82 100644
--- a/translations/README.ja.md
+++ b/translations/README.ja.md
@@ -54,7 +54,7 @@
 
 ��カリキュラムを構築�る���り�����2��教育方�を�����。**プロジェクトベース**�体験��**頻���テスト**を�む����。�ら���カリキュラム������りを���る���共通�**テーマ**��り��。
 
-内容�プロジェクト��整�性を������学生����学習プロセス�より魅力的��り�概念�定�度�高�り��。�ら��授業��軽��テスト�学生�学習�欲を高��授業後�2回目��テスト�より一層�定�����り��。��カリキュラム�柔軟��楽��も���るよ�デザイン�れ��り�����も���一部を�講����。プロジェクト����も��ら始�り�12週間�間�少���複雑�も���������。�����カリキュラム��機械学習�実世界��応用�関�る����も�ん��り�追加����る��議論�題����使用����。
+内容�プロジェクト��整�性を������学生����学習プロセス�より魅力的��り�概念�定�度�高�り��。�ら��授業��軽��テスト�学生�学習�欲を高��授業後�2回目��テスト�より一層�定�����り��。��カリキュラム�柔軟��楽��も���るよ�デザイン�れ��り�����も���一部を�講�る����能��。プロジェクト����も��ら始�り�12週間�間�少���複雑�も���������。�����カリキュラム��機械学習�実世界��応用�関�る����も�ん��り�追加����る��議論�題����使用����。
 
 > [行動�範](../CODE_OF_CONDUCT.md)�[貢献](../CONTRIBUTING.md)�[翻訳](../TRANSLATIONS.md) �ガイドラインを�覧����。建設的���見を�待����り���
 

From a8bf4c95e120c33cdb0eb1d45e48cc87563da18f Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Wed, 21 Jul 2021 21:13:45 +0700
Subject: [PATCH 239/368] Add Indonesian translation for 1-4 README

---
 .../translations/README.id.md                 | 105 ++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 1-Introduction/4-techniques-of-ML/translations/README.id.md

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
new file mode 100644
index 0000000000..dc9459546c
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -0,0 +1,105 @@
+# Teknik-teknik Machine Learning
+
+Proses membangun, menggunakan, dan memelihara model machine learning dan data yang digunakan adalah proses yang sangat berbeda dari banyak alur kerja pengembangan lainnya. Dalam pelajaran ini, kita akan mengungkap prosesnya, dan menguraikan teknik utama yang perlu Kamu ketahui. Kamu akan: 
+
+- Memahami gambaran dari proses yang mendasari machine learning.
+- Menjelajahi konsep dasar seperti 'models', 'predictions', dan 'training data'. 
+  
+## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## Pengantar
+
+Gambaran membuat proses machine learning (ML) terdiri dari sejumlah langkah: 
+
+1. **Menentukan pertanyaan**. Sebagian besar proses ML dimulai dengan mengajukan pertanyaan yang tidak dapat dijawab oleh program kondisional sederhana atau mesin berbasis aturan (*rules-based engine*). Pertanyaan-pertanyaan ini sering berkisar seputar prediksi berdasarkan kumpulan data.
+2. **Mengumpulkan dan menyiapkan data**. Untuk dapat menjawab pertanyaanmu, Kamu memerlukan data. Bagaimana kualitas dan terkadang kuantitas data kamu akan menentukan seberapa baik kamu dapat menjawab pertanyaan awal kamu. Memvisualisasikan data merupakan aspek penting dari fase ini. Fase ini juga mencakup pemisahan data menjadi kelompok *training* dan *testing* untuk membangun model.
+3. **Memilih metode training**. Tergantung dari pertanyaan dan sifat datamu, Kamu perlu memilih bagaimana kamu ingin men-training sebuah model untuk mencerminkan data kamu dengan baik dan membuat prediksi yang akurat terhadapnya. Ini adalah bagian dari proses ML yang membutuhkan keahlian khusus dan seringkali perlu banyak eksperimen.
+4. **Melatih model**. Dengan menggunakan data *training*, kamu akan menggunakan berbagai algoritma untuk melatih model guna mengenali pola dalam data. Modelnya mungkin bisa memanfaatkan *internal weight* yang dapat disesuaikan untuk memberi hak istimewa pada bagian tertentu dari data dibandingkan bagian lainnya untuk membangun model yang lebih baik. 
+5. **Mengevaluasi model**. Gunakan data yang belum pernah dilihat sebelumnya (data *testing*) untuk melihat bagaimana kinerja model. 
+6. **Parameter tuning**. Berdasarkan kinerja modelmu, Kamu dapat mengulang prosesnya menggunakan parameter atau variabel yang berbeda, yang mengontrol perilaku algoritma yang digunakan untuk melatih model. 
+7. **Prediksi**. Gunakan input baru untuk menguji keakuratan model kamu. 
+
+## Pertanyaan apa yang harus ditanyakan?
+
+Komputer sangat ahli dalam menemukan pola tersembunyi dalam data. Hal ini sangat membantu peneliti yang memiliki pertanyaan tentang domain tertentu yang tidak dapat dijawab dengan mudah dari hanya membuat mesin berbasis aturan kondisional (*conditionally-based rules engine*). Untuk tugas aktuaria misalnya, seorang data scientist mungkin dapat membuat aturan secara manual seputar mortalitas perokok vs non-perokok. 
+
+Namun, ketika banyak variabel lain dimasukkan ke dalam persamaan, model ML mungkin terbukti lebih efisien untuk memprediksi tingkat mortalitas di masa depan berdasarkan riwayat kesehatan masa lalu. Contoh yang lebih menyenangkan mungkin membuat prediksi cuaca untuk bulan April di lokasi tertentu berdasarkan data yang mencakup garis lintang, garis bujur, perubahan iklim, kedekatan dengan laut, pola aliran udara (Jet Stream), dan banyak lagi. 
+
+✅ [Slide deck](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) ini menawarkan perspektif historis pada model cuaca dengan menggunakan ML dalam analisis cuaca.
+
+## Tugas Pra-Pembuatan
+
+Sebelum mulai membangun model kamu, ada beberapa tugas yang harus kamu selesaikan. Untuk menguji pertanyaan kamu dan membentuk hipotesis berdasarkan prediksi model, Kamu perlu mengidentifikasi dan mengonfigurasi beberapa elemen. 
+
+### Data
+
+Untuk dapat menjawab pertanyaan kamu dengan kepastian, Kamu memerlukan sejumlah besar data dengan jenis yang tepat. Ada dua hal yang perlu kamu lakukan pada saat ini: 
+
+- **Mengumpulkan data**. Ingat pelajaran sebelumnya tentang keadilan dalam analisis data, kumpulkan data kamu dengan hati-hati. Waspadai sumber datanya, bias bawaan apa pun yang mungkin dimiliki, dan dokumentasikan asalnya. 
+- **Menyiapkan data**. Ada beberapa langkah dalam proses persiapan data. Kamu mungkin perlu menyusun data dan melakukan normalisasi jika berasal dari berbagai sumber. Kamu dapat meningkatkan kualitas dan kuantitas data melalui berbagai metode seperti mengonversi string menjadi angka (seperti yang kita lakukan di [Clustering](../../5-Clustering/1-Visualize/README.md)). Kamu mungkin juga bisa membuat data baru berdasarkan data yang asli (seperti yang kita lakukan di [Classification](../../4-Classification/1-Introduction/README.md)). Kamu bisa membersihkan dan mengubah data (seperti yang kita lakukan sebelum pelajaran [Web App](../3-Web-App/README.md)). Terakhir, Kamu mungkin juga perlu mengacaknya dan mengubah urutannya, tergantung pada teknik *training* kamu. 
+
+✅ Setelah mengumpulkan dan memproses data kamu, luangkan waktu sejenak untuk melihat apakah bentuknya memungkinkan kamu untuk menjawab pertanyaan yang kamu maksudkan. Mungkin data tidak akan berkinerja baik dalam tugas yang kamu berikan, seperti yang kita temukan dalam pelajaran [Clustering](../../5-Clustering/1-Visualize/README.md).
+
+### Memilih variabel fiturmu
+
+Sebuah [fitur](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) adalah sebuah properti yang dapat diukur dalam data kamu. Dalam banyak dataset, properti dinyatakan sebagai sebuah heading kolom seperti 'date' 'size' atau 'color'. Variabel fitur kamu yang biasanya direpresentasikan sebagai `y` dalam kode, mewakili jawaban atas pertanyaan yang kamu coba tanyakan tentang data kamu: pada bulan Desember, labu dengan **warna** apa yang akan paling murah? di San Francisco, lingkungan mana yang menawarkan **harga** real estate terbaik?
+
+🎓 **Feature Selection dan Feature Extraction** Bagaimana kamu tahu variabel mana yang harus dipilih saat membangun model? Kamu mungkin akan melalui proses pemilihan fitur (*Feature Selection*) atau ekstraksi fitur (*Feature Extraction*) untuk memilih variabel yang tepat untuk model yang paling berkinerja. Namun, keduanya tidak sama: "Ekstraksi fitur membuat fitur baru dari fungsi fitur asli, sedangkan pemilihan fitur mengembalikan subset fitur." ([sumber](https://wikipedia.org/wiki/Feature_selection))
+### Visualisasikan datamu
+
+Aspek penting dari toolkit data scientist adalah kemampuan untuk memvisualisasikan data menggunakan beberapa *library* seperti Seaborn atau MatPlotLib. Merepresentasikan data kamu secara visual memungkinkan kamu mengungkap korelasi tersembunyi yang dapat kamu manfaatkan. Visualisasimu mungkin juga membantu kamu mengungkap data yang bias atau tidak seimbang (seperti yang kita temukan dalam [Classification](../../4-Classification/2-Classifiers-1/README.md)).
+### Membagi dataset
+
+Sebelum memulai *training*, Kamu perlu membagi dataset menjadi dua atau lebih bagian dengan ukuran yang tidak sama tapi masih mewakili data dengan baik.
+
+- **Training**. Bagian dataset ini digunakan untuk men-training model kamu. Bagian dataset ini merupakan mayoritas dari dataset asli. 
+- **Testing**. Sebuah dataset tes adalah kelompok data independen, seringkali dikumpulkan dari data yang asli yang akan digunakan untuk mengkonfirmasi kinerja dari model yang dibuat.
+- **Validating**. Dataset validasi adalah kumpulan contoh mandiri yang lebih kecil yang kamu gunakan untuk menyetel hyperparameter atau arsitektur model untuk meningkatkan model. Tergantung dari ukuran data dan pertanyaan yang kamu ajukan, Kamu mungkin tidak perlu membuat dataset ketiga ini (seperti yang kita catat dalam [Time Series Forecasting](../7-TimeSeries/1-Introduction/README.md)).
+
+## Membuat sebuah model
+
+Dengan menggunakan data *training*, tujuan kamu adalah membuat model atau representasi statistik data kamu, menggunakan berbagai algoritma untuk **melatihnya**. Melatih model berarti mengeksposnya dengan data dan mengizinkannya membuat asumsi tentang pola yang ditemukan, divalidasi, dan diterima atau ditolak. 
+
+### Tentukan metode training
+
+Tergantung dari pertanyaan dan sifat datamu, Kamu akan memilih metode untuk melatihnya. Melangkah menuju dokumentasi [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) - yang kita gunakan dalam pelajaran ini - kamu bisa menjelajahi banyak cara untuk melatih sebuah model. Tergantung dari pengalamanmu, kamu mungkin perlu mencoba beberapa metode yang berbeda untuk membuat model yang terbaik. Kemungkinan kamu akan melalui proses di mana data scientist mengevaluasi kinerja model dengan memasukkan data yang belum pernah dilihat, memeriksa akurasi, bias, dan masalah penurunan kualitas lainnya, dan memilih metode training yang paling tepat untuk tugas yang ada. 
+### Melatih sebuah model
+
+Berbekal data *training*, Kamu siap untuk menggunakannya untuk membuat model. Kamu akan melihat di banyak *library* ML mengenai kode 'model.fit' - pada saat inilah kamu mengirimkan data kamu sebagai *array* nilai (biasanya 'X') dan variabel fitur (biasanya 'y' ). 
+### Mengevaluasi model
+
+Setelah proses *training* selesai (ini mungkin membutuhkan banyak iterasi, atau 'epoch', untuk melatih model besar), Kamu akan dapat mengevaluasi kualitas model dengan menggunakan data tes untuk mengukur kinerjanya. Data ini merupakan subset dari data asli yang modelnya belum pernah dianalisis sebelumnya. Kamu dapat mencetak tabel metrik tentang kualitas model kamu. 
+
+🎓 **Model fitting**
+
+Dalam konteks machine learning, *model fitting* mengacu pada keakuratan dari fungsi yang mendasari model saat mencoba menganalisis data yang tidak familiar. 
+
+🎓 **Underfitting** dan **overfitting** adalah masalah umum yang menurunkan kualitas model, karena model tidak cukup akurat atau terlalu akurat. Hal ini menyebabkan model membuat prediksi yang terlalu selaras atau tidak cukup selaras dengan data pelatihannya. Model overfit memprediksi data *training* terlalu baik karena telah mempelajari detail dan noise data dengan terlalu baik. Model underfit tidak akurat karena tidak dapat menganalisis data *training* atau data yang belum pernah dilihat sebelumnya secara akurat. 
+
+![overfitting model](images/overfitting.png)
+> Infografis oleh [Jen Looper](https://twitter.com/jenlooper)
+
+## Parameter tuning
+
+Setelah *training* awal selesai, amati kualitas model dan pertimbangkan untuk meningkatkannya dengan mengubah 'hyperparameter' nya. Baca lebih lanjut tentang prosesnya [di dalam dokumentasi](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+
+## Prediksi
+
+Ini adalah saat di mana Kamu dapat menggunakan data yang sama sekali baru untuk menguji akurasi model kamu. Dalam setelan ML 'terapan', di mana kamu membangun aset web untuk menggunakan modelnya dalam produksi, proses ini mungkin melibatkan pengumpulan input pengguna (misalnya menekan tombol) untuk menyetel variabel dan mengirimkannya ke model untuk inferensi, atau evaluasi. 
+
+Dalam pelajaran ini, Kamu akan menemukan cara untuk menggunakan langkah-langkah ini untuk mempersiapkan, membangun, menguji, mengevaluasi, dan memprediksi - semua gestur data scientist dan banyak lagi, seiring kemajuanmu dalam perjalanan menjadi 'full stack' ML engineer. 
+
+---
+
+## 🚀Tantangan
+
+Gambarlah sebuah flow chart yang mencerminkan langkah-langkah seorang praktisi ML. Di mana kamu melihat diri kamu saat ini dalam prosesnya? Di mana kamu memprediksi kamu akan menemukan kesulitan? Apa yang tampak mudah bagi kamu? 
+
+## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+
+## Ulasan & Belajar Mandiri
+
+Cari di Internet mengenai wawancara dengan data scientist yang mendiskusikan pekerjaan sehari-hari mereka. Ini [salah satunya](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+
+## Tugas
+
+[Wawancara dengan data scientist](assignment.md)

From ae5d451e5ce921068b7b117a4abc85aca11633cb Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Wed, 21 Jul 2021 21:19:23 +0700
Subject: [PATCH 240/368] Update some translation

---
 1-Introduction/1-intro-to-ML/translations/README.id.md      | 4 ++--
 1-Introduction/1-intro-to-ML/translations/assignment.id.md  | 2 +-
 1-Introduction/2-history-of-ML/translations/README.id.md    | 4 ++--
 1-Introduction/4-techniques-of-ML/translations/README.id.md | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md
index 8d0044b018..4cef05dca4 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.id.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.id.md
@@ -2,7 +2,7 @@
 
 [![ML, AI, deep learning - Apa perbedaannya?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, deep learning - Apa perbedaannya?")
 
-> 🎥 Klik gambar diatas untuk melihat video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning.
+> 🎥 Klik gambar diatas untuk menonton video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning.
 
 ## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
 
@@ -12,7 +12,7 @@ Selamat datang di pelajaran Machine Learning klasik untuk pemula! Baik kamu yang
 
 [![Pengantar Machine Learning](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Pengantar Machine Learning")
 
-> 🎥 Klik gambar diatas untuk melihat video: John Guttag dari MIT yang memberikan pengantar Machine Learning.
+> 🎥 Klik gambar diatas untuk menonton video: John Guttag dari MIT yang memberikan pengantar Machine Learning.
 ### Memulai Machine Learning
 
 Sebelum memulai kurikulum ini, kamu perlu memastikan komputer kamu sudah dipersiapkan untuk menjalankan *notebook* secara lokal.
diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.id.md b/1-Introduction/1-intro-to-ML/translations/assignment.id.md
index 9de8a4bdd5..c6ba6e4a8d 100644
--- a/1-Introduction/1-intro-to-ML/translations/assignment.id.md
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.id.md
@@ -4,6 +4,6 @@
 
 Dalam tugas yang tidak dinilai ini, kamu akan mempelajari Python dan mempersiapkan *environment* kamu sehingga dapat digunakan untuk menjalankan *notebook*.
 
-Ambil [Jalur Belajar Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) ini, kemudian persiapkan sistem kamu dengan melihat video-video pengantar ini:
+Ambil [Jalur Belajar Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa) ini, kemudian persiapkan sistem kamu dengan menonton video-video pengantar ini:
 
 https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
index 4053785b3f..a0a4ce059f 100644
--- a/1-Introduction/2-history-of-ML/translations/README.id.md
+++ b/1-Introduction/2-history-of-ML/translations/README.id.md
@@ -56,7 +56,7 @@ Penelitian didanai dengan baik oleh lembaga pemerintah, banyak kemajuan dibuat d
 
     [![blocks world dengan SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world dengan SHRDLU")
     
-    > 🎥 Klik gambar diatas untuk melihat video: Blocks world with SHRDLU
+    > 🎥 Klik gambar diatas untuk menonton video: Blocks world with SHRDLU
 
 ## 1974 - 1980: "Musim Dingin AI"
 
@@ -94,7 +94,7 @@ Saat ini, *machine learning* dan AI hampir ada di setiap bagian dari kehidupan k
 Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk memahami sistem komputer dan perangkat lunak serta algoritma yang dijalankannya. Kami berharap kurikulum ini akan membantu kamu untuk mendapatkan pemahaman yang lebih baik sehingga kamu dapat memutuskan sendiri. 
 
 [![Sejarah Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "Sejarah Deep Learning")
-> 🎥 Klik gambar diatas untuk melihat video: Yann LeCun mendiskusikan sejarah dari Deep Learning dalam pelajaran ini
+> 🎥 Klik gambar diatas untuk menonton video: Yann LeCun mendiskusikan sejarah dari Deep Learning dalam pelajaran ini
 
 ---
 ## 🚀Tantangan
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
index dc9459546c..77f5880f42 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.id.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -3,7 +3,7 @@
 Proses membangun, menggunakan, dan memelihara model machine learning dan data yang digunakan adalah proses yang sangat berbeda dari banyak alur kerja pengembangan lainnya. Dalam pelajaran ini, kita akan mengungkap prosesnya, dan menguraikan teknik utama yang perlu Kamu ketahui. Kamu akan: 
 
 - Memahami gambaran dari proses yang mendasari machine learning.
-- Menjelajahi konsep dasar seperti 'models', 'predictions', dan 'training data'. 
+- Menjelajahi konsep dasar seperti '*models*', '*predictions*', dan '**training data*'. 
   
 ## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
 ## Pengantar
@@ -61,7 +61,7 @@ Dengan menggunakan data *training*, tujuan kamu adalah membuat model atau repres
 
 ### Tentukan metode training
 
-Tergantung dari pertanyaan dan sifat datamu, Kamu akan memilih metode untuk melatihnya. Melangkah menuju dokumentasi [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) - yang kita gunakan dalam pelajaran ini - kamu bisa menjelajahi banyak cara untuk melatih sebuah model. Tergantung dari pengalamanmu, kamu mungkin perlu mencoba beberapa metode yang berbeda untuk membuat model yang terbaik. Kemungkinan kamu akan melalui proses di mana data scientist mengevaluasi kinerja model dengan memasukkan data yang belum pernah dilihat, memeriksa akurasi, bias, dan masalah penurunan kualitas lainnya, dan memilih metode training yang paling tepat untuk tugas yang ada. 
+Tergantung dari pertanyaan dan sifat datamu, Kamu akan memilih metode untuk melatihnya. Buka dokumentasi [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) yang kita gunakan dalam pelajaran ini, kamu bisa menjelajahi banyak cara untuk melatih sebuah model. Tergantung dari pengalamanmu, kamu mungkin perlu mencoba beberapa metode yang berbeda untuk membuat model yang terbaik. Kemungkinan kamu akan melalui proses di mana data scientist mengevaluasi kinerja model dengan memasukkan data yang belum pernah dilihat, memeriksa akurasi, bias, dan masalah penurunan kualitas lainnya, dan memilih metode training yang paling tepat untuk tugas yang ada. 
 ### Melatih sebuah model
 
 Berbekal data *training*, Kamu siap untuk menggunakannya untuk membuat model. Kamu akan melihat di banyak *library* ML mengenai kode 'model.fit' - pada saat inilah kamu mengirimkan data kamu sebagai *array* nilai (biasanya 'X') dan variabel fitur (biasanya 'y' ). 

From 90ce8e8a8f9b378b4ead8aa97c1098040464722b Mon Sep 17 00:00:00 2001
From: edgargonarr <35715904+edgargonarr@users.noreply.github.com>
Date: Wed, 21 Jul 2021 12:52:56 -0500
Subject: [PATCH 241/368] Update README.md

---
 6-NLP/1-Introduction-to-NLP/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index ea244c7409..eeae6bec3e 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -162,4 +162,4 @@ Take a look at the references below as further reading opportunities.
 
 ## Assignment 
 
-[Search for a bot](../assignment.md)
+[Search for a bot](assignment.md)

From c27305513c23c72e943d0d55a435db253356347e Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Thu, 22 Jul 2021 18:25:17 +0900
Subject: [PATCH 242/368] fix: wrong quiz answers

---
 quiz-app/src/assets/translations/en.json | 4 ++--
 quiz-app/src/assets/translations/fr.json | 4 ++--
 quiz-app/src/assets/translations/tr.json | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index 21b8375e6d..b9ef44e264 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -412,7 +412,7 @@
               },
               {
                 "answerText": "you can discover potential cause for bias",
-                "isCorrect": "true"
+                "isCorrect": "false"
               },
               {
                 "answerText": "both of these",
@@ -1092,7 +1092,7 @@
             "answerOptions": [
               {
                 "answerText": "the task of classifying data points into multiple classes",
-                "isCorrect": "true"
+                "isCorrect": "false"
               },
               {
                 "answerText": "the task of classifying data points into one of several classes",
diff --git a/quiz-app/src/assets/translations/fr.json b/quiz-app/src/assets/translations/fr.json
index f3ecffc4f7..9b946ab574 100644
--- a/quiz-app/src/assets/translations/fr.json
+++ b/quiz-app/src/assets/translations/fr.json
@@ -412,7 +412,7 @@
                         },
                         {
 							"answerText": "Vous pouvez découvrir une cause potentielle de biais",
-                            "isCorrect": "true"
+                            "isCorrect": "false"
                         },
                         {
 							"answerText": "Les deux",
@@ -1091,7 +1091,7 @@
                     "answerOptions": [
 							{
 							"answerText": "La tâche de classer les points de données dans plusieurs classes",
-                            "isCorrect": "true"
+                            "isCorrect": "false"
                         },
                         {
 							"answerText": "La tâche de classifier les points de données dans l'une des plusieurs classes",
diff --git a/quiz-app/src/assets/translations/tr.json b/quiz-app/src/assets/translations/tr.json
index 050bbd2a29..aa479e2812 100644
--- a/quiz-app/src/assets/translations/tr.json
+++ b/quiz-app/src/assets/translations/tr.json
@@ -412,7 +412,7 @@
 							},
 							{
 								"answerText": "önyargı için potansiyel bir sebebi keşfedebilirsiniz",
-								"isCorrect": "true"
+								"isCorrect": "false"
 							},
 							{
 								"answerText": "bunların her ikisi",
@@ -1092,7 +1092,7 @@
 						"answerOptions": [
 							{
 								"answerText": "veri noktalarını birden çok sınıfa sınıflandırma görevi",
-								"isCorrect": "true"
+								"isCorrect": "false"
 							},
 							{
 								"answerText": "veri noktalarını birkaç sınıftan birine sınıflandırma görevi",

From 6767a5d7ff0f2d01463c75db08075ba2c2ea5aef Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 22 Jul 2021 18:28:04 +0900
Subject: [PATCH 243/368] add japanese translation of Regression/3-Linear

---
 .../1-Tools/translations/README.ja.md         |   2 +-
 2-Regression/2-Data/translations/README.ja.md |   2 +-
 .../3-Linear/translations/README.ja.md        | 334 ++++++++++++++++++
 3 files changed, 336 insertions(+), 2 deletions(-)
 create mode 100644 2-Regression/3-Linear/translations/README.ja.md

diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index 0bebf16d0a..427ef7b04b 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -215,4 +215,4 @@ s1 tc: T細胞（白血��一種）
 
 ## 課題
 
-[異�るデータセット](assignment.md)
+[異�るデータセット](../assignment.md)
diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index 1570be3c5e..07fd923bef 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -203,4 +203,4 @@ Matplotlib��供�る様々�タイプ�ビジュアライゼーション
 
 ## 課題
 
-[ビジュアライゼーション�探求](assignment.md)
+[ビジュアライゼーション�探求](../assignment.md)
diff --git a/2-Regression/3-Linear/translations/README.ja.md b/2-Regression/3-Linear/translations/README.ja.md
new file mode 100644
index 0000000000..2dd458b86d
--- /dev/null
+++ b/2-Regression/3-Linear/translations/README.ja.md
@@ -0,0 +1,334 @@
+# Scikit-learnを用��回帰モデル�構築: 回帰を行�2��方法
+
+![線形回帰 vs 多項�回帰 �インフォグラフィック](../images/linear-polynomial.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
+## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+### イントロダクション 
+
+�れ�����レッスン�使用�るカボ�ャ�価格データセット�ら集��サンプルデータを使���回帰��何�を探������。���Matplotlibを使���視化を行����。
+
+�れ��ML���る回帰をより深��解�る準備�整����。��レッスン���2種類�回帰����詳��説明���。基本的�線形回帰 (_basic linear regression_)�多項�回帰 (_polynomial regression_)�2種類�回帰�������基礎��る数学を学���。
+
+> ��カリキュラム���最低��数学�知識を�����他�分野�学生�も�解��るよ�����������解を助�る���メモ�🧮��出��図���学習ツールを�覧����。
+
+### 事�確�
+
+�����パンプキンデータ�構造����説明�����。��レッスン�_notebook.ipynb_ファイル���事��読�込�れ�整形�れ�データ�入�����。��ファイル���カボ�ャ�価格�ブッシェル���新��データフレーム�表示�れ����。 �れら�ノートブックを�Visual Studio Code�カー�ル�実行��る��を確�������。
+
+### 準備
+
+忘れ���ら�����データを読�込ん�ら����を行�����。
+
+- カボ�ャを買���最��時期������？
+- ミニカボ�ャ1ケース�価格����ら���ょ��？
+- �ブッシェル��スケット�買�����1 1/9ブッシェル�箱�買����。
+
+データを掘り下������ょ�。
+
+�回�レッスン���Pandas�データフレームを作���元�データセット�一部を入力���ブッシェル���価格を標準化����。������方法���約400�データ�イント��集�る���������も�れ�秋�期間�も����。
+
+��レッスン�付属�るノートブック���ら���読�込ん����データを見����ょ�。データ�事��読�込�れ�月毎�データ�散布図���表示�れ����。データをも��綺麗��る����データ�性質をも�少�知る�����る�も�れ��ん。
+
+## 線形回帰
+
+レッスン1�学ん�よ���線形回帰�演習���以下�よ��線を��るよ���る���目標��。
+
+- **変数間�関係を示�。**
+- **予測を行�。** 新��データ�イント����線�����置�る�を正確�予測�る�����る。
+ 
+��よ��線を������**最�二乗回帰 (Least-Squares Regression)** �典型的�例��。「最�二乗����言葉��回帰線を囲む����データ�イント���離�二乗�れ���後加算�れる��を�味�����。�想的���最終的��計���る������るよ�����。�れ�エラー�数���り「最�二乗��値を����る����。
+
+�れ������データ�イント�ら�累��離�最���る直線をモデル化�������。���方�����大���注目���る���足���項を二乗���。
+
+> **🧮 Show me the math** 
+> 
+> ��線��_line of best fit_ �呼�れ�[方程�](https://en.wikipedia.org/wiki/Simple_linear_regression) �表��������。
+> 
+> ```
+> Y = a + bX
+> ```
+>
+> `X`�「説明変数���。`Y`�「目的変数���。`a`�切片�`b`�直線�傾�を表���。`X=0`����`Y`�値�切片`a`��り��。
+>
+>![傾��計算](../images/slope.png)
+>
+> �����傾�`b`を計算�����。[Jen Looper](https://twitter.com/jenlooper) �よるインフォグラフィック。
+>
+> カボ�ャ�データ�関�る最��質���る�「月毎�ブッシェル����カボ�ャ�価格を予測��������言�����る��`X`�価格を�`Y`�販売�れ�月を表�����。
+>
+>![方程��計算](../images/calculation.png)
+>
+> Y�値を計算�����ょ�。$4�後払���る�ら�4月����り��ん�[Jen Looper](https://twitter.com/jenlooper) �よるインフォグラフィック。
+>
+> 直線を計算�る数学��直線�傾�を示�必���り�����れ�切片���り「X = 0�����「Y������置�る��も�存���。
+>
+> �れら�値�計算方法��[Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) ���サイト�見る�������。���[this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html) ���値�線���よ��影響を与�る�を見る�������。
+
+## 相関関係
+
+も�一���解���用語��与�られ�X�Y�変数間�**相関係数 (Correlation Coefficient)** ��。散布図を使�����係数を����視化�る�������。データ�イント��れ��直線上�散ら����るプロット��高�相関を��������データ�イント�X�Y�間��らゆる場所�散ら����るプロット��低�相関を������。
+
+良�線形回帰モデル���最�二乗法�よ��求��回帰線�高�相関係数 (0よりも1�近�)を��も���。
+
+✅ ��レッスン�ノートを開���「都市�価格��散布図を見����ょ�。散布図�視覚的�解釈�よる��カボ�ャ�販売�関�る「都市��「価格��関連データ��相関性�高�よ��見������れ�も低�よ��見����？
+
+## 回帰�用�るデータ�準備
+
+��演習�背景��る数学を�解�����回帰モデルを作������パッケージ�カボ�ャ�価格�最も高��を予測��る����を確�������。休日�パンプキンパッ�用�パンプキンを購入�る人��パッ�用�パンプキンパッケージ�購入を最�化�る������情報を必���る�も�れ��ん。
+
+����Scikit-learnを使用�る���手作業�行�必���り��ん。レッスンノート�メイン�データ処�ブロック��Scikit-learn�ライブラリを追加�������文字列データを自動的�数字�変����。
+
+```python
+from sklearn.preprocessing import LabelEncoder
+
+new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)
+```
+
+new_pumpkinsデータフレームを見る������文字列�数値�����る����り��。�れ�より�人�読む��難���り�����Scikit-learn�������も分�りや���り���。
+�れ��回帰�最も���データ�����（散布図を見�������）より高度�判断���るよ���り���。
+
+良�予測モデルを構築�る����データ�2点間�良�相関関係を見��よ�����。���果�「都市��「価格��間��弱�相関関係���������り���。
+
+```python
+print(new_pumpkins['City'].corr(new_pumpkins['Price']))
+0.32363971816089226
+```
+
+����パッケージ�価格�間��も�少�強�相関関係��り��。�れ��������る������？通常�箱�大��れ�大�����価格�高��り��。
+
+```python
+print(new_pumpkins['Package'].corr(new_pumpkins['Price']))
+0.6061712937226021
+```
+
+��データ�対�る良�質���次�よ���り��。「�るカボ�ャ�パッケージ�価格����ら���る�？�
+
+��回帰モデルを構築�����ょ��
+
+## 線形モデル�構築
+
+モデルを構築�る���も�一度データ�整�を�����ょ�。NULLデータを削除��データ���よ��見�る�をも�一度確����。
+
+```python
+new_pumpkins.dropna(inplace=True)
+new_pumpkins.info()
+```
+
+������最�セット�ら新��データフレームを作����れを出力���。
+
+```python
+new_columns = ['Package', 'Price']
+lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+lin_pumpkins
+```
+
+```output
+	Package	Price
+70	0	13.636364
+71	0	16.363636
+72	0	16.363636
+73	0	15.454545
+74	0	13.636364
+...	...	...
+1738	2	30.000000
+1739	2	28.750000
+1740	2	25.750000
+1741	2	24.000000
+1742	2	24.000000
+415 rows × 2 columns
+```
+
+1. �れ��X�Y�座標データを割り当�る�������。
+
+   ```python
+   X = lin_pumpkins.values[:, :1]
+   y = lin_pumpkins.values[:, 1:2]
+   ```
+✅ ����何を������？ Python�[スライス記法](https://stackoverflow.com/questions/509211/understanding-slice-notation/509295#509295) を使���`X`�`y`��列を作������。
+
+2. 次��回帰モデル構築����ルー�ンを開始���。
+
+   ```python
+   from sklearn.linear_model import LinearRegression
+   from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+   from sklearn.model_selection import train_test_split
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+   lin_reg = LinearRegression()
+   lin_reg.fit(X_train,y_train)
+
+   pred = lin_reg.predict(X_test)
+
+   accuracy_score = lin_reg.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   相関関係���り良������生��れ�モデルも��り正確���り��ん。
+   
+   ```output
+   Model Accuracy:  0.3315342327998987
+   ```
+
+3. 今回��程���れ�線を�視化���。
+
+   ```python
+   plt.scatter(X_test, y_test,  color='black')
+   plt.plot(X_test, pred, color='blue', linewidth=3)
+
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+
+   plt.show()
+   ```
+   ![パッケージ�価格�関係を表�散布図](../images/linear.png)
+
+4. 架空�値�対��モデルをテスト�る。
+
+   ```python
+   lin_reg.predict( np.array([ [2.75] ]) )
+   ```
+   
+   ��架空�値�対���以下�価格�返�れ��。
+
+   ```output
+   array([[33.15655975]])
+   ```
+
+回帰�線�正��引�れ��れ����数字����������。
+
+🎃 ����������������数種類�カボ�ャ�価格を予測�るモデルを作�����。����休日�パンプキンパッ��美��も���る��ょ�。�も�も��良�モデルを作れる�も�れ��ん。
+
+## 多項�回帰
+
+線形回帰�も�一��タイプ��多項�回帰��。時��変数�間�直線的�関係 (カボ�ャ���多����価格�高��る)��る��も�り�����れら�関係��平�や直線���プロット������も�り��。
+
+✅ 多項�回帰を使������るデータ�[�ら������例](https://online.stat.psu.edu/stat501/lesson/9/9.8) を示���。
+
+先���散布図�「�種��「価格��関係をも�一度見����ょ�。��散布図��必��も直線�分����れ��ら��よ��見����？�������も�れ��ん。��よ��場���多項�回帰を試�����ょ�。
+
+✅ 多項����1�以上�変数�係数�構��れる数学的表���る。
+
+多項�回帰����線形データをよりよ�����る���曲線を作����。
+
+1. 元�カボ�ャ�データ�一部を入力��データフレームを作������ょ�。
+
+   ```python
+   new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']
+   poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')
+
+   poly_pumpkins
+   ```
+
+データフレーム内�データ間�相関関係を視覚化�る���「coolwarm��ャート�表示�る��良���ょ�。
+
+2. `Background_gradient()` メソッド�引数� `coolwarm` を指定��使用���。
+
+   ```python
+   corr = poly_pumpkins.corr()
+   corr.style.background_gradient(cmap='coolwarm')
+   ```
+   
+　　��コード�ヒートマップを作����。
+   ![データ�相関関係を示�ヒートマップ](../images/heatmap.png)
+
+���ャートを見る��「パッケージ��「価格��間�正�相関関係��る���視覚化�れ����。��り��回�モデルよりも多少良�モデルを作る�����る����。
+
+### パイプライン�作�
+
+Scikit-learn���多項�回帰モデルを構築�る���便利�API��る`make_pipeline` [API](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html?highlight=pipeline#sklearn.pipeline.make_pipeline) �用��れ����。「パイプライン��推定��連鎖�作��れ��。今回�場��パイプライン��多項��特徴���線形�経路を形��る予測値���れ��。
+
+1. X列�y列を作り��。
+
+   ```python
+   X=poly_pumpkins.iloc[:,3:4].values
+   y=poly_pumpkins.iloc[:,4:5].values
+   ```
+
+2. `make_pipeline()` メソッドを呼�出��パイプラインを作����。
+
+   ```python
+   from sklearn.preprocessing import PolynomialFeatures
+   from sklearn.pipeline import make_pipeline
+
+   pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())
+
+   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+   pipeline.fit(np.array(X_train), y_train)
+
+   y_pred=pipeline.predict(X_test)
+   ```
+
+### 系列�作�
+
+��時点��パイプライン�系列を作���るよ���ソート�れ�データ�新��データフレームを作��る必���り��。
+
+以下�コードを追加���。
+
+   ```python
+   df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})
+   df.sort_values(by='x',inplace = True)
+   points = pd.DataFrame(df).to_numpy()
+
+   plt.plot(points[:, 0], points[:, 1],color="blue", linewidth=3)
+   plt.xlabel('Package')
+   plt.ylabel('Price')
+   plt.scatter(X,y, color="black")
+   plt.show()
+   ```
+
+`pd.DataFrame` を呼�出��新��データフレームを作�����。次�`sort_values()` を呼�出��値をソート����。最後�多項��プロットを作�����。
+
+![パッケージ�価格�関係を示�多項��プロット](../images/polynomial.png)
+
+よりデータ�フィット��曲線を確��る�������。
+
+モデル�精度を確������ょ�。
+
+   ```python
+   accuracy_score = pipeline.score(X_train,y_train)
+   print('Model Accuracy: ', accuracy_score)
+   ```
+
+   �れ�完����
+
+   ```output
+   Model Accuracy:  0.8537946517073784
+   ```
+
+��感����価格を予測�����ょ�。
+
+### 予測�実行
+
+新��値を入力��予測値を�得�����？
+
+`predict()` メソッドを呼�出���予測を行���。
+ 
+   ```python
+   pipeline.predict( np.array([ [2.75] ]) )
+   ```
+   以下�予測�果�得られ��。
+
+   ```output
+   array([[46.34509342]])
+   ```
+
+プロットを見��る���得�������������データを見���れ���モデルよりも良�モデル��れ��より高価�カボ�ャ����予算を組む必���り��。
+
+� �疲れ様����1��レッスン�2��回帰モデルを作�����。回帰�関�る最後�セクション���カテゴリーを決定�る���ロジスティック回帰����学���。
+
+---
+## 🚀�ャレンジ
+
+��ノートブック������異�る変数をテスト��相関関係�モデル�精度���よ��影響�る�を確���������。
+
+## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+
+## レビュー & 自主学習
+
+��レッスン���線形回帰����学����。回帰��他�も���種類��り��。Stepwise�Ridge�Lasso�Elasticnet���テクニックを�覧����。より詳��学����[Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning) �良���ょ�。
+
+## 課題 
+
+[モデル構築](../assignment.md)

From f16880941b47e0183bb275754b174c1018844225 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 22 Jul 2021 18:32:18 +0900
Subject: [PATCH 244/368] add japanese translation of Regression/3-Linear

---
 2-Regression/3-Linear/translations/README.ja.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/3-Linear/translations/README.ja.md b/2-Regression/3-Linear/translations/README.ja.md
index 2dd458b86d..3c87a7367a 100644
--- a/2-Regression/3-Linear/translations/README.ja.md
+++ b/2-Regression/3-Linear/translations/README.ja.md
@@ -197,13 +197,13 @@ lin_pumpkins
 
 回帰�線�正��引�れ��れ����数字����������。
 
-🎃 ����������������数種類�カボ�ャ�価格を予測�るモデルを作�����。����休日�パンプキンパッ��美��も���る��ょ�。�も�も��良�モデルを作れる�も�れ��ん。
+🎃 �����������数種類�カボ�ャ�価格を予測�るモデルを作�����。休日�パンプキンパッ��美��も���る��ょ�。�も�も��良�モデルを作れる�も�れ��ん。
 
 ## 多項�回帰
 
 線形回帰�も�一��タイプ��多項�回帰��。時��変数�間�直線的�関係 (カボ�ャ���多����価格�高��る)��る��も�り�����れら�関係��平�や直線���プロット������も�り��。
 
-✅ 多項�回帰を使������るデータ�[�ら������例](https://online.stat.psu.edu/stat501/lesson/9/9.8) を示���。
+✅ 多項�回帰を使������る�[�����例](https://online.stat.psu.edu/stat501/lesson/9/9.8) を示���。
 
 先���散布図�「�種��「価格��関係をも�一度見����ょ�。��散布図��必��も直線�分����れ��ら��よ��見����？�������も�れ��ん。��よ��場���多項�回帰を試�����ょ�。
 

From e299a0898e71b8f6518305d33c32ab9d1a2391a3 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 22 Jul 2021 18:56:32 +0900
Subject: [PATCH 245/368] add japanse assignment

---
 2-Regression/1-Tools/translations/README.ja.md      |  2 +-
 2-Regression/1-Tools/translations/assignment.ja.md  | 13 +++++++++++++
 2-Regression/2-Data/translations/README.ja.md       |  2 +-
 2-Regression/2-Data/translations/assignment.ja.md   |  9 +++++++++
 2-Regression/3-Linear/translations/README.ja.md     |  2 +-
 2-Regression/3-Linear/translations/assignment.ja.md | 11 +++++++++++
 6 files changed, 36 insertions(+), 3 deletions(-)
 create mode 100644 2-Regression/1-Tools/translations/assignment.ja.md
 create mode 100644 2-Regression/2-Data/translations/assignment.ja.md
 create mode 100644 2-Regression/3-Linear/translations/assignment.ja.md

diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index 427ef7b04b..bc264cb6a9 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -215,4 +215,4 @@ s1 tc: T細胞（白血��一種）
 
 ## 課題
 
-[異�るデータセット](../assignment.md)
+[異�るデータセット](./assignment.ja.mds)
diff --git a/2-Regression/1-Tools/translations/assignment.ja.md b/2-Regression/1-Tools/translations/assignment.ja.md
new file mode 100644
index 0000000000..6f7d9ef079
--- /dev/null
+++ b/2-Regression/1-Tools/translations/assignment.ja.md
@@ -0,0 +1,13 @@
+# Scikit-learnを用��回帰
+
+## 課題�指示
+
+Scikit-learn�[Linnerud dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_linnerud.html#sklearn.datasets.load_linnerud) を見����ょ�。��データセット�複数�[ターゲット](https://scikit-learn.org/stable/datasets/toy_dataset.html#linnerrud-dataset) を������。フィット�スクラブ�20人�中年男性�ら�集��3���動変数(data)�3��生�変数(target)�構��れ����。
+
+���自身�言葉��ウエストライン�腹筋�回数��関係をプロット�る回帰モデル�作�方法を説明������。��データセット�他�データ�イント����も�様�説明��������。
+
+## ルーブリック
+
+| 指標 | 模範的                           | �切                      | �改善          |
+| ------------------------------ | ----------------------------------- | ----------------------------- | -------------------------- |
+| 説明文を�出������。 | よ�書��文章��出�れ��る。 | �����文章��出�れ��る。 | 文章��出�れ����ん。 |
diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index 07fd923bef..f6e77d4f3c 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -203,4 +203,4 @@ Matplotlib��供�る様々�タイプ�ビジュアライゼーション
 
 ## 課題
 
-[ビジュアライゼーション�探求](../assignment.md)
+[ビジュアライゼーション�探求](./assignment.ja.md)
diff --git a/2-Regression/2-Data/translations/assignment.ja.md b/2-Regression/2-Data/translations/assignment.ja.md
new file mode 100644
index 0000000000..1838d76eba
--- /dev/null
+++ b/2-Regression/2-Data/translations/assignment.ja.md
@@ -0,0 +1,9 @@
+# ビジュアライゼーション�活用
+
+データ�ビジュアライゼーション��������異�るライブラリ��り��。��レッスン�Pumpkinデータを使���matplotlib�seabornを使���サンプルノートブック������ビジュアライゼーションを作�����ょ�。��ライブラリ�作業�や����ょ��？
+
+## ルーブリック
+
+| 指標 | 模範的 | �切 | �改善 |
+| -------- | --------- | -------- | ----------------- |
+|          | ノートブック��2��活用法/�視化方法�示�れ��る。         |   ノートブック��1��活用法/�視化方法�示�れ��る。       |  ノートブック��出�れ����。                 |
diff --git a/2-Regression/3-Linear/translations/README.ja.md b/2-Regression/3-Linear/translations/README.ja.md
index 3c87a7367a..cc3c3b7b07 100644
--- a/2-Regression/3-Linear/translations/README.ja.md
+++ b/2-Regression/3-Linear/translations/README.ja.md
@@ -331,4 +331,4 @@ Scikit-learn���多項�回帰モデルを構築�る���便利�AP
 
 ## 課題 
 
-[モデル構築](../assignment.md)
+[モデル構築](./assignment.ja.md)
diff --git a/2-Regression/3-Linear/translations/assignment.ja.md b/2-Regression/3-Linear/translations/assignment.ja.md
new file mode 100644
index 0000000000..d0f8a4c509
--- /dev/null
+++ b/2-Regression/3-Linear/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# 回帰モデル�作�
+
+## 課題�指示
+
+��レッスン���線形回帰�多項�回帰�両方を使��モデルを構築�る方法を紹介����。��知識をも���自分�データセットを探���Scikit-learn�ビルトインセット�1�を使用���新��モデルを構築������。手法を�ん��由をノートブック�書��モデル�精度を示������。精度��分���場������由も説明������。
+
+## ルーブリック
+
+| 指標 | 模範的                                                    | �切                   | �改善 |
+| -------- | ------------------------------------------------------------ | -------------------------- | ------------------------------- |
+|          | ドキュメント化�れ�ソリューションを�む完全�ノートブックを�示�る。 | 解決策��完全��る。 | 解決策�欠陥����グ��る。 |

From 17956345de401bdf8a37c1719ff4f8f2c929e987 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 22 Jul 2021 18:57:55 +0900
Subject: [PATCH 246/368] add japanse assignment

---
 2-Regression/1-Tools/translations/README.ja.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index bc264cb6a9..f65832c8a8 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -215,4 +215,4 @@ s1 tc: T細胞（白血��一種）
 
 ## 課題
 
-[異�るデータセット](./assignment.ja.mds)
+[異�るデータセット](./assignment.ja.md)

From c0c5de60bfc8e2ceab69a72f20f6e5e981d104da Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Thu, 22 Jul 2021 18:59:39 +0900
Subject: [PATCH 247/368] add japanse assignment

---
 2-Regression/2-Data/translations/assignment.ja.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/2-Data/translations/assignment.ja.md b/2-Regression/2-Data/translations/assignment.ja.md
index 1838d76eba..09f344d61f 100644
--- a/2-Regression/2-Data/translations/assignment.ja.md
+++ b/2-Regression/2-Data/translations/assignment.ja.md
@@ -1,4 +1,4 @@
-# ビジュアライゼーション�活用
+# ビジュアライゼーション�探求
 
 データ�ビジュアライゼーション��������異�るライブラリ��り��。��レッスン�Pumpkinデータを使���matplotlib�seabornを使���サンプルノートブック������ビジュアライゼーションを作�����ょ�。��ライブラリ�作業�や����ょ��？
 

From 5d3b11f4ed69575369c2373c14c4831e73206624 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Thu, 22 Jul 2021 18:31:08 +0300
Subject: [PATCH 248/368] Update links to translated lessons

---
 4-Classification/translations/README.tr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/4-Classification/translations/README.tr.md b/4-Classification/translations/README.tr.md
index 347999d875..9514dd0add 100644
--- a/4-Classification/translations/README.tr.md
+++ b/4-Classification/translations/README.tr.md
@@ -16,8 +16,8 @@ Bu bölümde, bu eğitim programının tamamen regresyon üzerine olan ilk böl
 
 1. [Sınıflandırmaya giriş](../1-Introduction/translations/README.tr.md)
 2. [Daha fazla sınıflandırıcı](../2-Classifiers-1/translations/README.tr.md)
-3. [Hatta daha fazla sınıflandırıcı](../3-Classifiers-2/README.md)
-4. [Uygulamalı Makine Öğrenimi: bir web uygulaması oluşturun](../4-Applied/README.md)
+3. [Hatta daha fazla sınıflandırıcı](../3-Classifiers-2/translations/README.tr.md)
+4. [Uygulamalı Makine Öğrenimi: bir web uygulaması oluşturun](../4-Applied/translations/README.tr.md)
 ## Katkıda bulunanlar
 
 "Sınıflandırmaya başlarken" [Cassie Breviu](https://www.twitter.com/cassieview) ve [Jen Looper](https://www.twitter.com/jenlooper) tarafından :hearts: ile yazılmıştır.

From 15386734715f270d5ddf507b889468fcccca9e60 Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Thu, 22 Jul 2021 18:32:03 +0300
Subject: [PATCH 249/368] Add Classifiers-2 translated to Turkish

---
 .../3-Classifiers-2/translations/README.tr.md | 235 ++++++++++++++++++
 .../translations/assignment.tr.md             |  11 +
 2 files changed, 246 insertions(+)
 create mode 100644 4-Classification/3-Classifiers-2/translations/README.tr.md
 create mode 100644 4-Classification/3-Classifiers-2/translations/assignment.tr.md

diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md
new file mode 100644
index 0000000000..24ea3dfd84
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/translations/README.tr.md
@@ -0,0 +1,235 @@
+# Mutfak sınıflandırıcıları 2
+
+Bu ikinci sınıflandırma dersinde, sayısal veriyi sınıflandırmak için daha fazla yöntem öğreneceksiniz. Ayrıca, bir sınıflandırıcıyı diğerlerine tercih etmenin sonuçlarını da öğreneceksiniz.
+
+## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/?loc=tr)
+
+### Ön koşul
+
+Önceki dersleri tamamladığınızı ve bu 4-ders klasörünün kökündeki `data` klasörünüzdeki _cleaned_cuisines.csv_ adlı veri setini temizlediğinizi varsayıyoruz.
+
+### Hazırlık
+
+Temizlenmiş veri setiyle _notebook.ipynb_ dosyanızı yükledik ve model oluşturma sürecine hazır olması için X ve y veri iskeletlerine böldük.
+
+## Bir sınıflandırma haritası
+
+Daha önce, Microsoft'un kopya kağıdını kullanarak veri sınıflandırmanın çeşitli yollarını öğrendiniz. Scikit-learn de buna benzer, öngörücülerinizi (sınıflandırıcı) sınırlandırmanıza ilaveten yardım edecek bir kopya kağıdı sunar.
+
+![Scikit-learn'den Makine Öğrenimi Haritası](../images/map.png)
+> Tavsiye: [Bu haritayı çevrim içi ziyaret edin](https://scikit-learn.org/stable/tutorial/machine_learning_map/) ve rotayı seyrederken dokümantasyonu okumak için tıklayın.
+
+### Plan
+
+Verinizi iyice kavradığınızda bu harita çok faydalı olacaktır, çünkü karara ulaşırken rotalarında 'yürüyebilirsiniz':
+
+- >50 adet örneğimiz var
+- Bir kategori öngörmek istiyoruz
+- EtiketlenmiÅŸ veri var
+- 100 binden az örneğimiz var
+- :sparkles: Bir Linear SVC (Doğrusal Destek Vektör Sınıflandırma) seçebiliriz
+- Eğer bu işe yaramazsa, verimiz sayısal olduğundan
+    - :sparkles: Bir KNeighbors (K Komşu) Sınıflandırıcı deneyebiliriz
+      - Eğer bu işe yaramazsa, :sparkles: SVC (Destek Vektör Sınıflandırma) ve :sparkles: Ensemble (Topluluk) Sınıflandırıcılarını deneyin
+
+Bu çok faydalı bir yol.
+
+## Alıştırma - veriyi bölün
+
+Bu yolu takip ederek, kullanmak için bazı kütüphaneleri alarak başlamalıyız.
+
+1. Gerekli kütüphaneleri alın:
+
+    ```python
+    from sklearn.neighbors import KNeighborsClassifier
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.svm import SVC
+    from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+    from sklearn.model_selection import train_test_split, cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+    import numpy as np
+    ```
+
+1. Eğitme ve sınama verinizi bölün:
+
+    ```python
+    X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+    ```
+
+## Linear SVC Sınıflandırıcısı
+
+Destek Vektör kümeleme (SVC), makine öğrenimi yöntemlerinden Destek Vektör Makinelerinin (Aşağıda bunun hakkında daha fazla bilgi edineceksiniz.) alt dallarından biridir. Bu yöntemde, etiketleri nasıl kümeleyeceğinize karar vermek için bir 'kernel' seçebilirsiniz. 'C' parametresi 'düzenlileştirme'yi ifade eder ve parametrelerin etkilerini düzenler. Kernel (çekirdek) [birçoğundan](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC) biri olabilir; burada, doğrusal SVC leveraj ettiğimizden emin olmak için, 'linear' olarak ayarlıyoruz. Olasılık varsayılan olarak 'false' olarak ayarlıdır; burada, olasılık öngörülerini toplamak için, 'true' olarak ayarlıyoruz. Rastgele durumu (random state), olasılıkları elde etmek için veriyi karıştırmak (shuffle) üzere, '0' olarak ayarlıyoruz.
+
+### Alıştırma - doğrusal SVC uygulayın
+
+Sınıflandırıcıardan oluşan bir dizi oluşturarak başlayın. Sınadıkça bu diziye ekleme yapacağız.
+
+1. Liner SVC ile başlayın:
+
+    ```python
+    C = 10
+    # Create different classifiers.
+    classifiers = {
+        'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0)
+    }
+    ```
+
+2. Linear SVC kullanarak modelinizi eğitin ve raporu bastırın:
+
+    ```python
+    n_classifiers = len(classifiers)
+    
+    for index, (name, classifier) in enumerate(classifiers.items()):
+        classifier.fit(X_train, np.ravel(y_train))
+    
+        y_pred = classifier.predict(X_test)
+        accuracy = accuracy_score(y_test, y_pred)
+        print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100))
+        print(classification_report(y_test,y_pred))
+    ```
+
+    Sonuç oldukça iyi:
+
+    ```output
+    Accuracy (train) for Linear SVC: 78.6% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.71      0.67      0.69       242
+          indian       0.88      0.86      0.87       234
+        japanese       0.79      0.74      0.76       254
+          korean       0.85      0.81      0.83       242
+            thai       0.71      0.86      0.78       227
+    
+        accuracy                           0.79      1199
+       macro avg       0.79      0.79      0.79      1199
+    weighted avg       0.79      0.79      0.79      1199
+    ```
+
+## K-Komşu sınıflandırıcısı
+
+K-Komşu, makine öğrenimi yöntemlerinden "neighbors" (komşular) ailesinin bir parçasıdır ve gözetimli ve gözetimsiz öğrenmenin ikisinde de kullanılabilir. Bu yöntemde, önceden tanımlanmış sayıda nokta üretilir ve veri bu noktalar etrafında, genelleştirilmiş etiketlerin veriler için öngörülebileceği şekilde toplanır.
+
+### Alıştırma - K-Komşu sınıflandırıcısını uygulayın
+
+Önceki sınıflandırıcı iyiydi ve veriyle iyi çalıştı, ancak belki daha iyi bir doğruluk elde edebiliriz. K-Komşu sınıflandırıcısını deneyin.
+
+1. Sınıflandırıcı dizinize bir satır ekleyin (Linear SVC ögesinden sonra bir virgül ekleyin):
+
+    ```python
+    'KNN classifier': KNeighborsClassifier(C),
+    ```
+
+    Sonuç biraz daha kötü:
+
+    ```output
+    Accuracy (train) for KNN classifier: 73.8% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.64      0.67      0.66       242
+          indian       0.86      0.78      0.82       234
+        japanese       0.66      0.83      0.74       254
+          korean       0.94      0.58      0.72       242
+            thai       0.71      0.82      0.76       227
+    
+        accuracy                           0.74      1199
+       macro avg       0.76      0.74      0.74      1199
+    weighted avg       0.76      0.74      0.74      1199
+    ```
+
+    :white_check_mark: [K-Komşu](https://scikit-learn.org/stable/modules/neighbors.html#neighbors) hakkında bilgi edinin
+
+## Destek Vektör Sınıflandırıcısı
+
+Destek Vektör sınıflandırıcıları, makine öğrenimi yöntemlerinden [Destek Vektörü Makineleri](https://wikipedia.org/wiki/Support-vector_machine) ailesinin bir parçasıdır ve sınıflandırma ve regresyon görevlerinde kullanılır. SVM'ler (Destek Vektör Makineleri), iki kategori arasındaki uzaklığı en yükseğe getirmek için eğitme örneklerini boşluktaki noktalara eşler. Sonraki veri, kategorisinin öngörülebilmesi için bu boşluğa eşlenir.
+
+### Alıştırma - bir Destek Vektör Sınıflandırıcısı uygulayın
+
+Bir Destek Vektör Sınıflandırıcısı ile daha iyi bir doğruluk elde etmeye çalışalım.
+
+1. K-Neighbors ögesinden sonra bir virgül ekleyin, sonra bu satırı ekleyin:
+
+    ```python
+    'SVC': SVC(),
+    ```
+
+    Sonuç oldukça iyi!
+
+    ```output
+    Accuracy (train) for SVC: 83.2% 
+                  precision    recall  f1-score   support
+    
+         chinese       0.79      0.74      0.76       242
+          indian       0.88      0.90      0.89       234
+        japanese       0.87      0.81      0.84       254
+          korean       0.91      0.82      0.86       242
+            thai       0.74      0.90      0.81       227
+    
+        accuracy                           0.83      1199
+       macro avg       0.84      0.83      0.83      1199
+    weighted avg       0.84      0.83      0.83      1199
+    ```
+
+    :white_check_mark: [Destek Vektörleri](https://scikit-learn.org/stable/modules/svm.html#svm) hakkında bilgi edinin
+
+## Topluluk Sınıflandırıcıları
+
+Önceki sınamanın oldukça iyi olmasına rağmen rotayı sonuna kadar takip edelim. Bazı Topluluk Sınıflandırıcılarını deneyelim, özellikle Random Forest ve AdaBoost'u:
+
+```python
+'RFST': RandomForestClassifier(n_estimators=100),
+  'ADA': AdaBoostClassifier(n_estimators=100)
+```
+
+Sonuç çok iyi, özellikle Random Forest sonuçları:
+
+```output
+Accuracy (train) for RFST: 84.5% 
+              precision    recall  f1-score   support
+
+     chinese       0.80      0.77      0.78       242
+      indian       0.89      0.92      0.90       234
+    japanese       0.86      0.84      0.85       254
+      korean       0.88      0.83      0.85       242
+        thai       0.80      0.87      0.83       227
+
+    accuracy                           0.84      1199
+   macro avg       0.85      0.85      0.84      1199
+weighted avg       0.85      0.84      0.84      1199
+
+Accuracy (train) for ADA: 72.4% 
+              precision    recall  f1-score   support
+
+     chinese       0.64      0.49      0.56       242
+      indian       0.91      0.83      0.87       234
+    japanese       0.68      0.69      0.69       254
+      korean       0.73      0.79      0.76       242
+        thai       0.67      0.83      0.74       227
+
+    accuracy                           0.72      1199
+   macro avg       0.73      0.73      0.72      1199
+weighted avg       0.73      0.72      0.72      1199
+```
+
+:white_check_mark: [Topluluk Sınıflandırıcıları](https://scikit-learn.org/stable/modules/ensemble.html) hakkında bilgi edinin
+
+Makine Öğreniminin bu yöntemi, modelin kalitesini artırmak için, "birçok temel öngörücünün öngörülerini birleştirir." Bizim örneğimizde, Random Trees ve AdaBoost kullandık.
+
+- [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#forest) bir ortalama alma yöntemidir, aşırı öğrenmeden kaçınmak için rastgelelikle doldurulmuş 'karar ağaçları'ndan oluşan bir 'orman' oluşturur. n_estimators parametresi, ağaç sayısı olarak ayarlanmaktadır.
+
+- [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html), bir sınıflandıcıyı bir veri setine uydurur ve sonra o sınıflandırıcının kopyalarını aynı veri setine uydurur. Yanlış sınıflandırılmış ögelerin ağırlıklarına odaklanır ve bir sonraki sınıflandırıcının düzeltmesi için uydurma/oturtmayı ayarlar.
+
+---
+
+## :rocket: Meydan okuma
+
+Bu yöntemlerden her biri değiştirebileceğiniz birsürü parametre içeriyor. Her birinin varsayılan parametrelerini araştırın ve bu parametreleri değiştirmenin modelin kalitesi için ne anlama gelebileceği hakkında düşünün.
+
+## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/?loc=tr)
+
+## Gözden Geçirme & Kendi Kendine Çalışma
+
+Bu derslerde çok fazla jargon var, bu yüzden yararlı terminoloji içeren [bu listeyi](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) incelemek için bir dakika ayırın.
+
+## Ödev
+
+[Parametre oyunu](assignment.tr.md)
\ No newline at end of file
diff --git a/4-Classification/3-Classifiers-2/translations/assignment.tr.md b/4-Classification/3-Classifiers-2/translations/assignment.tr.md
new file mode 100644
index 0000000000..fbc7409279
--- /dev/null
+++ b/4-Classification/3-Classifiers-2/translations/assignment.tr.md
@@ -0,0 +1,11 @@
+# Parametre Oyunu
+
+## Yönergeler
+
+Bu sınıflandırıcılarla çalışırken varsayılan olarak ayarlanmış birçok parametre var. VS Code'daki Intellisense, onları derinlemesine incelemenize yardımcı olabilir. Bu dersteki Makine Öğrenimi Sınıflandırma Yöntemlerinden birini seçin ve çeşitli parametre değerlerini değiştirerek modelleri yeniden eğitin. Neden bazı değişikliklerin modelin kalitesini artırdığını ve bazılarının azalttığını açıklayan bir not defteri yapın. Cevabınız açıklayıcı olmalı.
+
+## Rubrik
+
+|  Ölçüt   | Örnek Alınacak Nitelikte                                                                                                           | Yeterli                                               | Geliştirme Gerekli              |
+| -------- | ---------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | ------------------------------- |
+|          | Bir sınıflandırıcının tamamen oluşturulduğu ve parametrelerinin değiştirilip yazı kutularında açıklandığı bir not defteri sunulmuş | Not defteri kısmen sunulmuş veya az açıklanmış        | Not defteri hatalı veya kusurlu |
\ No newline at end of file

From c7cc3cadd62e920ae45ab9160f39488e77b80b0e Mon Sep 17 00:00:00 2001
From: Buse Orak <buseorak@hotmail.com>
Date: Thu, 22 Jul 2021 18:33:18 +0300
Subject: [PATCH 250/368] Add Applied lesson translated to Turkish

---
 .../4-Applied/translations/README.tr.md       | 336 ++++++++++++++++++
 .../4-Applied/translations/assignment.tr.md   |  11 +
 2 files changed, 347 insertions(+)
 create mode 100644 4-Classification/4-Applied/translations/README.tr.md
 create mode 100644 4-Classification/4-Applied/translations/assignment.tr.md

diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md
new file mode 100644
index 0000000000..12423ee0cd
--- /dev/null
+++ b/4-Classification/4-Applied/translations/README.tr.md
@@ -0,0 +1,336 @@
+# Mutfak Önerici Bir Web Uygulaması Oluşturun
+
+Bu derste, önceki derslerde öğrendiğiniz bazı yöntemleri kullanarak, bu seri boyunca kullanılan leziz mutfak veri setiyle bir sınıflandırma modeli oluşturacaksınız. Ayrıca, kaydettiğiniz modeli kullanmak üzere, Onnx'un web çalışma zamanından yararlanan küçük bir web uygulaması oluşturacaksınız.
+
+Makine öğreniminin en faydalı pratik kullanımlarından biri, önerici/tavsiyeci sistemler oluşturmaktır ve bu yöndeki ilk adımınızı bugün atabilirsiniz!
+
+[![Önerici Sistemler Tanıtımı](https://img.youtube.com/vi/giIXNoiqO_U/0.jpg)](https://youtu.be/giIXNoiqO_U "Recommendation Systems Introduction")
+
+> :movie_camera: Video için yukarıdaki fotoğrafa tıklayın: Andrew Ng introduces recommendation system design (Andrew Ng önerici sistem tasarımını tanıtıyor)
+
+## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/?loc=tr)
+
+Bu derste şunları öğreneceksiniz:
+
+- Bir model nasıl oluşturulur ve Onnx modeli olarak kaydedilir
+- Modeli denetlemek için Netron nasıl kullanılır
+- Modeliniz çıkarım için bir web uygulamasında nasıl kullanılabilir
+
+## Modelinizi oluÅŸturun
+
+Uygulamalı Makine Öğrenimi sistemleri oluşturmak, bu teknolojilerden kendi iş sistemleriniz için yararlanmanızın önemli bir parçasıdır. Onnx kullanarak modelleri kendi web uygulamalarınız içerisinde kullanabilirsiniz (Böylece gerektiğinde çevrim dışı bir içerikte kullanabilirsiniz.).
+
+[Önceki bir derste](../../../3-Web-App/1-Web-App/README.md) UFO gözlemleriyle ilgili bir Regresyon modeli oluşturmuş, "pickle" kullanmış ve bir Flask uygulamasında kullanmıştınız. Bu mimariyi bilmek çok faydalıdır, ancak bu tam yığın Python uygulamasıdır ve bir JavaScript uygulaması kullanımı gerekebilir.
+
+Bu derste, çıkarım için temel JavaScript tabanlı bir sistem oluşturabilirsiniz. Ancak öncelikle, bir model eğitmeniz ve Onnx ile kullanım için dönüştürmeniz gerekmektedir.
+
+## Alıştırma - sınıflandırma modelini eğitin
+
+Öncelikle, kullandığımız temiz mutfak veri setini kullanarak bir sınıflandırma modeli eğitin.
+
+1. Faydalı kütüphaneler almakla başlayın:
+
+    ```python
+    !pip install skl2onnx
+    import pandas as pd 
+    ```
+
+    Scikit-learn modelinizi Onnx biçimine dönüştürmeyi sağlamak için '[skl2onnx](https://onnx.ai/sklearn-onnx/)'a ihtiyacınız var.
+
+1. Sonra, önceki derslerde yaptığınız şekilde, `read_csv()` kullanarak bir CSV dosyasını okuyarak veriniz üzerinde çalışın:
+
+    ```python
+    data = pd.read_csv('../data/cleaned_cuisines.csv')
+    data.head()
+    ```
+
+1. İlk iki gereksiz sütunu kaldırın ve geriye kalan veriyi 'X' olarak kaydedin:
+
+    ```python
+    X = data.iloc[:,2:]
+    X.head()
+    ```
+
+1. Etiketleri 'y' olarak kaydedin:
+
+    ```python
+    y = data[['cuisine']]
+    y.head()
+    
+    ```
+
+### Eğitme rutinine başlayın
+
+İyi doğruluğu olan 'SVC' kütüphanesini kullanacağız.
+
+1. Scikit-learn'den uygun kütüphaneleri alın:
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    from sklearn.svm import SVC
+    from sklearn.model_selection import cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report
+    ```
+
+1. Eğitme ve sınama kümelerini ayırın:
+
+    ```python
+    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
+    ```
+
+1. Önceki derste yaptığınız gibi bir SVC Sınıflandırma modeli oluşturun:
+
+    ```python
+    model = SVC(kernel='linear', C=10, probability=True,random_state=0)
+    model.fit(X_train,y_train.values.ravel())
+    ```
+
+1. Şimdi, `predict()` fonksiyonunu çağırarak modelinizi sınayın:
+
+    ```python
+    y_pred = model.predict(X_test)
+    ```
+
+1. Modelin kalitesini kontrol etmek için bir sınıflandırma raporu bastırın:
+
+    ```python
+    print(classification_report(y_test,y_pred))
+    ```
+
+    Daha önce de gördüğümüz gibi, doğruluk iyi:
+
+    ```output
+                    precision    recall  f1-score   support
+    
+         chinese       0.72      0.69      0.70       257
+          indian       0.91      0.87      0.89       243
+        japanese       0.79      0.77      0.78       239
+          korean       0.83      0.79      0.81       236
+            thai       0.72      0.84      0.78       224
+    
+        accuracy                           0.79      1199
+       macro avg       0.79      0.79      0.79      1199
+    weighted avg       0.79      0.79      0.79      1199
+    ```
+
+### Modelinizi Onnx'a dönüştürün
+
+Dönüştürmeyi uygun Tensor sayısıyla yaptığınıza emin olun. Bu veri seti listelenmiş 380 malzeme içeriyor, dolayısıyla bu sayıyı `FloatTensorType` içinde belirtmeniz gerekiyor:
+
+1. 380 tensor sayısını kullanarak dönüştürün.
+
+    ```python
+    from skl2onnx import convert_sklearn
+    from skl2onnx.common.data_types import FloatTensorType
+    
+    initial_type = [('float_input', FloatTensorType([None, 380]))]
+    options = {id(model): {'nocl': True, 'zipmap': False}}
+    ```
+
+1. onx'u oluÅŸturun ve **model.onnx** diye bir dosya olarak kaydedin:
+
+    ```python
+    onx = convert_sklearn(model, initial_types=initial_type, options=options)
+    with open("./model.onnx", "wb") as f:
+        f.write(onx.SerializeToString())
+    ```
+
+    > Not olarak, dönüştürme senaryonuzda [seçenekler](https://onnx.ai/sklearn-onnx/parameterized.html) geçirebilirsiniz. Biz bu durumda, 'nocl' parametresini True ve 'zipmap' parametresini 'False' olarak geçirdik. Bu bir sınıflandırma modeli olduğundan, bir sözlük listesi üreten (gerekli değil) ZipMap'i kaldırma seçeneğiniz var. `nocl`, modelde sınıf bilgisinin barındırılmasını ifade eder. `nocl` parametresini 'True' olarak ayarlayarak modelinizin boyutunu küçültün.
+
+Tüm not defterini çalıştırmak şimdi bir Onnx modeli oluşturacak ve bu klasöre kaydedecek.
+
+## Modelinizi inceleyin
+
+Onnx modelleri Visual Studio code'da pek görünür değiller ama birçok araştırmacının modelin doğru oluştuğundan emin olmak üzere modeli görselleştirmek için kullandığı çok iyi bir yazılım var. [Netron](https://github.com/lutzroeder/Netron)'u indirin ve model.onnx dosyanızı açın. 380 girdisi ve sınıflandırıcısıyla basit modelinizin görselleştirildiğini görebilirsiniz:
+
+![Netron görseli](../images/netron.png)
+
+Netron, modellerinizi incelemek için faydalı bir araçtır.
+
+Şimdi, bu düzenli modeli web uygulamanızda kullanmak için hazırsınız. Buzdolabınıza baktığınızda ve verilen bir mutfak için artık malzemelerin hangi birleşimini kullanabileceğinizi bulmayı denediğinizde kullanışlı olacak bir uygulama oluşturalım. Bu birleşim modeliniz tarafından belirlenecek.
+
+## Önerici bir web uygulaması oluşturun
+
+Modelinizi doğrudan bir web uygulamasında kullanabilirsiniz. Bu mimari, modelinizi yerelde ve hatta gerektiğinde çevrim dışı çalıştırabilmenizi de sağlar. `model.onnx` dosyanızı kaydettiğiniz klasörde `index.html` dosyasını oluşturarak başlayın.
+
+1. Bu _index.html_ dosyasında aşağıdaki işaretlemeyi ekleyin:
+
+    ```html
+    <!DOCTYPE html>
+    <html>
+        <header>
+            <title>Cuisine Matcher</title>
+        </header>
+        <body>
+            ...
+        </body>
+    </html>
+    ```
+
+1. Şimdi, `body` etiketleri içinde çalışarak, bazı malzemeleri ifade eden bir onay kutusu listesi göstermek için küçük bir işaretleme ekleyin:
+
+    ```html
+    <h1>Check your refrigerator. What can you create?</h1>
+            <div id="wrapper">
+                <div class="boxCont">
+                    <input type="checkbox" value="4" class="checkbox">
+                    <label>apple</label>
+                </div>
+            
+                <div class="boxCont">
+                    <input type="checkbox" value="247" class="checkbox">
+                    <label>pear</label>
+                </div>
+            
+                <div class="boxCont">
+                    <input type="checkbox" value="77" class="checkbox">
+                    <label>cherry</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="126" class="checkbox">
+                    <label>fenugreek</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="302" class="checkbox">
+                    <label>sake</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="327" class="checkbox">
+                    <label>soy sauce</label>
+                </div>
+    
+                <div class="boxCont">
+                    <input type="checkbox" value="112" class="checkbox">
+                    <label>cumin</label>
+                </div>
+            </div>
+            <div style="padding-top:10px">
+                <button onClick="startInference()">What kind of cuisine can you make?</button>
+            </div> 
+    ```
+
+    Her bir onay kutusuna bir değer verildiğine dikkat edin. Bu, veri setine göre malzemenin bulunduğu indexi ifade eder. Örneğin bu alfabetik listede elma beşinci sütundadır, dolayısıyla onun değeri '4'tür çünkü saymaya 0'dan başlıyoruz. Verilen malzemenin indexini görmek için [malzemeler tablosuna](../../data/ingredient_indexes.csv) başvurabilirsiniz.
+
+    index.html dosyasındaki işinize devam ederek, son `</div>` kapamasından sonra modelinizin çağrılacağı bir script bloğu ekleyin.
+
+1. Öncelikle, [Onnx Runtime](https://www.onnxruntime.ai/) alın:
+
+    ```html
+    <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.8.0-dev.20210608.0/dist/ort.min.js"></script> 
+    ```
+
+    > Onnx Runtime, Onnx modelinizin, eniyileştirmeler ve kullanmak için bir API da dahil olmak üzere, geniş bir donanım platform yelpazesinde çalışmasını sağlamak için kullanılır.
+
+1. Runtime uygun hale geldiğinde, onu çağırabilirsiniz:
+
+    ```javascript
+    <script>
+                const ingredients = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+                
+                const checks = [].slice.call(document.querySelectorAll('.checkbox'));
+    
+                // use an async context to call onnxruntime functions.
+                function init() {
+                    
+                    checks.forEach(function (checkbox, index) {
+                        checkbox.onchange = function () {
+                            if (this.checked) {
+                                var index = checkbox.value;
+    
+                                if (index !== -1) {
+                                    ingredients[index] = 1;
+                                }
+                                console.log(ingredients)
+                            }
+                            else {
+                                var index = checkbox.value;
+    
+                                if (index !== -1) {
+                                    ingredients[index] = 0;
+                                }
+                                console.log(ingredients)
+                            }
+                        }
+                    })
+                }
+    
+                function testCheckboxes() {
+                        for (var i = 0; i < checks.length; i++)
+                            if (checks[i].type == "checkbox")
+                                if (checks[i].checked)
+                                    return true;
+                        return false;
+                }
+    
+                async function startInference() {
+    
+                    let checked = testCheckboxes()
+    
+                    if (checked) {
+    
+                    try {
+                        // create a new session and load the model.
+                        
+                        const session = await ort.InferenceSession.create('./model.onnx');
+    
+                        const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
+                        const feeds = { float_input: input };
+    
+                        // feed inputs and run
+    
+                        const results = await session.run(feeds);
+    
+                        // read from results
+                        alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
+    
+                    } catch (e) {
+                        console.log(`failed to inference ONNX model: ${e}.`);
+                    }
+                }
+                else alert("Please check an ingredient")
+                    
+                }
+        init();
+               
+            </script>
+    ```
+
+Bu kodda birçok şey gerçekleşiyor:
+
+1. Ayarlanması ve çıkarım için modele gönderilmesi için, bir malzeme onay kutusunun işaretli olup olmadığına bağlı 380 muhtemel değerden (ya 1 ya da 0) oluşan bir dizi oluşturdunuz.
+2. Onay kutularından oluşan bir dizi ve uygulama başladığında çağrılan bir `init` fonksiyonunda işaretli olup olmadıklarını belirleme yolu oluşturdunuz. Eğer onay kutusu işaretliyse, `ingredients` dizisi, seçilen malzemeyi ifade etmek üzere değiştirilir.
+3. Herhangi bir onay kutusunun işaretli olup olmadığını kontrol eden bir `testCheckboxes` fonksiyonu oluşturdunuz.
+4. Düğmeye basıldığında o fonksiyonu kullanıyor ve eğer herhangi bir onay kutusu işaretlenmişse çıkarıma başlıyorsunuz.
+5. Çıkarım rutini şunları içerir:
+   1. Makinenin eşzamansız bir yüklemesini ayarlama
+   2. Modele göndermek için bir Tensor yapısı oluşturma
+   3. Modelinizi eğitirken oluşturduğunuz `float_input` (Bu adı doğrulamak için Netron kullanabilirsiniz.) girdisini ifade eden 'feeds' oluşturma
+   4. Bu 'feeds'i modele gönderme ve yanıt için bekleme
+
+## Uygulamanızı test edin
+
+index.html dosyanızın olduğu klasördeyken Visual Studio Code'da bir terminal açın. Global kapsamda `[http-server](https://www.npmjs.com/package/http-server)` indirilmiş olduğundan emin olun ve istemde `http-server` yazın. Bir yerel ana makine açılmalı ve web uygulamanızı görebilirsiniz. Çeşitli malzemeleri baz alarak hangi mutfağın önerildiğine bakın:
+
+![malzeme web uygulaması](../images/web-app.png)
+
+Tebrikler, birkaç değişkenle bir 'önerici' web uygulaması oluşturdunuz! Bu sistemi oluşturmak için biraz zaman ayırın!
+## :rocket: Meydan okuma
+
+Web uygulamanız çok minimal, bu yüzden [ingredient_indexes](../../data/ingredient_indexes.csv) verisinden malzemeleri ve indexlerini kullanarak web uygulamanızı oluşturmaya devam edin. Verilen bir ulusal yemeği yapmak için hangi tat birleşimleri işe yarıyor?
+
+## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/?loc=tr)
+
+## Gözden Geçirme & Kendi Kendine Çalışma
+
+Bu dersin sadece yemek malzemeleri için bir öneri sistemi oluşturmanın olanaklarına değinmesiyle beraber, makine öğrenimi uygulamalarının bu alanı örnekler açısından çok zengin. Bu sistemlerin nasıl oluşturulduğu hakkında biraz daha okuyun:
+
+- https://www.sciencedirect.com/topics/computer-science/recommendation-engine
+- https://www.technologyreview.com/2014/08/25/171547/the-ultimate-challenge-for-recommendation-engines/
+- https://www.technologyreview.com/2015/03/23/168831/everything-is-a-recommendation/
+
+## Ödev 
+
+[Yeni bir önerici oluşturun](assignment.tr.md)
\ No newline at end of file
diff --git a/4-Classification/4-Applied/translations/assignment.tr.md b/4-Classification/4-Applied/translations/assignment.tr.md
new file mode 100644
index 0000000000..f561bf48a0
--- /dev/null
+++ b/4-Classification/4-Applied/translations/assignment.tr.md
@@ -0,0 +1,11 @@
+# Bir önerici oluşturun
+
+## Yönergeler
+
+Bu dersteki alıştırmalar göz önünde bulundurulursa, Onnx Runtime ve dönüştürülmüş bir Onnx modeli kullanarak JavaScript tabanlı web uygulamasının nasıl oluşturulacağını artık biliyorsunuz. Bu derslerdeki verileri veya başka bir yerden kaynaklandırılmış verileri (Lütfen kaynakça verin.) kullanarak yeni bir önerici oluşturma deneyimi kazanın. Verilen çeşitli kişilik özellikleriyle bir evcil hayvan önericisi veya kişinin ruh haline göre bir müzik türü önericisi oluşturabilirsiniz. Yaratıcı olun!
+
+## Rubrik
+
+|  Ölçüt   | Örnek Alınacak Nitelikte                                               | Yeterli                               | Geliştirme Gerekli                |
+| -------- | ---------------------------------------------------------------------- | ------------------------------------- | --------------------------------- |
+|          | İyi belgelenen ve çalışan bir web uygulaması ve not defteri sunulmuş   | İkisinden biri eksik veya kusurlu     | İkisi ya eksik ya da kusurlu      |
\ No newline at end of file

From 97fee635f40080496b8f02feded656fee5b5b48e Mon Sep 17 00:00:00 2001
From: R-icntay <63848664+R-icntay@users.noreply.github.com>
Date: Thu, 22 Jul 2021 22:49:43 +0300
Subject: [PATCH 251/368] Add R resources for lessons 05 and 06

---
 2-Regression/1-Tools/images/encouRage.jpg     | Bin 0 -> 571629 bytes
 .../1-Tools/solution/lesson_1-R.ipynb         | 436 ++++++++++++
 2-Regression/1-Tools/solution/lesson_1.Rmd    | 250 +++++++
 .../2-Data/images/dplyr_wrangling.png         | Bin 0 -> 1064382 bytes
 2-Regression/2-Data/images/unruly_data.jpg    | Bin 0 -> 128312 bytes
 2-Regression/2-Data/solution/lesson_2-R.ipynb | 644 ++++++++++++++++++
 2-Regression/2-Data/solution/lesson_2.Rmd     | 345 ++++++++++
 7 files changed, 1675 insertions(+)
 create mode 100644 2-Regression/1-Tools/images/encouRage.jpg
 create mode 100644 2-Regression/1-Tools/solution/lesson_1-R.ipynb
 create mode 100644 2-Regression/1-Tools/solution/lesson_1.Rmd
 create mode 100644 2-Regression/2-Data/images/dplyr_wrangling.png
 create mode 100644 2-Regression/2-Data/images/unruly_data.jpg
 create mode 100644 2-Regression/2-Data/solution/lesson_2-R.ipynb
 create mode 100644 2-Regression/2-Data/solution/lesson_2.Rmd

diff --git a/2-Regression/1-Tools/images/encouRage.jpg b/2-Regression/1-Tools/images/encouRage.jpg
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diff --git a/2-Regression/1-Tools/solution/lesson_1-R.ipynb b/2-Regression/1-Tools/solution/lesson_1-R.ipynb
new file mode 100644
index 0000000000..6b6a0c42de
--- /dev/null
+++ b/2-Regression/1-Tools/solution/lesson_1-R.ipynb
@@ -0,0 +1,436 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "lesson_1-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YJUHCXqK57yz"
+      },
+      "source": [
+        "#Build a regression model: Get started with R and Tidymodels for regression models"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LWNNzfqd6feZ"
+      },
+      "source": [
+        "## Introduction to Regression - Lesson 1\n",
+        "\n",
+        "#### Putting it into perspective\n",
+        "\n",
+        "✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use `linear regression`, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use `logistic regression`. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.\n",
+        "\n",
+        "In this section, you will work with a [small dataset about diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.\n",
+        "\n",
+        "That said, let's get started on this task!\n",
+        "\n",
+        "![Artwork by \\@allison_horst](../images/encouRage.jpg){width=\"630\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FIo2YhO26wI9"
+      },
+      "source": [
+        "## 1. Loading up our tool set\n",
+        "\n",
+        "For this task, we'll require the following packages:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+        "\n",
+        "The script below checks whether you have the packages required to complete this module and installs them for you in case some are missing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cIA9fz9v7Dss",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+      },
+      "source": [
+        "if (!require(\"pacman\")) install.packages(\"pacman\")\n",
+        "pacman::p_load(tidyverse, tidymodels)"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loading required package: pacman\n",
+            "\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gpO_P_6f9WUG"
+      },
+      "source": [
+        "Now, let's load these awesome packages and make them available in our current R session.(This is for mere illustration, `pacman::p_load()` already did that for you)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NLMycgG-9ezO"
+      },
+      "source": [
+        "# load the core Tidyverse packages\n",
+        "library(tidyverse)\n",
+        "\n",
+        "# load the core Tidymodels packages\n",
+        "library(tidymodels)\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KM6iXLH996Cl"
+      },
+      "source": [
+        "## 2. The diabetes dataset\n",
+        "\n",
+        "In this exercise, we'll put our regression skills into display by making predictions on a diabetes dataset. The [diabetes dataset](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) includes `442 samples` of data around diabetes, with 10 predictor feature variables, `age`, `sex`, `body mass index`, `average blood pressure`, and `six blood serum measurements` as well as an outcome variable `y`: a quantitative measure of disease progression one year after baseline.\n",
+        "\n",
+        "|Number of observations|442|\n",
+        "|----------------------|:---|\n",
+        "|Number of predictors|First 10 columns are numeric predictive|\n",
+        "|Outcome/Target|Column 11 is a quantitative measure of disease progression one year after baseline|\n",
+        "|Predictor Information|- age in years\n",
+        "||- sex\n",
+        "||- bmi body mass index\n",
+        "||- bp average blood pressure\n",
+        "||- s1 tc, total serum cholesterol\n",
+        "||- s2 ldl, low-density lipoproteins\n",
+        "||- s3 hdl, high-density lipoproteins\n",
+        "||- s4 tch, total cholesterol / HDL\n",
+        "||- s5 ltg, possibly log of serum triglycerides level\n",
+        "||- s6 glu, blood sugar level|\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "> 🎓 Remember, this is supervised learning, and we need a named 'y' target.\n",
+        "\n",
+        "Before you can manipulate data with R, you need to import the data into R's memory, or build a connection to the data that R can use to access the data remotely.\n",
+        "\n",
+        "> The [readr](https://readr.tidyverse.org/) package, which is part of the Tidyverse, provides a fast and friendly way to read rectangular data into R.\n",
+        "\n",
+        "Now, let's load the diabetes dataset provided in this source URL: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+        "\n",
+        "Also, we'll perform a sanity check on our data using `glimpse()` and dsiplay the first 5 rows using `slice()`.\n",
+        "\n",
+        "Before going any further, let's also introduce something you will encounter often in R code ��: the pipe operator `%>%`\n",
+        "\n",
+        "The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying \"and then\" in your code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Z1geAMhM-bSP"
+      },
+      "source": [
+        "# Import the data set\n",
+        "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\n",
+        "\n",
+        "\n",
+        "# Get a glimpse and dimensions of the data\n",
+        "glimpse(diabetes)\n",
+        "\n",
+        "\n",
+        "# Select the first 5 rows of the data\n",
+        "diabetes %>% \n",
+        "  slice(1:5)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UwjVT1Hz-c3Z"
+      },
+      "source": [
+        "`glimpse()` shows us that this data has 442 rows and 11 columns with all the columns being of data type `double` \n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "\n",
+        "\n",
+        "> glimpse() and slice() are functions in [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, part of the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges\n",
+        "\n",
+        "<br>\n",
+        "\n",
+        "Now that we have the data, let's narrow down to one feature (`bmi`) to target for this exercise. This will require us to select the desired columns. So, how do we do this?\n",
+        "\n",
+        "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) allows us to *select* (and optionally rename) columns in a data frame."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RDY1oAKI-m80"
+      },
+      "source": [
+        "# Select predictor feature `bmi` and outcome `y`\n",
+        "diabetes_select <- diabetes %>% \n",
+        "  select(c(bmi, y))\n",
+        "\n",
+        "# Print the first 5 rows\n",
+        "diabetes_select %>% \n",
+        "  slice(1:10)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SDk668xK-tc3"
+      },
+      "source": [
+        "## 3. Training and Testing data\n",
+        "\n",
+        "It's common practice in supervised learning to *split* the data into two subsets; a (typically larger) set with which to train the model, and a smaller \"hold-back\" set with which to see how the model performed.\n",
+        "\n",
+        "Now that we have data ready, we can see if a machine can help determine a logical split between the numbers in this dataset. We can use the [rsample](https://tidymodels.github.io/rsample/) package, which is part of the Tidymodels framework, to create an object that contains the information on *how* to split the data, and then two more rsample functions to extract the created training and testing sets:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EqtHx129-1h-"
+      },
+      "source": [
+        "set.seed(2056)\n",
+        "# Split 67% of the data for training and the rest for tesing\n",
+        "diabetes_split <- diabetes_select %>% \n",
+        "  initial_split(prop = 0.67)\n",
+        "\n",
+        "# Extract the resulting train and test sets\n",
+        "diabetes_train <- training(diabetes_split)\n",
+        "diabetes_test <- testing(diabetes_split)\n",
+        "\n",
+        "# Print the first 3 rows of the training set\n",
+        "diabetes_train %>% \n",
+        "  slice(1:10)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sBOS-XhB-6v7"
+      },
+      "source": [
+        "## 4. Train a linear regression model with Tidymodels\n",
+        "\n",
+        "Now we are ready to train our model!\n",
+        "\n",
+        "In Tidymodels, you specify models using `parsnip()` by specifying three concepts:\n",
+        "\n",
+        "-   Model **type** differentiates models such as linear regression, logistic regression, decision tree models, and so forth.\n",
+        "\n",
+        "-   Model **mode** includes common options like regression and classification; some model types support either of these while some only have one mode.\n",
+        "\n",
+        "-   Model **engine** is the computational tool which will be used to fit the model. Often these are R packages, such as **`\"lm\"`** or **`\"ranger\"`**\n",
+        "\n",
+        "This modeling information is captured in a model specification, so let's build one!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "20OwEw20--t3"
+      },
+      "source": [
+        "# Build a linear model specification\n",
+        "lm_spec <- \n",
+        "  # Type\n",
+        "  linear_reg() %>% \n",
+        "  # Engine\n",
+        "  set_engine(\"lm\") %>% \n",
+        "  # Mode\n",
+        "  set_mode(\"regression\")\n",
+        "\n",
+        "\n",
+        "# Print the model specification\n",
+        "lm_spec"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_oDHs89k_CJj"
+      },
+      "source": [
+        "After a model has been *specified*, the model can be `estimated` or `trained` using the [`fit()`](https://parsnip.tidymodels.org/reference/fit.html) function, typically using a formula and some data.\n",
+        "\n",
+        "`y ~ .` means we'll fit `y` as the predicted quantity/target, explained by all the predictors/features ie, `.` (in this case, we only have one predictor: `bmi` )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YlsHqd-q_GJQ"
+      },
+      "source": [
+        "# Build a linear model specification\n",
+        "lm_spec <- linear_reg() %>% \n",
+        "  set_engine(\"lm\") %>%\n",
+        "  set_mode(\"regression\")\n",
+        "\n",
+        "\n",
+        "# Train a linear regression model\n",
+        "lm_mod <- lm_spec %>% \n",
+        "  fit(y ~ ., data = diabetes_train)\n",
+        "\n",
+        "# Print the model\n",
+        "lm_mod"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kGZ22RQj_Olu"
+      },
+      "source": [
+        "From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.\n",
+        "<br>\n",
+        "\n",
+        "## 5. Make predictions on the test set\n",
+        "\n",
+        "Now that we've trained a model, we can use it to predict the disease progression y for the test dataset using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). This will be used to draw the line between data groups."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nXHbY7M2_aao"
+      },
+      "source": [
+        "# Make predictions for the test set\n",
+        "predictions <- lm_mod %>% \n",
+        "  predict(new_data = diabetes_test)\n",
+        "\n",
+        "# Print out some of the predictions\n",
+        "predictions %>% \n",
+        "  slice(1:5)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R_JstwUY_bIs"
+      },
+      "source": [
+        "Woohoo! 💃🕺 We just trained a model and used it to make predictions!\n",
+        "\n",
+        "When making predictions, the tidymodels convention is to always produce a tibble/data frame of results with standardized column names. This makes it easy to combine the original data and the predictions in a usable format for subsequent operations such as plotting.\n",
+        "\n",
+        "`dplyr::bind_cols()` efficiently binds multiple data frames column."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RybsMJR7_iI8"
+      },
+      "source": [
+        "# Combine the predictions and the original test set\n",
+        "results <- diabetes_test %>% \n",
+        "  bind_cols(predictions)\n",
+        "\n",
+        "\n",
+        "results %>% \n",
+        "  slice(1:5)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XJbYbMZW_n_s"
+      },
+      "source": [
+        "## 6. Plot modelling results\n",
+        "\n",
+        "Now, its time to see this visually 📈. We'll create a scatter plot of all the `y` and `bmi` values of the test set, then use the predictions to draw a line in the most appropriate place, between the model's data groupings.\n",
+        "\n",
+        "R has several systems for making graphs, but `ggplot2` is one of the most elegant and most versatile. This allows you to compose graphs by **combining independent components**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "R9tYp3VW_sTn"
+      },
+      "source": [
+        "# Set a theme for the plot\n",
+        "theme_set(theme_light())\n",
+        "# Create a scatter plot\n",
+        "results %>% \n",
+        "  ggplot(aes(x = bmi)) +\n",
+        "  # Add a scatter plot\n",
+        "  geom_point(aes(y = y), size = 1.6) +\n",
+        "  # Add a line plot\n",
+        "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zrPtHIxx_tNI"
+      },
+      "source": [
+        "> ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.\n",
+        "\n",
+        "Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/2-Regression/1-Tools/solution/lesson_1.Rmd b/2-Regression/1-Tools/solution/lesson_1.Rmd
new file mode 100644
index 0000000000..d6a0c0eac6
--- /dev/null
+++ b/2-Regression/1-Tools/solution/lesson_1.Rmd
@@ -0,0 +1,250 @@
+---
+title: 'Build a regression model: Get started with R and Tidymodels for regression models'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Introduction to Regression - Lesson 1
+
+#### Putting it into perspective
+
+✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use `linear regression`, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use `logistic regression`. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.
+
+In this section, you will work with a [small dataset about diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.
+
+That said, let's get started on this task!
+
+![Artwork by \@allison_horst](../images/encouRage.jpg){width="630"}
+
+## 1. Loading up our tool set
+
+For this task, we'll require the following packages:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels"))`
+
+The script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+if (!require("pacman")) install.packages("pacman")
+pacman::p_load(tidyverse, tidymodels)
+```
+
+Now, let's load these awesome packages and make them available in our current R session. (This is for mere illustration, `pacman::p_load()` already did that for you)
+
+```{r load_tidy_verse_models, message=F, warning=F}
+# load the core Tidyverse packages
+library(tidyverse)
+
+# load the core Tidymodels packages
+library(tidymodels)
+
+
+```
+
+## 2. The diabetes dataset
+
+In this exercise, we'll put our regression skills into display by making predictions on a diabetes dataset. The [diabetes dataset](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) includes `442 samples` of data around diabetes, with 10 predictor feature variables, `age`, `sex`, `body mass index`, `average blood pressure`, and `six blood serum measurements` as well as an outcome variable `y`: a quantitative measure of disease progression one year after baseline.
+
++----------------------------+------------------------------------------------------------------------------------+
+| **Number of observations** | **442**                                                                            |
++============================+====================================================================================+
+| **Number of predictors**   | First 10 columns are numeric predictive values                                     |
++----------------------------+------------------------------------------------------------------------------------+
+| **Outcome/Target**         | Column 11 is a quantitative measure of disease progression one year after baseline |
++----------------------------+------------------------------------------------------------------------------------+
+| **Predictor Information**  | -   age age in years                                                               |
+|                            | -   sex                                                                            |
+|                            | -   bmi body mass index                                                            |
+|                            | -   bp average blood pressure                                                      |
+|                            | -   s1 tc, total serum cholesterol                                                 |
+|                            | -   s2 ldl, low-density lipoproteins                                               |
+|                            | -   s3 hdl, high-density lipoproteins                                              |
+|                            | -   s4 tch, total cholesterol / HDL                                                |
+|                            | -   s5 ltg, possibly log of serum triglycerides level                              |
+|                            | -   s6 glu, blood sugar level                                                      |
++----------------------------+------------------------------------------------------------------------------------+
+
+> 🎓 Remember, this is supervised learning, and we need a named 'y' target.
+
+Before you can manipulate data with R, you need to import the data into R's memory, or build a connection to the data that R can use to access the data remotely.\
+
+> The [readr](https://readr.tidyverse.org/) package, which is part of the Tidyverse, provides a fast and friendly way to read rectangular data into R.
+
+Now, let's load the diabetes dataset provided in this source URL: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>
+
+Also, we'll perform a sanity check on our data using `glimpse()` and dsiplay the first 5 rows using `slice()`.
+
+Before going any further, let's introduce something you will encounter quite often in R code: the pipe operator `%>%`
+
+The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying "and then" in your code.\
+
+```{r load_dataset, message=F, warning=F}
+# Import the data set
+diabetes <- read_table2(file = "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt")
+
+
+# Get a glimpse and dimensions of the data
+glimpse(diabetes)
+
+
+# Select the first 5 rows of the data
+diabetes %>% 
+  slice(1:5)
+
+```
+
+`glimpse()` shows us that this data has 442 rows and 11 columns with all the columns being of data type `double`
+
+> glimpse() and slice() are functions in [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, part of the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges
+
+Now that we have the data, let's narrow down to one feature (`bmi`) to target for this exercise. This will require us to select the desired columns. So, how do we do this?
+
+[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) allows us to *select* (and optionally rename) columns in a data frame.
+
+```{r select, message=F, warning=F}
+# Select predictor feature `bmi` and outcome `y`
+diabetes_select <- diabetes %>% 
+  select(c(bmi, y))
+
+# Print the first 5 rows
+diabetes_select %>% 
+  slice(1:5)
+```
+
+## 3. Training and Testing data
+
+It's common practice in supervised learning to *split* the data into two subsets; a (typically larger) set with which to train the model, and a smaller "hold-back" set with which to see how the model performed.
+
+Now that we have data ready, we can see if a machine can help determine a logical split between the numbers in this dataset. We can use the [rsample](https://tidymodels.github.io/rsample/) package, which is part of the Tidymodels framework, to create an object that contains the information on *how* to split the data, and then two more rsample functions to extract the created training and testing sets:
+
+```{r split, message=F, warning=F}
+set.seed(2056)
+# Split 67% of the data for training and the rest for tesing
+diabetes_split <- diabetes_select %>% 
+  initial_split(prop = 0.67)
+
+# Extract the resulting train and test sets
+diabetes_train <- training(diabetes_split)
+diabetes_test <- testing(diabetes_split)
+
+# Print the first 3 rows of the training set
+diabetes_train %>% 
+  slice(1:3)
+
+```
+
+## 4. Train a linear regression model with Tidymodels
+
+Now we are ready to train our model!
+
+In Tidymodels, you specify models using `parsnip()` by specifying three concepts:
+
+-   Model **type** differentiates models such as linear regression, logistic regression, decision tree models, and so forth.
+
+-   Model **mode** includes common options like regression and classification; some model types support either of these while some only have one mode.
+
+-   Model **engine** is the computational tool which will be used to fit the model. Often these are R packages, such as **`"lm"`** or **`"ranger"`**
+
+This modeling information is captured in a model specification, so let's build one!
+
+```{r lm_model_spec, message=F, warning=F}
+# Build a linear model specification
+lm_spec <- 
+  # Type
+  linear_reg() %>% 
+  # Engine
+  set_engine("lm") %>% 
+  # Mode
+  set_mode("regression")
+
+
+# Print the model specification
+lm_spec
+
+```
+
+After a model has been *specified*, the model can be `estimated` or `trained` using the [`fit()`](https://parsnip.tidymodels.org/reference/fit.html) function, typically using a formula and some data.
+
+`y ~ .` means we'll fit `y` as the predicted quantity/target, explained by all the predictors/features ie, `.` (in this case, we only have one predictor: `bmi` )
+
+```{r train, message=F, warning=F}
+# Build a linear model specification
+lm_spec <- linear_reg() %>% 
+  set_engine("lm") %>%
+  set_mode("regression")
+
+
+# Train a linear regression model
+lm_mod <- lm_spec %>% 
+  fit(y ~ ., data = diabetes_train)
+
+# Print the model
+lm_mod
+```
+
+From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.
+
+## 5. Make predictions on the test set
+
+Now that we've trained a model, we can use it to predict the disease progression y for the test dataset using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). This will be used to draw the line between data groups.
+
+```{r test, message=F, warning=F}
+# Make predictions for the test set
+predictions <- lm_mod %>% 
+  predict(new_data = diabetes_test)
+
+# Print out some of the predictions
+predictions %>% 
+  slice(1:5)
+```
+
+Woohoo! 💃🕺 We just trained a model and used it to make predictions!
+
+When making predictions, the tidymodels convention is to always produce a tibble/data frame of results with standardized column names. This makes it easy to combine the original data and the predictions in a usable format for subsequent operations such as plotting.
+
+`dplyr::bind_cols()` efficiently binds multiple data frames column.
+
+```{r test_pred, message=F, warning=F}
+# Combine the predictions and the original test set
+results <- diabetes_test %>% 
+  bind_cols(predictions)
+
+
+results %>% 
+  slice(1:5)
+```
+
+## 6. Plot modelling results
+
+Now, its time to see this visually 📈. We'll create a scatter plot of all the `y` and `bmi` values of the test set, then use the predictions to draw a line in the most appropriate place, between the model's data groupings.
+
+R has several systems for making graphs, but `ggplot2` is one of the most elegant and most versatile. This allows you to compose graphs by **combining independent components**.
+
+```{r plot_pred, message=F, warning=F}
+# Set a theme for the plot
+theme_set(theme_light())
+# Create a scatter plot
+results %>% 
+  ggplot(aes(x = bmi)) +
+  # Add a scatter plot
+  geom_point(aes(y = y), size = 1.6) +
+  # Add a line plot
+  geom_line(aes(y = .pred), color = "blue", size = 1.5)
+  
+```
+
+> ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.
+
+Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!
diff --git a/2-Regression/2-Data/images/dplyr_wrangling.png b/2-Regression/2-Data/images/dplyr_wrangling.png
new file mode 100644
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diff --git a/2-Regression/2-Data/solution/lesson_2-R.ipynb b/2-Regression/2-Data/solution/lesson_2-R.ipynb
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index 0000000000..632de0d40d
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+++ b/2-Regression/2-Data/solution/lesson_2-R.ipynb
@@ -0,0 +1,644 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "lesson_2-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pg5aexcOPqAZ"
+      },
+      "source": [
+        "# Build a regression model: prepare and visualize data\n",
+        "\n",
+        "## **Linear Regression for Pumpkins - Lesson 2**\n",
+        "#### Introduction\n",
+        "\n",
+        "Now that you are set up with the tools you need to start tackling machine learning model building with Tidymodels and the Tidyverse, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.\n",
+        "\n",
+        "In this lesson, you will learn:\n",
+        "\n",
+        "-   How to prepare your data for model-building.\n",
+        "\n",
+        "-   How to use `ggplot2` for data visualization.\n",
+        "\n",
+        "The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.\n",
+        "\n",
+        "Let's see this by working through a practical exercise.\n",
+        "\n",
+        "![Artwork by \\@allison_horst](../images/unruly_data.jpg){width=\"700\"} <br>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dc5WhyVdXAjR"
+      },
+      "source": [
+        "## 1. Importing pumpkins data and summoning the Tidyverse\n",
+        "\n",
+        "We'll require the following packages to slice and dice this lesson:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\"))`\n",
+        "\n",
+        "The script below checks whether you have the packages required to complete this module and installs them for you in case some are missing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GqPYUZgfXOBt"
+      },
+      "source": [
+        "if (!require(\"pacman\")) install.packages(\"pacman\")\n",
+        "pacman::p_load(tidyverse)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kvjDTPDSXRr2"
+      },
+      "source": [
+        "Now, let's fire up some packages and load the [data](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) provided for this lesson!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VMri-t2zXqgD"
+      },
+      "source": [
+        "# Load the core Tidyverse packages\n",
+        "library(tidyverse)\n",
+        "\n",
+        "# Import the pumpkins data\n",
+        "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+        "\n",
+        "\n",
+        "# Get a glimpse and dimensions of the data\n",
+        "glimpse(pumpkins)\n",
+        "\n",
+        "\n",
+        "# Print the first 50 rows of the data set\n",
+        "pumpkins %>% \n",
+        "  slice_head(n =50)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "REWcIv9yX29v"
+      },
+      "source": [
+        "A quick `glimpse()` immediately shows that there are blanks and a mix of strings (`chr`) and numeric data (`dbl`). The `Date` is of type character and there's also a strange column called `Package` where the data is a mix between `sacks`, `bins` and other values. The data, in fact, is a bit of a mess 😤.\n",
+        "\n",
+        "In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. But worry not, in this lesson, you will learn how to prepare a raw dataset using standard R libraries 🧑�🔧. You will also learn various techniques to visualize the data.📈📊\n",
+        "<br>\n",
+        "\n",
+        "> A refresher: The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying \"and then\" in your code.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zxfb3AM5YbUe"
+      },
+      "source": [
+        "## 2. Check for missing data\n",
+        "\n",
+        "One of the most common issues data scientists need to deal with is incomplete or missing data. R represents missing, or unknown values, with special sentinel value: `NA` (Not Available).\n",
+        "\n",
+        "So how would we know that the data frame contains missing values?\n",
+        "<br>\n",
+        "-   One straight forward way would be to use the base R function `anyNA` which returns the logical objects `TRUE` or `FALSE`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "G--DQutAYltj"
+      },
+      "source": [
+        "pumpkins %>% \n",
+        "  anyNA()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mU-7-SB6YokF"
+      },
+      "source": [
+        "Great, there seems to be some missing data! That's a good place to start.\n",
+        "\n",
+        "-   Another way would be to use the function `is.na()` that indicates which individual column elements are missing with a logical `TRUE`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "W-DxDOR4YxSW"
+      },
+      "source": [
+        "pumpkins %>% \n",
+        "  is.na() %>% \n",
+        "  head(n = 7)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xUWxipKYY0o7"
+      },
+      "source": [
+        "Okay, got the job done but with a large data frame such as this, it would be inefficient and practically impossible to review all of the rows and columns individually😴.\n",
+        "\n",
+        "-   A more intuitive way would be to calculate the sum of the missing values for each column:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZRBWV6P9ZArL"
+      },
+      "source": [
+        "pumpkins %>% \n",
+        "  is.na() %>% \n",
+        "  colSums()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9gv-crB6ZD1Y"
+      },
+      "source": [
+        "Much better! There is missing data, but maybe it won't matter for the task at hand. Let's see what further analysis brings forth.\n",
+        "\n",
+        "> Along with the awesome sets of packages and functions, R has a very good documentation. For instance, use `help(colSums)` or `?colSums` to find out more about the function."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o4jLY5-VZO2C"
+      },
+      "source": [
+        "## 3. Dplyr: A Grammar of Data Manipulation\n",
+        "\n",
+        "![Artwork by \\@allison_horst](../images/dplyr_wrangling.png){width=\"569\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i5o33MQBZWWw"
+      },
+      "source": [
+        "[`dplyr`](https://dplyr.tidyverse.org/), a package in the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges. In this section, we'll explore some of dplyr's verbs!\n",
+        "<br>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x3VGMAGBZiUr"
+      },
+      "source": [
+        "#### dplyr::select()\n",
+        "\n",
+        "`select()` is a function in the package `dplyr` which helps you pick columns to keep or exclude.\n",
+        "\n",
+        "To make your data frame easier to work with, drop several of its columns, using `select()`, keeping only the columns you need.\n",
+        "\n",
+        "For instance, in this exercise, our analysis will involve the columns `Package`, `Low Price`, `High Price` and `Date`. Let's select these columns."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "F_FgxQnVZnM0"
+      },
+      "source": [
+        "# Select desired columns\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  select(Package, `Low Price`, `High Price`, Date)\n",
+        "\n",
+        "\n",
+        "# Print data set\n",
+        "pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2KKo0Ed9Z1VB"
+      },
+      "source": [
+        "#### dplyr::mutate()\n",
+        "\n",
+        "`mutate()` is a function in the package `dplyr` which helps you create or modify columns, while keeping the existing columns.\n",
+        "\n",
+        "The general structure of mutate is:\n",
+        "\n",
+        "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+        "\n",
+        "Let's take `mutate` out for a spin using the `Date` column by doing the following operations:\n",
+        "\n",
+        "1.  Convert the dates (currently of type character) to a month format (these are US dates, so the format is `MM/DD/YYYY`).\n",
+        "\n",
+        "2.  Extract the month from the dates to a new column.\n",
+        "\n",
+        "In R, the package [lubridate](https://lubridate.tidyverse.org/) makes it easier to work with Date-time data. So, let's use `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` and see how to achieve the above objectives. We can drop the Date column since we won't be needing it again in subsequent operations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5joszIVSZ6xe"
+      },
+      "source": [
+        "# Load lubridate\n",
+        "library(lubridate)\n",
+        "\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  # Convert the Date column to a date object\n",
+        "  mutate(Date = mdy(Date)) %>% \n",
+        "  # Extract month from Date\n",
+        "  mutate(Month = month(Date)) %>% \n",
+        "  # Drop Date column\n",
+        "  select(-Date)\n",
+        "\n",
+        "# View the first few rows\n",
+        "pumpkins %>% \n",
+        "  slice_head(n = 7)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nIgLjNMCZ-6Y"
+      },
+      "source": [
+        "Woohoo! 🤩\n",
+        "\n",
+        "Next, let's create a new column `Price`, which represents the average price of a pumpkin. Now, let's take the average of the `Low Price` and `High Price` columns to populate the new Price column.\n",
+        "<br>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Zo0BsqqtaJw2"
+      },
+      "source": [
+        "# Create a new column Price\n",
+        "pumpkins <- pumpkins %>% \n",
+        "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+        "\n",
+        "# View the first few rows of the data\n",
+        "pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p77WZr-9aQAR"
+      },
+      "source": [
+        "Yeees!💪\n",
+        "\n",
+        "\"But wait!\", you'll say after skimming through the whole data set with `View(pumpkins)`, \"There's something odd here!\"🤔\n",
+        "\n",
+        "If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in `1 1/9 bushel` measures, and some in `1/2 bushel` measures, some per pumpkin, some per pound, and some in big boxes with varying widths.\n",
+        "\n",
+        "Let's verify this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XISGfh0IaUy6"
+      },
+      "source": [
+        "# Verify the distinct observations in Package column\n",
+        "pumpkins %>% \n",
+        "  distinct(Package)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7sMjiVujaZxY"
+      },
+      "source": [
+        "Amazing!�\n",
+        "\n",
+        "Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string *bushel* in the `Package` column and put this in a new data frame `new_pumpkins`.\n",
+        "<br>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L8Qfcs92ageF"
+      },
+      "source": [
+        "#### dplyr::filter() and stringr::str_detect()\n",
+        "\n",
+        "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): creates a subset of the data only containing **rows** that satisfy your conditions, in this case, pumpkins with the string *bushel* in the `Package` column.\n",
+        "\n",
+        "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): detects the presence or absence of a pattern in a string.\n",
+        "\n",
+        "The [`stringr`](https://github.com/tidyverse/stringr) package provides simple functions for common string operations."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hy_SGYREampd"
+      },
+      "source": [
+        "# Retain only pumpkins with \"bushel\"\n",
+        "new_pumpkins <- pumpkins %>% \n",
+        "       filter(str_detect(Package, \"bushel\"))\n",
+        "\n",
+        "# Get the dimensions of the new data\n",
+        "dim(new_pumpkins)\n",
+        "\n",
+        "# View a few rows of the new data\n",
+        "new_pumpkins %>% \n",
+        "  slice_head(n = 5)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VrDwF031avlR"
+      },
+      "source": [
+        "You can see that we have narrowed down to 415 or so rows of data containing pumpkins by the bushel.🤩\n",
+        "<br>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mLpw2jH4a0tx"
+      },
+      "source": [
+        "#### dplyr::case_when()\n",
+        "\n",
+        "**But wait! There's one more thing to do**\n",
+        "\n",
+        "Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel. Time to do some math to standardize it.\n",
+        "\n",
+        "We'll use the function [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) to *mutate* the Price column depending on some conditions. `case_when` allows you to vectorise multiple `if_else()`statements.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "P68kLVQmbM6I"
+      },
+      "source": [
+        "# Convert the price if the Package contains fractional bushel values\n",
+        "new_pumpkins <- new_pumpkins %>% \n",
+        "  mutate(Price = case_when(\n",
+        "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+        "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+        "    TRUE ~ Price))\n",
+        "\n",
+        "# View the first few rows of the data\n",
+        "new_pumpkins %>% \n",
+        "  slice_head(n = 30)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pS2GNPagbSdb"
+      },
+      "source": [
+        "Now, we can analyze the pricing per unit based on their bushel measurement. All this study of bushels of pumpkins, however, goes to show how very `important` it is to `understand the nature of your data`!\n",
+        "\n",
+        "> ✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. \"A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds.\" It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!\n",
+        ">\n",
+        "> ✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.\n",
+        "<br>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qql1SowfbdnP"
+      },
+      "source": [
+        "Now lastly, for the sheer sake of adventure ��♀�, let's also move the Month column to the first position i.e `before` column `Package`.\n",
+        "\n",
+        "`dplyr::relocate()` is used to change column positions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JJ1x6kw8bixF"
+      },
+      "source": [
+        "# Create a new data frame new_pumpkins\n",
+        "new_pumpkins <- new_pumpkins %>% \n",
+        "  relocate(Month, .before = Package)\n",
+        "\n",
+        "new_pumpkins %>% \n",
+        "  slice_head(n = 7)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y8TJ0Za_bn5Y"
+      },
+      "source": [
+        "Good job!👌 You now have a clean, tidy dataset on which you can build your new regression model!\n",
+        "<br>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mYSH6-EtbvNa"
+      },
+      "source": [
+        "## 4. Data visualization with ggplot2\n",
+        "\n",
+        "![Infographic by Dasani Madipalli](../images/data-visualization.png){width=\"600\"}\n",
+        "\n",
+        "There is a *wise* saying that goes like this:\n",
+        "\n",
+        "> \"The simple graph has brought more information to the data analyst's mind than any other device.\" --- John Tukey\n",
+        "\n",
+        "Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.\n",
+        "\n",
+        "Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.\n",
+        "\n",
+        "R offers a number of several systems for making graphs, but [`ggplot2`](https://ggplot2.tidyverse.org/index.html) is one of the most elegant and most versatile. `ggplot2` allows you to compose graphs by **combining independent components**.\n",
+        "\n",
+        "Let's start with a simple scatter plot for the Price and Month columns.\n",
+        "\n",
+        "So in this case, we'll start with [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), supply a dataset and aesthetic mapping (with [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) then add a layers (like [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) for scatter plots.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "g2YjnGeOcLo4"
+      },
+      "source": [
+        "# Set a theme for the plots\n",
+        "theme_set(theme_light())\n",
+        "\n",
+        "# Create a scatter plot\n",
+        "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+        "p + geom_point()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ml7SDCLQcPvE"
+      },
+      "source": [
+        "Is this a useful plot 🤷? Does anything about it surprise you?\n",
+        "\n",
+        "It's not particularly useful as all it does is display in your data as a spread of points in a given month.\n",
+        "<br>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jMakvJZIcVkh"
+      },
+      "source": [
+        "### **How do we make it useful?**\n",
+        "\n",
+        "To get charts to display useful data, you usually need to group the data somehow. For instance in our case, finding the average price of pumpkins for each month would provide more insights to the underlying patterns in our data. This leads us to one more **dplyr** flyby:\n",
+        "\n",
+        "#### `dplyr::group_by() %>% summarize()`\n",
+        "\n",
+        "Grouped aggregation in R can be easily computed using\n",
+        "\n",
+        "`dplyr::group_by() %>% summarize()`\n",
+        "\n",
+        "-   `dplyr::group_by()` changes the unit of analysis from the complete dataset to individual groups such as per month.\n",
+        "\n",
+        "-   `dplyr::summarize()` creates a new data frame with one column for each grouping variable and one column for each of the summary statistics that you have specified.\n",
+        "\n",
+        "For example, we can use the `dplyr::group_by() %>% summarize()` to group the pumpkins into groups based on the **Month** columns and then find the **mean price** for each month."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6kVSUa2Bcilf"
+      },
+      "source": [
+        "# Find the average price of pumpkins per month\n",
+        "new_pumpkins %>%\n",
+        "  group_by(Month) %>% \n",
+        "  summarise(mean_price = mean(Price))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kds48GUBcj3W"
+      },
+      "source": [
+        "Succinct!✨\n",
+        "\n",
+        "Categorical features such as months are better represented using a bar plot 📊. The layers responsible for bar charts are `geom_bar()` and `geom_col()`. Consult `?geom_bar` to find out more.\n",
+        "\n",
+        "Let's whip up one!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VNbU1S3BcrxO"
+      },
+      "source": [
+        "# Find the average price of pumpkins per month then plot a bar chart\n",
+        "new_pumpkins %>%\n",
+        "  group_by(Month) %>% \n",
+        "  summarise(mean_price = mean(Price)) %>% \n",
+        "  ggplot(aes(x = Month, y = mean_price)) +\n",
+        "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\n",
+        "  ylab(\"Pumpkin Price\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zDm0VOzzcuzR"
+      },
+      "source": [
+        "🤩🤩This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?\n",
+        "\n",
+        "Congratulations on finishing the second lesson �! You did prepared your data for model building, then uncovered more insights using visualizations!"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/2-Regression/2-Data/solution/lesson_2.Rmd b/2-Regression/2-Data/solution/lesson_2.Rmd
new file mode 100644
index 0000000000..7f72b1a84e
--- /dev/null
+++ b/2-Regression/2-Data/solution/lesson_2.Rmd
@@ -0,0 +1,345 @@
+---
+title: 'Build a regression model: prepare and visualize data'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## **Linear Regression for Pumpkins - Lesson 2**
+
+#### Introduction
+
+Now that you are set up with the tools you need to start tackling machine learning model building with Tidymodels and the Tidyverse, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.
+
+In this lesson, you will learn:
+
+-   How to prepare your data for model-building.
+
+-   How to use `ggplot2` for data visualization.
+
+The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.
+
+Let's see this by working through a practical exercise.
+
+![Artwork by \@allison_horst](../images/unruly_data.jpg){width="700"}
+
+## 1. Importing pumpkins data and summoning the Tidyverse
+
+We'll require the following packages to slice and dice this lesson:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+You can have them installed as:
+
+`install.packages(c("tidyverse"))`
+
+The script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+if (!require("pacman")) install.packages("pacman")
+pacman::p_load(tidyverse)
+```
+
+Now, let's fire up some packages and load the [data](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) provided for this lesson!
+
+```{r load_tidy_verse_models, message=F, warning=F}
+# Load the core Tidyverse packages
+library(tidyverse)
+
+# Import the pumpkins data
+pumpkins <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv")
+
+
+# Get a glimpse and dimensions of the data
+glimpse(pumpkins)
+
+
+# Print the first 50 rows of the data set
+pumpkins %>% 
+  slice_head(n =50)
+
+```
+
+A quick `glimpse()` immediately shows that there are blanks and a mix of strings (`chr`) and numeric data (`dbl`). The `Date` is of type character and there's also a strange column called `Package` where the data is a mix between `sacks`, `bins` and other values. The data, in fact, is a bit of a mess 😤.
+
+In fact, it is not very common to be gifted a dataset that is completely ready to use to create a ML model out of the box. But worry not, in this lesson, you will learn how to prepare a raw dataset using standard R libraries 🧑�🔧. You will also learn various techniques to visualize the data.📈📊
+
+
+
+> A refresher: The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying "and then" in your code.
+
+
+## 2. Check for missing data
+
+One of the most common issues data scientists need to deal with is incomplete or missing data. R represents missing, or unknown values, with special sentinel value: `NA` (Not Available).
+
+So how would we know that the data frame contains missing values?
+
+-   One straight forward way would be to use the base R function `anyNA` which returns the logical objects `TRUE` or `FALSE`
+
+```{r anyNA, message=F, warning=F}
+pumpkins %>% 
+  anyNA()
+```
+
+Great, there seems to be some missing data! That's a good place to start.
+
+-   Another way would be to use the function `is.na()` that indicates which individual column elements are missing with a logical `TRUE`.
+
+```{r is_na, message=F, warning=F}
+pumpkins %>% 
+  is.na() %>% 
+  head(n = 7)
+```
+
+Okay, got the job done but with a large data frame such as this, it would be inefficient and practically impossible to review all of the rows and columns individually😴.
+
+-   A more intuitive way would be to calculate the sum of the missing values for each column:
+
+```{r colSum_NA, message=F, warning=F}
+pumpkins %>% 
+  is.na() %>% 
+  colSums()
+```
+
+Much better! There is missing data, but maybe it won't matter for the task at hand. Let's see what further analysis brings forth.
+
+> Along with the awesome sets of packages and functions, R has a very good documentation. For instance, use `help(colSums)` or `?colSums` to find out more about the function.
+
+## 3. Dplyr: A Grammar of Data Manipulation
+
+![Artwork by \@allison_horst](../images/dplyr_wrangling.png){width="569"}
+
+[`dplyr`](https://dplyr.tidyverse.org/), a package in the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges. In this section, we'll explore some of dplyr's verbs!
+
+#### dplyr::select()
+
+`select()` is a function in the package `dplyr` which helps you pick columns to keep or exclude.
+
+To make your data frame easier to work with, drop several of its columns, using `select()`, keeping only the columns you need.
+
+For instance, in this exercise, our analysis will involve the columns `Package`, `Low Price`, `High Price` and `Date`. Let's select these columns.
+
+```{r select, message=F, warning=F}
+# Select desired columns
+pumpkins <- pumpkins %>% 
+  select(Package, `Low Price`, `High Price`, Date)
+
+
+# Print data set
+pumpkins %>% 
+  slice_head(n = 5)
+```
+
+#### dplyr::mutate()
+
+`mutate()` is a function in the package `dplyr` which helps you create or modify columns, while keeping the existing columns.
+
+The general structure of mutate is:
+
+`data %>%   mutate(new_column_name = what_it_contains)`
+
+Let's take `mutate` out for a spin using the `Date` column by doing the following operations:
+
+1.  Convert the dates (currently of type character) to a month format (these are US dates, so the format is `MM/DD/YYYY`).
+
+2.  Extract the month from the dates to a new column.
+
+In R, the package [lubridate](https://lubridate.tidyverse.org/) makes it easier to work with Date-time data. So, let's use `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` and see how to achieve the above objectives. We can drop the Date column since we won't be needing it again in subsequent operations.
+
+```{r mut_date, message=F, warning=F}
+# Load lubridate
+library(lubridate)
+
+pumpkins <- pumpkins %>% 
+  # Convert the Date column to a date object
+  mutate(Date = mdy(Date)) %>% 
+  # Extract month from Date
+  mutate(Month = month(Date)) %>% 
+  # Drop Date column
+  select(-Date)
+
+# View the first few rows
+pumpkins %>% 
+  slice_head(n = 7)
+```
+
+Woohoo! 🤩
+
+Next, let's create a new column `Price`, which represents the average price of a pumpkin. Now, let's take the average of the `Low Price` and `High Price` columns to populate the new Price column.
+
+```{r price, message=F, warning=F}
+# Create a new column Price
+pumpkins <- pumpkins %>% 
+  mutate(Price = (`Low Price` + `High Price`)/2)
+
+# View the first few rows of the data
+pumpkins %>% 
+  slice_head(n = 5)
+```
+
+Yeees!💪
+
+"But wait!", you'll say after skimming through the whole data set with `View(pumpkins)`, "There's something odd here!"🤔
+
+If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in `1 1/9 bushel` measures, and some in `1/2 bushel` measures, some per pumpkin, some per pound, and some in big boxes with varying widths.
+
+Let's verify this:
+
+```{r Package, message=F, warning=F}
+# Verify the distinct observations in Package column
+pumpkins %>% 
+  distinct(Package)
+
+```
+
+Amazing!�
+
+Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string *bushel* in the `Package` column and put this in a new data frame `new_pumpkins`.
+
+#### dplyr::filter() and stringr::str_detect()
+
+[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): creates a subset of the data only containing **rows** that satisfy your conditions, in this case, pumpkins with the string *bushel* in the `Package` column.
+
+[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): detects the presence or absence of a pattern in a string.
+
+The [`stringr`](https://github.com/tidyverse/stringr) package provides simple functions for common string operations.
+
+```{r filter, message=F, warning=F}
+# Retain only pumpkins with "bushel"
+new_pumpkins <- pumpkins %>% 
+       filter(str_detect(Package, "bushel"))
+
+# Get the dimensions of the new data
+dim(new_pumpkins)
+
+# View a few rows of the new data
+new_pumpkins %>% 
+  slice_head(n = 5)
+```
+
+You can see that we have narrowed down to 415 or so rows of data containing pumpkins by the bushel.🤩
+
+#### dplyr::case_when()
+
+**But wait! There's one more thing to do**
+
+Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel. Time to do some math to standardize it.
+
+We'll use the function [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) to *mutate* the Price column depending on some conditions. `case_when` allows you to vectorise multiple `if_else()`statements.
+
+```{r normalize_price, message=F, warning=F}
+# Convert the price if the Package contains fractional bushel values
+new_pumpkins <- new_pumpkins %>% 
+  mutate(Price = case_when(
+    str_detect(Package, "1 1/9") ~ Price/(1 + 1/9),
+    str_detect(Package, "1/2") ~ Price/(1/2),
+    TRUE ~ Price))
+
+# View the first few rows of the data
+new_pumpkins %>% 
+  slice_head(n = 30)
+```
+
+Now, we can analyze the pricing per unit based on their bushel measurement. All this study of bushels of pumpkins, however, goes to show how very `important` it is to `understand the nature of your data`!
+
+> ✅ According to [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), a bushel's weight depends on the type of produce, as it's a volume measurement. "A bushel of tomatoes, for example, is supposed to weigh 56 pounds... Leaves and greens take up more space with less weight, so a bushel of spinach is only 20 pounds." It's all pretty complicated! Let's not bother with making a bushel-to-pound conversion, and instead price by the bushel. All this study of bushels of pumpkins, however, goes to show how very important it is to understand the nature of your data!
+>
+> ✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.
+
+Now lastly, for the sheer sake of adventure ��♀�, let's also move the Month column to the first position i.e `before` column `Package`.
+
+`dplyr::relocate()` is used to change column positions.
+
+```{r new_pumpkins, message=F, warning=F}
+# Create a new data frame new_pumpkins
+new_pumpkins <- new_pumpkins %>% 
+  relocate(Month, .before = Package)
+
+new_pumpkins %>% 
+  slice_head(n = 7)
+  
+```
+
+Good job!👌 You now have a clean, tidy dataset on which you can build your new regression model!
+
+## 4. Data visualization with ggplot2
+
+![Infographic by Dasani Madipalli](../images/data-visualization.png){width="600"}
+
+There is a *wise* saying that goes like this:
+
+> "The simple graph has brought more information to the data analyst's mind than any other device." --- John Tukey
+
+Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.
+
+Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.
+
+R offers a number of several systems for making graphs, but [`ggplot2`](https://ggplot2.tidyverse.org/index.html) is one of the most elegant and most versatile. `ggplot2` allows you to compose graphs by **combining independent components**.
+
+Let's start with a simple scatter plot for the Price and Month columns.
+
+So in this case, we'll start with [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), supply a dataset and aesthetic mapping (with [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) then add a layers (like [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) for scatter plots.
+
+```{r scatter_plt, message=F, warning=F}
+# Set a theme for the plots
+theme_set(theme_light())
+
+# Create a scatter plot
+p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))
+p + geom_point()
+```
+
+Is this a useful plot 🤷? Does anything about it surprise you?
+
+It's not particularly useful as all it does is display in your data as a spread of points in a given month.
+
+### **How do we make it useful?**
+
+To get charts to display useful data, you usually need to group the data somehow. For instance in our case, finding the average price of pumpkins for each month would provide more insights to the underlying patterns in our data. This leads us to one more **dplyr** flyby:
+
+#### `dplyr::group_by() %>% summarize()`
+
+Grouped aggregation in R can be easily computed using
+
+`dplyr::group_by() %>% summarize()`
+
+-   `dplyr::group_by()` changes the unit of analysis from the complete dataset to individual groups such as per month.
+
+-   `dplyr::summarize()` creates a new data frame with one column for each grouping variable and one column for each of the summary statistics that you have specified.
+
+For example, we can use the `dplyr::group_by() %>% summarize()` to group the pumpkins into groups based on the **Month** columns and then find the **mean price** for each month.
+
+```{r grp_sumry, message=F, warning=F}
+# Find the average price of pumpkins per month
+new_pumpkins %>%
+  group_by(Month) %>% 
+  summarise(mean_price = mean(Price))
+```
+
+Succinct!✨
+
+Categorical features such as months are better represented using a bar plot 📊. The layers responsible for bar charts are `geom_bar()` and `geom_col()`. Consult
+
+`?geom_bar` to find out more.
+
+Let's whip up one!
+
+```{r bar_plt, message=F, warning=F}
+# Find the average price of pumpkins per month then plot a bar chart
+new_pumpkins %>%
+  group_by(Month) %>% 
+  summarise(mean_price = mean(Price)) %>% 
+  ggplot(aes(x = Month, y = mean_price)) +
+  geom_col(fill = "midnightblue", alpha = 0.7) +
+  ylab("Pumpkin Price")
+```
+
+🤩🤩This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?
+
+Congratulations on finishing the second lesson �! You did prepared your data for model building, then uncovered more insights using visualizations!\

From d3be7249cfec05fad7bacf23add1df43634d9c3c Mon Sep 17 00:00:00 2001
From: AidarSource <34319725+AidarSource@users.noreply.github.com>
Date: Fri, 23 Jul 2021 15:44:51 +0600
Subject: [PATCH 252/368] fix broken site link

---
 3-Web-App/1-Web-App/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index 7c91acb57e..f8db7afc97 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -1,6 +1,6 @@
 # Build a Web App to use a ML Model
 
-In this lesson, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from [NUFORC's database](https://www.nuforc.org).
+In this lesson, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from NUFORC's database.
 
 You will learn:
 

From f73b84f84a0844d6c02d48250c495da325284af8 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Fri, 23 Jul 2021 16:29:48 +0200
Subject: [PATCH 253/368] Italian Translation - Chapter 4 complete

---
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diff --git a/4-Classification/1-Introduction/translations/README.it.md b/4-Classification/1-Introduction/translations/README.it.md
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+# Introduzione alla classificazione
+
+In queste quattro lezioni si esplorerà un focus fondamentale del machine learning classico: _la classificazione_. Verrà analizzato l'utilizzo di vari algoritmi di classificazione con un insieme di dati su tutte le brillanti cucine dell'Asia e dell'India. Si spera siate affamati!
+
+![solo un pizzico!](../images/pinch.png)
+
+> In queste lezioni di celebrano le cucine panasiatiche! Immagine di [Jen Looper](https://twitter.com/jenlooper)
+
+La classificazione è una forma di [apprendimento supervisionato](https://it.wikipedia.org/wiki/Apprendimento_supervisionato) che ha molto in comune con le tecniche di regressione. Se machine learning riguarda la previsione di valori o nomi di cose utilizzando insiemi di dati, la classificazione generalmente rientra in due gruppi: _classificazione binaria_ e _classificazione multiclasse_.
+
+[![Introduzione allaclassificazione](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduzione alla classificazione")
+
+> 🎥 Fare clic sull'immagine sopra per un video: John Guttag del MIT introduce la classificazione
+
+Ricordare:
+
+- La **regressione lineare** ha aiutato a prevedere le relazioni tra le variabili e a fare previsioni accurate su dove un nuovo punto dati si sarebbe posizionato in relazione a quella linea. Quindi, si potrebbe prevedere _quale prezzo avrebbe una zucca a settembre rispetto a dicembre_, ad esempio.
+- La **regressione logistica** ha aiutato a scoprire le "categorie binarie": a questo prezzo, _questa zucca è arancione o non arancione_?
+
+La classificazione utilizza vari algoritmi per determinare altri modi per definire l'etichetta o la classe di un punto dati. Si lavorerà con questi dati di cucina per vedere se, osservando un gruppo di ingredienti, è possibile determinarne la cucina di origine.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+
+### Introduzione
+
+La classificazione è una delle attività fondamentali del ricercatore di machine learning e data scientist. Dalla classificazione basica di un valore binario ("questa email è spam o no?"), alla complessa classificazione e segmentazione di immagini utilizzando la visione artificiale, è sempre utile essere in grado di ordinare i dati in classi e porre domande su di essi.
+
+Per definire il processo in modo più scientifico, il metodo di classificazione crea un modello predittivo che consente di mappare la relazione tra le variabili di input e le variabili di output.
+
+![classificazione binaria vs. multiclasse](../images/binary-multiclass.png)
+
+> Problemi binari e multiclasse per la gestione di algoritmi di classificazione. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+Prima di iniziare il processo di pulizia dei dati, visualizzazione e preparazione per le attività di machine learning, si apprenderà qualcosa circa i vari modi in cui machine learning può essere sfruttato per classificare i dati.
+
+Derivata dalla [statistica](https://it.wikipedia.org/wiki/Classificazione_statistica), la classificazione che utilizza machine learning classico utilizza caratteristiche come l'`essere fumatore`, il `peso` e l'`età` per determinare _la probabilità di sviluppare la malattia X._ Essendo una tecnica di apprendimento supervisionata simile agli esercizi di regressione eseguiti in precedenza, i dati vengono etichettati e gli algoritmi ML utilizzano tali etichette per classificare e prevedere le classi (o "caratteristiche") di un insieme di dati e assegnarle a un gruppo o risultato.
+
+✅ Si prenda un momento per immaginare un insieme di dati sulle cucine. A cosa potrebbe rispondere un modello multiclasse? A cosa potrebbe rispondere un modello binario? Se si volesse determinare se una determinata cucina potrebbe utilizzare il fieno greco? Se si volesse vedere se, regalando una busta della spesa piena di anice stellato, carciofi, cavolfiori e rafano, si possa creare un piatto tipico indiano?
+
+[![Cesti misteriosi pazzeschi](https://img.youtube.com/vi/GuTeDbaNoEU/0.jpg)](https://youtu.be/GuTeDbaNoEU " Cestini misteriosi pazzeschi")
+
+> 🎥 Fare clic sull'immagine sopra per un video. L'intera premessa dello spettacolo 'Chopped' è il 'cesto misterioso' dove gli chef devono preparare un piatto con una scelta casuale di ingredienti. Sicuramente un modello ML avrebbe aiutato!
+
+## Ciao 'classificatore'
+
+La domanda che si vuole porre a questo insieme di dati sulla cucina è in realtà una **domanda multiclasse**, poiché ci sono diverse potenziali cucine nazionali con cui lavorare. Dato un lotto di ingredienti, in quale di queste molte classi si identificheranno i dati?
+
+Scikit-learn offre diversi algoritmi da utilizzare per classificare i dati, a seconda del tipo di problema che si desidera risolvere. Nelle prossime due lezioni si impareranno a conoscere molti di questi algoritmi.
+
+## Esercizio: pulire e bilanciare i dati
+
+Il primo compito, prima di iniziare questo progetto, sarà pulire e **bilanciare** i dati per ottenere risultati migliori. Si inizia con il file vuoto _notebook.ipynb_ nella radice di questa cartella.
+
+La prima cosa da installare è [imblearn](https://imbalanced-learn.org/stable/). Questo è un pacchetto di apprendimento di Scikit che consentirà di bilanciare meglio i dati (si imparerà di più su questa attività tra un minuto).
+
+1. Per installare `imblearn`, eseguire `pip install`, in questo modo:
+
+   ```python
+   pip install imblearn
+   ```
+
+1. Importare i pacchetti necessari per caricare i dati e visualizzarli, importare anche `SMOTE` da `imblearn`.
+
+   ```python
+   import pandas as pd
+   import matplotlib.pyplot as plt
+   import matplotlib as mpl
+   import numpy as np
+   from imblearn.over_sampling import SMOTE
+   ```
+
+   Ora si è pronti per la successiva importazione dei dati.
+
+1. Il prossimo compito sarà quello di importare i dati:
+
+   ```python
+   df  = pd.read_csv('../data/cuisines.csv')
+   ```
+
+   Usando `read_csv()` si leggerà il contenuto del file csv _cusines.csv_ e lo posizionerà nella variabile `df`.
+
+1. Controllare la forma dei dati:
+
+   ```python
+   df.head()
+   ```
+
+   Le prime cinque righe hanno questo aspetto:
+
+   ```output
+   |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+   | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+   | 0   | 65         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 1   | 66         | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 2   | 67         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 3   | 68         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 4   | 69         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+   ```
+
+1. Si possono ottienere informazioni su questi dati chiamando `info()`:
+
+   ```python
+   df.info()
+   ```
+
+   Il risultato assomiglia a:
+
+   ```output
+   <class 'pandas.core.frame.DataFrame'>
+   RangeIndex: 2448 entries, 0 to 2447
+   Columns: 385 entries, Unnamed: 0 to zucchini
+   dtypes: int64(384), object(1)
+   memory usage: 7.2+ MB
+   ```
+
+## Esercizio - conoscere le cucine
+
+Ora il lavoro inizia a diventare più interessante. Si scoprirà la distribuzione dei dati, per cucina
+
+1. Tracciare i dati come barre chiamando `barh()`:
+
+   ```python
+   df.cuisine.value_counts().plot.barh()
+   ```
+
+   ![distribuzione dati cuisine](../images/cuisine-dist.png)
+
+   Esiste un numero finito di cucine, ma la distribuzione dei dati non è uniforme. Si può sistemare! Prima di farlo, occorre esplorare un po' di più.
+
+1. Si deve scoprire quanti dati sono disponibili per cucina e stamparli:
+
+   ```python
+   thai_df = df[(df.cuisine == "thai")]
+   japanese_df = df[(df.cuisine == "japanese")]
+   chinese_df = df[(df.cuisine == "chinese")]
+   indian_df = df[(df.cuisine == "indian")]
+   korean_df = df[(df.cuisine == "korean")]
+
+   print(f'thai df: {thai_df.shape}')
+   print(f'japanese df: {japanese_df.shape}')
+   print(f'chinese df: {chinese_df.shape}')
+   print(f'indian df: {indian_df.shape}')
+   print(f'korean df: {korean_df.shape}')
+   ```
+
+   il risultato si presenta così:
+
+   ```output
+   thai df: (289, 385)
+   japanese df: (320, 385)
+   chinese df: (442, 385)
+   indian df: (598, 385)
+   korean df: (799, 385)
+   ```
+
+## Alla scoperta degli ingredienti
+
+Ora si possono approfondire i dati e scoprire quali sono gli ingredienti tipici per cucina. Si dovrebbero ripulire i dati ricorrenti che creano confusione tra le cucine, quindi si affronterà questo problema.
+
+1. Creare una funzione `create_ingredient()` in Python per creare un dataframe ingredient Questa funzione inizierà eliminando una colonna non utile e ordinando gli ingredienti in base al loro conteggio:
+
+   ```python
+   def create_ingredient_df(df):
+       ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
+       ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
+       ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+       inplace=False)
+       return ingredient_df
+   ```
+
+   Ora si può usare questa funzione per farsi un'idea dei primi dieci ingredienti più popolari per cucina.
+
+1. Chiamare `create_ingredient_df()` e tracciare il grafico chiamando `barh()`:
+
+   ```python
+   thai_ingredient_df = create_ingredient_df(thai_df)
+   thai_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![thai](../images/thai.png)
+
+1. Fare lo stesso per i dati giapponesi:
+
+   ```python
+   japanese_ingredient_df = create_ingredient_df(japanese_df)
+   japanese_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![Giapponese](../images/japanese.png)
+
+1. Ora per gli ingredienti cinesi:
+
+   ```python
+   chinese_ingredient_df = create_ingredient_df(chinese_df)
+   chinese_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![cinese](../images/chinese.png)
+
+1. Tracciare gli ingredienti indiani:
+
+   ```python
+   indian_ingredient_df = create_ingredient_df(indian_df)
+   indian_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![indiano](../images/indian.png)
+
+1. Infine, tracciare gli ingredienti coreani:
+
+   ```python
+   korean_ingredient_df = create_ingredient_df(korean_df)
+   korean_ingredient_df.head(10).plot.barh()
+   ```
+
+   ![Coreano](../images/korean.png)
+
+1. Ora, eliminare gli ingredienti più comuni che creano confusione tra le diverse cucine, chiamando `drop()`:
+
+   Tutti amano il riso, l'aglio e lo zenzero!
+
+   ```python
+   feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)
+   labels_df = df.cuisine #.unique()
+   feature_df.head()
+   ```
+
+## Bilanciare l'insieme di dati
+
+Ora che i dati sono puliti, si usa [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html) - "Tecnica di sovracampionamento della minoranza sintetica" - per bilanciarlo.
+
+1. Chiamare `fit_resample()`, questa strategia genera nuovi campioni per interpolazione.
+
+   ```python
+   oversample = SMOTE()
+   transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
+   ```
+
+   Bilanciando i dati, si otterranno risultati migliori quando si classificano. Si pensi a una classificazione binaria. Se la maggior parte dei dati è una classe, un modello ML prevederà quella classe più frequentemente, solo perché ci sono più dati per essa. Il bilanciamento dei dati prende tutti i dati distorti e aiuta a rimuovere questo squilibrio.
+
+1. Ora si può controllare il numero di etichette per ingrediente:
+
+   ```python
+   print(f'new label count: {transformed_label_df.value_counts()}')
+   print(f'old label count: {df.cuisine.value_counts()}')
+   ```
+
+   il risultato si presenta così:
+
+   ```output
+   new label count: korean      799
+   chinese     799
+   indian      799
+   japanese    799
+   thai        799
+   Name: cuisine, dtype: int64
+   old label count: korean      799
+   indian      598
+   chinese     442
+   japanese    320
+   thai        289
+   Name: cuisine, dtype: int64
+   ```
+
+   I dati sono belli e puliti, equilibrati e molto deliziosi!
+
+1. L'ultimo passaggio consiste nel salvare i dati bilanciati, incluse etichette e caratteristiche, in un nuovo dataframe che può essere esportato in un file:
+
+   ```python
+   transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
+   ```
+
+1. Si può dare un'altra occhiata ai dati usando `transform_df.head()` e `transform_df.info()`. Salvare una copia di questi dati per utilizzarli nelle lezioni future:
+
+   ```python
+   transformed_df.head()
+   transformed_df.info()
+   transformed_df.to_csv("../data/cleaned_cuisine.csv")
+   ```
+
+   Questo nuovo CSV può ora essere trovato nella cartella data in radice.
+
+---
+
+## 🚀 Sfida
+
+Questo programma di studi contiene diversi insiemi di dati interessanti. Esaminare le cartelle `data` e vedere se contiene insiemi di dati che sarebbero appropriati per la classificazione binaria o multiclasse. Quali domande si farebbero a questo insieme di dati?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+
+## Revisione e Auto Apprendimento
+
+Esplorare l'API di SMOTE. Per quali casi d'uso è meglio usarla? Quali problemi risolve?
+
+## Compito
+
+[Esplorare i metodi di classificazione](assignment.it.md)
diff --git a/4-Classification/1-Introduction/translations/assignment.it.md b/4-Classification/1-Introduction/translations/assignment.it.md
new file mode 100644
index 0000000000..1283401791
--- /dev/null
+++ b/4-Classification/1-Introduction/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Esplorare i metodi di classificazione
+
+## Istruzioni
+
+Nella [documentazione](https://scikit-learn.org/stable/supervised_learning.html) di Scikit-learn si troverà un ampio elenco di modi per classificare i dati. Fare una piccola caccia al tesoro in questi documenti: l'obiettivo è cercare metodi di classificazione e abbinare un insieme di dati in questo programma di studi, una domanda che si può porre e una tecnica di classificazione. Creare un foglio di calcolo o una tabella in un file .doc e spiegare come funzionerebbe l'insieme di dati con l'algoritmo di classificazione.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ----------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+|          | viene presentato un documento che riporta una panoramica di 5 algoritmi insieme a una tecnica di classificazione. La panoramica è ben spiegata e dettagliata. | viene presentato un documento che riporta una panoramica di 3 algoritmi insieme a una tecnica di classificazione. La panoramica è ben spiegata e dettagliata. | viene presentato un documento che riporta una panoramica di meno di tre algoritmi insieme a una tecnica di classificazione e la panoramica non è né ben spiegata né dettagliata. |
diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md
new file mode 100644
index 0000000000..674a59c269
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+++ b/4-Classification/2-Classifiers-1/translations/README.it.md
@@ -0,0 +1,241 @@
+# Classificatori di cucina 1
+
+In questa lezione, si utilizzerà l'insieme di dati salvati dall'ultima lezione, pieno di dati equilibrati e puliti relativi alle cucine.
+
+Si utilizzerà questo insieme di dati con una varietà di classificatori per _prevedere una determinata cucina nazionale in base a un gruppo di ingredienti_. Mentre si fa questo, si imparerà di più su alcuni dei modi in cui gli algoritmi possono essere sfruttati per le attività di classificazione.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+# Preparazione
+
+Supponendo che la [Lezione 1](../1-Introduction/README.md) sia stata completata, assicurarsi che _esista_ un file clean_cuisines.csv nella cartella in radice `/data` per queste quattro lezioni.
+
+## Esercizio - prevedere una cucina nazionale
+
+1. Lavorando con il  _notebook.ipynb_ di questa lezione nella cartella radice, importare quel file insieme alla libreria Pandas:
+
+   ```python
+   import pandas as pd
+   cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+   cuisines_df.head()
+   ```
+
+   I dati si presentano così:
+
+   ```output
+   |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+   | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+   | 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+   | 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+   ```
+
+1. Ora importare molte altre librerie:
+
+   ```python
+   from sklearn.linear_model import LogisticRegression
+   from sklearn.model_selection import train_test_split, cross_val_score
+   from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+   from sklearn.svm import SVC
+   import numpy as np
+   ```
+
+1. Dividere le coordinate X e y in due dataframe per l'addestramento. `cuisine` può essere il dataframe delle etichette:
+
+   ```python
+   cuisines_label_df = cuisines_df['cuisine']
+   cuisines_label_df.head()
+   ```
+
+   Apparirà così
+
+   ```output
+   0    indian
+   1    indian
+   2    indian
+   3    indian
+   4    indian
+   Name: cuisine, dtype: object
+   ```
+
+1. Scartare la colonna `Unnamed: 0` e la colonna `cuisine` , chiamando `drop()`. Salvare il resto dei dati come caratteristiche addestrabili:
+
+   ```python
+   cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
+   cuisines_feature_df.head()
+   ```
+
+   Le caratteristiche sono così:
+
+    | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     |
+    | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
+    |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+
+Ora si è pronti per addestrare il modello!
+
+## Scegliere il classificatore
+
+Ora che i dati sono puliti e pronti per l'addestramento, si deve decidere quale algoritmo utilizzare per il lavoro.
+
+Scikit-learn raggruppa la classificazione in Supervised Learning, e in quella categoria si troveranno molti modi per classificare. [La varietà](https://scikit-learn.org/stable/supervised_learning.html) è piuttosto sconcertante a prima vista. I seguenti metodi includono tutti tecniche di classificazione:
+
+- Modelli Lineari 
+- Macchine a Vettori di Supporto
+- Discesa stocastica del gradiente
+- Nearest Neighbors
+- Processi Gaussiani
+- Alberi di Decisione
+- Apprendimento ensemble (classificatore di voto)
+- Algoritmi multiclasse e multioutput (classificazione multiclasse e multietichetta, classificazione multiclasse-multioutput)
+
+> Si possono anche usare [le reti neurali per classificare i dati](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), ma questo esula dall'ambito di questa lezione.
+
+### Con quale classificatore andare?
+
+Quale classificatore si dovrebbe scegliere? Spesso, scorrerne diversi e cercare un buon risultato è un modo per testare. Scikit-learn offre un [confronto fianco](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html) a fianco su un insieme di dati creato, confrontando KNeighbors, SVC in due modi, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB e QuadraticDiscrinationAnalysis, mostrando i risultati visualizzati:
+
+![confronto di classificatori](../images/comparison.png)
+> Grafici generati sulla documentazione di Scikit-learn
+
+> AutoML risolve questo problema in modo ordinato eseguendo questi confronti nel cloud, consentendo di scegliere l'algoritmo migliore per i propri dati. Si può provare [qui](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)
+
+### Un approccio migliore
+
+Un modo migliore che indovinare a caso, tuttavia, è seguire le idee su questo [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa) scaricabile. Qui si scopre che, per questo problema multiclasse, si dispone di  alcune scelte:
+
+![cheatsheet per problemi multiclasse](../images/cheatsheet.png)
+> Una sezione dell'Algorithm Cheat Sheet di Microsoft, che descrive in dettaglio le opzioni di classificazione multiclasse
+
+✅ Scaricare questo cheat sheet, stamparlo e appenderlo alla parete!
+
+### Motivazione
+
+Si prova a ragionare attraverso diversi approcci dati i vincoli presenti:
+
+- **Le reti neurali sono troppo pesanti**. Dato l'insieme di dati pulito, ma minimo, e il fatto che si sta eseguendo l'addestramento localmente tramite notebook, le reti neurali sono troppo pesanti per questo compito.
+- **Nessun classificatore a due classi**. Non si usa un classificatore a due classi, quindi questo esclude uno contro tutti.
+- L'**albero decisionale o la regressione logistica potrebbero funzionare**. Potrebbe funzionare un albero decisionale o una regressione logistica per dati multiclasse.
+- **Gli alberi decisionali potenziati multiclasse risolvono un problema diverso**. L'albero decisionale potenziato multiclasse è più adatto per attività non parametriche, ad esempio attività progettate per costruire classifiche, quindi non è utile in questo caso.
+
+### Utilizzo di Scikit-learn
+
+Si userà Scikit-learn per analizzare i  dati. Tuttavia, ci sono molti modi per utilizzare la regressione logistica in Scikit-learn. Dare un'occhiata ai [parametri da passare](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
+
+Essenzialmente ci sono due importanti parametri `multi_class` e `solver`, che occorre specificare, quando si chiede a Scikit-learn di eseguire una regressione logistica. Il valore `multi_class` si applica un certo comportamento. Il valore del risolutore è quale algoritmo utilizzare. Non tutti i risolutori possono essere associati a tutti i valori `multi_class` .
+
+Secondo la documentazione, nel caso multiclasse, l'algoritmo di addestramento:
+
+- **Utilizza lo schema one-vs-rest (OvR)** - uno contro tutti, se l'opzione `multi_class` è impostata su `ovr`
+- **Utilizza la perdita di entropia incrociata**, se l 'opzione `multi_class` è impostata su `multinomial`. (Attualmente l'opzione multinomiale è supportata solo dai solutori 'lbfgs', 'sag', 'saga' e 'newton-cg')."
+
+> 🎓 Lo 'schema' qui può essere 'ovr' (one-vs-rest) - uno contro tutti - o 'multinomiale'. Poiché la regressione logistica è realmente progettata per supportare la classificazione binaria, questi schemi consentono di gestire meglio le attività di classificazione multiclasse. [fonte](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+
+> 🎓 Il 'solver' è definito come "l'algoritmo da utilizzare nel problema di ottimizzazione". [fonte](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
+
+Scikit-learn offre questa tabella per spiegare come i risolutori gestiscono le diverse sfide presentate da diversi tipi di strutture dati:
+
+![risolutori](../images/solvers.png)
+
+## Esercizio: dividere i dati
+
+Ci si può concentrare sulla regressione logistica per la prima prova di addestramento poiché di recente si è appreso di quest'ultima in una lezione precedente.
+Dividere i dati in gruppi di addestramento e test chiamando `train_test_split()`:
+
+```python
+X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+```
+
+## Esercizio: applicare la regressione logistica
+
+Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_ utilizzare e quale _solutore_ impostare. Usare LogisticRegression con un'impostazione multiclasse e il solutore **liblinear** da addestrare.
+
+1. Creare una regressione logistica con multi_class impostato su `ovr` e il risolutore impostato su `liblinear`:
+
+   ```python
+   lr = LogisticRegression(multi_class='ovr',solver='liblinear')
+   model = lr.fit(X_train, np.ravel(y_train))
+
+   accuracy = model.score(X_test, y_test)
+   print ("Accuracy is {}".format(accuracy))
+   ```
+
+   ✅ Provare un risolutore diverso come `lbfgs`, che è spesso impostato come predefinito
+
+   > Nota, usare la funzione [`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) di Pandas per appiattire i dati quando necessario.
+
+   La precisione è buona oltre l'**80%**!
+
+1. Si può vedere questo modello in azione testando una riga di dati (#50):
+
+   ```python
+   print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
+   print(f'cuisine: {y_test.iloc[50]}')
+   ```
+
+   Il risultato viene stampato:
+
+   ```output
+   ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
+   cuisine: indian
+   ```
+
+   ✅ Provare un numero di riga diverso e controllare i risultati
+
+1. Scavando più a fondo, si può verificare l'accuratezza di questa previsione:
+
+   ```python
+   test= X_test.iloc[50].values.reshape(-1, 1).T
+   proba = model.predict_proba(test)
+   classes = model.classes_
+   resultdf = pd.DataFrame(data=proba, columns=classes)
+
+   topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
+   topPrediction.head()
+   ```
+
+   Il risultato è stampato: la cucina indiana è la sua ipotesi migliore, con buone probabilità:
+
+   |          | 0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+   | indiano | 0,715851 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | cinese | 0.229475 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Giapponese | 0,029763 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Coreano | 0.017277 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | thai | 0.007634 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+
+   ✅ Si è in grado di spiegare perché il modello è abbastanza sicuro che questa sia una cucina indiana?
+
+1. Ottenere maggiori dettagli stampando un rapporto di classificazione, come fatto nelle lezioni di regressione:
+
+   ```python
+   y_pred = model.predict(X_test)
+   print(classification_report(y_test,y_pred))
+   ```
+
+   | precisione | recall | punteggio f1 | supporto |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+   | cinese | 0,73 | 0,71 | 0,72 | 229 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | indiano | 0,91 | 0,93 | 0,92 | 254 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Giapponese | 0.70 | 0,75 | 0,72 | 220 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Coreano | 0,86 | 0,76 | 0,81 | 242 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | thai | 0,79 | 0,85 | 0.82 | 254 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | accuratezza | 0,80 | 1199 |         |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | macro media | 0,80 | 0,80 | 0,80 | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Media ponderata | 0,80 | 0,80 | 0,80 | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+
+## 🚀 Sfida
+
+In questa lezione, sono stati utilizzati dati puliti per creare un modello di apprendimento automatico in grado di prevedere una cucina nazionale basata su una serie di ingredienti. Si prenda del tempo per leggere le numerose opzioni fornite da Scikit-learn per classificare i dati. Approfondire il concetto di "risolutore" per capire cosa succede dietro le quinte.
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## Revisione e Auto Apprendimento
+
+Approfondire un po' la matematica alla base della regressione logistica in [questa lezione](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)
+## Compito
+
+[Studiare i risolutori](assignment.it.md)
diff --git a/4-Classification/2-Classifiers-1/translations/assignment.it.md b/4-Classification/2-Classifiers-1/translations/assignment.it.md
new file mode 100644
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+++ b/4-Classification/2-Classifiers-1/translations/assignment.it.md
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+# Studiare i risolutori
+## Istruzioni
+
+In questa lezione si è imparato a conoscere i vari risolutori che associano algoritmi a un processo di machine learning per creare un modello accurato. Esaminare i risolutori elencati nella lezione e sceglierne due. Con parole proprie, confrontare questi due risolutori. Che tipo di problema affrontano? Come funzionano con varie strutture di dati? Perché se ne dovrebbe sceglierne uno piuttosto che un altro?
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ---------------------------------------------------------------------------------------------- | ------------------------------------------------ | ---------------------------- |
+|          | Viene presentato un file .doc con due paragrafi, uno su ciascun risolutore, confrontandoli attentamente. | Un file .doc viene presentato con un solo paragrafo | Il compito è incompleto |
diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md
new file mode 100644
index 0000000000..4a3a431fe3
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+# Classificatori di cucina 2
+
+In questa seconda lezione sulla classificazione, si esploreranno più modi per classificare i dati numerici. Si Impareranno anche le ramificazioni per la scelta di un classificatore rispetto all'altro.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/)
+
+### Prerequisito
+
+Si parte dal presupposto che siano state completate le lezioni precedenti e si disponga di un insieme di dati pulito nella cartella `data` chiamato _clean_cuisine.csv_ nella radice di questa cartella di 4 lezioni.
+
+### Preparazione
+
+Il file _notebook.ipynb_ è stato caricato con l'insieme di dati pulito ed è stato diviso in dataframe di dati X e y, pronti per il processo di creazione del modello.
+
+## Una mappa di classificazione
+
+In precedenza, si sono apprese le varie opzioni a disposizione durante la classificazione dei dati utilizzando il cheat sheet di Microsoft. Scikit-learn offre un cheat sheet simile, ma più granulare che può aiutare ulteriormente a restringere i propri stimatori (un altro termine per i classificatori):
+
+![Mappa ML da Scikit-learn](../images/map.png)
+> Suggerimento: [visitare questa mappa online](https://scikit-learn.org/stable/tutorial/machine_learning_map/) e fare clic lungo il percorso per leggere la documentazione.
+
+### Il piano
+
+Questa mappa è molto utile una volta che si ha una chiara comprensione dei propri dati, poiché si può "camminare" lungo i suoi percorsi verso una decisione:
+
+- Ci sono >50 campioni
+- Si vuole pronosticare una categoria
+- I dati sono etichettati
+- Ci sono meno di 100K campioni
+- ✨ Si può scegliere un SVC lineare
+- Se non funziona, visto che ci sono dati numerici
+   - Si può provare un ✨ KNeighbors Classifier
+      - Se non funziona, si prova ✨ SVC e ✨ Classificatori di ensemble
+
+Questo è un percorso molto utile da seguire.
+
+## Esercizio: dividere i dati
+
+Seguendo questo percorso, si dovrebbe iniziare importando alcune librerie da utilizzare.
+
+1. Importare le librerie necessarie:
+
+   ```python
+   from sklearn.neighbors import KNeighborsClassifier
+   from sklearn.linear_model import LogisticRegression
+   from sklearn.svm import SVC
+   from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
+   from sklearn.model_selection import train_test_split, cross_val_score
+   from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+   import numpy as np
+   ```
+
+1. Dividere i dati per allenamento e test:
+
+   ```python
+   X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+   ```
+
+## Classificatore lineare SVC
+
+Il clustering Support-Vector (SVC) è figlio della famiglia di tecniche ML Support-Vector (ulteriori informazioni su queste di seguito). In questo metodo, si può scegliere un "kernel" per decidere come raggruppare le etichette. Il parametro 'C' si riferisce alla 'regolarizzazione' che regola l'influenza dei parametri. Il kernel può essere uno dei [tanti](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC); qui si imposta su 'lineare' per assicurarsi di sfruttare l'SVC lineare. Il valore predefinito di probabilità è 'false'; qui si imposta su 'true' per raccogliere stime di probabilità. Si imposta lo stato casuale su "0" per mescolare i dati per ottenere le probabilità.
+
+### Esercizio: applicare una SVC lineare
+
+Iniziare creando un array di classificatori. Si aggiungerà progressivamente a questo array durante il test.
+
+1. Iniziare con un SVC lineare:
+
+   ```python
+   C = 10
+   # Create different classifiers.
+   classifiers = {
+       'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0)
+   }
+   ```
+
+2. Addestrare il modello utilizzando Linear SVC e stampare un rapporto:
+
+   ```python
+   n_classifiers = len(classifiers)
+
+   for index, (name, classifier) in enumerate(classifiers.items()):
+       classifier.fit(X_train, np.ravel(y_train))
+
+       y_pred = classifier.predict(X_test)
+       accuracy = accuracy_score(y_test, y_pred)
+       print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100))
+       print(classification_report(y_test,y_pred))
+   ```
+
+   Il risultato è abbastanza buono:
+
+   ```output
+   Accuracy (train) for Linear SVC: 78.6%
+                 precision    recall  f1-score   support
+
+        chinese       0.71      0.67      0.69       242
+         indian       0.88      0.86      0.87       234
+       japanese       0.79      0.74      0.76       254
+         korean       0.85      0.81      0.83       242
+           thai       0.71      0.86      0.78       227
+
+       accuracy                           0.79      1199
+      macro avg       0.79      0.79      0.79      1199
+   weighted avg       0.79      0.79      0.79      1199
+   ```
+
+## Classificatore K-Neighbors
+
+K-Neighbors fa parte della famiglia dei metodi ML "neighbors" (vicini), che possono essere utilizzati sia per l'apprendimento supervisionato che non supervisionato. In questo metodo, viene creato un numero predefinito di punti e i dati vengono raccolti attorno a questi punti in modo tale da poter prevedere etichette generalizzate per i dati.
+
+### Esercizio: applicare il classificatore K-Neighbors
+
+Il classificatore precedente era buono e funzionava bene con i dati, ma forse si può ottenere una maggiore precisione. Provare un classificatore K-Neighbors.
+
+1. Aggiungere una riga all'array classificatore (aggiungere una virgola dopo l'elemento Linear SVC):
+
+   ```python
+   'KNN classifier': KNeighborsClassifier(C),
+   ```
+
+   Il risultato è un po' peggio:
+
+   ```output
+   Accuracy (train) for KNN classifier: 73.8%
+                 precision    recall  f1-score   support
+
+        chinese       0.64      0.67      0.66       242
+         indian       0.86      0.78      0.82       234
+       japanese       0.66      0.83      0.74       254
+         korean       0.94      0.58      0.72       242
+           thai       0.71      0.82      0.76       227
+
+       accuracy                           0.74      1199
+      macro avg       0.76      0.74      0.74      1199
+   weighted avg       0.76      0.74      0.74      1199
+   ```
+
+   ✅ Scoprire [K-Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#neighbors)
+
+## Classificatore Support Vector
+
+I classificatori Support-Vector fanno parte della famiglia di metodi ML [Support-Vector Machine](https://it.wikipedia.org/wiki/Macchine_a_vettori_di_supporto) utilizzati per le attività di classificazione e regressione. Le SVM "mappano esempi di addestramento in punti nello spazio" per massimizzare la distanza tra due categorie. I dati successivi vengono mappati in questo spazio in modo da poter prevedere la loro categoria.
+
+### Esercizio: applicare un classificatore di vettori di supporto
+
+Si prova a ottenere una precisione leggermente migliore con un classificatore di vettori di supporto.
+
+1. Aggiungere una virgola dopo l'elemento K-Neighbors, quindi aggiungere questa riga:
+
+   ```python
+   'SVC': SVC(),
+   ```
+
+   Il risultato è abbastanza buono!
+
+   ```output
+   Accuracy (train) for SVC: 83.2%
+                 precision    recall  f1-score   support
+
+        chinese       0.79      0.74      0.76       242
+         indian       0.88      0.90      0.89       234
+       japanese       0.87      0.81      0.84       254
+         korean       0.91      0.82      0.86       242
+           thai       0.74      0.90      0.81       227
+
+       accuracy                           0.83      1199
+      macro avg       0.84      0.83      0.83      1199
+   weighted avg       0.84      0.83      0.83      1199
+   ```
+
+   ✅ Scoprire i vettori di [supporto](https://scikit-learn.org/stable/modules/svm.html#svm)
+
+## Classificatori ensamble
+
+Si segue il percorso fino alla fine, anche se il test precedente è stato abbastanza buono. Si provano un po' di classificatori di ensemble, nello specifico Random Forest e AdaBoost:
+
+```python
+'RFST': RandomForestClassifier(n_estimators=100),
+  'ADA': AdaBoostClassifier(n_estimators=100)
+```
+
+Il risultato è molto buono, soprattutto per Random Forest:
+
+```output
+Accuracy (train) for RFST: 84.5%
+              precision    recall  f1-score   support
+
+     chinese       0.80      0.77      0.78       242
+      indian       0.89      0.92      0.90       234
+    japanese       0.86      0.84      0.85       254
+      korean       0.88      0.83      0.85       242
+        thai       0.80      0.87      0.83       227
+
+    accuracy                           0.84      1199
+   macro avg       0.85      0.85      0.84      1199
+weighted avg       0.85      0.84      0.84      1199
+
+Accuracy (train) for ADA: 72.4%
+              precision    recall  f1-score   support
+
+     chinese       0.64      0.49      0.56       242
+      indian       0.91      0.83      0.87       234
+    japanese       0.68      0.69      0.69       254
+      korean       0.73      0.79      0.76       242
+        thai       0.67      0.83      0.74       227
+
+    accuracy                           0.72      1199
+   macro avg       0.73      0.73      0.72      1199
+weighted avg       0.73      0.72      0.72      1199
+```
+
+✅ Ulteriori informazioni sui [classificatori di ensemble](https://scikit-learn.org/stable/modules/ensemble.html)
+
+Questo metodo di Machine Learning "combina le previsioni di diversi stimatori di base" per migliorare la qualità del modello. In questo  esempio, si è utilizzato Random Trees e AdaBoost.
+
+- [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#forest), un metodo di calcolo della media, costruisce una "foresta" di "alberi decisionali" infusi di casualità per evitare il sovradattamento. Il parametro n_estimators è impostato sul numero di alberi.
+
+- [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) adatta un classificatore a un insieme di dati e quindi adatta le copie di quel classificatore allo stesso insieme di dati. Si concentra sui pesi degli elementi classificati in modo errato e regola l'adattamento per il successivo classificatore da correggere.
+
+---
+
+## 🚀 Sfida
+
+Ognuna di queste tecniche ha un gran numero di parametri che si possono modificare. Ricercare i parametri predefiniti di ciascuno e pensare a cosa significherebbe modificare questi parametri per la qualità del modello.
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/)
+
+## Revisione e Auto Apprendimento
+
+C'è molto gergo in queste lezioni, quindi si prenda un minuto per rivedere [questo elenco](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-15963-cxa) di terminologia utile!
+
+## Compito
+
+[Giocore coi parametri](assignment.it.md)
diff --git a/4-Classification/3-Classifiers-2/translations/assignment.it.md b/4-Classification/3-Classifiers-2/translations/assignment.it.md
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+++ b/4-Classification/3-Classifiers-2/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Giocore coi parametri
+
+## Istruzioni
+
+Ci sono molti parametri impostati in modalità predefinita quando si lavora con questi classificatori. Intellisense in VS Code può aiutare a scavare in loro. Adottare una delle tecniche di classificazione ML in questa lezione e riaddestrare i modelli modificando i vari valori dei parametri. Costruire un notebook spiegando perché alcune modifiche aiutano la qualità del modello mentre altre la degradano. La risposta sia dettagliata.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ---------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------- | ----------------------------- |
+|          | Un notebook viene presentato con un classificatore completamente costruito e i suoi parametri ottimizzati e le modifiche spiegate nelle caselle di testo | Un quaderno è presentato parzialmente o spiegato male | Un notebook contiene errori o è difettoso |
diff --git a/4-Classification/4-Applied/translations/README.it.md b/4-Classification/4-Applied/translations/README.it.md
new file mode 100644
index 0000000000..4512aabacb
--- /dev/null
+++ b/4-Classification/4-Applied/translations/README.it.md
@@ -0,0 +1,336 @@
+# Costruire un'App Web per Consigliare una Cucina
+
+In questa lezione si creerà un modello di classificazione utilizzando alcune delle tecniche apprese nelle lezioni precedenti e con il delizioso insieme di dati sulla cucina utilizzato in questa serie. Inoltre, si creerà una piccola app web per utilizzare un modello salvato, sfruttando il runtime web di Onnx.
+
+Uno degli usi pratici più utili dell'apprendimento automatico è la creazione di sistemi di raccomandazione e oggi si può fare il primo passo in quella direzione!
+
+[![Introduzione ai Sistemi di Raccomandazione](https://img.youtube.com/vi/giIXNoiqO_U/0.jpg)](https://youtu.be/giIXNoiqO_U "Introduzione ai Sistemi di Raccomandazione")
+
+> 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng introduce la progettazione di un sistema di raccomandazione
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/)
+
+In questa lezione, si imparerà:
+
+- Come costruire un modello e salvarlo come modello Onnx
+- Come usare Netron per ispezionare il modello
+- Come utilizzare il modello in un'app web per l'inferenza
+
+## Costruire il modello
+
+La creazione di sistemi ML applicati è una parte importante dell'utilizzo di queste tecnologie per i sistemi aziendali. Si possono utilizzare i modelli all'interno delle proprie applicazioni web (e quindi utilizzarli in un contesto offline se necessario) utilizzando Onnx.
+
+In una [lezione precedente](../../../3-Web-App/1-Web-App/translations/README.it.md) si è costruito un modello di regressione sugli avvistamenti di UFO, è stato serializzato e lo si è utilizzato in un'app Flask. Sebbene questa architettura sia molto utile da conoscere, è un'app Python completa e i requisiti potrebbero includere l'uso di un'applicazione JavaScript.
+
+In questa lezione si può creare un sistema di inferenza di base utilizzando JavaScript. Prima, tuttavia, è necessario addestrare un modello e convertirlo per l'utilizzo con Onnx.
+
+## Esercizio - modello di classificazione di addestramento
+
+Innanzitutto, addestrare un modello di classificazione utilizzando l'insieme di dati pulito delle cucine precedentemente usato.
+
+1. Iniziare importando librerie utili:
+
+   ```python
+   !pip install skl2onnx
+   import pandas as pd
+   ```
+
+   Serve '[skl2onnx](https://onnx.ai/sklearn-onnx/)' per poter convertire il modello di Scikit-learn in formato Onnx.
+
+1. Quindi si  lavora con i dati nello stesso modo delle lezioni precedenti, leggendo un file CSV usando `read_csv()`:
+
+   ```python
+   data = pd.read_csv('../data/cleaned_cuisine.csv')
+   data.head()
+   ```
+
+1. Rimuovere le prime due colonne non necessarie e salvare i dati rimanenti come "X":
+
+   ```python
+   X = data.iloc[:,2:]
+   X.head()
+   ```
+
+1. Salvare le etichette come "y":
+
+   ```python
+   y = data[['cuisine']]
+   y.head()
+
+   ```
+
+### Iniziare la routine di addestramento
+
+Verrà usata la libreria 'SVC' che ha una buona precisione.
+
+1. Importare le librerie appropriate da Scikit-learn:
+
+   ```python
+   from sklearn.model_selection import train_test_split
+   from sklearn.svm import SVC
+   from sklearn.model_selection import cross_val_score
+   from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report
+   ```
+
+1. Separare gli insiemi di allenamento e test:
+
+   ```python
+   X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
+   ```
+
+1. Costruire un modello di classificazione SVC come fatto nella lezione precedente:
+
+   ```python
+   model = SVC(kernel='linear', C=10, probability=True,random_state=0)
+   model.fit(X_train,y_train.values.ravel())
+   ```
+
+1. Ora provare il modello, chiamando `predict()`:
+
+   ```python
+   y_pred = model.predict(X_test)
+   ```
+
+1. Stampare un rapporto di classificazione per verificare la qualità del modello:
+
+   ```python
+   print(classification_report(y_test,y_pred))
+   ```
+
+   Come visto prima, la precisione è buona:
+
+   ```output
+                   precision    recall  f1-score   support
+
+        chinese       0.72      0.69      0.70       257
+         indian       0.91      0.87      0.89       243
+       japanese       0.79      0.77      0.78       239
+         korean       0.83      0.79      0.81       236
+           thai       0.72      0.84      0.78       224
+
+       accuracy                           0.79      1199
+      macro avg       0.79      0.79      0.79      1199
+   weighted avg       0.79      0.79      0.79      1199
+   ```
+
+### Convertire il modello in Onnx
+
+Assicurarsi di eseguire la conversione con il numero tensore corretto. Questo insieme di dati ha 380 ingredienti elencati, quindi è necessario annotare quel numero in `FloatTensorType`:
+
+1. Convertire usando un numero tensore di 380.
+
+   ```python
+   from skl2onnx import convert_sklearn
+   from skl2onnx.common.data_types import FloatTensorType
+
+   initial_type = [('float_input', FloatTensorType([None, 380]))]
+   options = {id(model): {'nocl': True, 'zipmap': False}}
+   ```
+
+1. Creare l'onx e salvarlo come file **model.onnx**:
+
+   ```python
+   onx = convert_sklearn(model, initial_types=initial_type, options=options)
+   with open("./model.onnx", "wb") as f:
+       f.write(onx.SerializeToString())
+   ```
+
+   > Nota, si possono passare le[opzioni](https://onnx.ai/sklearn-onnx/parameterized.html) nello  script di conversione. In questo caso, si è passato 'nocl' come True e 'zipmap' come False. Poiché questo è un modello di classificazione, si ha la possibilità di rimuovere ZipMap che produce un elenco di dizionari (non necessario). `nocl` si riferisce alle informazioni sulla classe incluse nel modello. Ridurre le dimensioni del modello impostando `nocl` su 'True'.
+
+L'esecuzione dell'intero notebook ora creerà un modello Onnx e lo salverà in questa cartella.
+
+## Visualizzare il modello
+
+I modelli Onnx non sono molto visualizzabili in Visual Studio code, ma c'è un ottimo software gratuito che molti ricercatori usano per visualizzare il modello per assicurarsi che sia costruito correttamente. Scaricare [Netron](https://github.com/lutzroeder/Netron) e aprire il file model.onnx. Si può vedere il modello semplice visualizzato, con i suoi 380 input e classificatore elencati:
+
+![Vista Netron ](../images/netron.png)
+
+Netron è uno strumento utile per visualizzare i modelli.
+
+Ora si è pronti per utilizzare questo modello accurato in un'app web. Si costruisce un'app che tornerà utile quando si guarda nel frigorifero e si prova a capire quale combinazione di ingredienti avanzati si può usare per cucinare una determinata tipologia di cucina, come determinato dal  modello.
+
+## Creare un'applicazione web di raccomandazione
+
+Si può utilizzare il modello direttamente in un'app web. Questa architettura consente anche di eseguirlo localmente e anche offline se necessario. Iniziare creando un file `index.html` nella stessa cartella in cui si è salvato il file `model.onnx`.
+
+1. In questo file _index.html_, aggiungere il seguente codice markup:
+
+   ```html
+   <!DOCTYPE html>
+   <html>
+       <header>
+           <title>Cuisine Matcher</title>
+       </header>
+       <body>
+           ...
+       </body>
+   </html>
+   ```
+
+1. Ora, lavorando all'interno del tag `body` , aggiungere un piccolo markup per mostrare un elenco di caselle di controllo che riflettono alcuni ingredienti:
+
+   ```html
+   <h1>Check your refrigerator. What can you create?</h1>
+           <div id="wrapper">
+               <div class="boxCont">
+                   <input type="checkbox" value="4" class="checkbox">
+                   <label>apple</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="247" class="checkbox">
+                   <label>pear</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="77" class="checkbox">
+                   <label>cherry</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="126" class="checkbox">
+                   <label>fenugreek</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="302" class="checkbox">
+                   <label>sake</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="327" class="checkbox">
+                   <label>soy sauce</label>
+               </div>
+
+               <div class="boxCont">
+                   <input type="checkbox" value="112" class="checkbox">
+                   <label>cumin</label>
+               </div>
+           </div>
+           <div style="padding-top:10px">
+               <button onClick="startInference()">What kind of cuisine can you make?</button>
+           </div>
+   ```
+
+   Notare che a ogni casella di controllo viene assegnato un valore. Questo riflette l'indice in cui si trova l'ingrediente in base all'insieme di dati. Apple, ad esempio, in questo elenco alfabetico, occupa la quinta colonna, quindi il suo valore è "4" poiché si inizia a contare da 0. Si può consultare il [foglio di calcolo degli ingredienti](../../data/ingredient_indexes.csv) per scoprire l'indice di un determinato ingrediente.
+
+   Continuando il lavoro nel file index.html, aggiungere un blocco di script in cui viene chiamato il modello dopo la chiusura del tag `</div>` finale.
+
+1. Innanzitutto, importare il [runtime Onnx](https://www.onnxruntime.ai/):
+
+   ```html
+   <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.8.0-dev.20210608.0/dist/ort.min.js"></script>
+   ```
+
+   > Onnx Runtime viene utilizzato per consentire l'esecuzione dei modelli Onnx su un'ampia gamma di piattaforme hardware, comprese le ottimizzazioni e un'API da utilizzare.
+
+1. Una volta che il Runtime è a posto, lo si può chiamare:
+
+   ```javascript
+   <script>
+               const ingredients = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+               const checks = [].slice.call(document.querySelectorAll('.checkbox'));
+
+               // use an async context to call onnxruntime functions.
+               function init() {
+
+                   checks.forEach(function (checkbox, index) {
+                       checkbox.onchange = function () {
+                           if (this.checked) {
+                               var index = checkbox.value;
+
+                               if (index !== -1) {
+                                   ingredients[index] = 1;
+                               }
+                               console.log(ingredients)
+                           }
+                           else {
+                               var index = checkbox.value;
+
+                               if (index !== -1) {
+                                   ingredients[index] = 0;
+                               }
+                               console.log(ingredients)
+                           }
+                       }
+                   })
+               }
+
+               function testCheckboxes() {
+                       for (var i = 0; i < checks.length; i++)
+                           if (checks[i].type == "checkbox")
+                               if (checks[i].checked)
+                                   return true;
+                       return false;
+               }
+
+               async function startInference() {
+
+                   let checked = testCheckboxes()
+
+                   if (checked) {
+
+                   try {
+                       // create a new session and load the model.
+
+                       const session = await ort.InferenceSession.create('./model.onnx');
+
+                       const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
+                       const feeds = { float_input: input };
+
+                       // feed inputs and run
+
+                       const results = await session.run(feeds);
+
+                       // read from results
+                       alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
+
+                   } catch (e) {
+                       console.log(`failed to inference ONNX model: ${e}.`);
+                   }
+               }
+               else alert("Please check an ingredient")
+
+               }
+       init();
+
+           </script>
+   ```
+
+In questo codice, accadono diverse cose:
+
+1. Si è creato un array di 380 possibili valori (1 o 0) da impostare e inviare al modello per l'inferenza, a seconda che una casella di controllo dell'ingrediente sia selezionata.
+2. Si è creata una serie di caselle di controllo e un modo per determinare se sono state selezionate in una funzione `init` chiamata all'avvio dell'applicazione. Quando una casella di controllo è selezionata, l 'array `ingredients` viene modificato per riflettere l'ingrediente scelto.
+3. Si è creata una funzione `testCheckboxes` che controlla se una casella di controllo è stata selezionata.
+4. Si utilizza quella funzione quando si preme il pulsante e, se una casella di controllo è selezionata, si avvia l'inferenza.
+5. La routine di inferenza include:
+   1. Impostazione di un caricamento asincrono del modello
+   2. Creazione di una struttura tensoriale da inviare al modello
+   3. Creazione di "feed" che riflettano l'input `float_input` creato durante l'addestramento del modello (si può usare Netron per verificare quel nome)
+   4. Invio di questi "feed" al modello e attesa di una risposta
+
+## Verificare l'applicazione
+
+Aprire una sessione terminale in Visual Studio Code nella cartella in cui risiede il file index.html. Assicurarsi di avere [http-server](https://www.npmjs.com/package/http-server) installato globalmente e digitare `http-server` al prompt. Dovrebbe aprirsi nel browser un localhost e si può visualizzare l'app web. Controllare quale cucina è consigliata in base ai vari ingredienti:
+
+![app web degli ingredienti](../images/web-app.png)
+
+Congratulazioni, si è creato un'app web di "raccomandazione" con pochi campi. Si prenda del tempo per costruire questo sistema!
+## 🚀 Sfida
+
+L'app web è molto minimale, quindi continuare a costruirla usando gli ingredienti e i loro indici dai dati [ingredient_indexes](../../data/ingredient_indexes.csv) . Quali combinazioni di sapori funzionano per creare un determinato piatto nazionale?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/)
+
+## Revisione e Auto Apprendimento
+
+Sebbene questa lezione abbia appena toccato l'utilità di creare un sistema di raccomandazione per gli ingredienti alimentari, quest'area delle applicazioni ML è molto ricca di esempi. Leggere di più su come sono costruiti questi sistemi:
+
+- https://www.sciencedirect.com/topics/computer-science/recommendation-engine
+- https://www.technologyreview.com/2014/08/25/171547/the-ultimate-challenge-for-recommendation-engines/
+- https://www.technologyreview.com/2015/03/23/168831/everything-is-a-recommendation/
+
+## Compito
+
+[Creare un nuovo sistema di raccomandazione](assignment.it.md)
diff --git a/4-Classification/4-Applied/translations/assignment.it.md b/4-Classification/4-Applied/translations/assignment.it.md
new file mode 100644
index 0000000000..cc926c7278
--- /dev/null
+++ b/4-Classification/4-Applied/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Creare un sistema di raccomandazione
+
+## Istruzioni
+
+Dati gli esercizi di questa lezione, ora si conosce come creare un'app Web basata su JavaScript utilizzando Onnx Runtime e un modello Onnx convertito. Sperimentare con la creazione di un nuovo sistema di raccomandazione utilizzando i dati di queste lezioni o provenienti da altre parti (citare le fonti, per favore). Si potrebbe creare un sistema di raccomandazione di animali domestici in base a vari attributi della personalità o un sistema di raccomandazione di genere musicale basato sull'umore di una persona. Dare sfogo alla creatività!
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ---------------------------------------------------------------------- | ------------------------------------- | --------------------------------- |
+|          | Vengono presentati un'app Web e un notebook, entrambi ben documentati e funzionanti | Uno di quei due è mancante o difettoso | Entrambi sono mancanti o difettosi |
diff --git a/4-Classification/translations/README.it.md b/4-Classification/translations/README.it.md
new file mode 100644
index 0000000000..fbaa4720d5
--- /dev/null
+++ b/4-Classification/translations/README.it.md
@@ -0,0 +1,26 @@
+# Iniziare con la classificazione
+
+## Argomento regionale: Deliziose Cucine Asiatiche e Indiane �
+
+In Asia e in India, le tradizioni alimentari sono estremamente diverse e molto deliziose! Si darà un'occhiata ai dati sulle cucine regionali per cercare di capirne gli ingredienti.
+
+![Venditore di cibo tailandese](../images/thai-food.jpg)
+> Foto di <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> su <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Cosa si imparerà
+
+In questa sezione si approfondiranno le abilità sulla regressione apprese nella prima parte di questo programma di studi  per conoscere altri classificatori da poter utilizzare e che aiuteranno a conoscere i propri dati.
+
+> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di regressione. Si provi [Azure ML per questa attività](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+## Lezioni
+
+1. [Introduzione alla classificazione](../1-Introduction/translations/README.it.md)
+2. [Più classificatori](../2-Classifiers-1/translations/README.it.md)
+3. [Ancora altri classificatori](../3-Classifiers-2/translations/README.it.md)
+4. [Machine Learning applicato: sviluppare un'app web](../4-Applied/translations/README.it.md)
+## Crediti
+
+"Iniziare con la classificazione" è stato scritto con ♥� da [Cassie Breviu](https://www.twitter.com/cassieview) e [Jen Looper](https://www.twitter.com/jenlooper)
+
+L'insieme di dati sulle deliziose cucine proviene da [Kaggle](https://www.kaggle.com/hoandan/asian-and-indian-cuisines)

From 8b4173543b307d35139f16ae7edf9c970c2b3caf Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Thu, 22 Jul 2021 01:41:55 +0900
Subject: [PATCH 254/368] feat: add ja translations for quizzes

Refs #149
---
 quiz-app/src/assets/translations/ja.json | 2815 ++++++++++++++++++++++
 1 file changed, 2815 insertions(+)
 create mode 100644 quiz-app/src/assets/translations/ja.json

diff --git a/quiz-app/src/assets/translations/ja.json b/quiz-app/src/assets/translations/ja.json
new file mode 100644
index 0000000000..4696347f33
--- /dev/null
+++ b/quiz-app/src/assets/translations/ja.json
@@ -0,0 +1,2815 @@
+[
+  {
+    "title": "�心者����機械学習: �テスト",
+    "complete": "������������テストを完了�����",
+    "error": "����ん�も�一度試��������。",
+    "quizzes": [
+      {
+        "id": 1,
+        "title": "機械学習��導入: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "機械学習�応用�身近��る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "�典的機械学習�深層学習�技術的����何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "�典的機械学習����先�発明�れ�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ニューラル�ットワークを使用�る����",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "深層学習�ロボット�使用�れ��る",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "���業�機械学習�戦略を使���������ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "多元的��題�解決を自動化�る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "顧客�種類�応��ショッピング体験をカスタマイズ�る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 2,
+        "title": "機械学習��導入: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "機械学習�アルゴリズム�シミュレート�る��",
+            "answerOptions": [
+              {
+                "answerText": "賢�マシン",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "人間�脳",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "オランウータン",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "�典的機械学習�手法�例�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "自然言語処�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "深層学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ニューラル�ットワーク",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "��全員�機械学習�基礎を学�������ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "機械学習を学���誰�も始�や���楽���ら",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "機械学習�戦略�多��産業や領域�使用�れ��る�ら",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 3,
+        "title": "機械学習�歴�: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "「人工知能����言葉�生�れ�����頃��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "1980年代",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "1950年代",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "1930年代",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "機械学習���る先駆者������り�誰��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "アラン・�ューリング",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ビル・ゲイツ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "シェーキー",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "1970年代�AI�進歩��化���由�����何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "計算能力��界",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "熟練��エンジニア��足",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "国家間�紛争",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 4,
+        "title": "機械学習�歴�: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "「���ら���AIシステム�例�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "ELIZA",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "HACKML",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "SSYSTEM",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "「黄金期��開発�れ�技術�例�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "Blocks world",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Jibo",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ロボット犬",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "人工知能�分野����誕生�発展�礎����出�事��れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "�ューリングテスト",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ダートマス�期研究会",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "AI�冬",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 5,
+        "title": "公平性�機械学習: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "機械学習�����公平性�起�り�る��",
+            "answerOptions": [
+              {
+                "answerText": "故��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�失�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方�",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "機械学習����「�公平性���味�る��",
+            "answerOptions": [
+              {
+                "answerText": "�るグループ�人々�対�る弊害",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "��り�人�対�る弊害",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "大多数�人々�対�る弊害",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "5��主�弊害�",
+            "answerOptions": [
+              {
+                "answerText": "割り当�・サービス�質・�見・誹謗中傷・表����足",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "移�・サービス�質・�見・誹謗中傷・表����足",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "割り当�・サービス�質・ステレオ・誹謗中傷・表����足",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 6,
+        "title": "公平性�機械学習: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "モデル��公平性�発生�る原因�����",
+            "answerOptions": [
+              {
+                "answerText": "�去�データ�対�る�存度�高��る��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "�去�データ�対�る�存度�低��る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�去�データ��整�性�高��る��",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "�公平性を緩和�る�����る���",
+            "answerOptions": [
+              {
+                "answerText": "弊害��れを��るグループ�特定",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "公平性�関�る指標�定義",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Fairlearnパッケージ���る���",
+            "answerOptions": [
+              {
+                "answerText": "複数�モデル間�公平性�パフォーマンス�指標を比較",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ニーズ�応��最��モデル��択",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "何�公平��何��������判断を助�る��",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 7,
+        "title": "ツール�手法: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "モデルを構築�る際�������",
+            "answerOptions": [
+              {
+                "answerText": "データを準備���ら学習�る��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "学習方法を�ん��らデータを準備�る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "パラメータを調整���ら学習�る��",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "データ�〇〇�機械学習モデル�質�影響を与�る",
+            "answerOptions": [
+              {
+                "answerText": "�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "å½¢",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "特徴���",
+            "answerOptions": [
+              {
+                "answerText": "データ�質",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データ�測定�能�特性",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データ�列",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 8,
+        "title": "ツール�手法: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "データを�視化����由�",
+            "answerOptions": [
+              {
+                "answerText": "外れ値を発見��る�ら",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�イアス�原因を発見��る�ら",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "分割�るデータ�種類�",
+            "answerOptions": [
+              {
+                "answerText": "訓練データ��ューリングデータ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "訓練データ�テストデータ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "検証データ�評価データ",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "様々�機械学習ライブラリ�学習プロセスを開始�る一般的�コマンド�",
+            "answerOptions": [
+              {
+                "answerText": "model.travel",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.train",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "model.fit",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 9,
+        "title": "回帰��導入: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "次�変数����数値�変数��れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "身長",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "性別",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "髪�色",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "次�変数����カテゴリー�変数��れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "心�数",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "血液型",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "体�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "次��題����回帰分��基���題��れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "学生�期末試験�点数を予測�る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "�る人物�血液型を予測�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "メール�スパム����を判定�る",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 10,
+        "title": "回帰��導入: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "機械学習モデル�学習精度�95%�テスト精度�30%�場���ん�状態��る�呼�れる��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "�学習",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "未学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "二�学習",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "特徴��中�ら���も�を特定�るプロセス����",
+            "answerOptions": [
+              {
+                "answerText": "特徴�抽出",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "特徴��次元削減",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "特徴��択",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Scikit Learn � 'train_test_split()' メソッド/関数を使用���データセットを一定�割��訓練データセット�テストデータセット�分割�る処�����",
+            "answerOptions": [
+              {
+                "answerText": "交差検証",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ホールドアウト検証",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "���抜�検証",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 11,
+        "title": "回帰����データを準備���視化�る: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "次�Pythonモジュール����データを�視化�る���使用�れるも���れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "Numpy",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Scikit-learn",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Matplotlib",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "データセット�広�り方や��他�特性を�解�る���実行�る��",
+            "answerOptions": [
+              {
+                "answerText": "データ��視化",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データ��処�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "訓練データ�テストデータ�分割",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "次����機械学習プロジェクト����データ�視化ステップ�一部��るも���れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "特定�機械学習アルゴリズムを�り入れる",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "様々�プロット方法を使��データ�図解表�を作��る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データセット�値を正�化�る",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 12,
+        "title": "回帰����データを準備���視化�る: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "次�コードスニペット����データセット�欠�値���れ��る����を確��るも�������レッスン����正�����れ��ょ��？���データセット�Pandas�DataFrameオブジェクト��る 'dataset' ���変数�格��れ��るも�����。",
+            "answerOptions": [
+              {
+                "answerText": "dataset.isnull().sum()",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "findMissing(dataset)",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sum(null(dataset))",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "次�プロット方法����データセット�異�るデータグループ�広�り方を�解�る���有効�も���れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "散布図",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "折れ線グラフ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "棒グラフ",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "データ�視化�教���れ�����何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "データ�イント間�関係",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データセット��集�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データセット�外れ値���れ��る����",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 13,
+        "title": "線形�よ�多項�回帰: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "Matplotlib�",
+            "answerOptions": [
+              {
+                "answerText": "�画ライブラリ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データ�視化ライブラリ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "図書館",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "線形回帰�変数間�関係をプロット�る方法�",
+            "answerOptions": [
+              {
+                "answerText": "ç›´ç·š",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "円J",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "曲線",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "優れ�線形回帰モデル�〇〇相関係数を��",
+            "answerOptions": [
+              {
+                "answerText": "低�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "高�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "平��",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 14,
+        "title": "線形�よ�多項�回帰: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "データ�線形���場��〇〇回帰を試��良�",
+            "answerOptions": [
+              {
+                "answerText": "ç·šå½¢",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "多項�",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "���回帰法�種類���",
+            "answerOptions": [
+              {
+                "answerText": "フォルスステップ・リッジ・ラッソ・エラスティック�ット",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ステップワイズ・リッジ・ラッソ・エラスティック�ット",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ステップワイズ・リッジ・ラリアット・エラスティック�ット",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "最�二乗回帰�回帰直線���り�����データ�イント�",
+            "answerOptions": [
+              {
+                "answerText": "二乗���ら減算�れ��る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "乗算�れ��る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "二乗���ら加算�れ��る",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 15,
+        "title": "ロジスティック回帰: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "ロジスティック回帰�予測�る��",
+            "answerOptions": [
+              {
+                "answerText": "りん��熟���る����",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "�ケット�月����売れる�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "明日��後6時�空�何色��る�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "ロジスティック回帰�種類���れる��",
+            "answerOptions": [
+              {
+                "answerText": "多項�基本",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "多項�順�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "主��順�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "����データ��弱�相関��り��。最��回帰�種類�",
+            "answerOptions": [
+              {
+                "answerText": "ロジスティック",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ç·šå½¢",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "基本",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 16,
+        "title": "ロジスティック回帰: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "Seaborn�",
+            "answerOptions": [
+              {
+                "answerText": "データ�視化ライブラリ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "地図ライブラリ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "数学ライブラリ",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "混�行列�別��",
+            "answerOptions": [
+              {
+                "answerText": "誤差行列",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "真�行列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "精度行列",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "良�モデル�",
+            "answerOptions": [
+              {
+                "answerText": "混�行列�多���陽性�真陰性を�む",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "混�行列�多��真陽性�真陰性を�む",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "混�行列�多��真陽性��陰性を�む",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 17,
+        "title": "Webアプリを構築�る: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "ONNX�何�略��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "Over Neural Network Exchange",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Open Neural Network Exchange",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Output Neural Network Exchange",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Flask�作�者���よ��定義�れ��る��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "ミニフレームワーク",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ラージフレームワーク",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "マイクロフレームワーク",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Python�Pickleモジュール�行���",
+            "answerOptions": [
+              {
+                "answerText": "パイソンオブジェクト�シリアライズ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Pythonオブジェクト�デシリアライズ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Pythonオブジェクト�シリアライズ�デシリアライズ",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 18,
+        "title": "Webアプリを構築�る: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "事�学習済�モデルをWeb上�ホスティング�る���使�るPython�ツール�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "Flask",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "TensorFlow.js",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "onnx.js",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "SaaS�何�略��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "System as a Service",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Software as a Service",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Security as a Service",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Scikit-learn � LabelEncoder ライブラリ�行����何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "データをアルファベット�エンコード�る��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データを数値�エンコード�る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データをシリアル�エンコード�る��",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 19,
+        "title": "分類 1: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "分類�教師�り学習�一種��り�多��共通点を����",
+            "answerOptions": [
+              {
+                "answerText": "時系列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "回帰手法",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "自然言語処�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分類���よ��疑��答�られる��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "��メール�スパム��ょ��？",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "豚�飛�る��ょ��？",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "人生��味��何��ょ��？",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分類手法を使����最��ステップ�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "データ�クラスを作��る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データ�クリーニング��ランシング",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データ�イントをグループ����果�割り当�る��",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 20,
+        "title": "分類 1: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "多クラス�題��何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "データ�イントを複数�クラス�分類�るタスク",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データ�イントを複数�クラス��れ��分類�るタスク",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データ�イントを複数�方法�クリーニング�るタスク",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分類器��題を解決�る�����何度も�れるデータや役�立���データを除��������る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "データを�ランシング�る一番��由�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "��衡データ��視化�る�見栄��悪��ら",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "データを�ランシング�る�機械学習モデル�����クラス��ら��良��果�得られる�ら",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "データを�ランシング�る�より多��データ�イント�得られる�ら",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 21,
+        "title": "分類 2: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "�ランシング�よ�クリーニング�れ�データ�最も良�分類�果����る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "正��分類器���よ������ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "��分類器���場��最��を�解�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "経験�基��推測�確�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "分類�一種�",
+            "answerOptions": [
+              {
+                "answerText": "自然言語処�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "教師�り学習",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "プログラミング言語",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 22,
+        "title": "分類 2: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "「ソル����何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "自分�仕事をダブル�ェック���れる人",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "最�化�題�使用�るアルゴリズム",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "機械学習�手法",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "レッスン�使用��分類器��れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "ロジスティック回帰",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "決定木",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "一対他�多クラス",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分類アルゴリズム�期待通り�動作���る��������よ����知る�����る��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "予測�精度を確��る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "他�アルゴリズム�比較�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "似��題を解決��際��れ��優れ����を�去�データ�ら確��る",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 23,
+        "title": "分類 3: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "最��試������分類器�",
+            "answerOptions": [
+              {
+                "answerText": "線形サ�ートベクター分類器",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "K-Means",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "論�サ�ートベクター分類器",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "正則化�コントロール�る��",
+            "answerOptions": [
+              {
+                "answerText": "パラメータ�影響",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "学習スピード�影響",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "外れ値�影響",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "k近�分類器�使�る��",
+            "answerOptions": [
+              {
+                "answerText": "教師�り学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "教師��学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 24,
+        "title": "分類 3: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "サ�ートベクター分類器�使�る��",
+            "answerOptions": [
+              {
+                "answerText": "分類",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "回帰",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "ランダムフォレスト�〇〇�分類器�一種��る",
+            "answerOptions": [
+              {
+                "answerText": "アンサンブル",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ディセンブル",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "アセンブル",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "アダブースト�次�よ��知られ��る",
+            "answerOptions": [
+              {
+                "answerText": "誤��分類�れ��素�����目�る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "外れ値��目�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "誤��データ��目�る",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 25,
+        "title": "分類 4: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "推薦システム�使�る��",
+            "answerOptions": [
+              {
+                "answerText": "良�レストラン�推薦",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "試���ファッション�推薦",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Webアプリ�モデルを埋�込む���オフライン対応��能��る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Onnx Runtime �使�る��",
+            "answerOptions": [
+              {
+                "answerText": "Webアプリ�中�モデルを実行�る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "モデルを学習�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�イパーパラメータ��ューニング",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 26,
+        "title": "分類 4: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "Netronアプリ�役立���",
+            "answerOptions": [
+              {
+                "answerText": "データ��視化",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "モデル構造��視化",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Webアプリ�テスト",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Scikit-learn モデルをOnnx�扱�るよ���る���使���",
+            "answerOptions": [
+              {
+                "answerText": "sklearn-app",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-web",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "sklearn-onnx",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "Webアプリ�モデルを使�����通称",
+            "answerOptions": [
+              {
+                "answerText": "推論",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "干渉",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�険",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 27,
+        "title": "クラスタリング��導入: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "クラスタリング�実例�",
+            "answerOptions": [
+              {
+                "answerText": "食��準備",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "洗濯物�分類",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "食料��買�物",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "クラスタリング�手法�使�る産業�",
+            "answerOptions": [
+              {
+                "answerText": "銀行",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "電�商�引",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "クラスタリング�一種�",
+            "answerOptions": [
+              {
+                "answerText": "教師�り学習",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "教師��学習",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "強化学習",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 28,
+        "title": "クラスタリング��導入: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "ユークリッド幾何学��置�れる��",
+            "answerOptions": [
+              {
+                "answerText": "平�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "曲�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "��",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "クラスタリングデータ�密度�関係�る��",
+            "answerOptions": [
+              {
+                "answerText": "ノイズ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "深�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "妥当性",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "最も有��クラスタリングアルゴリズム�",
+            "answerOptions": [
+              {
+                "answerText": "k-means",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "k-middle",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "k-mart",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 29,
+        "title": "K-Means法: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "K-Means �派生元�",
+            "answerOptions": [
+              {
+                "answerText": "電気工学",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "信�処�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "計算言語学",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "良�シルエットスコア��",
+            "answerOptions": [
+              {
+                "answerText": "クラスタ�よ�分離�れ����よ�定義�れ��る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "クラスタ�数�少��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタ�数�多�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "分散��",
+            "answerOptions": [
+              {
+                "answerText": "平���差を二乗��値�平�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタリング����高��り��る��題��るも�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 30,
+        "title": "K-Means法: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "ボロノイ図�表���",
+            "answerOptions": [
+              {
+                "answerText": "クラスタ�分散",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタ�シード���領域",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "クラスタ�慣性",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "慣性��",
+            "answerOptions": [
+              {
+                "answerText": "�れ��内部�������るクラスタ�を表�指標",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "クラスタ��れ��動��を表�指標",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタ�質を表�指標",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "K-Means を使�際��最�� 'k' �値を決�る必���る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 31,
+        "title": "自然言語処���導入: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "レッスン����NLP�何�略��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "Neural Language Processing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "natural language processing",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Natural Linguistic Processing",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "�期�ボット��るEliza�演������",
+            "answerOptions": [
+              {
+                "answerText": "療法士",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "医者",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "看護師",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "アラン・�ューリング�「�ューリングテスト��判定�よ���������コンピュータ�",
+            "answerOptions": [
+              {
+                "answerText": "人間�見分����������",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�考���る����",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 32,
+        "title": "自然言語処���導入: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "ジョセフ・ワイゼン�ウム�発明��ボット�",
+            "answerOptions": [
+              {
+                "answerText": "Elisha",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Eliza",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Eloise",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "会話型�ボット�出力�る方法�",
+            "answerOptions": [
+              {
+                "answerText": "�ら���決�られ��択肢�らランダム���",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "入力を分���機械知能を使�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "ボットをより効果的��る�����れ�良���ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "ボット�対��より多��質�を�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ボット�より多��データを与����れ�応��学習を��る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "ボット�頭�悪���学習���� :(",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 33,
+        "title": "自然言語処��タスク: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "トークン化�",
+            "answerOptions": [
+              {
+                "answerText": "文章を�読点�分割�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "文章をトークン（�語）�分割�る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "文章をフレーズ�分割�る",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "埋�込��",
+            "answerOptions": [
+              {
+                "answerText": "�語をクラスタ化�る���文章を数値�変��る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "�語をフレーズ�埋�込む",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "文を段��埋�込む",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "�詞タグ付��",
+            "answerOptions": [
+              {
+                "answerText": "文を�詞�分�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "トークン化�れ��語を�詞�タグ付��る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "文を図�表�",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 34,
+        "title": "自然言語処��タスク: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "�語�出�頻度�関�る辞書を作る際�使���",
+            "answerOptions": [
+              {
+                "answerText": "�語�フレーズ�辞書",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�語�フレーズ�出�頻度",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "�語�フレーズ�ライブラリ",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "N-grams ��",
+            "answerOptions": [
+              {
+                "answerText": "一定�長���語列�分割��る文章",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "一定�長��文字列�分割��る�語",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "一定�長��段��分割��る文章",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "感情分��",
+            "answerOptions": [
+              {
+                "answerText": "フレーズ��ジティブ��ガティブ�を分��る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "フレーズ�感傷的����を分��る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "フレーズ�悲��を分��る",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 35,
+        "title": "自然言語処��翻訳: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "�純�翻訳�",
+            "answerOptions": [
+              {
+                "answerText": "�語��を翻訳�る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "文章構造を翻訳�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "感情を翻訳�る",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "文章�「コーパス���",
+            "answerOptions": [
+              {
+                "answerText": "少��文章",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "大��文章",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "����標準的�文章",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "も�モデルを構築�る���分�人間�翻訳��れ��機械学習モデル�",
+            "answerOptions": [
+              {
+                "answerText": "翻訳を�略��る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "翻訳を標準化��る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "翻訳�精度を高�られる",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 36,
+        "title": "自然言語処��翻訳: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "TextBlob �翻訳ライブラリ�基盤�",
+            "answerOptions": [
+              {
+                "answerText": "Google翻訳",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "Bing",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "独自�機械学習モデル",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "`blob.translate` を使用�る���必����",
+            "answerOptions": [
+              {
+                "answerText": "インター�ット接続",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "辞書",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "JavaScript",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "感情を判定�る���機械学習�アプロー��行���",
+            "answerOptions": [
+              {
+                "answerText": "手�作����見やスコア�回帰�手法を�用���パターンを探�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "手�作����見やスコア�自然言語処��手法を�用���パターンを探�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "手�作����見やスコア�クラスタリング�手法を�用���パターンを探�",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 37,
+        "title": "自然言語処� 4: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "人間�書��り話��文章�ら得られる情報�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "パターン�頻度",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "感情��味",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "感情分���何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "家��感傷的�価値��る�����研究",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "感情�状態や主観的�情報を体系的�識別・抽出・定�化・研究�る方法",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "�る人物�悲����楽����を見分�る能力",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "ホテル�レビュー�データセット・Python・感情分�を使����答�られる質��何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "レビュー�最もよ�使�れる�語やフレーズ�何��ょ��？",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "��リゾート�最も良�プール��る��ょ��？",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "��ホテル���レーパーキング��る��ょ��？",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 38,
+        "title": "自然言語処� 4: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "自然言語処��本質��何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "人間�言葉を楽��も��悲��も��分類�る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "人�手を借り���味や感情を読��る��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "感情�異常を見���調�る��",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "データをクリーニング�る際�気を付�����良����何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "他�言語�文字",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "空�行や列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "データを�作�る��データ���弱点を�解�る������る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 39,
+        "title": "自然言語処� 5: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "分��る��データをクリーニング�る������������ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "データ�欠�����り�正���り�る列��る�も�れ���ら",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "汚�データ�データセット�関�る誤���論����る�能性��る�ら",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "データクリーニング戦略�例�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "特定�質��答�る���有用���列や行�削除",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "仮説�����検証値�排除",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "外れ値を別�表�移����表�計算を行���一致�る����を確�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Tag列を使��データを分類�る�便利�����る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 40,
+        "title": "自然言語処� 5: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "データセット�目的�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "世界中�ホテル�対�る�定的�よ�肯定的�レビュー�����る�を確��る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "最も良�ホテルを�����役立��見�列を追加�る��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "人々���特定�レビューを残���を分��る��",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "ストップワード��何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "文章��象を変���一般的��語",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "感情分�を高速化�る���除去��る�語",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "感情分�をテスト�る�����レビュー�対�るレビュアー�スコア�一致���る��を確��る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 41,
+        "title": "時系列��導入: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "時系列予測�役立���",
+            "answerOptions": [
+              {
+                "answerText": "将��コストを決�る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "将��価格を予測�る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "時系列���次�よ��列��る",
+            "answerOptions": [
+              {
+                "answerText": "空間的�連続��等間隔�点",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "時間的�連続��等間隔�点",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時間的�よ�空間的�連続��等間隔�点",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "時系列�使用��る��",
+            "answerOptions": [
+              {
+                "answerText": "地震予測",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "コンピュータビジョン",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "色解�",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 42,
+        "title": "時系列��導入: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "時系列�トレンド��",
+            "answerOptions": [
+              {
+                "answerText": "時間�経��伴�測定�能�増加�減少",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時間�経��伴�減少�定�化",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "時間�経��伴�増加�減少�差",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "外れ値��",
+            "answerOptions": [
+              {
+                "answerText": "標準的�データ�分散�近�点",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "標準的�データ�分散�ら離れ�点",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "標準的�データ�分散�中��る点",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "時系列予測�最も有効���",
+            "answerOptions": [
+              {
+                "answerText": "計�経済学",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "歴�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "図書館",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 43,
+        "title": "時系列ARIMA: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "ARIMA�次�略��る",
+            "answerOptions": [
+              {
+                "answerText": "AutoRegressive Integral Moving Average",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Action",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "AutoRegressive Integrated Moving Average",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "定常性��",
+            "answerOptions": [
+              {
+                "answerText": "時間を�ら��も属性�変�ら��データ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "時間を�ら��も分布�変�ら��データ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時間を�ら��分布�変�るデータ",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "差分変��",
+            "answerOptions": [
+              {
+                "answerText": "トレンドや季節性を安定��る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "トレンドや季節性を悪化��る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "トレンドや季節性を排除�る",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 44,
+        "title": "時系列ARIMA: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "ARIMA��時系列データ�特殊�形�対��モデルを次�よ������る���使�れる",
+            "answerOptions": [
+              {
+                "answerText": "��る��平��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "��る��近�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "散布図�よ��",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "SARIMAXを使���",
+            "answerOptions": [
+              {
+                "answerText": "季節性ARIMAモデルを管��る��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "特別�ARIMAモデルを管��る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "統計的�ARIMAモデルを管��る��",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "「ウォークフォワード�検証��",
+            "answerOptions": [
+              {
+                "answerText": "モデルを検証���ら段階的��評価�る",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "モデルを検証���ら段階的��学習�る",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "モデルを検証���ら段階的��構��る",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 45,
+        "title": "強化 1: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "強化学習��何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "�解�る��何度も教�る��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "何回も試行�る�����る環境���るエージェント�最��行動を解読�る学習手法",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "複数�試行を一度�行�方法を�解�る��",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "方策��何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "任��状態�行動を返�関数",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "返���る����を示�書類",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "ランダム�目的�使用�れる関数",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "報酬関数��る環境���る�状態�対�るスコアを返�",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 46,
+        "title": "強化 1: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "Q学習��何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "�状態�「良��を記録�る仕組�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "Qテーブル�よ��方策�定義�れ��るアルゴリズム",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "上�両方",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "ランダムウォーク�対応�るQテーブル�値�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "�����値",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "-0.25",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�����値",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "レッスン�学習プロセス����よりも探索を行�����良���",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "true"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 47,
+        "title": "強化 2: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "�ェスや囲��連続��状態を��ゲーム��る",
+            "answerOptions": [
+              {
+                "answerText": "正��",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "正����",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "カート�ール�題��何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "外れ値を排除�るプロセス",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "買�物��を最�化�る方法",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "�ランシング�簡易版",
+                "isCorrect": "true"
+              }
+            ]
+          },
+          {
+            "questionText": "ゲーム�中�起�り�る状態���る様々�シナリオを行����使�るツール�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "推測�確�",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "シミュレーション環境",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "状態�移テスト",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 48,
+        "title": "強化 2: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "�る環境�起�り�る����状態を定義�る場所�����ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "メソッド",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "アクションスペース",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "アクションリスト",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "辞書�キー�リュー���使��ペア�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "キー�(state, action)��リュー�Qテーブル�エントリ",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "キー�state��リュー�action",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "キー�qvalues関数�値��リュー�action",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "Q学習�使用���イパーパラメータ�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "Qテーブル�値・�在�報酬・ランダム�アクション",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "学習率・割引率・探索/��率",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "累�報酬・学習率・探索率",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 49,
+        "title": "実世界��応用: 講義���テスト",
+        "quiz": [
+          {
+            "questionText": "金�業界���る機械学習�応用例�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "自然言語処�を使��カスタマージャーニー�パーソナライズ",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "線形回帰を使���康管�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時系列を使��エ�ルギー管�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "�入院を管��る���病院�使�る機械学習�手法�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "クラスタリング",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時系列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "自然言語処�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "エ�ルギー管��時系列を使用�る例�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "動物�モーションセンシング",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "スマートパーキングメーター",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "森林���追跡",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      },
+      {
+        "id": 50,
+        "title": "実世界��応用: 講義後��テスト",
+        "quiz": [
+          {
+            "questionText": "クレジットカード��正利用を検出�る���使用��る機械学習�手法��れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "回帰",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "クラスタリング",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "自然言語処�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "森林管��例示�れ��る機械学習�手法��れ��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "強化学習",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "時系列",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "自然言語処�",
+                "isCorrect": "false"
+              }
+            ]
+          },
+          {
+            "questionText": "ヘルスケア業界���る機械学習�応用例�何��ょ��？",
+            "answerOptions": [
+              {
+                "answerText": "回帰を使��学生�行動予測",
+                "isCorrect": "false"
+              },
+              {
+                "answerText": "分類器を使��臨床試験�管�",
+                "isCorrect": "true"
+              },
+              {
+                "answerText": "分類器を使��動物�モーションセンシング",
+                "isCorrect": "false"
+              }
+            ]
+          }
+        ]
+      }
+    ]
+  }
+]

From 369877160d966854544116ef8187ebc934aca39a Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Thu, 22 Jul 2021 01:40:16 +0900
Subject: [PATCH 255/368] feat: update links to quizzes

Refs #149
---
 1-Introduction/1-intro-to-ML/translations/README.ja.md   | 4 ++--
 1-Introduction/2-history-of-ML/translations/README.ja.md | 4 ++--
 1-Introduction/3-fairness/translations/README.ja.md      | 4 ++--
 2-Regression/1-Tools/translations/README.ja.md           | 4 ++--
 2-Regression/2-Data/translations/README.ja.md            | 4 ++--
 5 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md
index ada0055008..fd3c11d1e4 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ja.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md
@@ -4,7 +4,7 @@
 
 > 🎥 上�画�をクリック�る��機械学習�AI�深層学習�������説明��動画�表示�れ��。
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=ja)
 
 ### イントロダクション
 
@@ -94,7 +94,7 @@
 ## 🚀 Challenge
 AI�ML�深層学習�データサイエンス��������解���る��を�紙や[Excalidraw](https://excalidraw.com/)���オンラインアプリを使��スケッ�������。����れ�れ�技術�得���る�題�アイデアを加��������。
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=ja)
 
 ## 振り返り�自習
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md
index f9b4c0457f..9d791209bd 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ja.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ja.md
@@ -3,7 +3,7 @@
 ![機械学習�歴�を����スケッ�](../../../sketchnotes/ml-history.png)
 > [Tomomi Imura](https://www.twitter.com/girlie_mac)�よるスケッ�
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=ja)
 
 ��授業���機械学習�人工知能�歴����る主��出�事を紹介���。
 
@@ -99,7 +99,7 @@
 
 �れら�歴�的瞬間�1�を掘り下�����背後��る人々����学���ょ�。魅力的�人々������文化的�空白�状態�科学的発見���れ�����り��ん。��������見��る��ょ��？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=ja)
 
 ## 振り返り�自習
 
diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
index e844835909..afa92f2bd1 100644
--- a/1-Introduction/3-fairness/translations/README.ja.md
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -3,7 +3,7 @@
 ![機械学習���る公平性を����スケッ�](../../../sketchnotes/ml-fairness.png)
 > [Tomomi Imura](https://www.twitter.com/girlie_mac)�よるスケッ�
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5?loc=ja)
  
 ## イントロダクション
 
@@ -178,7 +178,7 @@ AIや機械学習���る公平性��証���然���複雑�社
 
 モデル�構築や使用������公平�明ら���るよ���実�シナリオを考��������。他���よ����を考�る����ょ��？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6?loc=ja)
 ## Review & Self Study 
  
 ��レッスン���機械学習���る公平��公平�概念�基礎を学����。
diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index 0bebf16d0a..e91a46810d 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -4,7 +4,7 @@
 
 > [Tomomi Imura](https://www.twitter.com/girlie_mac) �よ��制作�れ�スケッ�ノート
 
-## [講義�クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [講義�クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9?loc=ja)
 
 ## イントロダクション
 
@@ -205,7 +205,7 @@ s1 tc: T細胞（白血��一種）
 ## 🚀�ャレンジ
 
 ��データセット�ら別�変数を�択��プロット������。ヒント： `X = X[:, np.newaxis, 2]` �行を編集�る。今回�データセット�ターゲット��る�糖尿病���病気�進行�������よ��発見��る���ょ��？
-## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10?loc=ja)
 
 ## レビュー & 自主学習
 
diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index 1570be3c5e..05017477aa 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -4,7 +4,7 @@
 > 
 > [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
 
-## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11?loc=ja)
 
 ## イントロダクション
 
@@ -195,7 +195,7 @@ Jupyter notebook����利用��るテータ�視化ライブラリ�
 
 Matplotlib��供�る様々�タイプ�ビジュアライゼーションを探�����ょ�。回帰��題����タイプ�最も����る��ょ��？
 
-## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12?loc=ja)
 
 ## レビュー & 自主学習
 

From 281f30e2672418175244c47fe0188590ccdf76e7 Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Thu, 22 Jul 2021 01:41:20 +0900
Subject: [PATCH 256/368] feat: add option of ja

Refs #149
---
 quiz-app/src/App.vue                      | 1 +
 quiz-app/src/assets/translations/index.js | 4 +++-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/quiz-app/src/App.vue b/quiz-app/src/App.vue
index 6baabd0ca0..ef95dbed28 100644
--- a/quiz-app/src/App.vue
+++ b/quiz-app/src/App.vue
@@ -7,6 +7,7 @@
         <option>en</option>
         <option>tr</option>
         <option>fr</option>
+        <option>ja</option>
       </select>
     </nav>
     <div id="app">
diff --git a/quiz-app/src/assets/translations/index.js b/quiz-app/src/assets/translations/index.js
index 85931b8b73..f93b84e17b 100644
--- a/quiz-app/src/assets/translations/index.js
+++ b/quiz-app/src/assets/translations/index.js
@@ -2,13 +2,15 @@
 import en from './en.json';
 import tr from './tr.json';
 import fr from './fr.json';
+import ja from './ja.json';
 
 //export const defaultLocale = 'en';
 
 const messages = {
 	en: en[0],
 	tr: tr[0],
-	fr: fr[0]
+	fr: fr[0],
+	ja: ja[0]
 };
 
 export default messages;

From 146d557945cb6a61ee02cada529b96f259767237 Mon Sep 17 00:00:00 2001
From: AidarSource <34319725+AidarSource@users.noreply.github.com>
Date: Sat, 24 Jul 2021 15:38:43 +0600
Subject: [PATCH 257/368] Typo in text

reuirements.txt to requirements.txt
---
 3-Web-App/1-Web-App/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index f8db7afc97..c4b406fa73 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -187,7 +187,7 @@ Now you can build a Flask app to call your model and return similar results, but
    cd web-app
    ```
 
-1. In your terminal type `pip install`, to install the libraries listed in _reuirements.txt_:
+1. In your terminal type `pip install`, to install the libraries listed in _requirements.txt_:
 
    ```bash
    pip install -r requirements.txt

From ffb22b3fb60b28215179ea3a357b6f554cd227d1 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 24 Jul 2021 21:35:51 -0400
Subject: [PATCH 258/368] Update README.zh-cn.md

small edit to show correct file structure
---
 3-Web-App/1-Web-App/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/3-Web-App/1-Web-App/translations/README.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md
index cb8a051c57..9110f2b864 100644
--- a/3-Web-App/1-Web-App/translations/README.zh-cn.md
+++ b/3-Web-App/1-Web-App/translations/README.zh-cn.md
@@ -165,7 +165,7 @@ print(model.predict([[50,44,-12]]))
     web-app/
       static/
         css/
-        templates/
+      templates/
     notebook.ipynb
     ufo-model.pkl
     ``` 

From 3a93d131125d3f79fad6964c8000a763113d6273 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sat, 24 Jul 2021 21:38:49 -0400
Subject: [PATCH 259/368] Update README.md

typo fix for NLP
---
 6-NLP/1-Introduction-to-NLP/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index eeae6bec3e..924ce73c73 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -17,7 +17,7 @@ You will learn about:
 
 ## Computational linguistics
 
-Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. natural language processing (NLP) is a related field focused on how computers can process 'natural', or human, languages.
+Computational linguistics is an area of research and development over many decades that studies how computers can work with, and even understand, translate, and communicate with languages. Natural language processing (NLP) is a related field focused on how computers can process 'natural', or human, languages.
 
 ### Example - phone dictation
 

From 2dabc96f1feb9a0f109b831ec38ee807dde84044 Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Sun, 25 Jul 2021 08:50:24 +0700
Subject: [PATCH 260/368] Update the links

---
 1-Introduction/1-intro-to-ML/translations/README.id.md |  2 +-
 .../2-history-of-ML/translations/README.id.md          |  2 +-
 1-Introduction/3-fairness/translations/README.id.md    |  2 +-
 .../4-techniques-of-ML/translations/README.id.md       | 10 +++++-----
 1-Introduction/translations/README.id.md               |  8 ++++----
 5 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md
index 4cef05dca4..0a2366a4f8 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.id.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.id.md
@@ -104,4 +104,4 @@ Untuk mempelajari lebih lanjut tentang bagaimana kamu dapat menggunakan algoritm
 
 ## Tugas
 
-[Persiapan](assignment.md)
+[Persiapan](assignment.id.md)
diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
index a0a4ce059f..2b4bdb6a20 100644
--- a/1-Introduction/2-history-of-ML/translations/README.id.md
+++ b/1-Introduction/2-history-of-ML/translations/README.id.md
@@ -113,4 +113,4 @@ Berikut adalah item untuk ditonton dan didengarkan:
 
 ## Tugas
 
-[Membuat sebuah *timeline*](assignment.md)
+[Membuat sebuah *timeline*](assignment.id.md)
diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md
index 44056ab83f..99abc7d2cf 100644
--- a/1-Introduction/3-fairness/translations/README.id.md
+++ b/1-Introduction/3-fairness/translations/README.id.md
@@ -212,4 +212,4 @@ Baca mengenai *tools* Azure Machine Learning untuk memastikan keadilan
 
 ## Tugas
 
-[Jelajahi Fairlearn](assignment.md) 
+[Jelajahi Fairlearn](assignment.id.md) 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
index 77f5880f42..259a90d1c4 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.id.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -35,9 +35,9 @@ Sebelum mulai membangun model kamu, ada beberapa tugas yang harus kamu selesaika
 Untuk dapat menjawab pertanyaan kamu dengan kepastian, Kamu memerlukan sejumlah besar data dengan jenis yang tepat. Ada dua hal yang perlu kamu lakukan pada saat ini: 
 
 - **Mengumpulkan data**. Ingat pelajaran sebelumnya tentang keadilan dalam analisis data, kumpulkan data kamu dengan hati-hati. Waspadai sumber datanya, bias bawaan apa pun yang mungkin dimiliki, dan dokumentasikan asalnya. 
-- **Menyiapkan data**. Ada beberapa langkah dalam proses persiapan data. Kamu mungkin perlu menyusun data dan melakukan normalisasi jika berasal dari berbagai sumber. Kamu dapat meningkatkan kualitas dan kuantitas data melalui berbagai metode seperti mengonversi string menjadi angka (seperti yang kita lakukan di [Clustering](../../5-Clustering/1-Visualize/README.md)). Kamu mungkin juga bisa membuat data baru berdasarkan data yang asli (seperti yang kita lakukan di [Classification](../../4-Classification/1-Introduction/README.md)). Kamu bisa membersihkan dan mengubah data (seperti yang kita lakukan sebelum pelajaran [Web App](../3-Web-App/README.md)). Terakhir, Kamu mungkin juga perlu mengacaknya dan mengubah urutannya, tergantung pada teknik *training* kamu. 
+- **Menyiapkan data**. Ada beberapa langkah dalam proses persiapan data. Kamu mungkin perlu menyusun data dan melakukan normalisasi jika berasal dari berbagai sumber. Kamu dapat meningkatkan kualitas dan kuantitas data melalui berbagai metode seperti mengonversi string menjadi angka (seperti yang kita lakukan di [Clustering](../../5-Clustering/1-Visualize/translations/README.id.md)). Kamu mungkin juga bisa membuat data baru berdasarkan data yang asli (seperti yang kita lakukan di [Classification](../../4-Classification/1-Introduction/translations/README.id.md)). Kamu bisa membersihkan dan mengubah data (seperti yang kita lakukan sebelum pelajaran [Web App](../3-Web-App/translations/README.id.md)). Terakhir, Kamu mungkin juga perlu mengacaknya dan mengubah urutannya, tergantung pada teknik *training* kamu. 
 
-✅ Setelah mengumpulkan dan memproses data kamu, luangkan waktu sejenak untuk melihat apakah bentuknya memungkinkan kamu untuk menjawab pertanyaan yang kamu maksudkan. Mungkin data tidak akan berkinerja baik dalam tugas yang kamu berikan, seperti yang kita temukan dalam pelajaran [Clustering](../../5-Clustering/1-Visualize/README.md).
+✅ Setelah mengumpulkan dan memproses data kamu, luangkan waktu sejenak untuk melihat apakah bentuknya memungkinkan kamu untuk menjawab pertanyaan yang kamu maksudkan. Mungkin data tidak akan berkinerja baik dalam tugas yang kamu berikan, seperti yang kita temukan dalam pelajaran [Clustering](../../5-Clustering/1-Visualize/translations/README.id.md).
 
 ### Memilih variabel fiturmu
 
@@ -46,14 +46,14 @@ Sebuah [fitur](https://www.datasciencecentral.com/profiles/blogs/an-introduction
 🎓 **Feature Selection dan Feature Extraction** Bagaimana kamu tahu variabel mana yang harus dipilih saat membangun model? Kamu mungkin akan melalui proses pemilihan fitur (*Feature Selection*) atau ekstraksi fitur (*Feature Extraction*) untuk memilih variabel yang tepat untuk model yang paling berkinerja. Namun, keduanya tidak sama: "Ekstraksi fitur membuat fitur baru dari fungsi fitur asli, sedangkan pemilihan fitur mengembalikan subset fitur." ([sumber](https://wikipedia.org/wiki/Feature_selection))
 ### Visualisasikan datamu
 
-Aspek penting dari toolkit data scientist adalah kemampuan untuk memvisualisasikan data menggunakan beberapa *library* seperti Seaborn atau MatPlotLib. Merepresentasikan data kamu secara visual memungkinkan kamu mengungkap korelasi tersembunyi yang dapat kamu manfaatkan. Visualisasimu mungkin juga membantu kamu mengungkap data yang bias atau tidak seimbang (seperti yang kita temukan dalam [Classification](../../4-Classification/2-Classifiers-1/README.md)).
+Aspek penting dari toolkit data scientist adalah kemampuan untuk memvisualisasikan data menggunakan beberapa *library* seperti Seaborn atau MatPlotLib. Merepresentasikan data kamu secara visual memungkinkan kamu mengungkap korelasi tersembunyi yang dapat kamu manfaatkan. Visualisasimu mungkin juga membantu kamu mengungkap data yang bias atau tidak seimbang (seperti yang kita temukan dalam [Classification](../../4-Classification/2-Classifiers-1/translations/README.id.md)).
 ### Membagi dataset
 
 Sebelum memulai *training*, Kamu perlu membagi dataset menjadi dua atau lebih bagian dengan ukuran yang tidak sama tapi masih mewakili data dengan baik.
 
 - **Training**. Bagian dataset ini digunakan untuk men-training model kamu. Bagian dataset ini merupakan mayoritas dari dataset asli. 
 - **Testing**. Sebuah dataset tes adalah kelompok data independen, seringkali dikumpulkan dari data yang asli yang akan digunakan untuk mengkonfirmasi kinerja dari model yang dibuat.
-- **Validating**. Dataset validasi adalah kumpulan contoh mandiri yang lebih kecil yang kamu gunakan untuk menyetel hyperparameter atau arsitektur model untuk meningkatkan model. Tergantung dari ukuran data dan pertanyaan yang kamu ajukan, Kamu mungkin tidak perlu membuat dataset ketiga ini (seperti yang kita catat dalam [Time Series Forecasting](../7-TimeSeries/1-Introduction/README.md)).
+- **Validating**. Dataset validasi adalah kumpulan contoh mandiri yang lebih kecil yang kamu gunakan untuk menyetel hyperparameter atau arsitektur model untuk meningkatkan model. Tergantung dari ukuran data dan pertanyaan yang kamu ajukan, Kamu mungkin tidak perlu membuat dataset ketiga ini (seperti yang kita catat dalam [Time Series Forecasting](../7-TimeSeries/1-Introduction/translations/README.id.md)).
 
 ## Membuat sebuah model
 
@@ -102,4 +102,4 @@ Cari di Internet mengenai wawancara dengan data scientist yang mendiskusikan pek
 
 ## Tugas
 
-[Wawancara dengan data scientist](assignment.md)
+[Wawancara dengan data scientist](assignment.id.md)
diff --git a/1-Introduction/translations/README.id.md b/1-Introduction/translations/README.id.md
index d2b0ae53a3..4ae59fb148 100644
--- a/1-Introduction/translations/README.id.md
+++ b/1-Introduction/translations/README.id.md
@@ -8,10 +8,10 @@ sejarah serta teknik-teknik yang digunakan oleh para peneliti. Ayo jelajahi duni
   
 ### Pelajaran
 
-1. [Pengantar Machine Learning](1-intro-to-ML/README.md)
-1. [Sejarah dari Machine Learning dan AI](2-history-of-ML/README.md)
-1. [Keadilan dan Machine Learning](3-fairness/README.md)
-1. [Teknik-Teknik Machine Learning](4-techniques-of-ML/README.md)
+1. [Pengantar Machine Learning](../1-intro-to-ML/translations/README.id.md)
+1. [Sejarah dari Machine Learning dan AI](../2-history-of-ML/translations/README.id.md)
+1. [Keadilan dan Machine Learning](../3-fairness/translations/README.id.md)
+1. [Teknik-Teknik Machine Learning](../4-techniques-of-ML/translations/README.id.md)
 ### Penghargaan
 
 "Pengantar Machine Learning" ditulis dengan ♥� oleh sebuah tim yang terdiri dari [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan), [Ornella Altunyan](https://twitter.com/ornelladotcom) dan [Jen Looper](https://twitter.com/jenlooper)

From 8c5f5f02c606c5c42c44d96e7280355b23615195 Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Sun, 25 Jul 2021 10:43:26 +0700
Subject: [PATCH 261/368] Update some translation

---
 .../1-intro-to-ML/translations/README.id.md   |  4 +--
 .../2-history-of-ML/translations/README.id.md | 18 ++++++-------
 .../3-fairness/translations/README.id.md      | 26 +++++++++----------
 .../translations/README.id.md                 | 10 +++----
 4 files changed, 28 insertions(+), 30 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md
index 0a2366a4f8..2f30f213d1 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.id.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.id.md
@@ -17,10 +17,10 @@ Selamat datang di pelajaran Machine Learning klasik untuk pemula! Baik kamu yang
 
 Sebelum memulai kurikulum ini, kamu perlu memastikan komputer kamu sudah dipersiapkan untuk menjalankan *notebook* secara lokal.
 
-- **Konfigurasi komputer kamu dengan video-video ini**. Pelajari bagaimana menyiapkan komputer kamu dalam [video-video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) ini.
+- **Konfigurasi komputer kamu dengan video ini**. Pelajari bagaimana menyiapkan komputer kamu dalam [video-video](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6) ini.
 - **Belajar Python**. Disarankan juga untuk memiliki pemahaman dasar dari [Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), sebuah bahasa pemrograman yang digunakan oleh data scientist yang juga akan kita gunakan dalam pelajaran ini. 
 - **Belajar Node.js dan JavaScript**. Kita juga menggunakan JavaScript beberapa kali dalam pelajaran ini ketika membangun aplikasi web, jadi kamu perlu menginstal [node](https://nodejs.org) dan [npm](https://www.npmjs.com/), serta [Visual Studio Code](https://code.visualstudio.com/) yang tersedia untuk pengembangan Python dan JavaScript.
-- **Buat akun GitHub**. Karena kamu menemukan kami di [GitHub](https://github.com), kamu mungkin sudah punya akun, tapi jika belum, silahkan buat akun baru kemudian *fork* kurikulum ini untuk kamu pergunakan sendiri. (Jangan ragu untuk memberikan kami bintang juga 😊)
+- **Buat akun GitHub**. Karena kamu menemukan kami di [GitHub](https://github.com), kamu mungkin sudah punya akun, tapi jika belum, silakan buat akun baru kemudian *fork* kurikulum ini untuk kamu pergunakan sendiri. (Jangan ragu untuk memberikan kami bintang juga 😊)
 - **Jelajahi Scikit-learn**. Buat diri kamu familiar dengan [Scikit-learn]([https://scikit-learn.org/stable/user_guide.html), seperangkat *library* ML yang kita acu dalam pelajaran-pelajaran ini.
 
 ### Apa itu Machine Learning?
diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
index 2b4bdb6a20..ff66b01485 100644
--- a/1-Introduction/2-history-of-ML/translations/README.id.md
+++ b/1-Introduction/2-history-of-ML/translations/README.id.md
@@ -13,10 +13,10 @@ Sejarah Artifical Intelligence, AI, sebagai bidang terkait dengan sejarah Machin
 
 - 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) dan para pendahulu. Teorema ini dan penerapannya mendasari inferensi, mendeskripsikan kemungkinan suatu peristiwa terjadi berdasarkan pengetahuan sebelumnya. 
 - 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) oleh matematikawan Perancis Adrien-Marie Legendre. Teori ini yang akan kamu pelajari di unit Regresi, ini membantu dalam *data fitting*. 
-- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) dinamai setelah matematikawan Rusia, Andrey Markov, digunakan untuk mendeskripsikan sebuah urutan dari kejadian-kejadian yang mungkin terjadi berdasarkan kondisi sebelumnya.
+- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) dinamai dengan nama matematikawan Rusia, Andrey Markov, digunakan untuk mendeskripsikan sebuah urutan dari kejadian-kejadian yang mungkin terjadi berdasarkan kondisi sebelumnya.
 - 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) adalah sebuah tipe dari *linear classifier* yang ditemukan oleh psikolog Amerika, Frank Rosenblatt, yang mendasari kemajuan dalam *Deep Learning*.
 - 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) adalah sebuah algoritma yang pada awalnya didesain untuk memetakan rute. Dalam konteks ML, ini digunakan untuk mendeteksi berbagai pola.
-- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) digunakan untuk men-*train* [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network). 
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) digunakan untuk melatih [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network). 
 - 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) adalah *artificial neural networks* yang berasal dari *feedforward neural networks* yang membuat grafik sementara. 
 
 ✅ Lakukan sebuah riset kecil. Tanggal berapa lagi yang merupakan tanggal penting dalam sejarah ML dan AI?
@@ -36,9 +36,9 @@ Workshop ini dipuji karena telah memprakarsai dan mendorong beberapa diskusi ter
 
 ## 1956 - 1974: "Tahun-tahun Emas"
 
-Dari tahun 1950-an hingga pertengahan 70-an, optimisme memuncak dengan harapan bahwa AI dapat memecahkan banyak masalah. Pada tahun 1967, Marvin Minsky dengan yakin menyatakan bahwa "Dalam satu generasi ... masalah menciptakan '*artificial intelligence*' secara substansial akan terpecahkan." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+Dari tahun 1950-an hingga pertengahan 70-an, optimisme memuncak dengan harapan bahwa AI dapat memecahkan banyak masalah. Pada tahun 1967, Marvin Minsky dengan yakin menyatakan bahwa "Dalam satu generasi ... masalah menciptakan '*artificial intelligence*' akan terpecahkan secara substansial." (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
 
-penelitian *natural language processing* berkembang, pencarian disempurnakan dan dibuat lebih kuat, dan konsep '*micro-worlds*' diciptakan, di mana tugas-tugas sederhana diselesaikan menggunakan instruksi bahasa sederhana. 
+Penelitian *natural language processing* berkembang, pencarian disempurnakan dan dibuat lebih *powerful*, dan konsep '*micro-worlds*' diciptakan, di mana tugas-tugas sederhana diselesaikan menggunakan instruksi bahasa sederhana. 
 
 Penelitian didanai dengan baik oleh lembaga pemerintah, banyak kemajuan dibuat dalam komputasi dan algoritma, dan prototipe mesin cerdas dibangun. Beberapa mesin tersebut antara lain: 
 
@@ -63,19 +63,19 @@ Penelitian didanai dengan baik oleh lembaga pemerintah, banyak kemajuan dibuat d
 Pada pertengahan 1970-an, semakin jelas bahwa kompleksitas pembuatan 'mesin cerdas' telah diremehkan dan janjinya, mengingat kekuatan komputasi yang tersedia, telah dilebih-lebihkan. Pendanaan telah habis dan kepercayaan dalam bidang ini menurun. Beberapa masalah yang memengaruhi kepercayaan diri termasuk: 
 
 - **Keterbatasan**. Kekuatan komputasi terlalu terbatas.
-- **Ledakan kombinatorial**. Jumlah parameter yang perlu di-*train* bertambah secara eksponensial karena lebih banyak hal yang diminta dari komputer, tanpa evolusi paralel dari kekuatan dan kemampuan komputasi. 
+- **Ledakan kombinatorial**. Jumlah parameter yang perlu dilatih bertambah secara eksponensial karena lebih banyak hal yang diminta dari komputer, tanpa evolusi paralel dari kekuatan dan kemampuan komputasi. 
 - **Kekurangan data**. Adanya kekurangan data yang menghalangi proses pengujian, pengembangan, dan penyempurnaan algoritma.
 - **Apakah kita menanyakan pertanyaan yang tepat?**. Pertanyaan-pertanyaan yang diajukan pun mulai dipertanyakan kembali. Para peneliti mulai melontarkan kritik tentang pendekatan mereka 
   - Tes Turing mulai dipertanyakan, di antara ide-ide lain, dari 'teori ruang Cina' yang mengemukakan bahwa, "memprogram komputer digital mungkin membuatnya tampak memahami bahasa tetapi tidak dapat menghasilkan pemahaman yang sebenarnya." ([sumber](https://plato.stanford.edu/entries/chinese-room/))
-  - Etika memperkenalkan kecerdasan buatan seperti "terapis" ELIZA ke dalam masyarakat telah ditantang. 
+  - Tantangan etika ketika memperkenalkan kecerdasan buatan seperti si "terapis" ELIZA ke dalam masyarakat. 
 
-Pada saat yang sama, berbagai aliran pemikiran AI mulai terbentuk. Sebuah dikotomi didirikan antara praktek ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). Lab _Scruffy_ mengubah program selama berjam-jam sampai mendapat hasil yang diinginkan. Lab _Neat_ "berfokus pada logika dan penyelesaian masalah formal". ELIZA dan SHRDLU adalah sistem _scruffy_ yang terkenal. Pada tahun 1980-an, karena perkembangan permintaan untuk membuat sistem ML yang dapat direproduksi, pendekatan _neat_ secara bertahap menjadi yang terdepan karena hasilnya lebih dapat dijelaskan.
+Pada saat yang sama, berbagai aliran pemikiran AI mulai terbentuk. Sebuah dikotomi didirikan antara praktik ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). Lab _Scruffy_ mengubah program selama berjam-jam sampai mendapat hasil yang diinginkan. Lab _Neat_ "berfokus pada logika dan penyelesaian masalah formal". ELIZA dan SHRDLU adalah sistem _scruffy_ yang terkenal. Pada tahun 1980-an, karena perkembangan permintaan untuk membuat sistem ML yang dapat direproduksi, pendekatan _neat_ secara bertahap menjadi yang terdepan karena hasilnya lebih dapat dijelaskan.
 
 ## 1980s Sistem Pakar
 
 Seiring berkembangnya bidang ini, manfaatnya bagi bisnis menjadi lebih jelas, dan begitu pula dengan menjamurnya 'sistem pakar' pada tahun 1980-an. "Sistem pakar adalah salah satu bentuk perangkat lunak artificial intelligence (AI) pertama yang benar-benar sukses."  ([sumber](https://wikipedia.org/wiki/Expert_system)).
 
-Tipe sistem ini sebenarnya adalah _hybrid_, sebagian terdiri dari mesin aturan yang mendefinisikan *business requirement*, dan mesin inferensi yang memanfaatkan sistem aturan untuk menyimpulkan fakta baru. 
+Tipe sistem ini sebenarnya adalah _hybrid_, sebagian terdiri dari mesin aturan yang mendefinisikan kebutuhan bisnis, dan mesin inferensi yang memanfaatkan sistem aturan untuk menyimpulkan fakta baru. 
 
 Pada era ini juga terlihat adanya peningkatan perhatian pada jaringan saraf. 
 
@@ -89,7 +89,7 @@ Pada zaman ini memperlihatkan era baru bagi ML dan AI untuk dapat menyelesaikan
 
 ## Sekarang
 
-Saat ini, *machine learning* dan AI hampir ada di setiap bagian dari kehidupan kita. Era ini menuntut pemahaman yang cermat tentang risiko dan efek potensi dari algoritma ini pada kehidupan manusia. Seperti yang telah dinyatakan oleh Brad Smith dari Microsoft, "Teknologi informasi mengangkat isu-isu yang menjadi inti dari perlindungan hak asasi manusia yang mendasar seperti privasi dan kebebasan berekspresi. Masalah-masalah ini meningkatkan tanggung jawab bagi perusahaan teknologi yang menciptakan produk-produk ini. Dalam pandangan kami, mereka juga menyerukan peraturan pemerintah yang bijaksana dan untuk pengembangan norma-norma seputar penggunaan yang wajar" ([sumber](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+Saat ini, *machine learning* dan AI hampir ada di setiap bagian dari kehidupan kita. Era ini menuntut pemahaman yang cermat tentang risiko dan efek potensi dari berbagai algoritma yang ada pada kehidupan manusia. Seperti yang telah dinyatakan oleh Brad Smith dari Microsoft, "Teknologi informasi mengangkat isu-isu yang menjadi inti dari perlindungan hak asasi manusia yang mendasar seperti privasi dan kebebasan berekspresi. Masalah-masalah ini meningkatkan tanggung jawab bagi perusahaan teknologi yang menciptakan produk-produk ini. Dalam pandangan kami, mereka juga menyerukan peraturan pemerintah yang bijaksana dan untuk pengembangan norma-norma seputar penggunaan yang wajar" ([sumber](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
 
 Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk memahami sistem komputer dan perangkat lunak serta algoritma yang dijalankannya. Kami berharap kurikulum ini akan membantu kamu untuk mendapatkan pemahaman yang lebih baik sehingga kamu dapat memutuskan sendiri. 
 
diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md
index 99abc7d2cf..03bf796392 100644
--- a/1-Introduction/3-fairness/translations/README.id.md
+++ b/1-Introduction/3-fairness/translations/README.id.md
@@ -38,23 +38,23 @@ Menjamin keadilan dalam AI dan machine learning tetap menjadi tantangan sosiotek
 
 ### Kerugian Terkait Keadilan  
 
-Apa yang kamu maksud dengan ketidakadilan? "Ketidakadilan" mencakup dampak negatif, atau "bahaya", bagi sekelompok orang, seperti yang didefinisikan dalam hal ras, jenis kelamin, usia, atau status disabilitas. 
+Apa yang dimaksud dengan ketidakadilan? "Ketidakadilan" mencakup dampak negatif atau "bahaya" bagi sekelompok orang, seperti yang didefinisikan dalam hal ras, jenis kelamin, usia, atau status disabilitas. 
 
 Kerugian utama yang terkait dengan keadilan dapat diklasifikasikan sebagai: 
 
 - **Alokasi**, jika suatu jenis kelamin atau etnisitas misalkan lebih disukai daripada yang lain.
-- **Kualitas layanan**. Jika kamu melatih data untuk satu skenario tertentu tetapi kenyataannya jauh lebih kompleks, itu mengarah ke layanan yang berkinerja buruk.
+- **Kualitas layanan**. Jika kamu melatih data untuk satu skenario tertentu tetapi kenyataannya jauh lebih kompleks, hasilnya adalah layanan yang berkinerja buruk.
 - **Stereotip**. Mengaitkan grup tertentu dengan atribut yang ditentukan sebelumnya.
 - **Fitnah**. Untuk mengkritik dan melabeli sesuatu atau seseorang secara tidak adil.
-- **Representasi yang kurang atau berlebihan**. Idenya adalah bahwa kelompok tertentu tidak terlihat dalam profesi tertentu, dan layanan atau fungsi apa pun yang terus dipromosikan yang berkontribusi pada kerugian.
+- **Representasi yang kurang atau berlebihan**. Idenya adalah bahwa kelompok tertentu tidak terlihat dalam profesi tertentu, dan layanan atau fungsi apa pun yang terus dipromosikan yang menambah kerugian.
 
 Mari kita lihat contoh-contohnya.
 
 ### Alokasi
 
-Pertimbangkan sistem hipotetis untuk menyaring aplikasi pinjaman. Sistem cenderung memilih pria kulit putih sebagai kandidat yang lebih baik daripada kelompok lain. Akibatnya, pinjaman ditahan dari pemohon tertentu. 
+Bayangkan sebuah sistem untuk menyaring pengajuan pinjaman. Sistem cenderung memilih pria kulit putih sebagai kandidat yang lebih baik daripada kelompok lain. Akibatnya, pinjaman ditahan dari pemohon tertentu. 
 
-Contoh lain adalah alat perekrutan eksperimental yang dikembangkan oleh perusahaan besar untuk menyaring kandidat. Alat tersebut secara sistematis mendiskriminasi satu gender dengan menggunakan model yang dilatih untuk lebih memilih kata-kata yang terkait dengan yang lain. Hal ini mengakibatkan kandidat yang resumenya berisi kata-kata seperti "tim rugby wanita". 
+Contoh lain adalah alat perekrutan eksperimental yang dikembangkan oleh perusahaan besar untuk menyaring kandidat. Alat tersebut secara sistematis mendiskriminasi satu gender dengan menggunakan model yang dilatih untuk lebih memilih kata-kata yang terkait dengan gender lain. Hal ini mengakibatkan kandidat yang resumenya berisi kata-kata seperti "tim rugby wanita" tidak masuk kualifikasi. 
 
 ✅ Lakukan sedikit riset untuk menemukan contoh dunia nyata dari sesuatu seperti ini
 
@@ -62,12 +62,10 @@ Contoh lain adalah alat perekrutan eksperimental yang dikembangkan oleh perusaha
 
 Para peneliti menemukan bahwa beberapa pengklasifikasi gender komersial memiliki tingkat kesalahan yang lebih tinggi di sekitar gambar wanita dengan warna kulit lebih gelap dibandingkan dengan gambar pria dengan warna kulit lebih terang. [Referensi](https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification/) 
 
-Contoh terkenal lainnya adalah dispenser sabun tangan yang sepertinya tidak bisa merasakan orang dengan kulit gelap. [Referensi](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
+Contoh terkenal lainnya adalah dispenser sabun tangan yang sepertinya tidak bisa mendeteksi orang dengan kulit gelap. [Referensi](https://gizmodo.com/why-cant-this-soap-dispenser-identify-dark-skin-1797931773)
 
 ### Stereotip
 
-Stereotypical gender view was found in machine translation. When translating “he is a nurse and she is a doctor� into Turkish, problems were encountered. Turkish is a genderless language which has one pronoun, “o� to convey a singular third person, but translating the sentence back from Turkish to English yields the stereotypical and incorrect as “she is a nurse and he is a doctor�.
-
 Pandangan gender stereotip ditemukan dalam terjemahan mesin. Ketika menerjemahkan "dia (laki-laki) adalah seorang perawat dan dia (perempuan) adalah seorang dokter" ke dalam bahasa Turki, masalah muncul. Turki adalah bahasa tanpa gender yang memiliki satu kata ganti, "o" untuk menyampaikan orang ketiga tunggal, tetapi menerjemahkan kalimat kembali dari Turki ke Inggris menghasilkan stereotip dan salah sebagai "dia (perempuan) adalah seorang perawat dan dia (laki-laki) adalah seorang dokter". 
 
 ![terjemahan ke bahasa Turki](images/gender-bias-translate-en-tr.png)
@@ -101,7 +99,7 @@ Lima jenis bahaya utama ini tidak saling eksklusif, dan satu sistem dapat menunj
 
 ## Mendeteksi Ketidakadilan 
 
-Ada banyak alasan mengapa sistem tertentu berperilaku tidak adil. Bias sosial, misalnya, mungkin tercermin dalam kumpulan data yang digunakan untuk melatih mereka. Misalnya, ketidakadilan perekrutan mungkin telah diperburuk oleh ketergantungan yang berlebihan pada data historis. Dengan menggunakan pola dalam resume yang dikirimkan ke perusahaan selama periode 10 tahun, model tersebut menentukan bahwa pria lebih berkualitas karena mayoritas resume berasal dari pria, yang mencerminkan dominasi pria di masa lalu di industri teknologi. 
+Ada banyak alasan mengapa sistem tertentu berperilaku tidak adil. Bias sosial, misalnya, mungkin tercermin dalam kumpulan data yang digunakan untuk melatih. Misalnya, ketidakadilan perekrutan mungkin telah diperburuk oleh ketergantungan yang berlebihan pada data historis. Dengan menggunakan pola dalam resume yang dikirimkan ke perusahaan selama periode 10 tahun, model tersebut menentukan bahwa pria lebih berkualitas karena mayoritas resume berasal dari pria, yang mencerminkan dominasi pria di masa lalu di industri teknologi. 
 
 Data yang tidak memadai tentang sekelompok orang tertentu dapat menjadi alasan ketidakadilan. Misalnya, pengklasifikasi gambar memiliki tingkat kesalahan yang lebih tinggi untuk gambar orang berkulit gelap karena warna kulit yang lebih gelap kurang terwakili dalam data. 
 
@@ -133,13 +131,13 @@ Apa bahaya dan manfaat yang terkait dengan pinjaman? Pikirkan tentang skenario n
 
 ### Identifikasi kelompok yang terkena dampak
 
-Langkah selanjutnya adalah menentukan kelompok mana yang kemungkinan akan terpengaruh. Misalnya, dalam kasus permohonan kartu kredit, seorang model mungkin menentukan bahwa perempuan harus menerima batas kredit yang jauh lebih rendah dibandingkan dengan pasangan mereka yang berbagi aset rumah tangga. Dengan demikian, seluruh demografi, yang ditentukan berdasarkan jenis kelamin, terpengaruh. 
+Langkah selanjutnya adalah menentukan kelompok mana yang kemungkinan akan terpengaruh. Misalnya, dalam kasus permohonan kartu kredit, sebuah model mungkin menentukan bahwa perempuan harus menerima batas kredit yang jauh lebih rendah dibandingkan dengan pasangan mereka yang berbagi aset rumah tangga. Dengan demikian, seluruh demografi yang ditentukan berdasarkan jenis kelamin menjadi terpengaruh. 
 
 ### Tentukan metrik keadilan
 
-Kamu telah mengidentifikasi bahaya dan kelompok yang terpengaruh, dalam hal ini, digambarkan berdasarkan jenis kelamin. Sekarang, gunakan faktor terukur untuk memisahkan metriknya. Misalnya, dengan menggunakan data di bawah ini, Kamu dapat melihat bahwa wanita memiliki tingkat *false positive* terbesar dan pria memiliki yang terkecil, dan kebalikannya berlaku untuk *false negative*. 
+Kamu telah mengidentifikasi bahaya dan kelompok yang terpengaruh, dalam hal ini digambarkan berdasarkan jenis kelamin. Sekarang, gunakan faktor terukur (*quantified factors*) untuk memisahkan metriknya. Misalnya, dengan menggunakan data di bawah ini, Kamu dapat melihat bahwa wanita memiliki tingkat *false positive* terbesar dan pria memiliki yang terkecil, dan kebalikannya berlaku untuk *false negative*. 
 
-✅ Dalam pelajaran selanjutnya tentang Pengelompokan, Kamu akan melihat bagaimana membangun 'matriks kebingungan' ini dalam kode 
+✅ Dalam pelajaran selanjutnya tentang *Clustering*, Kamu akan melihat bagaimana membangun 'confusion matrix' ini dalam kode 
 
 |            | False positive rate | False negative rate | count |
 | ---------- | ------------------- | ------------------- | ----- |
@@ -148,7 +146,7 @@ Kamu telah mengidentifikasi bahaya dan kelompok yang terpengaruh, dalam hal ini,
 | Non-binary | 0.33                | 0.31                | 1266  |
 
  
-Tabel ini memberitahu kita beberapa hal. Pertama, kami mencatat bahwa ada sedikit orang non-biner dalam data. Datanya condong, jadi Kamu harus berhati-hati dalam menafsirkan angka-angka ini. 
+Tabel ini memberitahu kita beberapa hal. Pertama, kami mencatat bahwa ada sedikit orang non-biner dalam data. Datanya condong (*skewed*), jadi Kamu harus berhati-hati dalam menafsirkan angka-angka ini. 
 
 Dalam hal ini, kita memiliki 3 grup dan 2 metrik. Ketika kita memikirkan tentang bagaimana sistem kita memengaruhi kelompok pelanggan dengan permohonan pinjaman mereka, ini mungkin cukup, tetapi ketika Kamu ingin menentukan jumlah grup yang lebih besar, Kamu mungkin ingin menyaringnya menjadi kumpulan ringkasan yang lebih kecil. Untuk melakukannya, Kamu dapat menambahkan lebih banyak metrik, seperti perbedaan terbesar atau rasio terkecil dari setiap *false negative* dan *false positive*. 
 
@@ -198,7 +196,7 @@ Tonton workshop ini untuk menyelami lebih dalam kedalam topik:
 
 Kamu juga dapat membaca:
 
-- Pusat sumber daya RAI Microsoft: [Sumber daya Responsible AI – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
+- Pusat sumber daya RAI Microsoft: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4) 
 
 - Grup riset FATE Microsoft: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/) 
 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
index 259a90d1c4..7c27302474 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.id.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -1,9 +1,9 @@
 # Teknik-teknik Machine Learning
 
-Proses membangun, menggunakan, dan memelihara model machine learning dan data yang digunakan adalah proses yang sangat berbeda dari banyak alur kerja pengembangan lainnya. Dalam pelajaran ini, kita akan mengungkap prosesnya, dan menguraikan teknik utama yang perlu Kamu ketahui. Kamu akan: 
+Proses membangun, menggunakan, dan memelihara model machine learning dan data yang digunakan adalah proses yang sangat berbeda dari banyak alur kerja pengembangan lainnya. Dalam pelajaran ini, kita akan mengungkap prosesnya dan menguraikan teknik utama yang perlu Kamu ketahui. Kamu akan: 
 
 - Memahami gambaran dari proses yang mendasari machine learning.
-- Menjelajahi konsep dasar seperti '*models*', '*predictions*', dan '**training data*'. 
+- Menjelajahi konsep dasar seperti '*models*', '*predictions*', dan '*training data*'. 
   
 ## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
 ## Pengantar
@@ -43,7 +43,7 @@ Untuk dapat menjawab pertanyaan kamu dengan kepastian, Kamu memerlukan sejumlah
 
 Sebuah [fitur](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) adalah sebuah properti yang dapat diukur dalam data kamu. Dalam banyak dataset, properti dinyatakan sebagai sebuah heading kolom seperti 'date' 'size' atau 'color'. Variabel fitur kamu yang biasanya direpresentasikan sebagai `y` dalam kode, mewakili jawaban atas pertanyaan yang kamu coba tanyakan tentang data kamu: pada bulan Desember, labu dengan **warna** apa yang akan paling murah? di San Francisco, lingkungan mana yang menawarkan **harga** real estate terbaik?
 
-🎓 **Feature Selection dan Feature Extraction** Bagaimana kamu tahu variabel mana yang harus dipilih saat membangun model? Kamu mungkin akan melalui proses pemilihan fitur (*Feature Selection*) atau ekstraksi fitur (*Feature Extraction*) untuk memilih variabel yang tepat untuk model yang paling berkinerja. Namun, keduanya tidak sama: "Ekstraksi fitur membuat fitur baru dari fungsi fitur asli, sedangkan pemilihan fitur mengembalikan subset fitur." ([sumber](https://wikipedia.org/wiki/Feature_selection))
+🎓 **Feature Selection dan Feature Extraction** Bagaimana kamu tahu variabel mana yang harus dipilih saat membangun model? Kamu mungkin akan melalui proses pemilihan fitur (*Feature Selection*) atau ekstraksi fitur (*Feature Extraction*) untuk memilih variabel yang tepat untuk membuat model yang berkinerja paling baik. Namun, keduanya tidak sama: "Ekstraksi fitur membuat fitur baru dari fungsi fitur asli, sedangkan pemilihan fitur mengembalikan subset fitur." ([sumber](https://wikipedia.org/wiki/Feature_selection))
 ### Visualisasikan datamu
 
 Aspek penting dari toolkit data scientist adalah kemampuan untuk memvisualisasikan data menggunakan beberapa *library* seperti Seaborn atau MatPlotLib. Merepresentasikan data kamu secara visual memungkinkan kamu mengungkap korelasi tersembunyi yang dapat kamu manfaatkan. Visualisasimu mungkin juga membantu kamu mengungkap data yang bias atau tidak seimbang (seperti yang kita temukan dalam [Classification](../../4-Classification/2-Classifiers-1/translations/README.id.md)).
@@ -57,7 +57,7 @@ Sebelum memulai *training*, Kamu perlu membagi dataset menjadi dua atau lebih ba
 
 ## Membuat sebuah model
 
-Dengan menggunakan data *training*, tujuan kamu adalah membuat model atau representasi statistik data kamu, menggunakan berbagai algoritma untuk **melatihnya**. Melatih model berarti mengeksposnya dengan data dan mengizinkannya membuat asumsi tentang pola yang ditemukan, divalidasi, dan diterima atau ditolak. 
+Dengan menggunakan data *training*, tujuan kamu adalah membuat model atau representasi statistik data kamu menggunakan berbagai algoritma untuk **melatihnya**. Melatih model berarti mengeksposnya dengan data dan mengizinkannya membuat asumsi tentang pola yang ditemukan, divalidasi, dan diterima atau ditolak. 
 
 ### Tentukan metode training
 
@@ -73,7 +73,7 @@ Setelah proses *training* selesai (ini mungkin membutuhkan banyak iterasi, atau
 
 Dalam konteks machine learning, *model fitting* mengacu pada keakuratan dari fungsi yang mendasari model saat mencoba menganalisis data yang tidak familiar. 
 
-🎓 **Underfitting** dan **overfitting** adalah masalah umum yang menurunkan kualitas model, karena model tidak cukup akurat atau terlalu akurat. Hal ini menyebabkan model membuat prediksi yang terlalu selaras atau tidak cukup selaras dengan data pelatihannya. Model overfit memprediksi data *training* terlalu baik karena telah mempelajari detail dan noise data dengan terlalu baik. Model underfit tidak akurat karena tidak dapat menganalisis data *training* atau data yang belum pernah dilihat sebelumnya secara akurat. 
+🎓 **Underfitting** dan **overfitting** adalah masalah umum yang menurunkan kualitas model, karena model tidak cukup akurat atau terlalu akurat. Hal ini menyebabkan model membuat prediksi yang terlalu selaras atau tidak cukup selaras dengan data trainingnya. Model overfit memprediksi data *training* terlalu baik karena telah mempelajari detail dan noise data dengan terlalu baik. Model underfit tidak akurat karena tidak dapat menganalisis data *training* atau data yang belum pernah dilihat sebelumnya secara akurat. 
 
 ![overfitting model](images/overfitting.png)
 > Infografis oleh [Jen Looper](https://twitter.com/jenlooper)

From 384b4a589dc76506f050c40206178ec48690ba60 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sun, 25 Jul 2021 11:29:51 -0400
Subject: [PATCH 262/368] Update README.md

editing to show http server
---
 4-Classification/4-Applied/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index 1f3573c2e1..66597fbf6d 100644
--- a/4-Classification/4-Applied/README.md
+++ b/4-Classification/4-Applied/README.md
@@ -312,7 +312,7 @@ In this code, there are several things happening:
 
 ## Test your application
 
-Open a terminal session in Visual Studio Code in the folder where your index.html file resides. Ensure that you have `[http-server](https://www.npmjs.com/package/http-server)` installed globally, and type `http-server` at the prompt. A localhost should open and you can view your web app. Check what cuisine is recommended based on various ingredients:
+Open a terminal session in Visual Studio Code in the folder where your index.html file resides. Ensure that you have [http-server](https://www.npmjs.com/package/http-server) installed globally, and type `http-server` at the prompt. A localhost should open and you can view your web app. Check what cuisine is recommended based on various ingredients:
 
 ![ingredient web app](images/web-app.png)
 

From 19b5631f294f615c0e5c1fd0ae73d07cdd2662f6 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sun, 25 Jul 2021 11:31:27 -0400
Subject: [PATCH 263/368] Update README.tr.md

editing mistake in function name for Turkish translation
---
 4-Classification/1-Introduction/translations/README.tr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
index cba295463c..a2b1d92f32 100644
--- a/4-Classification/1-Introduction/translations/README.tr.md
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -157,7 +157,7 @@ Kurmanız gereken ilk şey [imblearn](https://imbalanced-learn.org/stable/). Bu,
 
 Şimdi veriyi daha derinlemesine inceleyebilirsiniz ve her mutfak için tipik malzemelerin neler olduğunu öğrenebilirsiniz. Mutfaklar arasında karışıklık yaratan tekrar eden veriyi temizlemelisiniz, dolayısıyla şimdi bu problemle ilgili bilgi edinelim.
 
-1. Python'da, malzeme veri iskeleti yaratmak için `create_ingredient()` diye bir fonksiyon oluşturun. Bu fonksiyon, yardımcı olmayan bir sütunu temizleyerek ve sayılarına göre malzemeleri sıralayarak başlar:
+1. Python'da, malzeme veri iskeleti yaratmak için `create_ingredient_df()` diye bir fonksiyon oluşturun. Bu fonksiyon, yardımcı olmayan bir sütunu temizleyerek ve sayılarına göre malzemeleri sıralayarak başlar:
 
     ```python
     def create_ingredient_df(df):
@@ -170,7 +170,7 @@ Kurmanız gereken ilk şey [imblearn](https://imbalanced-learn.org/stable/). Bu,
 
    Şimdi bu fonksiyonu, her mutfağın en yaygın ilk on malzemesi hakkında hakkında fikir edinmek için kullanabilirsiniz.
 
-1. `create_ingredient()` fonksiyonunu çağırın ve `barh()` fonksiyonunu çağırarak çizdirin:
+1. `create_ingredient_df()` fonksiyonunu çağırın ve `barh()` fonksiyonunu çağırarak çizdirin:
 
     ```python
     thai_ingredient_df = create_ingredient_df(thai_df)

From 25891843ba83d65a21991f08cd34713a76d5af0c Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sun, 25 Jul 2021 11:32:40 -0400
Subject: [PATCH 264/368] Update README.zh-cn.md

editing mistake in function name in Chinese translation
---
 4-Classification/1-Introduction/translations/README.zh-cn.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
index adef7c8adc..ae8d123b1e 100644
--- a/4-Classification/1-Introduction/translations/README.zh-cn.md
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -152,7 +152,7 @@ Scikit-learn项目�供多�对数�进行分类的算法，你需�根�
 
 现在你�以在数�中探索的更深一点并了解���肴的代表性食�。你需�将��出现的�容易造�混淆的数�清�出去，那么让我们�学习解决这个问题。
 
-1. 在Python中创建一个函数 `create_ingredient()` �创建一个食�的数�帧。这个函数会去掉数�中无用的列并按食�的数�进行分类。
+1. 在Python中创建一个函数 `create_ingredient_df()` �创建一个食�的数�帧。这个函数会去掉数�中无用的列并按食�的数�进行分类。
 
     ```python
     def create_ingredient_df(df):
@@ -164,7 +164,7 @@ Scikit-learn项目�供多�对数�进行分类的算法，你需�根�
     ```
 现在你�以使用这个函数�得到�想的���肴最��的10�食�。
 
-1. 调用函数 `create_ingredient()` 然�通过函数`barh()`�绘制图�:
+1. 调用函数 `create_ingredient_df()` 然�通过函数`barh()`�绘制图�:
 
     ```python
     thai_ingredient_df = create_ingredient_df(thai_df)

From d6bac5cb87830d1ab6a44098c9c42360f51fe1b3 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sun, 25 Jul 2021 11:33:27 -0400
Subject: [PATCH 265/368] Update README.tr.md

removing back ticks around link in Turkish translation
---
 4-Classification/4-Applied/translations/README.tr.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md
index 12423ee0cd..651fc85b7a 100644
--- a/4-Classification/4-Applied/translations/README.tr.md
+++ b/4-Classification/4-Applied/translations/README.tr.md
@@ -312,7 +312,7 @@ Bu kodda birçok şey gerçekleşiyor:
 
 ## Uygulamanızı test edin
 
-index.html dosyanızın olduğu klasördeyken Visual Studio Code'da bir terminal açın. Global kapsamda `[http-server](https://www.npmjs.com/package/http-server)` indirilmiş olduğundan emin olun ve istemde `http-server` yazın. Bir yerel ana makine açılmalı ve web uygulamanızı görebilirsiniz. Çeşitli malzemeleri baz alarak hangi mutfağın önerildiğine bakın:
+index.html dosyanızın olduğu klasördeyken Visual Studio Code'da bir terminal açın. Global kapsamda [http-server](https://www.npmjs.com/package/http-server) indirilmiş olduğundan emin olun ve istemde `http-server` yazın. Bir yerel ana makine açılmalı ve web uygulamanızı görebilirsiniz. Çeşitli malzemeleri baz alarak hangi mutfağın önerildiğine bakın:
 
 ![malzeme web uygulaması](../images/web-app.png)
 
@@ -333,4 +333,4 @@ Bu dersin sadece yemek malzemeleri için bir öneri sistemi oluşturmanın olana
 
 ## Ödev 
 
-[Yeni bir önerici oluşturun](assignment.tr.md)
\ No newline at end of file
+[Yeni bir önerici oluşturun](assignment.tr.md)

From 844349882fc2d6ddc199892b15c057a3ba6dfb1e Mon Sep 17 00:00:00 2001
From: Fajar Ru <kzofajar@gmail.com>
Date: Mon, 26 Jul 2021 09:21:55 +0700
Subject: [PATCH 266/368] Update the image links

---
 1-Introduction/1-intro-to-ML/translations/README.id.md    | 4 ++--
 1-Introduction/2-history-of-ML/translations/README.id.md  | 6 +++---
 1-Introduction/3-fairness/translations/README.id.md       | 8 ++++----
 .../4-techniques-of-ML/translations/README.id.md          | 2 +-
 4 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md
index 2f30f213d1..d0daadd89f 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.id.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.id.md
@@ -27,7 +27,7 @@ Sebelum memulai kurikulum ini, kamu perlu memastikan komputer kamu sudah dipersi
 
 Istilah 'Machine Learning' merupakan salah satu istilah yang paling populer dan paling sering digunakan saat ini. Ada kemungkinan kamu pernah mendengar istilah ini paling tidak sekali jika kamu familiar dengan teknologi. Tetapi untuk mekanisme Machine Learning sendiri, merupakan sebuah misteri bagi sebagian besar orang. Karena itu, penting untuk memahami sebenarnya apa itu Machine Learning, dan mempelajarinya langkah demi langkah melalui contoh praktis.
 
-![kurva tren ml](images/hype.png)
+![kurva tren ml](../images/hype.png)
 
 > Google Trends memperlihatkan 'kurva tren' dari istilah 'Machine Learning' belakangan ini.
 
@@ -39,7 +39,7 @@ Otak dan indera seorang anak memahami fakta-fakta di sekitarnya dan secara berta
 
 Meskipun istilah-stilahnya bisa membingungkan, Machine Learning (ML) adalah bagian penting dari Artificial Intelligence. **ML berkaitan dengan menggunakan algoritma-algoritma terspesialisasi untuk mengungkap informasi yang berarti dan mencari pola-pola tersembunyi dari data yang diterima untuk mendukung proses pembuatan keputusan rasional**.
 
-![AI, ML, deep learning, data science](images/ai-ml-ds.png)
+![AI, ML, deep learning, data science](../images/ai-ml-ds.png)
 
 > Sebuah diagram yang memperlihatkan hubungan antara AI, ML, Deep Learning, dan Data Science. Infografis oleh [Jen Looper](https://twitter.com/jenlooper) terinspirasi dari [infografis ini](https://softwareengineering.stackexchange.com/questions/366996/distinction-between-ai-ml-neural-networks-deep-learning-and-data-mining)
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
index ff66b01485..5e6a6f0f1f 100644
--- a/1-Introduction/2-history-of-ML/translations/README.id.md
+++ b/1-Introduction/2-history-of-ML/translations/README.id.md
@@ -1,6 +1,6 @@
 # Sejarah Machine Learning
 
-![Ringkasan dari Sejarah Machine Learning dalam sebuah catatan sketsa](../../sketchnotes/ml-history.png)
+![Ringkasan dari Sejarah Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-history.png)
 > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
@@ -44,12 +44,12 @@ Penelitian didanai dengan baik oleh lembaga pemerintah, banyak kemajuan dibuat d
 
 * [Shakey the robot](https://wikipedia.org/wiki/Shakey_the_robot), yang bisa bermanuver dan memutuskan bagaimana melakukan tugas-tugas secara 'cerdas'. 
 
-    ![Shakey, an intelligent robot](images/shakey.jpg)
+    ![Shakey, an intelligent robot](../images/shakey.jpg)
     > Shakey pada 1972
 
 * Eliza, sebuah 'chatterbot' awal, dapat mengobrol dengan orang-orang dan bertindak sebagai 'terapis' primitif. Kamu akan belajar lebih banyak tentang Eliza dalam pelajaran NLP. 
 
-    ![Eliza, a bot](images/eliza.png)
+    ![Eliza, a bot](../images/eliza.png)
     > Sebuah versi dari Eliza, sebuah *chatbot*
 
 * "Blocks world" adalah contoh sebuah *micro-world* dimana balok dapat ditumpuk dan diurutkan, dan pengujian eksperimen mesin pengajaran untuk membuat keputusan dapat dilakukan. Kemajuan yang dibuat dengan *library-library* seperti [SHRDLU](https://wikipedia.org/wiki/SHRDLU) membantu mendorong kemajuan pemrosesan bahasa.
diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md
index 03bf796392..6f09a14896 100644
--- a/1-Introduction/3-fairness/translations/README.id.md
+++ b/1-Introduction/3-fairness/translations/README.id.md
@@ -1,6 +1,6 @@
 # Keadilan dalam Machine Learning 
 
-![Ringkasan dari Keadilan dalam Machine Learning dalam sebuah catatan sketsa](../../sketchnotes/ml-fairness.png)
+![Ringkasan dari Keadilan dalam Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-fairness.png)
 > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
@@ -68,9 +68,9 @@ Contoh terkenal lainnya adalah dispenser sabun tangan yang sepertinya tidak bisa
 
 Pandangan gender stereotip ditemukan dalam terjemahan mesin. Ketika menerjemahkan "dia (laki-laki) adalah seorang perawat dan dia (perempuan) adalah seorang dokter" ke dalam bahasa Turki, masalah muncul. Turki adalah bahasa tanpa gender yang memiliki satu kata ganti, "o" untuk menyampaikan orang ketiga tunggal, tetapi menerjemahkan kalimat kembali dari Turki ke Inggris menghasilkan stereotip dan salah sebagai "dia (perempuan) adalah seorang perawat dan dia (laki-laki) adalah seorang dokter". 
 
-![terjemahan ke bahasa Turki](images/gender-bias-translate-en-tr.png)
+![terjemahan ke bahasa Turki](../images/gender-bias-translate-en-tr.png)
 
-![terjemahan kembali ke bahasa Inggris](images/gender-bias-translate-tr-en.png)
+![terjemahan kembali ke bahasa Inggris](../images/gender-bias-translate-tr-en.png)
 
 ### Fitnah
 
@@ -83,7 +83,7 @@ Sebuah teknologi pelabelan gambar yang terkenal salah memberi label gambar orang
 
 Hasil pencarian gambar yang condong ke hal tertentu (skewed) dapat menjadi contoh yang bagus dari bahaya ini. Saat menelusuri gambar profesi dengan persentase pria yang sama atau lebih tinggi daripada wanita, seperti teknik, atau CEO, perhatikan hasil yang lebih condong ke jenis kelamin tertentu. 
 
-![Pencarian CEO di Bing](images/ceos.png)
+![Pencarian CEO di Bing](../images/ceos.png)
 > Pencarian di Bing untuk 'CEO' ini menghasilkan hasil yang cukup inklusif
 
 Lima jenis bahaya utama ini tidak saling eksklusif, dan satu sistem dapat menunjukkan lebih dari satu jenis bahaya. Selain itu, setiap kasus bervariasi dalam tingkat keparahannya. Misalnya, memberi label yang tidak adil kepada seseorang sebagai penjahat adalah bahaya yang jauh lebih parah daripada memberi label yang salah pada gambar. Namun, penting untuk diingat bahwa bahkan kerugian yang relatif tidak parah dapat membuat orang merasa terasing atau diasingkan dan dampak kumulatifnya bisa sangat menekan. 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
index 7c27302474..6fee4f14a0 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.id.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -75,7 +75,7 @@ Dalam konteks machine learning, *model fitting* mengacu pada keakuratan dari fun
 
 🎓 **Underfitting** dan **overfitting** adalah masalah umum yang menurunkan kualitas model, karena model tidak cukup akurat atau terlalu akurat. Hal ini menyebabkan model membuat prediksi yang terlalu selaras atau tidak cukup selaras dengan data trainingnya. Model overfit memprediksi data *training* terlalu baik karena telah mempelajari detail dan noise data dengan terlalu baik. Model underfit tidak akurat karena tidak dapat menganalisis data *training* atau data yang belum pernah dilihat sebelumnya secara akurat. 
 
-![overfitting model](images/overfitting.png)
+![overfitting model](../images/overfitting.png)
 > Infografis oleh [Jen Looper](https://twitter.com/jenlooper)
 
 ## Parameter tuning

From 72325d073ac4df84bdc73ddb96492279e173585f Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Sun, 25 Jul 2021 23:28:47 -0400
Subject: [PATCH 267/368] Update README.id.md

---
 1-Introduction/translations/README.id.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/1-Introduction/translations/README.id.md b/1-Introduction/translations/README.id.md
index 4ae59fb148..0e6cc55750 100644
--- a/1-Introduction/translations/README.id.md
+++ b/1-Introduction/translations/README.id.md
@@ -3,7 +3,7 @@
 Di bagian kurikulum ini, Kamu akan berkenalan dengan konsep yang mendasari bidang Machine Learning, apa itu Machine Learning, dan belajar mengenai
 sejarah serta teknik-teknik yang digunakan oleh para peneliti. Ayo jelajahi dunia baru Machine Learning bersama! 
 
-![bola dunia](images/globe.jpg)
+![bola dunia](../images/globe.jpg)
 > Foto oleh <a href="https://unsplash.com/@bill_oxford?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Bill Oxford</a> di <a href="https://unsplash.com/s/photos/globe?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ### Pelajaran
@@ -20,4 +20,4 @@ sejarah serta teknik-teknik yang digunakan oleh para peneliti. Ayo jelajahi duni
 
 "Keadilan dan Machine Learning" ditulis dengan ♥� oleh [Tomomi Imura](https://twitter.com/girliemac) 
 
-"Teknik-Teknik Machine Learning" ditulis dengan ♥� oleh [Jen Looper](https://twitter.com/jenlooper) dan [Chris Noring](https://twitter.com/softchris) 
\ No newline at end of file
+"Teknik-Teknik Machine Learning" ditulis dengan ♥� oleh [Jen Looper](https://twitter.com/jenlooper) dan [Chris Noring](https://twitter.com/softchris) 

From c13f28c97104fdf3c0ca39ecd7a8f93d73ae66d5 Mon Sep 17 00:00:00 2001
From: RyanXin <xyz@ryanxin.cn>
Date: Mon, 26 Jul 2021 13:12:18 +0800
Subject: [PATCH 268/368] Correct the definitions of precision and recall.

---
 2-Regression/4-Logistic/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index a4488c11e4..8d89ce9d89 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -237,9 +237,9 @@ As you might have guessed it's preferable to have a larger number of true positi
 
 Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN:
 
-🎓 Precision: TP/(TP + FN) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
+🎓 Precision: TP/(TP + FP) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
 
-🎓 Recall: TP/(TP + FP) The fraction of relevant instances that were retrieved, whether well-labeled or not
+🎓 Recall: TP/(TP + FN) The fraction of relevant instances that were retrieved, whether well-labeled or not
 
 🎓 f1-score: (2 * precision * recall)/(precision + recall) A weighted average of the precision and recall, with best being 1 and worst being 0
 

From bd8f9617a0032e34b16cad261ac682add13a1e1b Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Mon, 26 Jul 2021 18:56:47 +0900
Subject: [PATCH 269/368] feat: add ja 1.2 assignment

Refs #149
---
 .../2-history-of-ML/translations/README.ja.md         |  2 +-
 .../2-history-of-ML/translations/assignment.ja.md     | 11 +++++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)
 create mode 100644 1-Introduction/2-history-of-ML/translations/assignment.ja.md

diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md
index 9d791209bd..5c17650c78 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ja.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ja.md
@@ -111,4 +111,4 @@
 
 ## 課題
 
-[時系列を制作������](../assignment.md)
+[年表を作��る](./assignment.ja.md)
diff --git a/1-Introduction/2-history-of-ML/translations/assignment.ja.md b/1-Introduction/2-history-of-ML/translations/assignment.ja.md
new file mode 100644
index 0000000000..f5f7879928
--- /dev/null
+++ b/1-Introduction/2-history-of-ML/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# 年表を作��る
+
+## 指示
+
+[��リ�ジトリ](https://github.com/Digital-Humanities-Toolkit/timeline-builder) を使���アルゴリズム・数学・統計学・人工知能・機械学習�����れら�組�����対���歴���������関�る年表を作�������。焦点を当�る�����り�人物・����アイディア・長期間���る�想���れ�も��も構���ん。マル�メディア��素を必�加�るよ��������。
+
+## 評価基準
+
+| 基準 | 模範的                                 | �分                                 | �改善                                                       |
+| ---- | -------------------------------------- | ------------------------------------ | ------------------------------------------------------------ |
+|      | GitHub page �年表�デプロイ�れ��る | コード�未完��デプロイ�れ���� | 年表�未完����分�調査�れ��ら��デプロイ�れ���� |

From 01109e7d0cabdbc2bb160a3a529b9137d60bfae7 Mon Sep 17 00:00:00 2001
From: RyanXin <xyz@ryanxin.cn>
Date: Mon, 26 Jul 2021 20:38:31 +0800
Subject: [PATCH 270/368] Update all translations on formula

---
 2-Regression/4-Logistic/translations/README.id.md    | 4 ++--
 2-Regression/4-Logistic/translations/README.it.md    | 4 ++--
 2-Regression/4-Logistic/translations/README.zh-cn.md | 4 ++--
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
index ac5a3a98a5..fa47aea32f 100644
--- a/2-Regression/4-Logistic/translations/README.id.md
+++ b/2-Regression/4-Logistic/translations/README.id.md
@@ -245,9 +245,9 @@ Mari kita lihat kembali istilah-istilah yang kita lihat tadi dengan bantuan matr
 > NB: Negatif benar
 > NP: Negatif palsu
 
-🎓 Presisi: PB/(PB + NP) Rasio titik data relevan antara semua titik data (seperti data mana yang benar dilabelkannya)
+🎓 Presisi: PB/(PB + PP) Rasio titik data relevan antara semua titik data (seperti data mana yang benar dilabelkannya)
 
-🎓 *Recall*: PB/(PB + PP) Rasio titk data relevan yang digunakan, maupun labelnya benar atau tidak.
+🎓 *Recall*: PB/(PB + NP) Rasio titk data relevan yang digunakan, maupun labelnya benar atau tidak.
 
 🎓 *f1-score*: (2 * Presisi * *Recall*)/(Presisi + *Recall*) Sebuah rata-rata tertimbang antara presisi dan *recall*. 1 itu baik dan 0 itu buruk.
 
diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md
index 6a31f5fb4e..3b30cf685c 100644
--- a/2-Regression/4-Logistic/translations/README.it.md
+++ b/2-Regression/4-Logistic/translations/README.it.md
@@ -238,9 +238,9 @@ Come si sarà intuito, è preferibile avere un numero maggiore di veri positivi
 
 I termini visti in precedenza vengono rivisitati con l'aiuto della mappatura della matrice di confusione di TP/TN e FP/FN:
 
-🎓 Precisione: TP/(TP + FN) La frazione di istanze rilevanti tra le istanze recuperate (ad es. quali etichette erano ben etichettate)
+🎓 Precisione: TP/(TP + FP) La frazione di istanze rilevanti tra le istanze recuperate (ad es. quali etichette erano ben etichettate)
 
-🎓 Richiamo: TP/(TP + FP) La frazione di istanze rilevanti che sono state recuperate, ben etichettate o meno
+🎓 Richiamo: TP/(TP + FN) La frazione di istanze rilevanti che sono state recuperate, ben etichettate o meno
 
 🎓 f1-score: (2 * precisione * richiamo)/(precisione + richiamo) Una media ponderata della precisione e del richiamo, dove il migliore è 1 e il peggiore è 0
 
diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index b4397856b5..3a5ff92609 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -238,9 +238,9 @@ Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�
 
 让我们借助混淆矩阵对TP/TN和FP/FN的映射，�新审视一下我们之�看到的术语：
 
-🎓 准确率：TP/（TP+FN）检索实例中相关实例的分数（例如，哪些标签标记得很好）
+🎓 准确率：TP/(TP + FP) 检索实例中相关实例的分数（例如，哪些标签标记得很好）
 
-🎓 �回率: TP/(TP + FP) 检索到的相关实例的比例，无论是�标记良好
+🎓 �回率: TP/(TP + FN) 检索到的相关实例的比例，无论是�标记良好
 
 🎓 F1分数: (2 * 准确率 * �回率)/(准确率 + �回率) 准确率和�回率的加�平�值，最好为1，最差为0
 

From 125f5c747065654e009586a633ccc251383e1532 Mon Sep 17 00:00:00 2001
From: "Michael.Wang" <wzh@Michael.Wang>
Date: Mon, 26 Jul 2021 23:25:45 +0800
Subject: [PATCH 271/368] Translated 3-Web-App assignment.md into Simplified
 Chinese.

---
 3-Web-App/1-Web-App/translations/assignment.zh-cn.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 3-Web-App/1-Web-App/translations/assignment.zh-cn.md

diff --git a/3-Web-App/1-Web-App/translations/assignment.zh-cn.md b/3-Web-App/1-Web-App/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..016dfa5228
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/assignment.zh-cn.md
@@ -0,0 +1,12 @@
+# ³¢ÊÔ²»Í¬µÄÄ£ÐÍ
+
+## ˵Ã÷
+
+ÏÖÔÚ£¬ÄãÒѾ­Äܹ»Ê¹ÓÃÒ»¸ö¾­¹ýѵÁ·µÄ»Ø¹éÄ£ÐÍÀ´´î½¨webÓ¦ÓóÌÐò£¬ÄÇôÇëÄã´ÓÇ°ÃæµÄ»Ø¹é¿Î³ÌÖÐÖØÐÂÑ¡ÔñÒ»¸öÄ£ÐÍÀ´ÖØ×öÒ»±éwebÓ¦ÓóÌÐò¡£Äã¿ÉÒÔʹÓÃÔ­À´µÄ·ç¸ñ»òÕßÆäËû²»Í¬µÄ·ç¸ñ½øÐÐÉè¼Æ£¬À´Õ¹Ê¾pumpkinÊý¾Ý¡£×¢Òâ¸ü¸ÄÊäÈëÒÔ·´Ó³Ä£Ð͵ÄѵÁ··½·¨¡£
+
+
+## ÆÀÅбê×¼
+
+| ±ê×¼                   | ÓÅÐã                                                 | ÖйæÖÐ¾Ø                                                  |  ÈÔÐèŬÁ¦                      |
+| -------------------------- | --------------------------------------------------------- | --------------------------------------------------------- | -------------------------------------- |
+| | webÓ¦ÓóÌÐò°´Ô¤ÆÚÔËÐУ¬²¢²¿Êðµ½ÔÆ¶Ë | webÓ¦ÓóÌÐò´æÔÚȱÏÝ»òÕßÏÔʾÒâÏë²»µ½µÄ½á¹û | webÓ¦ÓóÌÐòÎÞ·¨Õý³£ÔËÐÐ |

From 7bdbcad6449b8beeec68a3de78cf3633bf66acf0 Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Mon, 26 Jul 2021 23:21:02 -0300
Subject: [PATCH 272/368] 1-Introduction/1-intro-to-ML/assignment.es.md

---
 1-Introduction/1-intro-to-ML/assignment.es.md | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 1-Introduction/1-intro-to-ML/assignment.es.md

diff --git a/1-Introduction/1-intro-to-ML/assignment.es.md b/1-Introduction/1-intro-to-ML/assignment.es.md
new file mode 100644
index 0000000000..5241ca9624
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/assignment.es.md
@@ -0,0 +1,9 @@
+# Lévantate y corre
+
+## Instrucciones
+
+En esta tarea no calificada, debe repasar Python y hacer que su entorno esté en funcionamiento y sea capaz de ejecutar cuadernos.
+
+Tome esta [Ruta de aprendizaje de Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), y luego configure sus sistemas con estos videos introductorios:
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6

From 675c8f79380a322081865b91e40c132321bd681b Mon Sep 17 00:00:00 2001
From: Daniel Qian <chanjarster@gmail.com>
Date: Tue, 27 Jul 2021 14:03:21 +0800
Subject: [PATCH 273/368] Update README.zh-cn.md

add a right parentheses to avoid right text being a part of the link.
---
 2-Regression/1-Tools/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index 41b167e37c..bb7f37c11f 100644
--- a/2-Regression/1-Tools/translations/README.zh-cn.md
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -30,7 +30,7 @@
 
    > 通过学习这一系列的 [学习模�](https://docs.microsoft.com/users/jenlooper-2911/collections/mp1pagggd5qrq7?WT.mc_id=academic-15963-cxa)熟悉Python
 
-3. **按照[这些说明]安装Scikit learn**(https://scikit-learn.org/stable/install.html)。由于你需�确�使用Python3，因此建议你使用虚拟环境。注�，如果你是在M1 Mac上安装这个库，在上�链接的页�上有特别的说明。
+3. **按照[这些说明]安装Scikit learn**(https://scikit-learn.org/stable/install.html )。由于你需�确�使用Python3，因此建议你使用虚拟环境。注�，如果你是在M1 Mac上安装这个库，在上�链接的页�上有特别的说明。
 
 4. **安装Jupyter Notebook**。你需�[安装Jupyter包](https://pypi.org/project/jupyter/)。
 

From eb37fcbc3e48936e8db915350e0f6827f5297b18 Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Tue, 27 Jul 2021 18:31:49 +0900
Subject: [PATCH 274/368] feat: add ja 1.3 assignment

Refs #149
---
 1-Introduction/3-fairness/translations/README.ja.md   |  2 +-
 .../3-fairness/translations/assignment.ja.md          | 11 +++++++++++
 2 files changed, 12 insertions(+), 1 deletion(-)
 create mode 100644 1-Introduction/3-fairness/translations/assignment.ja.md

diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
index afa92f2bd1..9bb32639eb 100644
--- a/1-Introduction/3-fairness/translations/README.ja.md
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -201,4 +201,4 @@ Azure Machine Learning�よる�公平性を確��る���ツール�
 
 ## 課題
 
-[Fairlearnを調査�る](../assignment.md) 
+[Fairlearnを調査�る](./assignment.ja.md) 
diff --git a/1-Introduction/3-fairness/translations/assignment.ja.md b/1-Introduction/3-fairness/translations/assignment.ja.md
new file mode 100644
index 0000000000..dbf7b2b46b
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# Fairlearnを調査�る
+
+## 指示
+
+��レッスン���「データサイエンティスト�AIシステム�公平性を�上��る����オープンソース�コミュニティ主導�プロジェクト���るFairlearn����学習����。��課題���Fairlearn� [ノートブック](https://fairlearn.org/v0.6.2/auto_examples/index.html) �������を調査��������をレ�ートやプレゼンテーション�形�報告������。
+
+## 評価基準
+
+| 基準 | 模範的                                                                                                                                  | �分                               | �改善                     |
+| ---- | --------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------- | -------------------------- |
+|      | Fairlearn�システム・実行��ノートブック・実行�よ��得られ��果��レ�ートやパワー�イント�プレゼンテーション����示�れ��る | �論���レ�ート��示�れ��る | レ�ート��示�れ���� |

From 6f61468fef1248ac34748396fae457e10ee2559e Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Wed, 28 Jul 2021 18:51:19 +0900
Subject: [PATCH 275/368] feat: add ja 1.4 assignment

Refs #149
---
 .../4-techniques-of-ML/translations/assignment.ja.md  | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/4-techniques-of-ML/translations/assignment.ja.md

diff --git a/1-Introduction/4-techniques-of-ML/translations/assignment.ja.md b/1-Introduction/4-techniques-of-ML/translations/assignment.ja.md
new file mode 100644
index 0000000000..b3690e770f
--- /dev/null
+++ b/1-Introduction/4-techniques-of-ML/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# データサイエンティスト�インタビュー�る
+
+## 指示
+
+会社・ユーザグループ・�人・学生仲間�中��データサイエンティスト���専門的�����る人�話を������ょ�。��人�日々�仕事����短�レ�ート（500語）を書������。��人�専門家��ょ��？�れ�も「フルスタック��������る��ょ��？
+
+## 評価基準
+
+| 基準 | 模範的                                                                 | �分                                                           | �改善                     |
+| ---- | ---------------------------------------------------------------------- | -------------------------------------------------------------- | -------------------------- |
+|      | 出典�明記�れ��切�長��レ�ート�.docファイル����示�れ��る | レ�ート�出典�明記�れ�����も���必��長�よりも短� | レ�ート��示�れ���� |

From dc7d81b5a42f1178ce284e71755ab65406b10dc5 Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com>
Date: Wed, 28 Jul 2021 19:12:54 +0530
Subject: [PATCH 276/368] Update README.md

---
 2-Regression/4-Logistic/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index a4488c11e4..f694a554a2 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -284,7 +284,7 @@ In future lessons on classifications, you will learn how to iterate to improve y
 ---
 ## 🚀Challenge
 
-There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://kaggle.com) for interesting datasets.
+There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets.
 ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
 
 ## Review & Self Study

From 03015f91c4073aa8ab281e84eb46f23c0210e60b Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com>
Date: Wed, 28 Jul 2021 19:18:39 +0530
Subject: [PATCH 277/368] Update README.md

---
 2-Regression/1-Tools/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index e36c34fe68..6225f7cd7e 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -95,7 +95,7 @@ For this task we will import some libraries:
 
 - **matplotlib**. It's a useful [graphing tool](https://matplotlib.org/) and we will use it to create a line plot.
 - **numpy**. [numpy](https://numpy.org/doc/stable/user/whatisnumpy.html) is a useful library for handling numeric data in Python.
-- **sklearn**. This is the Scikit-learn library.
+- **sklearn**. This is the [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) library.
 
 Import some libraries to help with your tasks.
 

From 31826eec95ac7821b4ab8b74d369a1da6473f385 Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com>
Date: Wed, 28 Jul 2021 20:13:21 +0530
Subject: [PATCH 278/368] Update README.md

Stanford's K-Means Simulator had been removed from the parent directory. Added new K-Means Simulator
---
 5-Clustering/2-K-Means/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md
index 153932e631..bd59e080d8 100644
--- a/5-Clustering/2-K-Means/README.md
+++ b/5-Clustering/2-K-Means/README.md
@@ -242,7 +242,7 @@ Hint: Try to scale your data. There's commented code in the notebook that adds s
 
 ## Review & Self Study
 
-Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+Take a look at K-Means Simulator [here](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
 
 Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.
 

From 779d95a4afc2a88205f384a3f0ac9648e134b0d9 Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Wed, 28 Jul 2021 21:15:40 -0300
Subject: [PATCH 279/368] 1-Introduction/3-fairness/assignment.es.md

---
 1-Introduction/3-fairness/assignment.es.md | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/3-fairness/assignment.es.md

diff --git a/1-Introduction/3-fairness/assignment.es.md b/1-Introduction/3-fairness/assignment.es.md
new file mode 100644
index 0000000000..2f6d1035d0
--- /dev/null
+++ b/1-Introduction/3-fairness/assignment.es.md
@@ -0,0 +1,11 @@
+# Explore Fairlearn
+
+## Instrucciones
+
+En esta lección, aprendió sobre Fairlearn, un "proyecto open-source impulsado por la comunidad para ayudar a los científicos de datos a mejorar la equidad de los sistemas de AI." Para esta tarea, explore uno de los  [cuadernos](https://fairlearn.org/v0.6.2/auto_examples/index.html) de Fairlearn e informe sus hallazgos en un documento o presentación.
+
+## Rúbrica
+
+| Criterios | Ejemplar | Adecuado | Necesita mejorar |
+| -------- | --------- | -------- | ----------------- |
+|          |  Un documento o presentación powerpoint es presentado discutiendo los sistemas de Fairlearn, el cuadernos que fue ejecutado, y las concluiosnes extraídas al ejecutarlo        |   Un documento es presentado sin conclusiones       |  No se presenta ningún documento    |

From aaea81476239446d3b843988ec11938ea695c9ff Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Wed, 28 Jul 2021 21:24:40 -0300
Subject: [PATCH 280/368] 
 1-Introduction/1-intro-to-ML/translations/assignment.es.md

---
 .../1-intro-to-ML/translations/assignment.es.md          | 9 +++++++++
 1-Introduction/3-fairness/assignment.es.md               | 2 +-
 2 files changed, 10 insertions(+), 1 deletion(-)
 create mode 100644 1-Introduction/1-intro-to-ML/translations/assignment.es.md

diff --git a/1-Introduction/1-intro-to-ML/translations/assignment.es.md b/1-Introduction/1-intro-to-ML/translations/assignment.es.md
new file mode 100644
index 0000000000..5241ca9624
--- /dev/null
+++ b/1-Introduction/1-intro-to-ML/translations/assignment.es.md
@@ -0,0 +1,9 @@
+# Lévantate y corre
+
+## Instrucciones
+
+En esta tarea no calificada, debe repasar Python y hacer que su entorno esté en funcionamiento y sea capaz de ejecutar cuadernos.
+
+Tome esta [Ruta de aprendizaje de Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), y luego configure sus sistemas con estos videos introductorios:
+
+https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6
diff --git a/1-Introduction/3-fairness/assignment.es.md b/1-Introduction/3-fairness/assignment.es.md
index 2f6d1035d0..cf83256ef4 100644
--- a/1-Introduction/3-fairness/assignment.es.md
+++ b/1-Introduction/3-fairness/assignment.es.md
@@ -8,4 +8,4 @@ En esta lección, aprendió sobre Fairlearn, un "proyecto open-source impulsado
 
 | Criterios | Ejemplar | Adecuado | Necesita mejorar |
 | -------- | --------- | -------- | ----------------- |
-|          |  Un documento o presentación powerpoint es presentado discutiendo los sistemas de Fairlearn, el cuadernos que fue ejecutado, y las concluiosnes extraídas al ejecutarlo        |   Un documento es presentado sin conclusiones       |  No se presenta ningún documento    |
+|          |  Un documento o presentación powerpoint es presentado discutiendo los sistemas de Fairlearn, el cuadernos que fue ejecutado, y las conclusiones extraídas al ejecutarlo        |   Un documento es presentado sin conclusiones       |  No se presenta ningún documento    |

From 8f372da2a9e3a1906bf27037dc9743e5bf1dc287 Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Wed, 28 Jul 2021 21:25:17 -0300
Subject: [PATCH 281/368] 
 1-Introduction/3-fairness/translations/assignment.es.md

---
 .../3-fairness/translations/assignment.es.md          | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 1-Introduction/3-fairness/translations/assignment.es.md

diff --git a/1-Introduction/3-fairness/translations/assignment.es.md b/1-Introduction/3-fairness/translations/assignment.es.md
new file mode 100644
index 0000000000..cf83256ef4
--- /dev/null
+++ b/1-Introduction/3-fairness/translations/assignment.es.md
@@ -0,0 +1,11 @@
+# Explore Fairlearn
+
+## Instrucciones
+
+En esta lección, aprendió sobre Fairlearn, un "proyecto open-source impulsado por la comunidad para ayudar a los científicos de datos a mejorar la equidad de los sistemas de AI." Para esta tarea, explore uno de los  [cuadernos](https://fairlearn.org/v0.6.2/auto_examples/index.html) de Fairlearn e informe sus hallazgos en un documento o presentación.
+
+## Rúbrica
+
+| Criterios | Ejemplar | Adecuado | Necesita mejorar |
+| -------- | --------- | -------- | ----------------- |
+|          |  Un documento o presentación powerpoint es presentado discutiendo los sistemas de Fairlearn, el cuadernos que fue ejecutado, y las conclusiones extraídas al ejecutarlo        |   Un documento es presentado sin conclusiones       |  No se presenta ningún documento    |

From 620eb25358d2e3d3c0b644cd93ecf2d9ca9e0752 Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Wed, 28 Jul 2021 22:03:33 -0300
Subject: [PATCH 282/368] 1-Introduction/3-fairness/assignment.es.md

---
 1-Introduction/3-fairness/assignment.es.md | 11 -----------
 1 file changed, 11 deletions(-)
 delete mode 100644 1-Introduction/3-fairness/assignment.es.md

diff --git a/1-Introduction/3-fairness/assignment.es.md b/1-Introduction/3-fairness/assignment.es.md
deleted file mode 100644
index cf83256ef4..0000000000
--- a/1-Introduction/3-fairness/assignment.es.md
+++ /dev/null
@@ -1,11 +0,0 @@
-# Explore Fairlearn
-
-## Instrucciones
-
-En esta lección, aprendió sobre Fairlearn, un "proyecto open-source impulsado por la comunidad para ayudar a los científicos de datos a mejorar la equidad de los sistemas de AI." Para esta tarea, explore uno de los  [cuadernos](https://fairlearn.org/v0.6.2/auto_examples/index.html) de Fairlearn e informe sus hallazgos en un documento o presentación.
-
-## Rúbrica
-
-| Criterios | Ejemplar | Adecuado | Necesita mejorar |
-| -------- | --------- | -------- | ----------------- |
-|          |  Un documento o presentación powerpoint es presentado discutiendo los sistemas de Fairlearn, el cuadernos que fue ejecutado, y las conclusiones extraídas al ejecutarlo        |   Un documento es presentado sin conclusiones       |  No se presenta ningún documento    |

From 4a1c1471996d374f31312952c56007ade23273f1 Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Wed, 28 Jul 2021 22:08:32 -0300
Subject: [PATCH 283/368] rm  1-Introduction/1-intro-to-ML/assignment.es.md

---
 1-Introduction/1-intro-to-ML/assignment.es.md | 9 ---------
 1 file changed, 9 deletions(-)
 delete mode 100644 1-Introduction/1-intro-to-ML/assignment.es.md

diff --git a/1-Introduction/1-intro-to-ML/assignment.es.md b/1-Introduction/1-intro-to-ML/assignment.es.md
deleted file mode 100644
index 5241ca9624..0000000000
--- a/1-Introduction/1-intro-to-ML/assignment.es.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Lévantate y corre
-
-## Instrucciones
-
-En esta tarea no calificada, debe repasar Python y hacer que su entorno esté en funcionamiento y sea capaz de ejecutar cuadernos.
-
-Tome esta [Ruta de aprendizaje de Python](https://docs.microsoft.com/learn/paths/python-language/?WT.mc_id=academic-15963-cxa), y luego configure sus sistemas con estos videos introductorios:
-
-https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6

From c1945983ef5c0c21c4a8d03abb49b69bc47a57b1 Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Thu, 29 Jul 2021 09:15:48 +0800
Subject: [PATCH 284/368] Simplified Chinese Translation v1

---
 .../translations/README.zh-cn.md              | 245 ++++++++++++++++++
 1 file changed, 245 insertions(+)
 create mode 100644 4-Classification/2-Classifiers-1/translations/README.zh-cn.md

diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
new file mode 100644
index 0000000000..00327c92de
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -0,0 +1,245 @@
+# ��分类器1
+
+在本节中，将使用你在上一个课程中所�存的全部�过�衡和清洗的��数�。
+
+You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
+你将使用这份数�集，并通过多�分类器 _在给出了���料�预测这是那一个国家的��_。在此过程中，你将学到更多能够用�调节分类任务算法的方法。   
+
+## [课�测试](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+# 准备工作
+
+�设你已�完�了[课程1](../1-Introduction/README.md), 确�在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 文件�进行接下�的四节课程。
+
+## 练习 - 预测�国的��
+
+1. 在本节课的 _notebook.ipynb_ 文件中，导入Pandas的�时载入相应的数�文件：
+
+    ```python
+    import pandas as pd
+    cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv")
+    cuisines_df.head()
+    ```
+
+    数�如下所示:
+
+    ```output
+    |     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+    | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+    | 0   | 0          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 1   | 1          | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 2   | 2          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 3   | 3          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+    | 4   | 4          | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
+    ```
+
+1. 现在，�多导入一些库：
+
+    ```python
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.model_selection import train_test_split, cross_val_score
+    from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
+    from sklearn.svm import SVC
+    import numpy as np
+    ```
+
+1. 接下�需�将数�分训练模型所需的X和y两个dataframe。首先�将`cuisine`列的数��独�存为标签（label）的dataframe。
+
+    ```python
+    cuisines_label_df = cuisines_df['cuisine']
+    cuisines_label_df.head()
+    ```
+
+    输出看上去会是这样:
+
+    ```output
+    0    indian
+    1    indian
+    2    indian
+    3    indian
+    4    indian
+    Name: cuisine, dtype: object
+    ```
+
+1. 调用`drop()`函数将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数�作为�以用于训练的特�（feature）:
+
+    ```python
+    cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
+    cuisines_feature_df.head()
+    ```
+
+    你的特�（feature）数�看上去将会是这样:
+
+    | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     |
+    | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
+    |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
+    |      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+
+现在，你已�准备好�以开始训练你的模型了�
+
+## 选则你的分类器
+
+你的数�已�清洗干净并已�准备好�以进行训练了，现在需�决定你想使用的算法�完�这项任务。
+
+Scikit_learn将分类任务归在了监�学习目录中，在这个目录中你将会找到很多方法�进行分类。�一看上去，有点[��满目](https://scikit-learn.org/stable/supervised_learning.html)。下�这些方法都包�了分类技术：
+
+- 线性模型（Linear Models）
+- 支���机（Support Vector Machines）
+- �机梯度下�（Stochastic Gradient Descent）
+- 最近邻（Nearest Neighbors）
+- 高斯过程（Gaussian Processes）
+- 决策树（Decision Trees）
+- 集�方法（投票分类器）（Ensemble methods（voting classifier）） 
+- 多类别多输出算法（多类别多标签分类，多类别多输出分类）（Multiclass and multioutput algorithms (multiclass and multilabel classification, multiclass-multioutput classification)）
+
+> 你也�以使用[神�网络�分类数�](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), 但这对于本课程�说有点超纲了。
+
+### 如何选择分类器?
+
+那么，你应该选择哪一个分类器呢？一般�说，�以选择多个方法并对比他们�行�的结果。Scikit-learn�供了��算法（包括KNeighbors� SVC two ways� GaussianProcessClassifier� DecisionTreeClassifier� RandomForestClassifier� MLPClassifier� AdaBoostClassifier� GaussianNB以�QuadraticDiscrinationAnalysis）的[比较](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且对比较的结果进行了�视化的展示：
+
+![�分类器比较](../images/comparison.png)
+> 图表��于Scikit-learn的官方文档
+
+> AutoML通过在云端�行这些比较�常完美地解决的这个问题，使得你能够根�你的数�选择最佳的算法。试试[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)。
+
+### 一�更好的方法
+
+�过，比起无脑地猜测，根�这份�以下载的[机器学习作弊表]中的方法是一个更好的选择。在表中我们�以�现对于这个多类型的分类任务，�以有一些选择：
+
+A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:
+
+![多类型问题作弊表](../images/cheatsheet.png)
+> 微软算法作弊表中关于多类型分类任务�选算法的部分
+
+✅ 下载这份作弊表，打�出�，挂在你的墙上��
+
+### 推导过程
+
+Let's see if we can reason our way through different approaches given the constraints we have:让我们看看根�我们所有的�制�件推导下�中方法的�行性：
+
+- **神�网络（Neural Network）太过��了**。我们的数�很清晰但数��比较�，此外我们是通过notebook在本地进行训练，神�网络对于这个任务�说过于��了。
+- **二分类法(two-class classifier)��行**。我们�能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 
+- **决策树以�逻辑回归�行**. 决策树也许有用，或者也�以使用逻辑回归�处�多类型数�。
+- **多类型增强决策数�以解决��的问题**. 多类型增强决策树最适���数化的任务，�任务目标是建立一个排�，这对我们的任务并没有作用。
+
+### 使用Scikit-learn 
+
+我们将使用Scikit-learn�分�我们的数�。然而，在Scikit-learn中有很多�方法�使用逻辑回归。�以看一看逻辑回归算法�以[传递的�数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。
+
+当我们需�Scikit-learn进行逻辑回归�算时，`multi_class` 以� `solver`是最��的两个�数，我们需�特别说明一下哎。 `multi_class` 的值决定了特定的行为。`solver`的值是我们需�使用的算法。并�是所有的solvers都�以匹�`multi_class`的值的。
+
+According to the docs, in the multiclass case, the training algorithm根�文档，在多类型问题�，训练的算法:
+
+- **使用“一对其余�(OvR)策略（scheme）**, 如果`multi_class`被设置为`ovr`
+- **使用交�熵�失（cross entropy loss）**, 如果`multi_class`被设置为`multinomial` (目�，`multinomial`�支�‘lbfgs’, ‘sag’, ‘saga’以�‘newton-cg’等 solver)。
+
+> 🎓 其中“scheme��以是“ovr(one-vs-rest)�也�以是“multinomial�。 因为逻辑回归事实上是设计用于支�二分类任务的，这些scheme将使其�以更好的支�多类型分类任务。[��](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+
+> 🎓 “solver�被定义为是"用于解决优化问题的算法"。[��](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
+
+Scikit-learn�供了以下这个表格�解释solver是如何应对的��的数�结构所带�的��的挑战的:
+
+![solvers](../images/solvers.png)
+
+## 练习 - 分割数�
+
+因为你刚刚在上一节课中学习了逻辑回归，因此我们�以�焦于此，�演练一下如何进行第一个模型的训练。通过调用`train_test_split()`�以把你的数�分割�训练集和测试集：
+
+
+```python
+X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
+```
+
+## 练习 - 应用逻辑回归
+
+因为我们正在进行多类型分类的案例，你需�决定选用什么  _scheme_ 以�使用什么 _solver_ 。使用带有multiclass设置的LogisticRegression，并将solver设置为**liblinear**�进行模型训练。
+
+1. 创建逻辑回归，并将multi_class设置为`ovr`，�时将solver设置为 `liblinear`:
+
+    ```python
+    lr = LogisticRegression(multi_class='ovr',solver='liblinear')
+    model = lr.fit(X_train, np.ravel(y_train))
+    
+    accuracy = model.score(X_test, y_test)
+    print ("Accuracy is {}".format(accuracy))
+    ```
+
+    ✅ 也�以试试其他solver比如`lbfgs`, 它通常�以作为默认的设置
+
+    > 注�, 使用Pandas的[`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 函数�以在需�的时候将你的数�进行�维
+
+    准确率高达了**80%**!
+
+1. 你也�以通过查看一行数�（比如第50行）�观察到模型�行的情况:
+
+    ```python
+    print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
+    print(f'cuisine: {y_test.iloc[50]}')
+    ```
+
+    �行�的输出如下:
+
+   ```output
+   ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
+   cuisine: indian
+   ```
+
+   ✅ 试试��的行��检查一下结果�
+
+1. 让我们�深入研究一下，你�以检查一下这回预测的准确率:
+
+    ```python
+    test= X_test.iloc[50].values.reshape(-1, 1).T
+    proba = model.predict_proba(test)
+    classes = model.classes_
+    resultdf = pd.DataFrame(data=proba, columns=classes)
+    
+    topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
+    topPrediction.head()
+    ```
+
+    �行�的输出如下———这是一��度�的�能性最大，是最��的猜测:
+
+    |          |        0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+    |   indian | 0.715851 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |  chinese | 0.229475 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | japanese | 0.029763 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |   korean | 0.017277 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |     thai | 0.007634 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+
+    ✅ 你能解释下为什么模型会如此确定这是一��度�么？
+
+1. 就和你在回归的课程中所�的一样，通过输出分类的报告，我们�以得到更多的细节：
+
+    ```python
+    y_pred = model.predict(X_test)
+    print(classification_report(y_test,y_pred))
+    ```
+
+    | precision    | recall | f1-score | support |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+    | chinese      | 0.73   | 0.71     | 0.72    | 229  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | indian       | 0.91   | 0.93     | 0.92    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | japanese     | 0.70   | 0.75     | 0.72    | 220  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | korean       | 0.86   | 0.76     | 0.81    | 242  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | thai         | 0.79   | 0.85     | 0.82    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | accuracy     | 0.80   | 1199     |         |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+
+## 挑战
+
+在本课程中，你使用了清洗�的数�建立了一个机器学习的模型，能够根�一系列的�料�预测���自于哪个国家。请�花点时间阅读一下Scikit-learn所�供的�以用�分类数�的其他选择。�时也�以深入研究一下“solver�的概念并�试一下�解其背�的原�。
+
+## [课��测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## 回顾与自学
+
+[这个课程](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)将对逻辑回归背�的数学原�进行更加深入的讲解
+
+## 作业 
+
+[学习solver](assignment.md)

From ab83692b57b7d9dfec46088388755f697577b165 Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Thu, 29 Jul 2021 09:21:57 +0800
Subject: [PATCH 285/368] Update README.zh-cn.md

---
 4-Classification/2-Classifiers-1/translations/README.zh-cn.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
index 00327c92de..3386412b59 100644
--- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -2,7 +2,6 @@
 
 在本节中，将使用你在上一个课程中所�存的全部�过�衡和清洗的��数�。
 
-You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
 你将使用这份数�集，并通过多�分类器 _在给出了���料�预测这是那一个国家的��_。在此过程中，你将学到更多能够用�调节分类任务算法的方法。   
 
 ## [课�测试](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)

From dc0eda9f923f18444e269c53f197538b98f9aaa8 Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com>
Date: Thu, 29 Jul 2021 07:19:32 +0530
Subject: [PATCH 286/368] Update README.md

address link broken
---
 6-NLP/1-Introduction-to-NLP/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index 924ce73c73..4a7a88d638 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -69,7 +69,7 @@ The idea for this came from a party game called *The Imitation Game* where an in
 
 ### Developing Eliza
 
-In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https:/wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". 
+In the 1960's an MIT scientist called *Joseph Weizenbaum* developed [*Eliza*](https://wikipedia.org/wiki/ELIZA), a computer 'therapist' that would ask the human questions and give the appearance of understanding their answers. However, while Eliza could parse a sentence and identify certain grammatical constructs and keywords so as to give a reasonable answer, it could not be said to *understand* the sentence. If Eliza was presented with a sentence following the format "**I am** <u>sad</u>" it might rearrange and substitute words in the sentence to form the response "How long have **you been** <u>sad</u>". 
 
 This gave the impression that Eliza understood the statement and was asking a follow-on question, whereas in reality, it was changing the tense and adding some words. If Eliza could not identify a keyword that it had a response for, it would instead give a random response that could be applicable to many different statements. Eliza could be easily tricked, for instance if a user wrote "**You are** a <u>bicycle</u>" it might respond with "How long have **I been** a <u>bicycle</u>?", instead of a more reasoned response.
 

From 12c49120a22e386446a57e9f3d80f0224c0e2079 Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Thu, 29 Jul 2021 09:54:53 +0800
Subject: [PATCH 287/368] Correct Typo

---
 .../translations/README.zh-cn.md              | 64 +++++++++----------
 1 file changed, 31 insertions(+), 33 deletions(-)

diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
index 3386412b59..2174423a29 100644
--- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -1,17 +1,17 @@
 # ��分类器1
 
-在本节中，将使用你在上一个课程中所�存的全部�过�衡和清洗的��数�。
+本节课程将使用你在上一个课程中所�存的全部�过�衡和清洗的��数�。
 
-你将使用这份数�集，并通过多�分类器 _在给出了���料�预测这是那一个国家的��_。在此过程中，你将学到更多能够用�调节分类任务算法的方法。   
+你将使用这份数�集，并通过多�分类器 _在给出了���料�预测这是那一个国家的��_。在此过程中，你将学到更多能够用�调试分类任务算法的方法。   
 
 ## [课�测试](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
 # 准备工作
 
-�设你已�完�了[课程1](../1-Introduction/README.md), 确�在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 文件�进行接下�的四节课程。
+�设你已�完�了[课程1](../1-Introduction/README.md), 确�在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件�进行接下�的四节课程。
 
 ## 练习 - 预测�国的��
 
-1. 在本节课的 _notebook.ipynb_ 文件中，导入Pandas的�时载入相应的数�文件：
+1. 在本节课的 _notebook.ipynb_ 文件中，导入Pandas，并读�相应的数�文件：
 
     ```python
     import pandas as pd
@@ -41,14 +41,14 @@
     import numpy as np
     ```
 
-1. 接下�需�将数�分训练模型所需的X和y两个dataframe。首先�将`cuisine`列的数��独�存为标签（label）的dataframe。
+1. 接下�需�将数�分为训练模型所需的X（译者注：代表特�数�）和y（译者注：代表标签数�）两个dataframe。首先�将`cuisine`列的数��独�存为的一个dataframe作为标签（label）。
 
     ```python
     cuisines_label_df = cuisines_df['cuisine']
     cuisines_label_df.head()
     ```
 
-    输出看上去会是这样:
+    输出如下:
 
     ```output
     0    indian
@@ -59,7 +59,7 @@
     Name: cuisine, dtype: object
     ```
 
-1. 调用`drop()`函数将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数�作为�以用于训练的特�（feature）:
+1. 调用`drop()`函数将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数�作为�以用于训练的特�（feature）数�:
 
     ```python
     cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
@@ -80,9 +80,9 @@
 
 ## 选则你的分类器
 
-你的数�已�清洗干净并已�准备好�以进行训练了，现在需�决定你想使用的算法�完�这项任务。
+你的数�已�清洗干净并已�准备好�以进行训练了，现在需�决定你想�使用的算法�完�这项任务。
 
-Scikit_learn将分类任务归在了监�学习目录中，在这个目录中你将会找到很多方法�进行分类。�一看上去，有点[��满目](https://scikit-learn.org/stable/supervised_learning.html)。下�这些方法都包�了分类技术：
+Scikit_learn将分类任务归在了监�学习类别中，在这个类别中你将�以找到很多�以用�分类的方法。�一看上去，有点[��满目](https://scikit-learn.org/stable/supervised_learning.html)。以下这些方法都包�了分类技术：
 
 - 线性模型（Linear Models）
 - 支���机（Support Vector Machines）
@@ -97,45 +97,43 @@ Scikit_learn将分类任务归在了监�学习目录中，在这个目录中
 
 ### 如何选择分类器?
 
-那么，你应该选择哪一个分类器呢？一般�说，�以选择多个方法并对比他们�行�的结果。Scikit-learn�供了��算法（包括KNeighbors� SVC two ways� GaussianProcessClassifier� DecisionTreeClassifier� RandomForestClassifier� MLPClassifier� AdaBoostClassifier� GaussianNB以�QuadraticDiscrinationAnalysis）的[比较](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且对比较的结果进行了�视化的展示：
+那么，你应该选择哪一个分类器呢？一般�说，�以多选择几个并对比他们�行�的结果。Scikit-learn�供了��算法（包括KNeighbors� SVC two ways� GaussianProcessClassifier� DecisionTreeClassifier� RandomForestClassifier� MLPClassifier� AdaBoostClassifier� GaussianNB以�QuadraticDiscrinationAnalysis）的效果[对比](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且将比较的结果进行了�视化的展示：
 
 ![�分类器比较](../images/comparison.png)
 > 图表��于Scikit-learn的官方文档
 
-> AutoML通过在云端�行这些比较�常完美地解决的这个问题，使得你能够根�你的数�选择最佳的算法。试试[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)。
+> AutoML通过在云端�行这些对比�常完美地解决的选择算法的这个问题，使得你能够根�你的数�特性选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)了解更多。
 
-### 一�更好的方法
+### 一�更好的方法�选择分类器
 
-�过，比起无脑地猜测，根�这份�以下载的[机器学习作弊表]中的方法是一个更好的选择。在表中我们�以�现对于这个多类型的分类任务，�以有一些选择：
-
-A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices:
+�过，比起无脑地猜测，你�以下载这份[机器学习作弊表（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)，对�算法进行对比，这是一个选择算法更有效的办法。在表中我们�以�现对于本课程中涉�的多类型的分类任务，�以有以下这些选择：
 
 ![多类型问题作弊表](../images/cheatsheet.png)
 > 微软算法作弊表中关于多类型分类任务�选算法的部分
 
 ✅ 下载这份作弊表，打�出�，挂在你的墙上��
 
-### 推导过程
+### 选择的过程
 
-Let's see if we can reason our way through different approaches given the constraints we have:让我们看看根�我们所有的�制�件推导下�中方法的�行性：
+让我们看看根�我们所有的�制�件�次判断下��方法的�行性：
 
 - **神�网络（Neural Network）太过��了**。我们的数�很清晰但数��比较�，此外我们是通过notebook在本地进行训练，神�网络对于这个任务�说过于��了。
 - **二分类法(two-class classifier)��行**。我们�能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 
-- **决策树以�逻辑回归�行**. 决策树也许有用，或者也�以使用逻辑回归�处�多类型数�。
-- **多类型增强决策数�以解决��的问题**. 多类型增强决策树最适���数化的任务，�任务目标是建立一个排�，这对我们的任务并没有作用。
+- **决策树以�逻辑回归�行**。决策树应该是有用的，此外也�以使用逻辑回归�处�多类型数�。
+- **多类型增强决策树是用于解决其他问题的**. 多类型增强决策树最适���数化的任务，�任务目标是建立一个排�，这对我们当�的任务并没有作用。
 
 ### 使用Scikit-learn 
 
-我们将使用Scikit-learn�分�我们的数�。然而，在Scikit-learn中有很多�方法�使用逻辑回归。�以看一看逻辑回归算法�以[传递的�数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。
+我们将会使用Scikit-learn�对我们的数�进行分�。然而，在Scikit-learn中使用逻辑回归也有很很多方法。�以看一看逻辑回归算法需�[传递的�数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。
 
-当我们需�Scikit-learn进行逻辑回归�算时，`multi_class` 以� `solver`是最��的两个�数，我们需�特别说明一下哎。 `multi_class` 的值决定了特定的行为。`solver`的值是我们需�使用的算法。并�是所有的solvers都�以匹�`multi_class`的值的。
+当我们需�Scikit-learn进行逻辑回归�算时，`multi_class` 以� `solver`是最��的两个�数，因此我们需�特别说明一下。 `multi_class` 的值是分类任务�求的�一�特定的行为。`solver`的值是我们需�使用的算法。并�是所有的solvers都�以匹�`multi_class`的值的。
 
-According to the docs, in the multiclass case, the training algorithm根�文档，在多类型问题�，训练的算法:
+根�文档，在多类型问题中，训练的算法应:
 
-- **使用“一对其余�(OvR)策略（scheme）**, 如果`multi_class`被设置为`ovr`
-- **使用交�熵�失（cross entropy loss）**, 如果`multi_class`被设置为`multinomial` (目�，`multinomial`�支�‘lbfgs’, ‘sag’, ‘saga’以�‘newton-cg’等 solver)。
+- **使用“一对其余�(OvR)策略（scheme）**, 当`multi_class`被设置为`ovr`时
+- **使用交�熵�失（cross entropy loss）**, 当`multi_class`被设置为`multinomial` (目�，`multinomial`�支�‘lbfgs’, ‘sag’, ‘saga’以�‘newton-cg’等 solver)时。
 
-> 🎓 其中“scheme��以是“ovr(one-vs-rest)�也�以是“multinomial�。 因为逻辑回归事实上是设计用于支�二分类任务的，这些scheme将使其�以更好的支�多类型分类任务。[��](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+> 🎓 其中“scheme��以是“ovr(one-vs-rest)�也�以是“multinomial�。 因为逻辑回归本�是设计�用于进行二分类任务的，这两个scheme都�以使得逻辑回归能更好的支�多类型分类任务。[��](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
 
 > 🎓 “solver�被定义为是"用于解决优化问题的算法"。[��](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
 
@@ -145,7 +143,7 @@ Scikit-learn�供了以下这个表格�解释solver是如何应对的��
 
 ## 练习 - 分割数�
 
-因为你刚刚在上一节课中学习了逻辑回归，因此我们�以�焦于此，�演练一下如何进行第一个模型的训练。通过调用`train_test_split()`�以把你的数�分割�训练集和测试集：
+你刚刚在上一节课中学习了逻辑回归，因此我们�以�焦于此，�演练一下如何进行第一个模型的训练。首先，需�通过调用`train_test_split()`�以把你的数�分割�训练集和测试集：
 
 
 ```python
@@ -154,7 +152,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
 
 ## 练习 - 应用逻辑回归
 
-因为我们正在进行多类型分类的案例，你需�决定选用什么  _scheme_ 以�使用什么 _solver_ 。使用带有multiclass设置的LogisticRegression，并将solver设置为**liblinear**�进行模型训练。
+接�，你需�决定选用什么  _scheme_ 以� _solver_ �进行我们这个多类型分类的案例。这里我们使用LogisticRegression方法，并设置相应的multi_class�数，�时将solver设置为**liblinear**�进行模型训练。
 
 1. 创建逻辑回归，并将multi_class设置为`ovr`，�时将solver设置为 `liblinear`:
 
@@ -166,13 +164,13 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
     print ("Accuracy is {}".format(accuracy))
     ```
 
-    ✅ 也�以试试其他solver比如`lbfgs`, 它通常�以作为默认的设置
+    ✅ 也�以试试其他solver比如`lbfgs`, 这通常是默认的设置
 
     > 注�, 使用Pandas的[`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 函数�以在需�的时候将你的数�进行�维
 
-    准确率高达了**80%**!
+    计算结果准确率高达了**80%**!
 
-1. 你也�以通过查看一行数�（比如第50行）�观察到模型�行的情况:
+1. 你也�以通过查看�一行数�（比如第50行）�观察到模型�行的情况:
 
     ```python
     print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
@@ -186,9 +184,9 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
    cuisine: indian
    ```
 
-   ✅ 试试��的行��检查一下结果�
+   ✅ 试试��的行索引�检查一下结果�
 
-1. 让我们�深入研究一下，你�以检查一下这回预测的准确率:
+1. 让我们�深入研究一下，你�以检查一下本次预测的准确率:
 
     ```python
     test= X_test.iloc[50].values.reshape(-1, 1).T
@@ -200,7 +198,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
     topPrediction.head()
     ```
 
-    �行�的输出如下———这是一��度�的�能性最大，是最��的猜测:
+    �行�的输出如下———�以�现这是一��度�的�能性最大，是最��的猜测:
 
     |          |        0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
     | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

From d9e007b72916a46a34173fd59be83c89f22f9ce1 Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com>
Date: Thu, 29 Jul 2021 07:26:26 +0530
Subject: [PATCH 288/368] Update README.md

Fix bot.py address
---
 6-NLP/1-Introduction-to-NLP/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index 4a7a88d638..5123585624 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -133,7 +133,7 @@ Let's create the bot next. We'll start by defining some phrases.
     It was nice talking to you, goodbye!
     ```
 
-    One possible solution to the task is [here](../solution/bot.py)
+    One possible solution to the task is [here](solution/bot.py)
 
     ✅ Stop and consider
 

From f29dc516b89d5da7de6f72a2259881e7644ab177 Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com>
Date: Thu, 29 Jul 2021 07:34:20 +0530
Subject: [PATCH 289/368] Update README.md

Address Link fix
---
 6-NLP/3-Translation-Sentiment/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md
index bcd6cdd1ae..0c6b568bf5 100644
--- a/6-NLP/3-Translation-Sentiment/README.md
+++ b/6-NLP/3-Translation-Sentiment/README.md
@@ -143,7 +143,7 @@ Your task is to determine, using sentiment polarity, if *Pride and Prejudice* ha
    1. If the polarity is 1 or -1 store the sentence in an array or list of positive or negative messages
 5. At the end, print out all the positive sentences and negative sentences (separately) and the number of each.
 
-Here is a sample [solution](solutions/notebook.ipynb).
+Here is a sample [solution](solution/notebook.ipynb).
 
 ✅ Knowledge Check
 

From f17f05ace97acfa46b0b6a9a4c7f28fa066908b1 Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com>
Date: Thu, 29 Jul 2021 07:48:06 +0530
Subject: [PATCH 290/368] Update README.md

Fix link to Jupyter-notebook
---
 6-NLP/5-Hotel-Reviews-2/README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md
index 7d8a4d031a..12b9a15af0 100644
--- a/6-NLP/5-Hotel-Reviews-2/README.md
+++ b/6-NLP/5-Hotel-Reviews-2/README.md
@@ -347,13 +347,13 @@ print("Saving results to Hotel_Reviews_NLP.csv")
 df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False)
 ```
 
-You should run the entire code for [the analysis notebook](solution/notebook-sentiment-analysis.ipynb) (after you've run [your filtering notebook](solution/notebook-filtering.ipynb) to generate the Hotel_Reviews_Filtered.csv file).
+You should run the entire code for [the analysis notebook](solution/3-notebook.ipynb) (after you've run [your filtering notebook](solution/1-notebook.ipynb) to generate the Hotel_Reviews_Filtered.csv file).
 
 To review, the steps are:
 
-1. Original dataset file **Hotel_Reviews.csv** is explored in the previous lesson with [the explorer notebook](../4-Hotel-Reviews-1/solution/notebook-explorer.ipynb)
-2. Hotel_Reviews.csv is filtered by [the filtering notebook](solution/notebook-filtering.ipynb) resulting in **Hotel_Reviews_Filtered.csv**
-3. Hotel_Reviews_Filtered.csv is processed by [the sentiment analysis notebook](solution/notebook-sentiment-analysis.ipynb) resulting in **Hotel_Reviews_NLP.csv**
+1. Original dataset file **Hotel_Reviews.csv** is explored in the previous lesson with [the explorer notebook](../4-Hotel-Reviews-1/solution/notebook.ipynb)
+2. Hotel_Reviews.csv is filtered by [the filtering notebook](solution/1-notebook.ipynb) resulting in **Hotel_Reviews_Filtered.csv**
+3. Hotel_Reviews_Filtered.csv is processed by [the sentiment analysis notebook](solution/3-notebook.ipynb) resulting in **Hotel_Reviews_NLP.csv**
 4. Use Hotel_Reviews_NLP.csv in the NLP Challenge below
 
 ### Conclusion

From 7539844e90c7c1921fafacc4aa923799c510f132 Mon Sep 17 00:00:00 2001
From: Abhinav Sharma <63901956+abhi-bhatra@users.noreply.github.com>
Date: Thu, 29 Jul 2021 08:30:58 +0530
Subject: [PATCH 291/368] Update README.md

Link to K-Means Clustering Simulator
---
 5-Clustering/2-K-Means/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md
index bd59e080d8..6e0724b53c 100644
--- a/5-Clustering/2-K-Means/README.md
+++ b/5-Clustering/2-K-Means/README.md
@@ -242,7 +242,7 @@ Hint: Try to scale your data. There's commented code in the notebook that adds s
 
 ## Review & Self Study
 
-Take a look at K-Means Simulator [here](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+Take a look at K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
 
 Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.
 

From d6e61a30afdbcbcbc711cc929951be94416f6a93 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:03:23 -0400
Subject: [PATCH 292/368] Update README.md

---
 5-Clustering/2-K-Means/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md
index 6e0724b53c..d85654ae35 100644
--- a/5-Clustering/2-K-Means/README.md
+++ b/5-Clustering/2-K-Means/README.md
@@ -224,7 +224,7 @@ Previously, you surmised that, because you have targeted 3 song genres, you shou
 
 ## Variance
 
-Variance is defined as "the average of the squared differences from the Mean."[source](https://www.mathsisfun.com/data/standard-deviation.html) In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean. 
+Variance is defined as "the average of the squared differences from the Mean" [source](https://www.mathsisfun.com/data/standard-deviation.html). In the context of this clustering problem, it refers to data that the numbers of our dataset tend to diverge a bit too much from the mean. 
 
 ✅ This is a great moment to think about all the ways you could correct this issue. Tweak the data a bit more? Use different columns? Use a different algorithm? Hint: Try [scaling your data](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) to normalize it and test other columns.
 
@@ -242,7 +242,7 @@ Hint: Try to scale your data. There's commented code in the notebook that adds s
 
 ## Review & Self Study
 
-Take a look at K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+Take a look at a K-Means Simulator [such as this one](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). You can use this tool to visualize sample data points and determine its centroids. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
 
 Also, take a look at [this handout on k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) from Stanford.
 

From 23d4e4c216758c09aadb93f8b8b639b6b98d7c9f Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:08:47 -0400
Subject: [PATCH 293/368] Update README.md

---
 2-Regression/4-Logistic/README.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 2f31b6ed44..0708b36530 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -227,9 +227,6 @@ What's going on here? Let's say our model is asked to classify items between two
 - If your model predicts something as a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a false negative, shown by the bottom left number. 
 - If your model predicts something as not a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a true negative, shown by the bottom right number.
 
-![Confusion Matrix](images/confusion-matrix.png)
-
-> Infographic by [Jen Looper](https://twitter.com/jenlooper)
 
 As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.
 
@@ -285,6 +282,7 @@ In future lessons on classifications, you will learn how to iterate to improve y
 ## 🚀Challenge
 
 There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets.
+
 ## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
 
 ## Review & Self Study

From 929103d8aca803324f4ca91f25882717965dc360 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:09:34 -0400
Subject: [PATCH 294/368] removing confusion matrix image

---
 2-Regression/4-Logistic/translations/README.id.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
index fa47aea32f..213ef4aa3d 100644
--- a/2-Regression/4-Logistic/translations/README.id.md
+++ b/2-Regression/4-Logistic/translations/README.id.md
@@ -230,10 +230,6 @@ Apa yang sedang terjadi di sini? Mari kita asumsi dulu bahwa model kita ditanyak
 - Kalau modelmu memprediksi sesuati sebagai sebuah labu tetapi sebenarnya bukan sebuah labu, itu disebut negatif palsu yang diindikasi angka di pojok kiri bawah.
 - Kalau modelmu memprediksi sesuati sebagai bukan sebuah labu dan memang benar sesuatu itu bukan sebuah labu, itu disebut negatif benar yang diindikasi angka di pojok kanan bawah.
 
-![Matriks Kebingungan](../images/confusion-matrix.png)
-
-> Infografik oleh [Jen Looper](https://twitter.com/jenlooper)
-
 Sebagaimana kamu mungkin sudah pikirkan, lebih baik dapat banyak positif benar dan negatif benar dan sedikit positif palsu dan negatif palsu. Implikasinya adalah performa modelnya bagus.
 
 ✅ Pertanyaan: Berdasarkan matriks kebingungan, modelnya baik tidak? Jawaban: Tidak buruk; ada banyak positif benar dan sedikit negatif palsu.

From 46d3bd3986f021a017a41cb86141c91954cc52fb Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:10:05 -0400
Subject: [PATCH 295/368] Update README.it.md

---
 2-Regression/4-Logistic/translations/README.it.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md
index 3b30cf685c..e9940203ce 100644
--- a/2-Regression/4-Logistic/translations/README.it.md
+++ b/2-Regression/4-Logistic/translations/README.it.md
@@ -228,10 +228,6 @@ Cosa sta succedendo qui? Si supponga che al modello venga chiesto di classificar
 - Se il modello prevede qualcosa come una zucca e appartiene alla categoria 'non-una-zucca' in realtà si chiama falso negativo, mostrato dal numero in basso a sinistra.
 - Se il modello prevede qualcosa come non una zucca e appartiene alla categoria 'non-una-zucca' in realtà lo si chiama un vero negativo, mostrato dal numero in basso a destra.
 
-![Matrice di Confusione](../images/confusion-matrix.png)
-
-> Infografica di [Jen Looper](https://twitter.com/jenlooper)
-
 Come si sarà intuito, è preferibile avere un numero maggiore di veri positivi e veri negativi e un numero inferiore di falsi positivi e falsi negativi, il che implica che il modello funziona meglio.
 
 ✅ Domanda: Secondo la matrice di confusione, come si è comportato il modello? Risposta: Non male; ci sono un buon numero di veri positivi ma anche diversi falsi negativi.

From c9e2bdf2635c2422d2407c089ed54360973e3ca3 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:10:55 -0400
Subject: [PATCH 296/368] removing confusion matrix image

---
 2-Regression/4-Logistic/translations/README.zh-cn.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 3a5ff92609..5de169fbec 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -228,10 +228,6 @@ Seaborn�供了一些巧妙的方法��视化你的数�。例如，你�
 - 如果你的模型将�物预测为�瓜并且它实际上属于“��瓜�类别，我们将其称为�阴性，由左下角的数字显示。
 - 如果你的模型预测�物�是�瓜，并且它实际上属于“��瓜�类别，我们将其称为真阴性，如�下角的数字所示。
 
-![混淆矩阵](../images/confusion-matrix.png)
-
-> 作者[Jen Looper](https://twitter.com/jenlooper)
-
 正如你�能已�猜到的那样，最好有更多的真阳性和真阴性以�较少的�阳性和�阴性，这�味�模型性能更好。
 
 ✅ Q：根�混淆矩阵，模型怎么样？ A：还�错；有很多真阳性，但也有一些�阴性。

From 4e7e75c8c22855bd243f41019c50fe9c4a8e5cb1 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:15:13 -0400
Subject: [PATCH 297/368] Update lesson_2-R.ipynb

---
 2-Regression/2-Data/solution/lesson_2-R.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/2-Regression/2-Data/solution/lesson_2-R.ipynb b/2-Regression/2-Data/solution/lesson_2-R.ipynb
index 632de0d40d..9090e18360 100644
--- a/2-Regression/2-Data/solution/lesson_2-R.ipynb
+++ b/2-Regression/2-Data/solution/lesson_2-R.ipynb
@@ -216,7 +216,7 @@
       "source": [
         "## 3. Dplyr: A Grammar of Data Manipulation\n",
         "\n",
-        "![Artwork by \\@allison_horst](../images/dplyr_wrangling.png){width=\"569\"}"
+        "![Artwork by \\@allison_horst](../images/dplyr_wrangling.png)"
       ]
     },
     {
@@ -637,8 +637,8 @@
       "source": [
         "🤩🤩This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?\n",
         "\n",
-        "Congratulations on finishing the second lesson �! You did prepared your data for model building, then uncovered more insights using visualizations!"
+        "Congratulations on finishing the second lesson �! You prepared your data for model building, then uncovered more insights using visualizations!"
       ]
     }
   ]
-}
\ No newline at end of file
+}

From 4d24d0cc7e43141feb8f2157faafc714f7764744 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:17:38 -0400
Subject: [PATCH 298/368] art links

---
 2-Regression/2-Data/solution/lesson_2-R.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/2-Data/solution/lesson_2-R.ipynb b/2-Regression/2-Data/solution/lesson_2-R.ipynb
index 9090e18360..adb3a50376 100644
--- a/2-Regression/2-Data/solution/lesson_2-R.ipynb
+++ b/2-Regression/2-Data/solution/lesson_2-R.ipynb
@@ -40,7 +40,7 @@
         "\n",
         "Let's see this by working through a practical exercise.\n",
         "\n",
-        "![Artwork by \\@allison_horst](../images/unruly_data.jpg){width=\"700\"} <br>"
+        "![Artwork by \\@allison_horst](../images/unruly_data.jpg)<br>Artwork by \\@allison_horst"
       ]
     },
     {
@@ -216,7 +216,7 @@
       "source": [
         "## 3. Dplyr: A Grammar of Data Manipulation\n",
         "\n",
-        "![Artwork by \\@allison_horst](../images/dplyr_wrangling.png)"
+        "![Artwork by \\@allison_horst](../images/dplyr_wrangling.png)<br/>Artwork by \\@allison_horst"
       ]
     },
     {

From bb375dd2567d1700333dd28edde3b4bee344af59 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:23:07 -0400
Subject: [PATCH 299/368] Update README.md

---
 2-Regression/2-Data/README.md | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 2c7f23adcd..03d17fe7ed 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -1,10 +1,13 @@
 # Build a regression model using Scikit-learn: prepare and visualize data
 
-> ![Data visualization infographic](./images/data-visualization.png)
-> Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
+![Data visualization infographic](./images/data-visualization.png)
+
+Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
 
+> ### [This lesson is available in R!](../solution/lesson_2-R.ipynb)
+
 ## Introduction
 
 Now that you are set up with the tools you need to start tackling machine learning model building with Scikit-learn, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.

From eb5ae91609cb8c150ae0d9c41c20ecf3f4bb3433 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:24:20 -0400
Subject: [PATCH 300/368] editing art link

---
 2-Regression/1-Tools/solution/lesson_1-R.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/2-Regression/1-Tools/solution/lesson_1-R.ipynb b/2-Regression/1-Tools/solution/lesson_1-R.ipynb
index 6b6a0c42de..2af5f3e1db 100644
--- a/2-Regression/1-Tools/solution/lesson_1-R.ipynb
+++ b/2-Regression/1-Tools/solution/lesson_1-R.ipynb
@@ -42,7 +42,7 @@
         "\n",
         "That said, let's get started on this task!\n",
         "\n",
-        "![Artwork by \\@allison_horst](../images/encouRage.jpg){width=\"630\"}"
+        "![Artwork by \\@allison_horst](../images/encouRage.jpg){width=\"630\"}<br>Artwork by @allison_horst"
       ]
     },
     {
@@ -433,4 +433,4 @@
       ]
     }
   ]
-}
\ No newline at end of file
+}

From 1048989ed6218f9ac2680becb90501250e0f9d1a Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:24:51 -0400
Subject: [PATCH 301/368] edit for art link

---
 2-Regression/1-Tools/solution/lesson_1-R.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/solution/lesson_1-R.ipynb b/2-Regression/1-Tools/solution/lesson_1-R.ipynb
index 2af5f3e1db..cba2408151 100644
--- a/2-Regression/1-Tools/solution/lesson_1-R.ipynb
+++ b/2-Regression/1-Tools/solution/lesson_1-R.ipynb
@@ -42,7 +42,7 @@
         "\n",
         "That said, let's get started on this task!\n",
         "\n",
-        "![Artwork by \\@allison_horst](../images/encouRage.jpg){width=\"630\"}<br>Artwork by @allison_horst"
+        "![Artwork by \\@allison_horst](../images/encouRage.jpg)<br>Artwork by @allison_horst"
       ]
     },
     {

From 132068cd4d2042f088df47753a9c1e59bec4398c Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:25:46 -0400
Subject: [PATCH 302/368] Update README.md

---
 2-Regression/1-Tools/README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index 6225f7cd7e..fc222cb673 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -5,6 +5,9 @@
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+
+> ### [This lesson is available in R!](../solution/lesson_1-R.ipynb)
+
 ## Introduction
 
 In these four lessons, you will discover how to build regression models. We will discuss what these are for shortly. But before you do anything, make sure you have the right tools in place to start the process!

From 6fff369c0e475a85636778030d0c610eb0b6c19e Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:27:38 -0400
Subject: [PATCH 303/368] R lesson!

---
 2-Regression/1-Tools/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index fc222cb673..2bd3f7f2d7 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -6,7 +6,7 @@
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
 
-> ### [This lesson is available in R!](../solution/lesson_1-R.ipynb)
+> ### [This lesson is available in R!](./solution/lesson_1-R.ipynb)
 
 ## Introduction
 

From e468f6cbecfd89f85d6e4b5672e0e704a99bc5a7 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:28:03 -0400
Subject: [PATCH 304/368] Update README.md

---
 2-Regression/2-Data/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 03d17fe7ed..56493188bc 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -6,7 +6,7 @@ Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
 
-> ### [This lesson is available in R!](../solution/lesson_2-R.ipynb)
+> ### [This lesson is available in R!](./solution/lesson_2-R.ipynb)
 
 ## Introduction
 

From 32aa697fa4a605e0827fd6de543b2ce86a87c81d Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:32:16 -0400
Subject: [PATCH 305/368] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 8e7dca0039..64ab0a82e2 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,8 @@ Travel with us around the world as we apply these classic techniques to data fro
 
 **� Special thanks � to our Microsoft Student Ambassador authors, reviewers and content contributors**, notably Rishit Dagli, Muhammad Sakib Khan Inan, Rohan Raj, Alexandru Petrescu, Abhishek Jaiswal, Nawrin Tabassum, Ioan Samuila, and Snigdha Agarwal
 
+**🤩 Extra gratitude to Microsoft Student Ambassador Eric Wanjau for our R lessons!** 
+
 ---
 
 # Getting Started

From f5049f8e9fd910cc9563480124a06ed7eb33184a Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 28 Jul 2021 23:56:04 -0400
Subject: [PATCH 306/368] confusion matrices are confusing

---
 2-Regression/4-Logistic/README.md | 27 ++++++++++++++++++++-------
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 0708b36530..62697103cc 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -206,7 +206,7 @@ While you can get a scoreboard report [terms](https://scikit-learn.org/stable/mo
 
 > 🎓 A '[confusion matrix](https://wikipedia.org/wiki/Confusion_matrix)' (or 'error matrix') is a table that expresses your model's true vs. false positives and negatives, thus gauging the accuracy of predictions.
 
-1. To use a confusion metrics, call `confusin_matrix()`:
+1. To use a confusion metrics, call `confusion_matrix()`:
 
     ```python
     from sklearn.metrics import confusion_matrix
@@ -220,17 +220,29 @@ While you can get a scoreboard report [terms](https://scikit-learn.org/stable/mo
            [ 33,   0]])
     ```
 
-What's going on here? Let's say our model is asked to classify items between two binary categories, category 'pumpkin' and category 'not-a-pumpkin'.
+In Scikit-learn, confusion matrices Rows (axis 0) are actual labels and columns (axis 1) are predicted labels.
 
-- If your model predicts something as a pumpkin and it belongs to category 'pumpkin' in reality we call it a true positive, shown by the top left number. 
-- If your model predicts something as not a pumpkin and it belongs to category 'pumpkin' in reality we call it a false positive, shown by the top right number. 
-- If your model predicts something as a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a false negative, shown by the bottom left number. 
-- If your model predicts something as not a pumpkin and it belongs to category 'not-a-pumpkin' in reality we call it a true negative, shown by the bottom right number.
+|  |0|1|
+|:-:|:-:|:-:|
+|0|TN|FP|
+|1|FN|TP|
 
+What's going on here? Let's say our model is asked to classify pumpkins between two binary categories, category 'orange' and category 'not-orange'.
+
+- If your model predicts a pumpkin as not orange and it belongs to category 'not-orange' in reality we call it a true negative, shown by the top left number.
+- If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a false negative, shown by the bottom left number. 
+- If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a false positive, shown by the top right number. 
+- If your model predicts a pumpkin as orange and it belongs to category 'orange' in reality we call it a true positive, shown by the bottom right number.
 
 As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.
 
-✅ Q: According to the confusion matrix, how did the model do? A: Not too bad; there are a good number of true positives but also several false negatives. 
+How does the confusion matrix relate to precision and recall? Remember, the classification report printed above showed precision (0.83) and recall (0.98).
+
+Precision = tp / (tp + fp) = 162 / (162 + 33) = 0.8307692307692308
+
+Recall = tp / (tp + fn) = 162 / (162 + 4) = 0.9759036144578314
+
+✅ Q: According to the confusion matrix, how did the model do? A: Not too bad; there are a good number of true negatives but also several false negatives. 
 
 Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN:
 
@@ -249,6 +261,7 @@ Let's revisit the terms we saw earlier with the help of the confusion matrix's m
 🎓 Weighted Avg: The calculation of the mean metrics for each label, taking label imbalance into account by weighting them by their support (the number of true instances for each label).
 
 ✅ Can you think which metric you should watch if you want your model to reduce the number of false negatives?
+
 ## Visualize the ROC curve of this model
 
 This is not a bad model; its accuracy is in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables.

From 83f8a7980b5edfe05c15333e21c7273ccd650b54 Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Fri, 30 Jul 2021 10:46:50 +0800
Subject: [PATCH 307/368] Standardize some terms as other translation file

---
 .../2-Classifiers-1/translations/README.zh-cn.md     | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
index 2174423a29..ac5d80bae0 100644
--- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -4,10 +4,10 @@
 
 你将使用这份数�集，并通过多�分类器 _在给出了���料�预测这是那一个国家的��_。在此过程中，你将学到更多能够用�调试分类任务算法的方法。   
 
-## [课�测试](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
 # 准备工作
 
-�设你已�完�了[课程1](../1-Introduction/README.md), 确�在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件�进行接下�的四节课程。
+�如你已�完�了[课程1](../1-Introduction/README.md), 确�在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件�进行接下�的四节课程。
 
 ## 练习 - 预测�国的��
 
@@ -59,14 +59,14 @@
     Name: cuisine, dtype: object
     ```
 
-1. 调用`drop()`函数将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数�作为�以用于训练的特�（feature）数�:
+1. 调用`drop()`方法将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数�作为�以用于训练的特�（feature）数�:
 
     ```python
     cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
     cuisines_feature_df.head()
     ```
 
-    你的特�（feature）数�看上去将会是这样:
+    你的特�集看上去将会是这样:
 
     | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     |
     | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
@@ -78,7 +78,7 @@
 
 现在，你已�准备好�以开始训练你的模型了�
 
-## 选则你的分类器
+## 选择你的分类器
 
 你的数�已�清洗干净并已�准备好�以进行训练了，现在需�决定你想�使用的算法�完�这项任务。
 
@@ -232,7 +232,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
 
 在本课程中，你使用了清洗�的数�建立了一个机器学习的模型，能够根�一系列的�料�预测���自于哪个国家。请�花点时间阅读一下Scikit-learn所�供的�以用�分类数�的其他选择。�时也�以深入研究一下“solver�的概念并�试一下�解其背�的原�。
 
-## [课��测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
 ## 回顾与自学
 
 [这个课程](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)将对逻辑回归背�的数学原�进行更加深入的讲解

From 70bc7e558193177622bdb46f4c511411f35377f8 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Thu, 29 Jul 2021 23:07:09 -0400
Subject: [PATCH 308/368] typo fix for quiz

---
 quiz-app/src/assets/translations/en.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index b9ef44e264..337b086759 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -293,11 +293,11 @@
             "questionText": "Unfairness in a model can be caused by",
             "answerOptions": [
               {
-                "answerText": "overrreliance on historical data",
+                "answerText": "over reliance on historical data",
                 "isCorrect": "true"
               },
               {
-                "answerText": "underreliance on historical data",
+                "answerText": "under reliance on historical data",
                 "isCorrect": "false"
               },
               {

From 41f429311328e339710f8e09a43b3f66338d4506 Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Fri, 30 Jul 2021 11:32:43 +0800
Subject: [PATCH 309/368] optimize translation, more fluent

---
 .../translations/README.zh-cn.md              | 64 +++++++++----------
 1 file changed, 32 insertions(+), 32 deletions(-)

diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
index ac5d80bae0..36273b91bb 100644
--- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -82,7 +82,7 @@
 
 你的数�已�清洗干净并已�准备好�以进行训练了，现在需�决定你想�使用的算法�完�这项任务。
 
-Scikit_learn将分类任务归在了监�学习类别中，在这个类别中你将�以找到很多�以用�分类的方法。�一看上去，有点[��满目](https://scikit-learn.org/stable/supervised_learning.html)。以下这些方法都包�了分类技术：
+Scikit_learn将分类任务归在了监�学习类别中，在这个类别中你�以找到很多�以用�分类的方法。�一看上去，有点[��满目](https://scikit-learn.org/stable/supervised_learning.html)。以下这些算法都�以用于分类：
 
 - 线性模型（Linear Models）
 - 支���机（Support Vector Machines）
@@ -97,64 +97,64 @@ Scikit_learn将分类任务归在了监�学习类别中，在这个类别中
 
 ### 如何选择分类器?
 
-那么，你应该选择哪一个分类器呢？一般�说，�以多选择几个并对比他们�行�的结果。Scikit-learn�供了��算法（包括KNeighbors� SVC two ways� GaussianProcessClassifier� DecisionTreeClassifier� RandomForestClassifier� MLPClassifier� AdaBoostClassifier� GaussianNB以�QuadraticDiscrinationAnalysis）的效果[对比](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且将比较的结果进行了�视化的展示：
+那么，你应该如何从中选择分类器呢？一般�说，�以选择多个分类器并对比他们的�行结果。Scikit-learn�供了��算法（包括KNeighbors� SVC two ways� GaussianProcessClassifier� DecisionTreeClassifier� RandomForestClassifier� MLPClassifier� AdaBoostClassifier� GaussianNB以�QuadraticDiscrinationAnalysis）的[对比](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且将结果进行了�视化的展示：
 
 ![�分类器比较](../images/comparison.png)
 > 图表��于Scikit-learn的官方文档
 
-> AutoML通过在云端�行这些对比�常完美地解决的选择算法的这个问题，使得你能够根�你的数�特性选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)了解更多。
+> AutoML通过在云端�行这些算法并进行了对比，�常巧妙地解决的算法选择的问题，能帮助你根�数�集的特点�选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)了解更多。
 
-### 一�更好的方法�选择分类器
+### �外一�效果更佳的分类器选择方法
 
-�过，比起无脑地猜测，你�以下载这份[机器学习作弊表（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)，对�算法进行对比，这是一个选择算法更有效的办法。在表中我们�以�现对于本课程中涉�的多类型的分类任务，�以有以下这些选择：
+比起无脑地猜测，你�以下载这份[机器学习�抄（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)。这里�将�算法进行了比较，能更有效地帮助我们选择算法。根�这份�抄，我们�以找到�完�本课程中涉�的多类型的分类任务，�以有以下这些选择：
 
 ![多类型问题作弊表](../images/cheatsheet.png)
-> 微软算法作弊表中关于多类型分类任务�选算法的部分
+> 微软算法�抄中部分关于多类型分类任务�选算法
 
-✅ 下载这份作弊表，打�出�，挂在你的墙上��
+✅ 下载这份�抄，并打�出�，挂在你的墙上��
 
-### 选择的过程
+### 选择的�程
 
-让我们看看根�我们所有的�制�件�次判断下��方法的�行性：
+让我们根�所有�制�件�次对��算法的�行性进行判断：
 
-- **神�网络（Neural Network）太过��了**。我们的数�很清晰但数��比较�，此外我们是通过notebook在本地进行训练，神�网络对于这个任务�说过于��了。
-- **二分类法(two-class classifier)��行**。我们�能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 
-- **决策树以�逻辑回归�行**。决策树应该是有用的，此外也�以使用逻辑回归�处�多类型数�。
-- **多类型增强决策树是用于解决其他问题的**. 多类型增强决策树最适���数化的任务，�任务目标是建立一个排�，这对我们当�的任务并没有作用。
+- **神�网络（Neural Network）太过��了**。我们的数�很清晰但数��比较�，此外我们是通过notebook在本地进行训练的，神�网络对于这个任务�说过于��了。
+- **二分类法(two-class classifier)是��行的**。我们�能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 
+- **�以选择决策树以�逻辑回归算法**。决策树应该是�行的，此外也�以使用逻辑回归�处�多类型数�。
+- **多类型增强决策树是用于解决其他问题的**. 多类型增强决策树最适�的是��数化的任务，�任务目标是建立一个排�，这对我们当�的任务并没有作用。
 
 ### 使用Scikit-learn 
 
-我们将会使用Scikit-learn�对我们的数�进行分�。然而，在Scikit-learn中使用逻辑回归也有很很多方法。�以看一看逻辑回归算法需�[传递的�数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。
+我们将会使用Scikit-learn�对我们的数�进行分�。然而在Scikit-learn中使用逻辑回归也有很多方法。�以先了解一下逻辑回归算法需�[传递的�数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。
 
-当我们需�Scikit-learn进行逻辑回归�算时，`multi_class` 以� `solver`是最��的两个�数，因此我们需�特别说明一下。 `multi_class` 的值是分类任务�求的�一�特定的行为。`solver`的值是我们需�使用的算法。并�是所有的solvers都�以匹�`multi_class`的值的。
+当我们需�Scikit-learn进行逻辑回归�算时，`multi_class` 以� `solver`是最��的两个�数，因此我们需�特别说明一下。 `multi_class` 是分类方�选择�数，而`solver`优化算法选择�数。值得注�的是，并�是所有的solvers都�以与`multi_class`�数进行匹�的。
 
-根�文档，在多类型问题中，训练的算法应:
+根�官方文档，在多类型分类问题中:
 
-- **使用“一对其余�(OvR)策略（scheme）**, 当`multi_class`被设置为`ovr`时
-- **使用交�熵�失（cross entropy loss）**, 当`multi_class`被设置为`multinomial` (目�，`multinomial`�支�‘lbfgs’, ‘sag’, ‘saga’以�‘newton-cg’等 solver)时。
+- 当`multi_class`被设置为`ovr`时，将使用 **“一对其余�(OvR)策略（scheme）**。
+- 当`multi_class`被设置为`multinomial`时，则使用的是**交�熵�失（cross entropy loss）** 作为�失函数。(注�，目�`multinomial`�支�‘lbfgs’, ‘sag’, ‘saga’以�‘newton-cg’等solver作为�失函数的优化方法)
 
-> 🎓 其中“scheme��以是“ovr(one-vs-rest)�也�以是“multinomial�。 因为逻辑回归本�是设计�用于进行二分类任务的，这两个scheme都�以使得逻辑回归能更好的支�多类型分类任务。[��](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
+> 🎓 在本课程的任务中“scheme��以是“ovr(one-vs-rest)�也�以是“multinomial�。因为逻辑回归本�是设计�用于进行二分类任务的，这两个scheme�数的选择都�以使得逻辑回归很好的完�多类型分类任务。[��](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/)
 
 > 🎓 “solver�被定义为是"用于解决优化问题的算法"。[��](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression).
 
-Scikit-learn�供了以下这个表格�解释solver是如何应对的��的数�结构所带�的��的挑战的:
+Scikit-learn�供了以下这个表格�解释��solver是如何应对的��的数�结构所带�的��的挑战的:
 
 ![solvers](../images/solvers.png)
 
 ## 练习 - 分割数�
 
-你刚刚在上一节课中学习了逻辑回归，因此我们�以�焦于此，�演练一下如何进行第一个模型的训练。首先，需�通过调用`train_test_split()`�以把你的数�分割�训练集和测试集：
+因为你刚刚在上一节课中学习了逻辑回归，我们这里就通过逻辑回归算法，�演练一下如何进行你的第一个机器学习模型的训练。首先，需�通过调用`train_test_split()`方法�以把你的数�分割�训练集和测试集：
 
 
 ```python
 X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
 ```
 
-## 练习 - 应用逻辑回归
+## 练习 - 调用逻辑回归算法
 
-接�，你需�决定选用什么  _scheme_ 以� _solver_ �进行我们这个多类型分类的案例。这里我们使用LogisticRegression方法，并设置相应的multi_class�数，�时将solver设置为**liblinear**�进行模型训练。
+接下�，你需�决定选用什么 _scheme_ 以� _solver_ �进行我们这个多类型分类的案例。在这里我们使用LogisticRegression方法，并设置相应的multi_class�数，�时将solver设置为**liblinear**�进行模型训练。
 
-1. 创建逻辑回归，并将multi_class设置为`ovr`，�时将solver设置为 `liblinear`:
+1. 创建一个逻辑回归模型，并将multi_class设置为`ovr`，�时将solver设置为 `liblinear`:
 
     ```python
     lr = LogisticRegression(multi_class='ovr',solver='liblinear')
@@ -164,13 +164,13 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
     print ("Accuracy is {}".format(accuracy))
     ```
 
-    ✅ 也�以试试其他solver比如`lbfgs`, 这通常是默认的设置
+    ✅ 也�以试试其他solver比如`lbfgs`, 这也是默认�数
 
-    > 注�, 使用Pandas的[`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 函数�以在需�的时候将你的数�进行�维
+    > 注�, 使用Pandas的[`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 方法�以在需�的时候将你的数�进行�维
 
-    计算结果准确率高达了**80%**!
+    �算之�，�以看到准确率高达了**80%**!
 
-1. 你也�以通过查看�一行数�（比如第50行）�观察到模型�行的情况:
+1. 你也�以通过查看�一行数�（比如第50行）�观测到模型�行的情况:
 
     ```python
     print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
@@ -184,9 +184,9 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
    cuisine: indian
    ```
 
-   ✅ 试试��的行索引�检查一下结果�
+   ✅ 试试��的行索引�检查一下计算的结果�
 
-1. 让我们�深入研究一下，你�以检查一下本次预测的准确率:
+1. 我们�以�进行一部深入的研究，检查一下本轮预测结果的准确率:
 
     ```python
     test= X_test.iloc[50].values.reshape(-1, 1).T
@@ -210,7 +210,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
 
     ✅ 你能解释下为什么模型会如此确定这是一��度�么？
 
-1. 就和你在回归的课程中所�的一样，通过输出分类的报告，我们�以得到更多的细节：
+1. 和你在之�的回归的课程中所�的一样，我们也�以通过输出分类的报告得到关于模型的更多的细节：
 
     ```python
     y_pred = model.predict(X_test)
@@ -230,7 +230,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
 
 ## 挑战
 
-在本课程中，你使用了清洗�的数�建立了一个机器学习的模型，能够根�一系列的�料�预测���自于哪个国家。请�花点时间阅读一下Scikit-learn所�供的�以用�分类数�的其他选择。�时也�以深入研究一下“solver�的概念并�试一下�解其背�的原�。
+在本课程中，你使用了清洗�的数�建立了一个机器学习的模型，这个模型能够根�输入的一系列的�料�预测���自于哪个国家。请�花点时间阅读一下Scikit-learn所�供的关于�以用�分类数�的其他方法的资料。此外，你也�以深入研究一下“solver�的概念并�试一下�解其背�的原�。
 
 ## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
 ## 回顾与自学

From 369554be38fcdfccbdf20b060f370e89fd987210 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Fri, 30 Jul 2021 17:06:41 +0200
Subject: [PATCH 310/368] Italian translation - Chapter 5 complete

---
 .../1-Visualize/translations/README.it.md     | 332 ++++++++++++++++++
 .../1-Visualize/translations/assignment.it.md |  11 +
 .../2-K-Means/translations/README.it.md       | 251 +++++++++++++
 .../2-K-Means/translations/assignment.it.md   |  10 +
 5-Clustering/translations/README.it.md        |  29 ++
 5 files changed, 633 insertions(+)
 create mode 100644 5-Clustering/1-Visualize/translations/README.it.md
 create mode 100644 5-Clustering/1-Visualize/translations/assignment.it.md
 create mode 100644 5-Clustering/2-K-Means/translations/README.it.md
 create mode 100644 5-Clustering/2-K-Means/translations/assignment.it.md
 create mode 100644 5-Clustering/translations/README.it.md

diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md
new file mode 100644
index 0000000000..104507c914
--- /dev/null
+++ b/5-Clustering/1-Visualize/translations/README.it.md
@@ -0,0 +1,332 @@
+# Introduzione al clustering
+
+Il clustering è un tipo di [apprendimento non supervisionato](https://wikipedia.org/wiki/Unsupervised_learning) che presuppone che un insieme di dati non sia etichettato o che i suoi input non siano abbinati a output predefiniti. Utilizza vari algoritmi per ordinare i dati non etichettati e fornire raggruppamenti in base ai modelli che individua nei dati.
+
+[![No One Like You di PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You di PSquare")
+
+> 🎥 Fare clic sull'immagine sopra per un video. Mentre si studia machine learning con il clustering, si potranno gradire brani della Nigerian Dance Hall: questa è una canzone molto apprezzata del 2014 di PSquare.
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+
+### Introduzione
+
+[Il clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) è molto utile per l'esplorazione dei dati. Si vedrà se può aiutare a scoprire tendenze e modelli nel modo in cui il pubblico nigeriano consuma la musica.
+
+✅ Ci si prenda un minuto per pensare agli usi del clustering. Nella vita reale, il clustering si verifica ogni volta che si ha una pila di biancheria e si devono sistemare i vestiti dei propri familiari 🧦👕👖🩲. Nella scienza dei dati, il clustering si verifica quando si tenta di analizzare le preferenze di un utente o di determinare le caratteristiche di qualsiasi insieme di dati senza etichetta. Il clustering, in un certo senso, aiuta a dare un senso al caos, come un cassetto dei calzini.
+
+[![Introduzione a ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduzione al Clustering")
+
+> 🎥 Fare clic sull'immagine sopra per un video: John Guttag del MIT introduce il clustering
+
+In un ambiente professionale, il clustering può essere utilizzato per determinare cose come la segmentazione del mercato, determinare quali fasce d'età acquistano quali articoli, ad esempio. Un altro uso sarebbe il rilevamento di anomalie, forse per rilevare le frodi da un insieme di dati delle transazioni con carta di credito. Oppure si potrebbe usare il clustering per determinare i tumori in una serie di scansioni mediche.
+
+✅ Si pensi un minuto a come si potrebbe aver incontrato il clustering 'nel mondo reale', in un ambiente bancario, e-commerce o aziendale.
+
+> 🎓 È interessante notare che l'analisi dei cluster ha avuto origine nei campi dell'antropologia e della psicologia negli anni '30. Si riusce a immaginare come potrebbe essere stato utilizzato?
+
+In alternativa, lo si può utilizzare per raggruppare i risultati di ricerca, ad esempio tramite link per acquisti, immagini o recensioni. Il clustering è utile quando si dispone di un insieme di dati di grandi dimensioni che si desidera ridurre e sul quale si desidera eseguire un'analisi più granulare, quindi la tecnica può essere utilizzata per conoscere i dati prima che vengano costruiti altri modelli.
+
+✅ Una volta che i dati sono organizzati in cluster, viene assegnato un ID cluster e questa tecnica può essere utile quando si preserva la privacy di un insieme di dati; si può invece fare riferimento a un punto dati tramite il suo ID cluster, piuttosto che dati identificabili più rivelatori. Si riesce a pensare ad altri motivi per cui fare riferimento a un ID cluster piuttosto che ad altri elementi del cluster per identificarlo?
+
+In questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa) si approfondirà la propria comprensione delle tecniche di clustering
+
+## Iniziare con il clustering
+
+[Scikit-learn offre una vasta gamma](https://scikit-learn.org/stable/modules/clustering.html) di metodi per eseguire il clustering. Il tipo scelto dipenderà dal caso d'uso. Secondo la documentazione, ogni metodo ha diversi vantaggi. Ecco una tabella semplificata dei metodi supportati da Scikit-learn e dei loro casi d'uso appropriati:
+
+| Nome del metodo | Caso d'uso |
+| :--------------------------- | :--------------------------------------------------------------------- |
+| K-MEANS | uso generale, induttivo |
+| Affinity propagation (Propagazione dell'affinità) | molti, cluster irregolari, induttivo |
+| Mean-shift (Spostamento medio) | molti, cluster irregolari, induttivo |
+| Spectral clustering (Raggruppamento spettrale) | pochi, anche grappoli, trasduttivi |
+| Ward hierarchical clustering (Cluster gerarchico) | molti, cluster vincolati, trasduttivi |
+| Agglomerative clustering (Raggruppamento agglomerativo) | molte, vincolate, distanze non euclidee, trasduttive |
+| DBSCAN | geometria non piatta, cluster irregolari, trasduttivo |
+| OPTICS | geometria non piatta, cluster irregolari con densità variabile, trasduttivo |
+| Gaussian mixtures (miscele gaussiane) | geometria piana, induttiva |
+| BIRCH | insiemi di dati di grandi dimensioni con valori anomali, induttivo |
+
+> 🎓 Il modo in cui si creno i cluster ha molto a che fare con il modo in cui si raccolgono punti dati in gruppi. Si esamina un po' di vocabolario:
+>
+> 🎓 ['trasduttivo' vs. 'induttivo'](https://wikipedia.org/wiki/Transduction_(machine_learning))
+>
+> L'inferenza trasduttiva è derivata da casi di addestramento osservati che mappano casi di test specifici. L'inferenza induttiva è derivata da casi di addestramento che mappano regole generali che vengono poi applicate ai casi di test.
+>
+> Un esempio: si immagini di avere un insieme di dati che è solo parzialmente etichettato. Alcune cose sono "dischi", alcune "cd" e altre sono vuote. Il compito è fornire etichette per gli spazi vuoti. Se si scegliesse un approccio induttivo, si addestrerebbe un modello alla ricerca di "dischi" e "cd" e si applicherebbero quelle etichette ai dati non etichettati. Questo approccio avrà problemi a classificare cose che sono in realtà "cassette". Un approccio trasduttivo, d'altra parte, gestisce questi dati sconosciuti in modo più efficace poiché funziona raggruppando elementi simili e quindi applica un'etichetta a un gruppo. In questo caso, i cluster potrebbero riflettere "cose musicali rotonde" e "cose musicali quadrate".
+>
+> 🎓 [Geometria 'non piatta' (non-flat) vs. 'piatta' (flat)](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)
+>
+> Derivato dalla terminologia matematica, la geometria non piatta rispetto a quella piatta si riferisce alla misura delle distanze tra i punti mediante metodi geometrici "piatti" ([euclidei](https://wikipedia.org/wiki/Euclidean_geometry)) o "non piatti" (non euclidei).
+>
+> "Piatto" in questo contesto si riferisce alla geometria euclidea (parti della quale vengono insegnate come geometria "piana") e non piatto si riferisce alla geometria non euclidea. Cosa ha a che fare la geometria con machine learning? Bene, come due campi che sono radicati nella matematica, ci deve essere un modo comune per misurare le distanze tra i punti nei cluster, e questo può essere fatto in modo "piatto" o "non piatto", a seconda della natura dei dati . [Le distanze euclidee](https://wikipedia.org/wiki/Euclidean_distance) sono misurate come la lunghezza di un segmento di linea tra due punti. [Le distanze non euclidee](https://wikipedia.org/wiki/Non-Euclidean_geometry) sono misurate lungo una curva. Se i dati, visualizzati, sembrano non esistere su un piano, si potrebbe dover utilizzare un algoritmo specializzato per gestirli.
+>
+![Infografica con geometria piatta e non piatta](../images/flat-nonflat.png)
+> Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+>
+> [' Distanze'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)
+>
+> I cluster sono definiti dalla loro matrice di distanza, ad esempio le distanze tra i punti. Questa distanza può essere misurata in alcuni modi. I cluster euclidei sono definiti dalla media dei valori dei punti e contengono un 'centroide' o baricentro. Le distanze sono quindi misurate dalla distanza da quel baricentro. Le distanze non euclidee si riferiscono a "clustroidi", il punto più vicino ad altri punti. I clustroidi a loro volta possono essere definiti in vari modi.
+>
+> 🎓 ['Vincolato'](https://wikipedia.org/wiki/Constrained_clustering)
+>
+> [Constrained Clustering](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) introduce l'apprendimento 'semi-supervisionato' in questo metodo non supervisionato. Le relazioni tra i punti sono contrassegnate come "non è possibile collegare" o "è necessario collegare", quindi alcune regole sono imposte sull'insieme di dati.
+>
+> Un esempio: se un algoritmo viene applicato su un batch di dati non etichettati o semi-etichettati, i cluster che produce potrebbero essere di scarsa qualità. Nell'esempio sopra, i cluster potrebbero raggruppare "cose musicali rotonde" e "cose musicali quadrate" e "cose triangolari" e "biscotti". Se vengono dati dei vincoli, o delle regole da seguire ("l'oggetto deve essere di plastica", "l'oggetto deve essere in grado di produrre musica"), questo può aiutare a "vincolare" l'algoritmo a fare scelte migliori.
+>
+> 'Densità'
+>
+> I dati "rumorosi" sono considerati "densi". Le distanze tra i punti in ciascuno dei suoi cluster possono rivelarsi, all'esame, più o meno dense, o "affollate" e quindi questi dati devono essere analizzati con il metodo di clustering appropriato. [Questo articolo](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) dimostra la differenza tra l'utilizzo del clustering K-Means rispetto agli algoritmi HDBSCAN per esplorare un insieme di dati rumoroso con densità di cluster non uniforme.
+
+## Algoritmi di clustering
+
+Esistono oltre 100 algoritmi di clustering e il loro utilizzo dipende dalla natura dei dati a portata di mano. Si discutono alcuni dei principali:
+
+- **Raggruppamento gerarchico**. Se un oggetto viene classificato in base alla sua vicinanza a un oggetto vicino, piuttosto che a uno più lontano, i cluster vengono formati in base alla distanza dei loro membri da e verso altri oggetti. Il clustering agglomerativo di Scikit-learn è gerarchico.
+
+   ![Infografica sul clustering gerarchico](../images/hierarchical.png)
+   > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+- **Raggruppamento centroide**. Questo popolare algoritmo richiede la scelta di 'k', o il numero di cluster da formare, dopodiché l'algoritmo determina il punto centrale di un cluster e raccoglie i dati attorno a quel punto. [Il clustering K-means](https://wikipedia.org/wiki/K-means_clustering) è una versione popolare del clustering centroide. Il centro è determinato dalla media più vicina, da qui il nome. La distanza al quadrato dal cluster è ridotta al minimo.
+
+   ![Infografica sul clustering del centroide](../images/centroid.png)
+   > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
+
+- **Clustering basato sulla distribuzione**. Basato sulla modellazione statistica, il clustering basato sulla distribuzione è incentrato sulla determinazione della probabilità che un punto dati appartenga a un cluster e sull'assegnazione di conseguenza. I metodi di miscelazione gaussiana appartengono a questo tipo.
+
+- **Clustering basato sulla densità**. I punti dati vengono assegnati ai cluster in base alla loro densità o al loro raggruppamento l'uno intorno all'altro. I punti dati lontani dal gruppo sono considerati valori anomali o rumore. DBSCAN, Mean-shift e OPTICS appartengono a questo tipo di clustering.
+
+- **Clustering basato su griglia**. Per gli insiemi di dati multidimensionali, viene creata una griglia e i dati vengono divisi tra le celle della griglia, creando così dei cluster.
+
+## Esercizio: raggruppare i dati
+
+Il clustering come tecnica è notevolmente aiutato da una corretta visualizzazione, quindi si inizia visualizzando i  dati musicali. Questo esercizio aiuterà a decidere quale dei metodi di clustering si dovranno utilizzare in modo più efficace per la natura di questi dati.
+
+1. Aprire il file _notebook.ipynb_ in questa cartella.
+
+1. Importare il pacchetto `Seaborn` per una buona visualizzazione dei dati.
+
+   ```python
+   !pip install seaborn
+   ```
+
+1. Aggiungere i dati dei brani da _nigerian-songs.csv_. Caricare un dataframe con alcuni dati sulle canzoni. Prepararsi a esplorare questi dati importando le librerie e scaricando i dati:
+
+   ```python
+   import matplotlib.pyplot as plt
+   import pandas as pd
+
+   df = pd.read_csv("../data/nigerian-songs.csv")
+   df.head()
+   ```
+
+   Controllare le prime righe di dati:
+
+    |     | name                     | album                        | artist              | artist_top_genre | release_date | length | popularity | danceability | acousticness | energy | instrumentalness | liveness | loudness | speechiness | tempo   | time_signature |
+    | --- | ------------------------ | ---------------------------- | ------------------- | ---------------- | ------------ | ------ | ---------- | ------------ | ------------ | ------ | ---------------- | -------- | -------- | ----------- | ------- | -------------- |
+    | 0   | Sparky                   | Mandy & The Jungle           | Cruel Santino       | alternative r&b  | 2019         | 144000 | 48         | 0.666        | 0.851        | 0.42   | 0.534            | 0.11     | -6.699   | 0.0829      | 133.015 | 5              |
+    | 1   | shuga rush               | EVERYTHING YOU HEARD IS TRUE | Odunsi (The Engine) | afropop          | 2020         | 89488  | 30         | 0.71         | 0.0822       | 0.683  | 0.000169         | 0.101    | -5.64    | 0.36        | 129.993 | 3              |
+    | 2   | LITT!                    | LITT!                        | AYLØ                | indie r&b        | 2018         | 207758 | 40         | 0.836        | 0.272        | 0.564  | 0.000537         | 0.11     | -7.127   | 0.0424      | 130.005 | 4              |
+    | 3   | Confident / Feeling Cool | Enjoy Your Life              | Lady Donli          | nigerian pop     | 2019         | 175135 | 14         | 0.894        | 0.798        | 0.611  | 0.000187         | 0.0964   | -4.961   | 0.113       | 111.087 | 4              |
+    | 4   | wanted you               | rare.                        | Odunsi (The Engine) | afropop          | 2018         | 152049 | 25         | 0.702        | 0.116        | 0.833  | 0.91             | 0.348    | -6.044   | 0.0447      | 105.115 | 4              |
+
+1. Ottenere alcune informazioni sul dataframe, chiamando `info()`:
+
+   ```python
+   df.info()
+   ```
+
+   Il risultato appare così:
+
+   ```output
+   <class 'pandas.core.frame.DataFrame'>
+   RangeIndex: 530 entries, 0 to 529
+   Data columns (total 16 columns):
+    #   Column            Non-Null Count  Dtype  
+   ---  ------            --------------  -----  
+    0   name              530 non-null    object
+    1   album             530 non-null    object
+    2   artist            530 non-null    object
+    3   artist_top_genre  530 non-null    object
+    4   release_date      530 non-null    int64  
+    5   length            530 non-null    int64  
+    6   popularity        530 non-null    int64  
+    7   danceability      530 non-null    float64
+    8   acousticness      530 non-null    float64
+    9   energy            530 non-null    float64
+    10  instrumentalness  530 non-null    float64
+    11  liveness          530 non-null    float64
+    12  loudness          530 non-null    float64
+    13  speechiness       530 non-null    float64
+    14  tempo             530 non-null    float64
+    15  time_signature    530 non-null    int64  
+   dtypes: float64(8), int64(4), object(4)
+   memory usage: 66.4+ KB
+   ```
+
+1. Ricontrollare i valori null, chiamando `isnull()` e verificando che la somma sia 0:
+
+   ```python
+   df.isnull().sum()
+   ```
+
+   Si presenta bene!
+
+   ```output
+   name                0
+   album               0
+   artist              0
+   artist_top_genre    0
+   release_date        0
+   length              0
+   popularity          0
+   danceability        0
+   acousticness        0
+   energy              0
+   instrumentalness    0
+   liveness            0
+   loudness            0
+   speechiness         0
+   tempo               0
+   time_signature      0
+   dtype: int64
+   ```
+
+1. Descrivere i dati:
+
+   ```python
+   df.describe()
+   ```
+
+   |       | release_date | lenght | popularity | danceability | acousticness | Energia | strumentale | vitalità | livello di percezione sonora | parlato | tempo | #ora_firma |
+   | ----- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ---------------- | -------- | --------- | ----------- | ---------- | -------------- |
+   | estero) | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 |
+   | mezzo | 2015.390566 | 222298.1698 | 17.507547 | 0.741619 | 0.265412 | 0.760623 | 0,016305 | 0,147308 | -4.953011 | 0,130748 | 116.487864 | 3.986792 |
+   | std | 3.131688 | 39696.82226 | 18.992212 | 0,117522 | 0.208342 | 0.148533 | 0.090321 | 0,123588 | 2.464186 | 0,092939 | 23.518601 | 0.333701 |
+   | min | 1998 | 89488 | 0 | 0,255 | 0,000665 | 0,111 | 0 | 0,0283 | -19,362 | 0,0278 | 61.695 | 3 |
+   | 25% | 2014 | 199305 | 0 | 0,681 | 0,089525 | 0,669 | 0 | 0,07565 | -6.29875 | 0,0591 | 102.96125 | 4 |
+   | 50% | 2016 | 218509 | 13 | 0,761 | 0.2205 | 0.7845 | 0.000004 | 0,1035 | -4.5585 | 0,09795 | 112.7145 | 4 |
+   | 75% | 2017 | 242098.5 | 31 | 0,8295 | 0.403 | 0.87575 | 0.000234 | 0,164 | -3.331 | 0,177 | 125.03925 | 4 |
+   | max | 2020 | 511738 | 73 | 0.966 | 0,954 | 0,995 | 0,91 | 0,811 | 0,582 | 0.514 | 206.007 | 5 |
+
+> 🤔 Se si sta lavorando con il clustering, un metodo non supervisionato che non richiede dati etichettati, perché si stanno mostrando questi dati con etichette? Nella fase di esplorazione dei dati, sono utili, ma non sono necessari per il funzionamento degli algoritmi di clustering. Si potrebbero anche rimuovere le intestazioni delle colonne e fare riferimento ai dati per numero di colonna.
+
+Dare un'occhiata ai valori generali dei dati. Si nota che la popolarità può essere "0", che mostra i brani che non hanno una classifica. Quelli verranno rimossi a breve.
+
+1. Usare un grafico a barre per scoprire i generi più popolari:
+
+   ```python
+   import seaborn as sns
+
+   top = df['artist_top_genre'].value_counts()
+   plt.figure(figsize=(10,7))
+   sns.barplot(x=top[:5].index,y=top[:5].values)
+   plt.xticks(rotation=45)
+   plt.title('Top genres',color = 'blue')
+   ```
+
+   ![I più popolari](../images/popular.png)
+
+✅ Se si desidera vedere più valori superiori, modificare il valore di top `[:5]` con un valore più grande o rimuoverlo per vederli tutti.
+
+Nota, quando un valore di top è descritto come "Missing", ciò significa che Spotify non lo ha classificato, quindi va rimosso.
+
+1. Eliminare i dati mancanti escludendoli via filtro
+
+   ```python
+   df = df[df['artist_top_genre'] != 'Missing']
+   top = df['artist_top_genre'].value_counts()
+   plt.figure(figsize=(10,7))
+   sns.barplot(x=top.index,y=top.values)
+   plt.xticks(rotation=45)
+   plt.title('Top genres',color = 'blue')
+   ```
+
+   Ora ricontrollare i generi:
+
+   ![I più popolari](../images/all-genres.png)
+
+1. Di gran lunga, i primi tre generi dominano questo insieme di dati. Si pone l'attenzione su `afrodancehall,` `afropop` e `nigerian pop`, filtrando inoltre l'insieme di dati per rimuovere qualsiasi cosa con un valore di popolarità 0 (il che significa che non è stato classificato con una popolarità nell'insieme di dati e può essere considerato rumore per gli scopi attuali):
+
+   ```python
+   df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]
+   df = df[(df['popularity'] > 0)]
+   top = df['artist_top_genre'].value_counts()
+   plt.figure(figsize=(10,7))
+   sns.barplot(x=top.index,y=top.values)
+   plt.xticks(rotation=45)
+   plt.title('Top genres',color = 'blue')
+   ```
+
+1. Fare un test rapido per vedere se i dati sono correlati in modo particolarmente forte:
+
+   ```python
+   corrmat = df.corr()
+   f, ax = plt.subplots(figsize=(12, 9))
+   sns.heatmap(corrmat, vmax=.8, square=True)
+   ```
+
+   ![correlazioni](../images/correlation.png)
+
+   L'unica forte correlazione è tra `energy` e `loudness` (volume), il che non è troppo sorprendente, dato che la musica ad alto volume di solito è piuttosto energica. Altrimenti, le correlazioni sono relativamente deboli. Sarà interessante vedere cosa può fare un algoritmo di clustering di questi dati.
+
+   > 🎓 Notare che la correlazione non implica la causalità! Ci sono prove di correlazione ma nessuna prova di causalità. Un [sito web divertente](https://tylervigen.com/spurious-correlations) ha alcune immagini che enfatizzano questo punto.
+
+C'è qualche convergenza in questo insieme di dati intorno alla popolarità e alla ballabilità percepite di una canzone? Una FacetGrid mostra che ci sono cerchi concentrici che si allineano, indipendentemente dal genere. Potrebbe essere che i gusti nigeriani convergano ad un certo livello di ballabilità per questo genere?
+
+✅ Provare diversi punti dati (energy, loudness, speachiness) e più o diversi generi musicali. Cosa si può scoprire? Dare un'occhiata alla tabella con `df.describe()` per vedere la diffusione generale dei punti dati.
+
+### Esercizio - distribuzione dei dati
+
+Questi tre generi sono significativamente differenti nella percezione della loro ballabilità, in base alla loro popolarità?
+
+1. Esaminare la distribuzione dei dati sui tre principali generi per la popolarità e la ballabilità lungo un dato asse x e y.
+
+   ```python
+   sns.set_theme(style="ticks")
+
+   g = sns.jointplot(
+       data=df,
+       x="popularity", y="danceability", hue="artist_top_genre",
+       kind="kde",
+   )
+   ```
+
+   Si possono scoprire cerchi concentrici attorno a un punto di convergenza generale, che mostra la distribuzione dei punti.
+
+   > 🎓 Si noti che questo esempio utilizza un grafico KDE (Kernel Density Estimate) che rappresenta i dati utilizzando una curva di densità di probabilità continua. Questo consente di interpretare i dati quando si lavora con più distribuzioni.
+
+   In generale, i tre generi si allineano liberamente in termini di popolarità e ballabilità. Determinare i cluster in questi dati vagamente allineati sarà una sfida:
+
+   ![distribuzione](../images/distribution.png)
+
+1. Crea un grafico a dispersione:
+
+   ```python
+   sns.FacetGrid(df, hue="artist_top_genre", size=5) \
+      .map(plt.scatter, "popularity", "danceability") \
+      .add_legend()
+   ```
+
+   Un grafico a dispersione degli stessi assi mostra un modello di convergenza simile
+
+   ![Facetgrid](../images/facetgrid.png)
+
+In generale, per il clustering è possibile utilizzare i grafici a dispersione per mostrare i cluster di dati, quindi è molto utile padroneggiare questo tipo di visualizzazione. Nella prossima lezione, si prenderanno questi dati filtrati e si utilizzerà il clustering k-means per scoprire gruppi in questi dati che si sovrappongono in modi interessanti.
+
+---
+
+## 🚀 Sfida
+
+In preparazione per la lezione successiva, creare un grafico sui vari algoritmi di clustering che si potrebbero scoprire e utilizzare in un ambiente di produzione. Che tipo di problemi sta cercando di affrontare il clustering?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+
+## Revisione e Auto Apprendimento
+
+Prima di applicare gli algoritmi di clustering, come si è appreso, è una buona idea comprendere la natura del proprio insieme di dati. Leggere di più su questo argomento [qui](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)
+
+[Questo utile articolo](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) illustra i diversi modi in cui si comportano i vari algoritmi di clustering, date diverse forme di dati.
+
+## Compito
+
+[Ricercare altre visualizzazioni per il clustering](assignment.it.md)
diff --git a/5-Clustering/1-Visualize/translations/assignment.it.md b/5-Clustering/1-Visualize/translations/assignment.it.md
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+# Ricercare altre visualizzazioni per il clustering
+
+## Istruzioni
+
+In questa lezione, si è lavorato con alcune tecniche di visualizzazione per capire come tracciare i propri dati in preparazione per il clustering. I grafici a dispersione, in particolare, sono utili per trovare gruppi di oggetti. Ricercare modi diversi e librerie diverse per creare grafici a dispersione e documentare il proprio lavoro in un notebook. Si possono utilizzare i dati di questa lezione, di altre lezioni o dei dati che si sono procurati in autonomia (per favore citare la fonte, comunque, nel proprio notebook). Tracciare alcuni dati usando i grafici a dispersione e spiegare cosa si scopre.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | -------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | ----------------------------------- |
+|          | Viene presentato un notebook con cinque grafici a dispersione ben documentati | Un notebook viene presentato con meno di cinque grafici a dispersione ed è meno ben documentato | Viene presentato un notebook incompleto |
diff --git a/5-Clustering/2-K-Means/translations/README.it.md b/5-Clustering/2-K-Means/translations/README.it.md
new file mode 100644
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+# Clustering K-Means
+
+[![Andrew Ng spiega Clustering](https://img.youtube.com/vi/hDmNF9JG3lo/0.jpg)](https://youtu.be/hDmNF9JG3lo " Andrew Ng spiega Clustering")
+
+> 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng spiega il clustering
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+
+In questa lezione si imparerà come creare cluster utilizzando Scikit-learn e l'insieme di dati di musica nigeriana importato in precedenza. Si tratteranno le basi di K-Means per Clustering. Si tenga presente che, come appreso nella lezione precedente, ci sono molti modi per lavorare con i cluster e il metodo usato dipende dai propri dati. Si proverà K-Means poiché è la tecnica di clustering più comune. Si inizia!
+
+Temini che si imparerà a conoscere:
+
+- Silhouette scoring (punteggio silhouette)
+- Elbow method (metodo del gomito)
+- Inerzia
+- Varianza
+
+## Introduzione
+
+[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering) è un metodo derivato dal campo dell'elaborazione del segnale. Viene utilizzato per dividere e partizionare gruppi di dati in cluster "k" utilizzando una serie di osservazioni. Ogni osservazione lavora per raggruppare un dato punto dati più vicino alla sua "media" più vicina, o punto centrale di un cluster.
+
+I cluster possono essere visualizzati come [diagrammi di Voronoi](https://wikipedia.org/wiki/Voronoi_diagram), che includono un punto (o 'seme') e la sua regione corrispondente.
+
+![diagramma di voronoi](../images/voronoi.png)
+
+> Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+Il processo di clustering K-Means [viene eseguito in tre fasi](https://scikit-learn.org/stable/modules/clustering.html#k-means):
+
+1. L'algoritmo seleziona il numero k di punti centrali campionando dall'insieme di dati. Dopo questo, esegue un ciclo:
+   1. Assegna ogni campione al centroide più vicino.
+   2. Crea nuovi centroidi prendendo il valore medio di tutti i campioni assegnati ai centroidi precedenti.
+   3. Quindi, calcola la differenza tra il nuovo e il vecchio centroide e ripete finché i centroidi non sono stabilizzati.
+
+Uno svantaggio dell'utilizzo di K-Means include il fatto che sarà necessario stabilire 'k', ovvero il numero di centroidi. Fortunatamente il "metodo del gomito" aiuta a stimare un buon valore iniziale per "k". Si proverà in un minuto.
+
+## Prerequisito
+
+Si lavorerà nel file _notebook.ipynb_ di questa lezione  che include l'importazione dei dati e la pulizia preliminare fatta nell'ultima lezione.
+
+## Esercizio - preparazione
+
+Iniziare dando un'altra occhiata ai dati delle canzoni.
+
+1. Creare un diagramma a scatola e baffi (boxplot), chiamando `boxplot()` per ogni colonna:
+
+   ```python
+   plt.figure(figsize=(20,20), dpi=200)
+
+   plt.subplot(4,3,1)
+   sns.boxplot(x = 'popularity', data = df)
+
+   plt.subplot(4,3,2)
+   sns.boxplot(x = 'acousticness', data = df)
+
+   plt.subplot(4,3,3)
+   sns.boxplot(x = 'energy', data = df)
+
+   plt.subplot(4,3,4)
+   sns.boxplot(x = 'instrumentalness', data = df)
+
+   plt.subplot(4,3,5)
+   sns.boxplot(x = 'liveness', data = df)
+
+   plt.subplot(4,3,6)
+   sns.boxplot(x = 'loudness', data = df)
+
+   plt.subplot(4,3,7)
+   sns.boxplot(x = 'speechiness', data = df)
+
+   plt.subplot(4,3,8)
+   sns.boxplot(x = 'tempo', data = df)
+
+   plt.subplot(4,3,9)
+   sns.boxplot(x = 'time_signature', data = df)
+
+   plt.subplot(4,3,10)
+   sns.boxplot(x = 'danceability', data = df)
+
+   plt.subplot(4,3,11)
+   sns.boxplot(x = 'length', data = df)
+
+   plt.subplot(4,3,12)
+   sns.boxplot(x = 'release_date', data = df)
+   ```
+
+   Questi dati sono un po' rumorosi: osservando ogni colonna come un boxplot, si possono vedere i valori anomali.
+
+   ![situazioni anomale](../images/boxplots.png)
+
+Si potrebbe esaminare l'insieme di dati e rimuovere questi valori anomali, ma ciò renderebbe i dati piuttosto minimi.
+
+1. Per ora, si scelgono quali colonne utilizzare per questo esercizio di clustering. Scegliere quelle con intervalli simili e codifica la colonna `artist_top_genre` come dati numerici:
+
+   ```python
+   from sklearn.preprocessing import LabelEncoder
+   le = LabelEncoder()
+
+   X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]
+
+   y = df['artist_top_genre']
+
+   X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])
+
+   y = le.transform(y)
+   ```
+
+1. Ora si deve scegliere quanti cluster scegliere come obiettivo. E' noto che ci sono 3 generi di canzoni ricavati dall'insieme di dati, quindi si prova 3:
+
+   ```python
+   from sklearn.cluster import KMeans
+
+   nclusters = 3
+   seed = 0
+
+   km = KMeans(n_clusters=nclusters, random_state=seed)
+   km.fit(X)
+
+   # Predict the cluster for each data point
+
+   y_cluster_kmeans = km.predict(X)
+   y_cluster_kmeans
+   ```
+
+Viene visualizzato un array con i cluster previsti (0, 1 o 2) per ogni riga del dataframe di dati.
+
+1. Usare questo array per calcolare un "punteggio silhouette":
+
+   ```python
+   from sklearn import metrics
+   score = metrics.silhouette_score(X, y_cluster_kmeans)
+   score
+   ```
+
+## Punteggio Silhouette
+
+Si vuole ottenere un punteggio silhouette più vicino a 1. Questo punteggio varia da -1 a 1 e, se il punteggio è 1, il cluster è denso e ben separato dagli altri cluster. Un valore vicino a 0 rappresenta cluster sovrapposti con campioni molto vicini al limite di decisione dei clusters vicini [fonte](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).
+
+Il punteggio è **.53**, quindi proprio nel mezzo. Ciò indica che i dati non sono particolarmente adatti a questo tipo di clustering, ma si prosegue.
+
+### Esercizio: costruire il proprio modello
+
+1. Importare `KMeans` e avviare il processo di clustering.
+
+   ```python
+   from sklearn.cluster import KMeans
+   wcss = []
+
+   for i in range(1, 11):
+       kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
+       kmeans.fit(X)
+       wcss.append(kmeans.inertia_)
+
+   ```
+
+   Ci sono alcune parti qui che meritano una spiegazione.
+
+   > 🎓 range: queste sono le iterazioni del processo di clustering
+
+   > 🎓 random_state: "Determina la generazione di numeri casuali per l'inizializzazione del centroide."[fonte](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)
+
+   > 🎓 WCSS: "somma dei quadrati all'interno del cluster" misura la distanza media al quadrato di tutti i punti all'interno di un cluster rispetto al cluster centroid [fonte](https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce).
+
+   > 🎓 Inerzia: gli algoritmi K-Means tentano di scegliere i centroidi per ridurre al minimo l’’inerzia’, "una misura di quanto siano coerenti i cluster".[fonte](https://scikit-learn.org/stable/modules/clustering.html). Il valore viene aggiunto alla variabile wcss ad ogni iterazione.
+
+   > 🎓 k-means++: in [Scikit-learn](https://scikit-learn.org/stable/modules/clustering.html#k-means) puoi utilizzare l'ottimizzazione 'k-means++', che "inizializza i centroidi in modo che siano (generalmente) distanti l'uno dall'altro, portando probabilmente a risultati migliori rispetto all'inizializzazione casuale.
+
+### Metodo del gomito
+
+In precedenza, si era supposto che, poiché sono stati presi di mira 3 generi di canzoni, si dovrebbero scegliere 3 cluster. E' questo il caso?
+
+1. Usare il "metodo del gomito" per assicurarsene.
+
+   ```python
+   plt.figure(figsize=(10,5))
+   sns.lineplot(range(1, 11), wcss,marker='o',color='red')
+   plt.title('Elbow')
+   plt.xlabel('Number of clusters')
+   plt.ylabel('WCSS')
+   plt.show()
+   ```
+
+   Usare la variabile `wcss` creata nel passaggio precedente per creare un grafico che mostra dove si trova la "piegatura" nel gomito, che indica il numero ottimale di cluster. Forse **sono** 3!
+
+   ![Metodo del gomito](../images/elbow.png)
+
+## Esercizio - visualizzare i cluster
+
+1. Riprovare il processo, questa volta impostando tre cluster e visualizzare i cluster come grafico a dispersione:
+
+   ```python
+   from sklearn.cluster import KMeans
+   kmeans = KMeans(n_clusters = 3)
+   kmeans.fit(X)
+   labels = kmeans.predict(X)
+   plt.scatter(df['popularity'],df['danceability'],c = labels)
+   plt.xlabel('popularity')
+   plt.ylabel('danceability')
+   plt.show()
+   ```
+
+1. Verificare la precisione del modello:
+
+   ```python
+   labels = kmeans.labels_
+
+   correct_labels = sum(y == labels)
+
+   print("Result: %d out of %d samples were correctly labeled." % (correct_labels, y.size))
+
+   print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))
+   ```
+
+   La precisione di questo modello non è molto buona e la forma dei grappoli fornisce un indizio sul perché.
+
+   ![cluster](../images/clusters.png)
+
+   Questi dati sono troppo sbilanciati, troppo poco correlati e c'è troppa varianza tra i valori della colonna per raggruppare bene. In effetti, i cluster che si formano sono probabilmente fortemente influenzati o distorti dalle tre categorie di genere definite sopra. È stato un processo di apprendimento!
+
+   Nella documentazione di Scikit-learn, si può vedere che un modello come questo, con cluster non molto ben delimitati, ha un problema di "varianza":
+
+   ![modelli problematici](../images/problems.png)
+   > Infografica da Scikit-learn
+
+## Varianza
+
+La varianza è definita come "la media delle differenze al quadrato dalla media" [fonte](https://www.mathsisfun.com/data/standard-deviation.html). Nel contesto di questo problema di clustering, si fa riferimento ai dati che i numeri dell'insieme di dati tendono a divergere un po' troppo dalla media.
+
+✅ Questo è un ottimo momento per pensare a tutti i modi in cui si potrebbe correggere questo problema. Modificare un po' di più i dati? Utilizzare colonne diverse? Utilizzare un algoritmo diverso? Suggerimento: provare a [ridimensionare i dati](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) per normalizzarli e testare altre colonne.
+
+> Provare questo "[calcolatore della varianza](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)" per capire un po’ di più il concetto.
+
+---
+
+## 🚀 Sfida
+
+Trascorrere un po' di tempo con questo notebook, modificando i parametri. E possibile migliorare l'accuratezza del modello pulendo maggiormente i dati (rimuovendo gli outlier, ad esempio)? È possibile utilizzare i pesi per dare più peso a determinati campioni di dati. Cos'altro si può fare per creare cluster migliori?
+
+Suggerimento: provare a ridimensionare i dati. C'è un codice commentato nel notebook che aggiunge il ridimensionamento standard per rendere le colonne di dati più simili tra loro in termini di intervallo. Si scoprirà che mentre il punteggio della silhouette diminuisce, il "kink" nel grafico del gomito si attenua. Questo perché lasciare i dati non scalati consente ai dati con meno varianza di avere più peso. Leggere un po' di più su questo problema [qui](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+
+## Revisione e Auto Apprendimento
+
+Dare un'occhiata a un simulatore di K-Means [tipo questo](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). È possibile utilizzare questo strumento per visualizzare i punti dati di esempio e determinarne i centroidi. Questo aiuta a farsi un'idea di come i dati possono essere raggruppati?
+
+Inoltre, dare un'occhiata a [questa dispensa sui k-means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) di Stanford.
+
+## Compito
+
+[Provare diversi metodi di clustering](assignment.it.md)
diff --git a/5-Clustering/2-K-Means/translations/assignment.it.md b/5-Clustering/2-K-Means/translations/assignment.it.md
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+# Provare diversi metodi di clustering
+
+## Istruzioni
+
+In questa lezione si è imparato a conoscere il clustering K-Means. A volte K-Means non è appropriato per i propri dati. Creare un notebook usando i dati da queste lezioni o da qualche altra parte (accreditare la fonte) e mostrare un metodo di clustering diverso NON usando K-Means. Che cosa si è imparato?
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------------------------------------------------------------- | -------------------------------------------------------------------- | ---------------------------- |
+|          | Viene presentato un notebook con un modello di clustering ben documentato | Un notebook è presentato senza una buona documentazione e/o incompleto | E' stato inviato un lavoro incompleto |
diff --git a/5-Clustering/translations/README.it.md b/5-Clustering/translations/README.it.md
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+# Modelli di clustering per machine learning
+
+Il clustering è un'attività di machine learning che cerca di trovare oggetti che si assomigliano per raggrupparli in gruppi chiamati cluster. Ciò che differenzia il clustering da altri approcci in machine learning è che le cose accadono automaticamente, infatti, è giusto dire che è l'opposto dell'apprendimento supervisionato.
+
+## Tema regionale: modelli di clustering per il gusto musicale di un pubblico nigeriano 🎧
+
+Il pubblico eterogeneo della Nigeria ha gusti musicali diversi. Usando i dati recuperati da Spotify (ispirato da [questo articolo](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421), si dà un'occhiata a un po' di musica popolare in Nigeria. Questo insieme di dati include dati sul punteggio di "danzabilità", acustica, volume, "speechness" (un numero compreso tra zero e uno che indica la probabilità che un particolare file audio sia parlato - n.d.t.) popolarità ed energia di varie canzoni. Sarà interessante scoprire modelli in questi dati!
+
+![Un giradischi](../images/turntable.jpg)
+
+Foto di <a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a> su <a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+In questa serie di lezioni si scopriranno nuovi modi per analizzare i dati utilizzando tecniche di clustering. Il clustering è particolarmente utile quando l'insieme di dati non ha etichette. Se ha etichette, le tecniche di classificazione come quelle apprese nelle lezioni precedenti potrebbero essere più utili. Ma nei casi in cui si sta cercando di raggruppare dati senza etichetta, il clustering è un ottimo modo per scoprire i modelli.
+
+> Esistono utili strumenti a basso codice che possono aiutare a imparare a lavorare con i modelli di clustering. Si provi [Azure ML per questa attività](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+## Lezioni
+
+
+1. [Introduzione al clustering](../1-Visualize/translations/README.it.md)
+2. [K-Means clustering](../2-K-Means/translations/README.it.md)
+
+## Crediti
+
+Queste lezioni sono state scritte con 🎶 da [Jen Looper](https://www.twitter.com/jenlooper) con utili recensioni di [Rishit Dagli](https://rishit_dagli) e [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).
+
+L'insieme di dati [Nigerian Songs](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) è stato prelevato da Kaggle, a sua volta recuperato da Spotify.
+
+Esempi utili di K-Means che hanno aiutato nella creazione di questa lezione includono questa [esplorazione dell'iride](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering), questo [notebook introduttivo](https://www.kaggle.com/prashant111/k-means-clustering-with-python) e questo [ipotetico esempio di ONG](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering).
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+# 機械学習�手法
+
+機械学習モデルや��モデル�使用�るデータを構築・使用・管��るプロセス��他�多��開発ワークフロー��全�異�るも���。��レッスン�����プロセスを明快����知������主�手法�概�を�����。�����
+
+- 機械学習を支�るプロセスを高�水準��解���。
+- 「モデル�「予測�「訓練データ����基本的�概念を調���。
+
+## [講義���テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7?loc=ja)
+
+## å°Žå…¥
+
+大���言���機械学習 (Machine Learning: ML) プロセスを作��る技術������ステップ�構��れ����。
+
+1. **質�を決�る**。��ん��機械学習プロセス���純��件�プログラムやルールベース�エンジン��答�られ��よ��質�を�る���ら始�り��。��よ��質���データ�集�を使��予測を中心��れる���多���。
+2. **データを集��準備�る**。質��答�る����データ�必���。データ�質���������最��質���れ�����答�られる�を決���。データ��視化���フェーズ��������。モデルを構築�る���データを訓練グループ�テストグループ�分�る��も��フェーズ�����。
+3. **学習方法を��**。質��内容やデータ�性質�応���データを最も良��映��正確�予測��るモデルを���よ��学習�る�を��必���り��。�れ�機械学習プロセス�中�も�特定�専門知識��多��場����り�試行回数�必���る部分��。
+4. **モデルを学習�る**。データ�パターンを�識�るモデルを学習�る����訓練データ�様々�アルゴリズムを使���。モデル�より良�モデルを構築�る����データ�特定�部分を優先�るよ��調整��る内部���を活用�る�も�れ��ん。
+5. **モデルを評価�る**。モデル���よ��動作���る�を確��る����集��データ�中�ら��見������も�（テストデータ）を使���。
+6. **パラメータ�ューニング**。モデル�性能�よ����モデルを学習�る���使�れる��アルゴリズム�挙動を制御�るパラメータや変数を変更��プロセスをやり直���も����。
+7. **予測�る**。モデル�精度をテスト�る���新��入力を使���。
+
+## ��よ��質�を�れ�良��
+
+コンピュータ�データ�中�隠れ��るパターンを見��る�����も得���。��有用性���件ベース�ルールエンジンを作��も簡���答�られ��よ���特定�領域�関�る質�を����る研究者�����常�役立���。������る�険数���題��������データサイエンティスト�喫煙者��喫煙者�死亡率�関�る法則を自分�手���も作れる�も�れ��ん。
+
+����他�も多��変数�方程����れる場���去��康状態�ら将��死亡率を予測�る機械学習モデル�方�効率的�も�れ��ん。も��明る�テーマ�例�����緯度�経度�気候変動�海��近��ジェット気��パターン���データ�基����特定�場所���る4月�天気を予測�る�������。
+
+✅ 気象モデル�関�る�� [スライド](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) ��気象解��機械学習を使�際�歴�的�考�方を示�����。
+
+## 構築��タスク
+
+モデル�構築を始�る��������タスクを完了��る必���り��。質�をテスト��りモデル�予測�基���仮説を立��り�る�����������素を特定��設定�る必���り��。
+
+### データ
+
+質��確実�答�る������切�種類�データ�大��必���り��。����やる�����2��り��。
+
+- **データを集�る**。データ解����る公平性�関�る�回�講義を��出���ら�慎��データを集������。特定��イアスを����る�も�れ��データ�ソース�注����れを記録���������。
+- **データを準備�る**。データを準備�るプロセス�������ステップ��り��。異�るソース�らデータを集��場��照��正�化�必���る�も�れ��ん。（[クラスタリング](../../../5-Clustering/1-Visualize/README.md) �行���るよ���）文字列を数値�変��る���様々�方法�データ�質��を�上��る�������。（[分類](../../../4-Classification/1-Introduction/README.md) �行���るよ���）元�データ�ら新��データを生��る��も����。（[Webアプリ](../../../3-Web-App/README.md) �講義���行�よ���）データをクリーニング��り編集��り�る�������。最後��学習�手法�よ����ランダム���りシャッフル��り�る必�も�る�も�れ��ん。
+
+✅ データを集��処���後����形��図��質��対応��る����を確������ょ�。[クラスタリング](../../../5-Clustering/1-Visualize/README.md) �講義���るよ���データ�与�られ�タスク�対��上手�機能����も�れ��ん�
+
+### 特徴���択
+
+[特徴](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) ���測定�能�データ�特性�����。多��データセット���「日付�「大���「色����列�見出����表�れ����。コード上�� `y` �表�れる���多�特徴���データ�関�る次�よ��質���回答を�味���。「12月��ん� **色** �カボ�ャ�一番安��？�「サンフランシスコ�����地域�最も�動産� **価格** �高��？�
+
+🎓 **特徴�択�特徴抽出** モデルを構築�る際���変数を����������れ���る��ょ��？最も性能�高�モデル���������変数を�択�る特徴�択や特徴抽出�プロセスを��る����る��ょ�。�����れら���も����り��ん。「特徴抽出�元�特徴�機能�ら新��特徴を作��る��対��特徴�択�特徴�一部を返�も���。� ([出典](https://wikipedia.org/wiki/Feature_selection))
+
+### データを�視化�る
+
+データサイエンティスト��具�関�る�������Seaborn や MatPlotLib ���優れ�ライブラリを使��データを�視化�る力��。データを視覚的�表��る����隠れ�相関関係を見���活用��る�も�れ��ん。���（[分類](../../../4-Classification/2-Classifiers-1/README.md) ���るよ���）視覚化�る�����イアスや�ランシング�れ����データを見��られる�も�れ��ん。
+
+### データセットを分割�る
+
+学習���データセットを2�以上�分割����れ�れ�データを表����分����等�大����る必���り��。
+
+- **学習**。データセット���部分��モデルを学習�る���������。�れ�元�データセット�大部分を����。
+- **テスト**。テストデータセット���構築��モデル�性能を確��る���使用�る独立��データグループ�����多��場��元�データ�ら集�られ��。
+- **検証**。検証セット����ら�����独立��サンプル�集������モデルを改善�る����イパーパラメータや構造を調整�る際�使用�れ��。（[時系列予測](../../../7-TimeSeries/1-Introduction/README.md) �記載���るよ���）データ�大��や質��内容�よ������3�目�セットを作る必���り��ん。
+
+## モデル�構築
+
+訓練データ�様々�アルゴリズムを使�� **学習** �よ���モデルも���データ�統計的�表�を構築�る���目標��。モデルを学習�る����データを扱�るよ�����り�発見�検証�肯定����定��パターン�関�る仮説を立�る������り���。
+
+### 学習方法を決�る
+
+質��内容やデータ�性質�応���モデルを学習�る方法を�択���。��コース�使用�る [Scikit-learn �ドキュメント](https://scikit-learn.org/stable/user_guide.html) を見る��モデルを学習�る様々�方法を調�られ��。経験次第���最��モデルを構築�る��������異�る方法を試�必���る�も�れ��ん。���モデル�見������データを与��り�質を下���る�題�精度��イアス����調��り�タスク�対��最��学習方法を�ん�り�る����データサイエンティスト�行���る�モデル�性能を評価�るプロセスを�む����る��ょ�。
+
+### モデルを学習�る
+
+訓練データを用������モデルを作��る����れを「�����る準備�整����。多��機械学習ライブラリ�� 'model.fit' ���コード��る���気����ょ�。データを（通常� 'X' �表�）値��列�（通常� 'y' �表�）特徴����渡�����。
+
+### モデルを評価�る
+
+（大��モデルを学習�る��多���復（エ�ック）�必���り����）学習プロセス�完了��ら�テストデータを使��モデル�質を評価�る�������。��データ�元�データ����モデル��れ���分������も���。モデル�質を表�指標�表を出力�る�������。
+
+🎓 **モデルフィッティング**
+
+機械学習���るモデルフィッティング��モデル���知ら��データを分��る際�根本的�機能�精度を�照���。
+
+🎓 **未学習** � **�学習** �モデル�質を下�る一般的��題��モデル��分����������������������。�れ�よ��モデル�訓練データ�近���り����り�る予測を行���。�学習モデル��データ�詳細やノイズもよ�学習���る���訓練データを上手�予測���������。未学習モデル��訓練データや��「見�������データを正確�分��る����������精度�高�����。
+
+![�学習モデル](../images/overfitting.png)
+> [Jen Looper](https://twitter.com/jenlooper) �ん�よる解説画�
+
+## パラメータ�ューニング
+
+最��トレーニング�完了��ら�モデル�質を観察���「�イパーパラメータ��調整�よるモデル�改善を検討���ょ�。��プロセス����� [ドキュメント](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa) を読ん�����。
+
+## 予測
+
+全�新��データを使��モデル�精度をテスト�る瞬間��。本番環境�モデルを使用�る���Webアセットを構築�るよ�「�用�れ��機械学習�設定������推論や評価����モデル�渡��り�変数を設定��り�る���ユーザ�入力（ボタン�押下��）を�集�る�����プロセス���れる�も�れ��ん。
+
+��講義���「フルスタック��機械学習エンジニア��る���旅を���ら�準備・構築・テスト・評価・予測���データサイエンティスト�行�����ステップ�使�方を学���。
+
+---
+
+## 🚀�ャレンジ
+
+機械学習�学習者�ステップを�映��フロー�ャートを�������。今�自分���プロセス�����る������？���困難��る�予想����？�������簡�������何���？
+
+## [講義後��テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8?loc=ja)
+
+## 振り返り�自主学習
+
+データサイエンティスト�日々�仕事����話���るインタビューを�ット�検索�����ょ�。���� [�れ](https://www.youtube.com/watch?v=Z3IjgbbCEfs) ��。
+
+## 課題
+
+[データサイエンティスト�インタビュー�る](assignment.ja.md)

From 0ec65f5633e14c2b6aa9a75ebae58706a007945a Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Sat, 31 Jul 2021 20:11:04 +0900
Subject: [PATCH 312/368] style: clean 1.4 README

---
 1-Introduction/4-techniques-of-ML/README.md | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index ae9d2c44be..b29c7b1882 100644
--- a/1-Introduction/4-techniques-of-ML/README.md
+++ b/1-Introduction/4-techniques-of-ML/README.md
@@ -4,8 +4,9 @@ The process of building, using, and maintaining machine learning models and the
 
 - Understand the processes underpinning machine learning at a high level.
 - Explore base concepts such as 'models', 'predictions', and 'training data'.
-  
+
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+
 ## Introduction
 
 On a high level, the craft of creating machine learning (ML) processes is comprised of a number of steps:
@@ -44,9 +45,11 @@ To be able to answer your question with any kind of certainty, you need a good a
 A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**?
 
 🎓 **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." ([source](https://wikipedia.org/wiki/Feature_selection))
+
 ### Visualize your data
 
-An important aspect of the data scientist's toolkit is the power to  visualize data using several excellent libraries such as Seaborn or MatPlotLib. Representing your data visually might allow you to uncover hidden correlations that you can leverage. Your visualizations might also help you to uncover bias or unbalanced data (as we discover in [Classification](../../4-Classification/2-Classifiers-1/README.md)).
+An important aspect of the data scientist's toolkit is the power to visualize data using several excellent libraries such as Seaborn or MatPlotLib. Representing your data visually might allow you to uncover hidden correlations that you can leverage. Your visualizations might also help you to uncover bias or unbalanced data (as we discover in [Classification](../../4-Classification/2-Classifiers-1/README.md)).
+
 ### Split your dataset
 
 Prior to training, you need to split your dataset into two or more parts of unequal size that still represent the data well.
@@ -61,10 +64,12 @@ Using your training data, your goal is to build a model, or a statistical repres
 
 ### Decide on a training method
 
-Depending on your question and the nature of your data, your will choose a method to train it. Stepping through [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html) - which we use in this course - you can explore many ways to train a model. Depending on your experience, you might have to try several different methods to build the best model. You are likely to go through a process whereby data scientists evaluate the performance of a model by feeding it unseen data, checking for accuracy, bias, and other quality-degrading issues, and selecting the most appropriate training method for the task at hand.
+Depending on your question and the nature of your data, you will choose a method to train it. Stepping through [Scikit-learn's documentation](https://scikit-learn.org/stable/user_guide.html) - which we use in this course - you can explore many ways to train a model. Depending on your experience, you might have to try several different methods to build the best model. You are likely to go through a process whereby data scientists evaluate the performance of a model by feeding it unseen data, checking for accuracy, bias, and other quality-degrading issues, and selecting the most appropriate training method for the task at hand.
+
 ### Train a model
 
 Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your data as an array of values (usually 'X') and a feature variable (usually 'y').
+
 ### Evaluate the model
 
 Once the training process is complete (it can take many iterations, or 'epochs', to train a large model), you will be able to evaluate the model's quality by using test data to gauge its performance. This data is a subset of the original data that the model has not previously analyzed. You can print out a table of metrics about your model's quality.

From 80099c7ca4723d641392772c69cbcd08b04e715d Mon Sep 17 00:00:00 2001
From: KAN <private@mars.com>
Date: Sun, 1 Aug 2021 17:16:40 +0800
Subject: [PATCH 313/368] Chinese Translation for Charpter 5

---
 .../1-Visualize/translations/README.zh-cn.md  | 337 ++++++++++++++++++
 .../translations/assignment.zh-cn.md          |  13 +
 .../2-K-Means/translations/README.zh-cn.md    | 253 +++++++++++++
 .../translations/assignment.zh-cn.md          |  12 +
 5-Clustering/translations/README.zh-cn.md     |  27 ++
 5 files changed, 642 insertions(+)
 create mode 100644 5-Clustering/1-Visualize/translations/README.zh-cn.md
 create mode 100644 5-Clustering/1-Visualize/translations/assignment.zh-cn.md
 create mode 100644 5-Clustering/2-K-Means/translations/README.zh-cn.md
 create mode 100644 5-Clustering/2-K-Means/translations/assignment.zh-cn.md
 create mode 100644 5-Clustering/translations/README.zh-cn.md

diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md
new file mode 100644
index 0000000000..1697e9c491
--- /dev/null
+++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md
@@ -0,0 +1,337 @@
+# 介��类
+
+�类是一�无监�学习，它�定数�集未标记或其输入与预定义的输出�匹�。它使用��算法对未标记的数�进行排�，并根�它在数�中识别的模��供分组。
+[![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
+
+> 🎥 点击上�的图片观看视频。当您通过�类学习机器学习时，请欣�一些尼日利亚舞厅曲目 - 这是2014 年PSquare上高度评价的歌曲。
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+### 介�
+
+[�类](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)对于数�探索�常有用。让我们看看它是�有助于�现尼日利亚观众消费音�的趋势和模�。
+
+✅花一点时间�考�类的用途。在现实生活中，�当你有一堆衣�需�整�家人的衣�时，就会�生�类🧦👕👖🩲. 在数�科学中，�类用于在�试分�用户的�好或确定任何未标记数�集的特�。在��程度上，�类有助于�解�乱的状�，就�一个袜�抽屉。
+
+[![Introduction to ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduction to Clustering")
+
+> 🎥�击上图观看视频：麻��工学院的 John Guttag 介��类
+
+在专业环境中，�类�用于确定诸如市场细分之类的事情，例如确定哪些年龄组购买哪些商�。�一个用途是异常检测，�能是从信用�交易数�集中检测欺诈。或者您�以使用�类�确定一批医学扫�中的肿瘤。
+
+✅ 想一想您是如何在银行�电�商务或商业环境中“�外��到�类的。
+
+> 🎓有趣的是，�类分�起�于 1930 年代的人类学和心�学领域。你能想象它是如何被使用的�？
+
+或者，您�以使用它对�索结果进行分组 - 例如，通过购物链接�图片或评论。当您有一个大型数�集想��少并且想�对其执行更细粒度的分�时，�类�常有用，因此该技术�用于在构建其他模型之�了解数�。
+
+✅一旦你的数�被组织��类，你就为它分�一个�类 ID，这个技术在�护数�集的��时很有用；您�以改为通过其�类 ID �引用数�点，而�是通过更多的�明显区分的数�。您能想到为什么�引用�类 ID 而�是�类的其他元素�识别它的其他原因�？
+
+在此[学习模�中](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)加深您对�类技术的�解
+
+## �类入门
+
+[Scikit-learn �供了大�](https://scikit-learn.org/stable/modules/clustering.html)的方法�执行�类。您选择的类型将�决于您的用例。根�文档，��方法都有��的好处。以下是 Scikit-learn 支�的方法�其适当用例的简化表：
+
+| 方法�称                     | 用例                                               |
+| ---------------------------- | -------------------------------------------------- |
+| K-Means                      | 通用目的，归纳的                                   |
+| Affinity propagation         | 许多，��匀的�类，归纳的                         |
+| Mean-shift                   | 许多，��匀的�类，归纳的                         |
+| Spectral clustering          | 少数，甚至�类，转导的                             |
+| Ward hierarchical clustering | 许多，�约�的�类，转导的                         |
+| Agglomerative clustering     | 许多，�约�的，�欧几里得�离，转导的             |
+| DBSCAN                       | �平�几何，��匀�类，转导的                     |
+| OPTICS                       | �平�的几何形状，具有��密度的��匀�类，转导的 |
+| Gaussian mixtures            | 平�几何，感应的                                   |
+| BIRCH                        | 具有异常值的大型数�集，归纳的                     |
+
+> 🎓我们如何创建�类与我们如何将数�点收集到组中有很大关系。让我们分�一些�汇：
+>
+> 🎓 [“转导�与“归纳�](https://wikipedia.org/wiki/Transduction_(machine_learning))
+>
+> 转导推��自观察到的映射到特定测试用例的训练用例。归纳推��自映射到一般规则的训练案例，然��应用于测试案例。
+>
+> 示例：�设您有一个仅部分标记的数�集。有些东西是“记录�，有些是“CD�，有些是空白的。您的工作是为空白�供标签。如果您选择归纳方法，您将训练一个寻找“记录�和“CD�的模型，并将这些标签应用于未标记的数�。这�方法将难以对实际上是“盒��带�的东西进行分类。�一方�，转导方法�以更有效地处�这些未知数�，因为它�以将相似的项目组�在一起，然�将标签应用于一个组。在这�情况下，�类�能�映“圆形音�事物�和“方形音�事物�。
+>
+> 🎓 [“�平��与“平��几何](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)
+>
+> �自数学术语，�平�与平�几何是指通过“平��（[欧几里德](https://wikipedia.org/wiki/Euclidean_geometry)）或“�平��（�欧几里得）几何方法测�点之间的�离。
+>
+> 在此上下文中，“平��是指欧几里得几何（其中一部分被教导为“平��几何），而�平�是指�欧几里得几何。几何与机器学习有什么关系？好�，作为�根于数学的两个领域，必须有一�通用的方法�测��类中点之间的�离，并且�以以“平��(flat)或“�平��(non-flat)的方�完�，具体�决于数�的性质. [欧几里得�离](https://wikipedia.org/wiki/Euclidean_distance)测�为两点之间线段的长度。[�欧�离](https://wikipedia.org/wiki/Non-Euclidean_geometry)是沿曲线测�的。如果您的�视化数�似乎�存在于平�上，您�能需�使用专门的算法�处�它。
+>
+> ![Flat vs Nonflat Geometry Infographic](../images/flat-nonflat.png)
+> [Dasani Madipalli ](https://twitter.com/dasani_decoded)作图
+>
+> 🎓 ['�离'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)
+>
+> �类由它们的�离矩阵定义，例如点之间的�离。这个�离�以通过几�方��测�。欧几里得�类由点值的平�值定义，并包�“质心�或中心点。因此，�离是通过到该质心的�离�测�的。�欧��离指的是“�类心�，�离其他点最近的点。�类心��以用��方�定义。
+>
+> 🎓 ['约�'](https://wikipedia.org/wiki/Constrained_clustering)
+>
+> [约��类](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf)将“�监��学习引入到这�无监�方法中。点之间的关系被标记为“无法链接�或“必须链接�，因此对数�集强加了一些规则。
+>
+> 一个例�：如果一个算法在一批未标记或�标记的数�上��约�，它产生的�类质��能很差。在上�的示例中，�类�能将“圆形音�事物�和“方形音�事物�以�“三角形事物�和“饼干�分组。如果给出一些约�或��循的规则（“物�必须由塑料制���“物�需�能够产生音��），这�以帮助“约��算法�出更好的选择。
+>
+> 🎓 '密度'
+>
+> “嘈��的数�被认为是“密集的�。在检查时，�个�类中的点之间的�离�能或多或少地密集或“拥挤�，因此需�使用适当的�类方法分�这些数�。[本文](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html)演示了使用 K-Means �类与 HDBSCAN 算法探索具有��匀�类密度的嘈�数�集之间的区别。
+
+## �类算法
+
+有超过 100 ��类算法，它们的使用�决于手头数�的性质。让我们讨论一些主�的：
+
+- **层次�类**。如果一个对象是根�其与附近对象的接近程度而�是较远对象�分类的，则�类是根�其�员与其他对象之间的�离�形�的。Scikit-learn 的���类是分层的。
+
+   ![Hierarchical clustering Infographic](../images/hierarchical.png)
+
+   > [Dasani Madipalli ](https://twitter.com/dasani_decoded)作图
+
+- **质心�类**。这��行的算法需�选择“k�或�形�的�类数�，然�算法确定�类的中心点并围绕该点收集数�。[K-means �类](https://wikipedia.org/wiki/K-means_clustering)是质心�类的�行版本。中心由最近的平�值确定，因此��质心。与�类的平方�离被最�化。
+
+   ![Centroid clustering Infographic](../images/centroid.png)
+
+   > [Dasani Madipalli](https://twitter.com/dasani_decoded)作图
+
+- **基于分布的�类**。基于统计建模，基于分布的�类中心确定一个数�点属于一个�类的概率，并相应地分�它。高斯混�方法属于这�类型。
+
+- **基于密度的�类**。数�点根�它们的密度或它们彼此的分组分�给�类。远离该组的数�点被视为异常值或噪声。DBSCAN�Mean-shift 和 OPTICS 属于此类�类。
+
+- **基于网格的�类**。对于多维数�集，创建一个网格并将数�划分到网格的�元格中，从而创建�类。
+
+
+
+## 练习 - 对你的数�进行�类
+
+适当的�视化对�类作为一�技术有很大帮助，所以让我们从�视化我们的音�数�开始。这个练习将帮助我们决定我们应该最有效地使用哪��类方法�处�这些数�的性质。
+
+1. 打开此文件夹中的*notebook.ipynb*文件。
+
+1. 导入`Seaborn`包以获得良好的数��视化。
+
+    ```python
+    !pip install seaborn
+    ```
+
+1. 附加�自*nigerian-songs.csv*的歌曲数�。加载包�有关歌曲的一些数�的数�帧。准备好通过导入库和转储数��探索这些数�：
+
+    ```python
+    import matplotlib.pyplot as plt
+    import pandas as pd
+    
+    df = pd.read_csv("../data/nigerian-songs.csv")
+    df.head()
+    ```
+
+    检查�几行数�：
+
+    |     | name                     | album                        | artist              | artist_top_genre | release_date | length | popularity | danceability | acousticness | energy | instrumentalness | liveness | loudness | speechiness | tempo   | time_signature |
+    | --- | ------------------------ | ---------------------------- | ------------------- | ---------------- | ------------ | ------ | ---------- | ------------ | ------------ | ------ | ---------------- | -------- | -------- | ----------- | ------- | -------------- |
+    | 0   | Sparky                   | Mandy & The Jungle           | Cruel Santino       | alternative r&b  | 2019         | 144000 | 48         | 0.666        | 0.851        | 0.42   | 0.534            | 0.11     | -6.699   | 0.0829      | 133.015 | 5              |
+    | 1   | shuga rush               | EVERYTHING YOU HEARD IS TRUE | Odunsi (The Engine) | afropop          | 2020         | 89488  | 30         | 0.71         | 0.0822       | 0.683  | 0.000169         | 0.101    | -5.64    | 0.36        | 129.993 | 3              |
+    | 2   | LITT!                    | LITT!                        | AYLØ                | indie r&b        | 2018         | 207758 | 40         | 0.836        | 0.272        | 0.564  | 0.000537         | 0.11     | -7.127   | 0.0424      | 130.005 | 4              |
+    | 3   | Confident / Feeling Cool | Enjoy Your Life              | Lady Donli          | nigerian pop     | 2019         | 175135 | 14         | 0.894        | 0.798        | 0.611  | 0.000187         | 0.0964   | -4.961   | 0.113       | 111.087 | 4              |
+    | 4   | wanted you               | rare.                        | Odunsi (The Engine) | afropop          | 2018         | 152049 | 25         | 0.702        | 0.116        | 0.833  | 0.91             | 0.348    | -6.044   | 0.0447      | 105.115 | 4              |
+
+1. 获�有关数�帧的一些信�，调用`info()`：
+
+    ```python
+    df.info()
+    ```
+
+   输出看起��这样：
+
+    ```output
+    <class 'pandas.core.frame.DataFrame'>
+    RangeIndex: 530 entries, 0 to 529
+    Data columns (total 16 columns):
+     #   Column            Non-Null Count  Dtype  
+    ---  ------            --------------  -----  
+     0   name              530 non-null    object 
+     1   album             530 non-null    object 
+     2   artist            530 non-null    object 
+     3   artist_top_genre  530 non-null    object 
+     4   release_date      530 non-null    int64  
+     5   length            530 non-null    int64  
+     6   popularity        530 non-null    int64  
+     7   danceability      530 non-null    float64
+     8   acousticness      530 non-null    float64
+     9   energy            530 non-null    float64
+     10  instrumentalness  530 non-null    float64
+     11  liveness          530 non-null    float64
+     12  loudness          530 non-null    float64
+     13  speechiness       530 non-null    float64
+     14  tempo             530 non-null    float64
+     15  time_signature    530 non-null    int64  
+    dtypes: float64(8), int64(4), object(4)
+    memory usage: 66.4+ KB
+    ```
+
+1. 通过调用`isnull()`和验�总和为 0 �仔细检查空值：
+
+    ```python
+    df.isnull().sum()
+    ```
+
+    看起��错：
+
+    ```output
+    name                0
+    album               0
+    artist              0
+    artist_top_genre    0
+    release_date        0
+    length              0
+    popularity          0
+    danceability        0
+    acousticness        0
+    energy              0
+    instrumentalness    0
+    liveness            0
+    loudness            0
+    speechiness         0
+    tempo               0
+    time_signature      0
+    dtype: int64
+    ```
+
+1. �述数�：
+
+    ```python
+    df.describe()
+    ```
+
+    |       | release_date | length      | popularity | danceability | acousticness | energy   | instrumentalness | liveness | loudness  | speechiness | tempo      | time_signature |
+    | ----- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ---------------- | -------- | --------- | ----------- | ---------- | -------------- |
+    | count | 530          | 530         | 530        | 530          | 530          | 530      | 530              | 530      | 530       | 530         | 530        | 530            |
+    | mean  | 2015.390566  | 222298.1698 | 17.507547  | 0.741619     | 0.265412     | 0.760623 | 0.016305         | 0.147308 | -4.953011 | 0.130748    | 116.487864 | 3.986792       |
+    | std   | 3.131688     | 39696.82226 | 18.992212  | 0.117522     | 0.208342     | 0.148533 | 0.090321         | 0.123588 | 2.464186  | 0.092939    | 23.518601  | 0.333701       |
+    | min   | 1998         | 89488       | 0          | 0.255        | 0.000665     | 0.111    | 0                | 0.0283   | -19.362   | 0.0278      | 61.695     | 3              |
+    | 25%   | 2014         | 199305      | 0          | 0.681        | 0.089525     | 0.669    | 0                | 0.07565  | -6.29875  | 0.0591      | 102.96125  | 4              |
+    | 50%   | 2016         | 218509      | 13         | 0.761        | 0.2205       | 0.7845   | 0.000004         | 0.1035   | -4.5585   | 0.09795     | 112.7145   | 4              |
+    | 75%   | 2017         | 242098.5    | 31         | 0.8295       | 0.403        | 0.87575  | 0.000234         | 0.164    | -3.331    | 0.177       | 125.03925  | 4              |
+    | max   | 2020         | 511738      | 73         | 0.966        | 0.954        | 0.995    | 0.91             | 0.811    | 0.582     | 0.514       | 206.007    | 5              |
+
+> 🤔如果我们正在使用�类，一��需�标记数�的无监�方法，为什么我们用标签显示这些数�？在数�探索阶段，它们派上用场，但它们�是�类算法工作所必需的。您也�以删除列标题并按列�引用数�。
+
+查看数�的普�值。请注�，�行度�以是“0�，表示没有排�的歌曲。让我们尽快删除它们。
+
+1. 使用�形图找出最�欢迎的类型：
+
+    ```python
+    import seaborn as sns
+    
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top[:5].index,y=top[:5].values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+    ![most popular](../images/popular.png)
+
+✅如果您想查看更多顶部值，请将顶部更改`[:5]`为更大的值，或将其删除以查看全部。
+
+请注�，当顶级�派被�述为“缺失�时，这�味� Spotify 没有对其进行分类，所以让我们��它。
+
+1. 通过过滤掉丢失的数���
+
+    ```python
+    df = df[df['artist_top_genre'] != 'Missing']
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top.index,y=top.values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+    现在�新检查genres：
+
+    ![most popular](../images/all-genres.png)
+
+1. 到目�为止，�三大�派主导了这个数�集。让我们专注于`afro dancehall`, `afropop`, 和`nigerian pop`，�外过滤数�集以删除任何具有 0 �行度值的内容（这�味�它在数�集中没有被归类为�行度并且�以被视为我们的目的的噪音）：
+
+    ```python
+    df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]
+    df = df[(df['popularity'] > 0)]
+    top = df['artist_top_genre'].value_counts()
+    plt.figure(figsize=(10,7))
+    sns.barplot(x=top.index,y=top.values)
+    plt.xticks(rotation=45)
+    plt.title('Top genres',color = 'blue')
+    ```
+
+1. �一个快速测试，看看数�是�以任何特别强的方�相关：
+
+    ```python
+    corrmat = df.corr()
+    f, ax = plt.subplots(figsize=(12, 9))
+    sns.heatmap(corrmat, vmax=.8, square=True)
+    ```
+
+    ![correlations](../images/correlation.png)
+
+    > 唯一强相关性是`energy`和之间`loudness`，这并�奇怪，因为嘈�的音�通常�常有活力。�则，相关性相对较弱。看看�类算法�以如何处�这些数�会很有趣。
+    >
+    > > 🎓请注�，相关性并��味�因果关系�我们有相关性的��，但没有因果关系的��。一个[有趣的网站](https://tylervigen.com/spurious-correlations)有一些强调这一点的视觉效果。
+
+这个数�集是�围绕歌曲的�行度和�舞性有任何收敛？FacetGrid 显示无论�派如何，都有�心圆排列。对于这�类型，尼日利亚人的�味是�会在��程度的�舞性上趋于一致？
+
+✅�试��的数�点（能���度�语音）和更多或��的音�类型。你能�现什么？查看`df.describe()`表格以了解数�点的一般分布。
+
+### 练习 - 数�分布
+
+这三��派是�因其�欢迎程度而对其�舞性的看法有显�差异？
+
+1. 检查我们沿给定 x 和 y 轴的�行度和�舞性的�三�类型数�分布。
+
+    ```python
+    sns.set_theme(style="ticks")
+    
+    g = sns.jointplot(
+        data=df,
+        x="popularity", y="danceability", hue="artist_top_genre",
+        kind="kde",
+    )
+    ```
+
+    您�以�现围绕一般收敛点的�心圆，显示点的分布。
+
+    > 🎓请注�，此示例使用 KDE（核密度估计）图，该图使用连续概率密度曲线表示数�。这�许我们在处�多个分布时解释数�。
+
+    总的�说，这三��派在�行度和�舞性方��散地对�。在这��散对�的数�中确定�类将是一个挑战：
+
+    ![distribution](../images/distribution.png)
+
+1. 创建散点图：
+
+    ```python
+    sns.FacetGrid(df, hue="artist_top_genre", size=5) \
+       .map(plt.scatter, "popularity", "danceability") \
+       .add_legend()
+    ```
+
+    相�轴的散点图显示了类似的收敛模�
+
+    ![Facetgrid](../images/facetgrid.png)
+
+一般�说，对于�类，你�以使用散点图�展示数�的�类，所以掌�这�类型的�视化是�常有用的。在下一课中，我们将使用过滤�的数�并使用 k-means �类��现这些数�中以有趣方���的组。
+
+---
+
+## 🚀挑战
+
+为下一课�准备，制作一张图表，说明您�能会在生产环境中�现和使用的���类算法。
+
+�类试图解决什么样的问题？
+
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+
+## �习与自学
+
+在应用�类算法之�，正如我们所了解的，了解数�集的性质是一个好主�。[在此处](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)阅读有关此主题的更多[信�](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)
+
+[这篇有用的文章将](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/)引导您了解���类算法在给定��数�形状的情况下的��行为方�。
+
+## 作业
+
+[研究用于�类的其他�视化](./assignment.zh-cn.md)
+
diff --git a/5-Clustering/1-Visualize/translations/assignment.zh-cn.md b/5-Clustering/1-Visualize/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..48f5ea24a4
--- /dev/null
+++ b/5-Clustering/1-Visualize/translations/assignment.zh-cn.md
@@ -0,0 +1,13 @@
+# 研究用于�类的其他�视化
+
+## 说明
+
+在本节课中，您使用了一些�视化技术�掌�绘制数�图，为�类数��准备。散点图在寻找一组对象时尤其有用。研究��的方法和��的库�创建散点图，并在notebook上记录你的工作。你�以使用这节课的数�，其他课的数�，或者你自己的数�(但是，请把它的��记在你的notebook上)。用散点图绘制一些数�，并解释你的�现。
+
+## 评判规则
+
+
+| 评判标准 | 优秀                             | 中规中矩                                        | �需努力             |
+| -------- | -------------------------------- | ----------------------------------------------- | -------------------- |
+|          | notebook上有五个详细文档的散点图 | notebook上的散点图少于5个，而且文档写得�太详细 | 一个�完整的notebook |
+
diff --git a/5-Clustering/2-K-Means/translations/README.zh-cn.md b/5-Clustering/2-K-Means/translations/README.zh-cn.md
new file mode 100644
index 0000000000..32a3301555
--- /dev/null
+++ b/5-Clustering/2-K-Means/translations/README.zh-cn.md
@@ -0,0 +1,253 @@
+# K-Means �类
+
+[![Andrew Ng explains Clustering](https://img.youtube.com/vi/hDmNF9JG3lo/0.jpg)](https://youtu.be/hDmNF9JG3lo "Andrew Ng explains Clustering")
+
+> 🎥 �击上图观看视频：Andrew Ng 解释�类
+
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+
+在本课中，您将学习如何使用 Scikit-learn 和您之�导入的尼日利亚音�数�集创建�类。我们将介� K-Means �类 的基础知识。请记�，正如您在上一课中学到的，使用�类的方法有很多�，您使用的方法�决于您的数�。我们将�试 K-Means，因为它是最常�的�类技术。让我们开始��
+
+您将了解的术语：
+
+- 轮廓打分
+- 手肘方法
+- 惯性
+- 方差
+
+## 介�
+
+[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering)是一��自信�处�领域的方法。它用于使用一系列观察将数�组划分和划分为“k�个�类。�个观察都用于对最接近其最近“平�值�或�类中心点的给定数�点进行分组。
+
+�类�以�视化为[Voronoi 图](https://wikipedia.org/wiki/Voronoi_diagram)，其中包括一个点（或“���）�其相应的区域。
+
+![voronoi diagram](../images/voronoi.png)
+
+> [Jen Looper](https://twitter.com/jenlooper)作图
+
+K-Means �类过程[分三步执行](https://scikit-learn.org/stable/modules/clustering.html#k-means)：
+
+1. 该算法通过从数�集中采样�选择 k 个中心点。在此之�，它循环：
+   1. 它将�个样本分�到最近的质心。
+   2. 它通过�分�给先�质心的所有样本的平�值�创建新质心。
+   3. 然�，它计算新旧质心之间的差异并��直到质心稳定。
+
+使用 K-Means 的一个缺点包括您需�建立“k�，�质心的数�。幸�的是，“肘部法则�有助于估计“k�的良好起始值。试一下�。
+
+## �置�件
+
+您将使用本课的*notebook.ipynb*文件，其中包�您在上一课中所�的数�导入和�步清�。
+
+## 练习 - 准备
+
+首先�看看歌曲数�。
+
+1. 创建一个箱线图，`boxplot()`为�一列调用：
+
+    ```python
+    plt.figure(figsize=(20,20), dpi=200)
+    
+    plt.subplot(4,3,1)
+    sns.boxplot(x = 'popularity', data = df)
+    
+    plt.subplot(4,3,2)
+    sns.boxplot(x = 'acousticness', data = df)
+    
+    plt.subplot(4,3,3)
+    sns.boxplot(x = 'energy', data = df)
+    
+    plt.subplot(4,3,4)
+    sns.boxplot(x = 'instrumentalness', data = df)
+    
+    plt.subplot(4,3,5)
+    sns.boxplot(x = 'liveness', data = df)
+    
+    plt.subplot(4,3,6)
+    sns.boxplot(x = 'loudness', data = df)
+    
+    plt.subplot(4,3,7)
+    sns.boxplot(x = 'speechiness', data = df)
+    
+    plt.subplot(4,3,8)
+    sns.boxplot(x = 'tempo', data = df)
+    
+    plt.subplot(4,3,9)
+    sns.boxplot(x = 'time_signature', data = df)
+    
+    plt.subplot(4,3,10)
+    sns.boxplot(x = 'danceability', data = df)
+    
+    plt.subplot(4,3,11)
+    sns.boxplot(x = 'length', data = df)
+    
+    plt.subplot(4,3,12)
+    sns.boxplot(x = 'release_date', data = df)
+    ```
+
+    这个数�有点嘈�：通过观察�一列作为箱线图，你�以看到异常值。
+
+    ![outliers](../images/boxplots.png)
+
+您�以�览数�集并删除这些异常值，但这会使数��常少。
+
+1. 现在，选择您将用于�类练习的列。选择具有相似范围的那些并将`artist_top_genre`列编�为数字类型的数�：
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    le = LabelEncoder()
+    
+    X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]
+    
+    y = df['artist_top_genre']
+    
+    X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])
+    
+    y = le.transform(y)
+    ```
+
+1. 现在您需�选择�定�的�类数�。您知�我们从数�集中挖掘出 3 �歌曲�派，所以让我们�试 3 �：
+
+    ```python
+    from sklearn.cluster import KMeans
+    
+    nclusters = 3 
+    seed = 0
+    
+    km = KMeans(n_clusters=nclusters, random_state=seed)
+    km.fit(X)
+    
+    # Predict the cluster for each data point
+    
+    y_cluster_kmeans = km.predict(X)
+    y_cluster_kmeans
+    ```
+
+您会看到打�出的数组，其中包�数�帧�一行的预测�类（0�1 或 2）。
+
+1. 使用此数组计算“轮廓分数�：
+
+    ```python
+    from sklearn import metrics
+    score = metrics.silhouette_score(X, y_cluster_kmeans)
+    score
+    ```
+
+## 轮廓分数
+
+寻找接近 1 的轮廓分数。该分数从 -1 到 1 �等，如果分数为 1，则该�类密集且与其他�类分离良好。接近 0 的值表示���类，样本�常接近相邻�类的决策边界。[��](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam)。
+
+我们的分数是**0.53**，所以正好在中间。这表明我们的数��是特别适�这�类型的�类，但让我们继续。
+
+### 练习 - 建立模型
+
+1. 导入`KMeans`并�动�类过程。
+
+    ```python
+    from sklearn.cluster import KMeans
+    wcss = []
+    
+    for i in range(1, 11):
+        kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)
+        kmeans.fit(X)
+        wcss.append(kmeans.inertia_)
+    
+    ```
+
+    这里有几个部分需�解释。
+
+    > 🎓 range：这些是�类过程的迭代
+
+    > 🎓random_state：“确定质心�始化的�机数生�。� [��](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)
+
+    > 🎓WCSS：“�类内平方和�测��类内所有点到�类质心的平方平��离。[��](https://medium.com/@ODSC/unsupervised-learning-evaluating-clusters-bd47eed175ce)。
+
+    > 🎓Inertia：K-Means 算法�试选择质心以最�化“惯性�，“惯性是衡�内部相干程度的一�方法�。[��](https://scikit-learn.org/stable/modules/clustering.html)。该值在�次迭代时附加到 wcss ��。
+
+    > 🎓k-means++：在[Scikit-learn 中，](https://scikit-learn.org/stable/modules/clustering.html#k-means)您�以使用“k-means++�优化，它“将质心�始化为（通常）彼此远离，导致�能比�机�始化更好的结果。
+
+### 手肘方法
+
+之�，您推测，因为您已�定�了 3 个歌曲genre，所以您应该选择 3 个�类。但真的是这样�？
+
+1. 使用手肘方法�确认。
+
+    ```python
+    plt.figure(figsize=(10,5))
+    sns.lineplot(range(1, 11), wcss,marker='o',color='red')
+    plt.title('Elbow')
+    plt.xlabel('Number of clusters')
+    plt.ylabel('WCSS')
+    plt.show()
+    ```
+
+    使用`wcss`您在上一步中构建的��创建一个图表，显示肘部“弯曲�的�置，这表示最佳�类数。也许**是**3�
+
+    ![elbow method](../images/elbow.png)
+
+## 练习 - 显示�类
+
+1. �次�试该过程，这次设置三个�类，并将�类显示为散点图：
+
+    ```python
+    from sklearn.cluster import KMeans
+    kmeans = KMeans(n_clusters = 3)
+    kmeans.fit(X)
+    labels = kmeans.predict(X)
+    plt.scatter(df['popularity'],df['danceability'],c = labels)
+    plt.xlabel('popularity')
+    plt.ylabel('danceability')
+    plt.show()
+    ```
+
+1. 检查模型的准确性：
+
+    ```python
+    labels = kmeans.labels_
+    
+    correct_labels = sum(y == labels)
+    
+    print("Result: %d out of %d samples were correctly labeled." % (correct_labels, y.size))
+    
+    print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))
+    ```
+
+    这个模型的准确性�是很好，�类的形状给了你一个�示。
+
+    ![clusters](../images/clusters.png)
+
+    这些数�太�平衡，相关性太低，列值之间的差异太大，无法很好地�类。事实上，形�的�类�能�到我们上�定义的三个类型类别的严�影�或扭曲。那是一个学习的过程�
+
+    在 Scikit-learn 的文档中，你�以看到�这样的模型，�类划分�是很好，有一个“方差�问题：
+
+    ![problem models](../images/problems.png)
+    
+    > 图�自 Scikit-learn
+
+## 方差
+
+> 方差被定义为“�自�值的平方差的平�值�[�](https://www.mathsisfun.com/data/standard-deviation.html)。在这个�类问题的上下文中，它指的是我们数�集的数�往往与平�值相差太多的数�。
+>
+> ✅这是考虑�以纠正此问题的所有方法的好时机。�微调整一下数�？使用��的列？使用��的算法？�示：�试[缩放数�](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/)以对其进行标准化并测试其他列。
+>
+> > 试试这个“[方差计算器](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)��更多地�解这个概念。
+
+---
+
+## 🚀挑战
+
+花一些时间在这个笔记本上，调整�数。您能�通过更多地清�数�（例如，去除异常值）��高模型的准确性？您�以使用��为给定的数�样本赋予更多��。你还能�些什么�创建更好的�类？
+
+�示：�试缩放您的数�。笔记本中的注释代�添加了标准缩放，使数�列在范围方�更加相似。您会�现，当轮廓分数下�时，肘部图中的“扭结��得平滑。这是因为�缩放数��以让方差较�的数�承载更多的��。在[这里](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)阅读更多关于这个问题的[信�](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)。
+
+## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+
+## �习与自学
+
+看看[�这样](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/)的 K-Means 模拟器。您�以使用此工具��视化样本数�点并确定其质心。您�以编辑数�的�机性��类数和质心数。这是�有助于您了解如何对数�进行分组？
+
+�外，看看斯��大学的[k-means 讲义](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html)。
+
+## 作业
+
+[�试��的�类方法](./assignment.zh-cn.md)
+
diff --git a/5-Clustering/2-K-Means/translations/assignment.zh-cn.md b/5-Clustering/2-K-Means/translations/assignment.zh-cn.md
new file mode 100644
index 0000000000..c21058d3c3
--- /dev/null
+++ b/5-Clustering/2-K-Means/translations/assignment.zh-cn.md
@@ -0,0 +1,12 @@
+# �试��的�类方法
+
+
+## 说明
+
+在本课中，您学习了 K-Means �类。有时 K-Means �适�您的数�。使用�自这些课程或其他地方的数�（归功于您的��）创建notebook，并展示�使用 K-Means 的���类方法。你学到了什么？
+## 评判规则
+
+| 评判标准 | 优秀                                                       | 中规中矩                                                             | �需努力            |
+| -------- | --------------------------------------------------------------- | -------------------------------------------------------------------- | ---------------------------- |
+|          | 一个具有良好文档记录的�类模型的notebook | 一个没有详细文档或�完整的notebook| �交了一个�完整的工作 |
+
diff --git a/5-Clustering/translations/README.zh-cn.md b/5-Clustering/translations/README.zh-cn.md
new file mode 100644
index 0000000000..88d29623dc
--- /dev/null
+++ b/5-Clustering/translations/README.zh-cn.md
@@ -0,0 +1,27 @@
+# 机器学习中的�类模型
+
+�类（clustering)是一项机器学习任务，用于寻找类似对象并将他们分���的组（这些组称�“�类�(cluster)）。�类与其它机器学习方法的��之处在于�类是自动进行的。事实上，我们�以说它是监�学习的对立�。
+
+## 区域性�题: 尼日利亚观众音��味的�类模型🎧
+
+尼日利亚多样化的观众有�多样化的音��味。使用从Spotify上抓�的数�（�到[本文](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)的��），让我们看看尼日利亚�行的一些音�。这个数�集包括关于��歌曲的舞蹈性�声学��度�言语��行度和活力的分数。从这些数�中�现一些模�（pattern）会是很有趣的事情!
+
+![A turntable](../images/turntable.jpg)
+
+<a href="https://unsplash.com/@marcelalaskoski?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Marcela Laskoski</a>在<a href="https://unsplash.com/s/photos/nigerian-music?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>上的照片
+
+在本系列课程中，您将�现使用�类技术分�数�的新方法。当数�集缺少标签的时候，�类特别有用。如果它有标签，那么分类技术(比如您在��的课程中所学的那些)�能会更有用。但是如果�对未标记的数�进行分组，�类是�现模�的好方法。
+
+> 这里有一些有用的低代�工具�以帮助您了解如何使用�类模型。�试 [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-clustering-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+## 课程安排
+
+1. [介��类](../1-Visualize/translations/README.zh-cn.md)
+2. [K-Means�类](../2-K-Means/translations/README.zh-cn.md)
+## 致谢
+
+这些课程由Jen Looper在🎶上撰写，并由 [Rishit Dagli](https://rishit_dagli) 和[Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan)进行了有帮助的评审。
+
+[尼日利亚歌曲数�集](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) �自Kaggle抓�的Spotify数�。
+
+一些帮助创造了这节课程的K-Means例�包括:[虹膜探索(iris exploration)](https://www.kaggle.com/bburns/iris-exploration-pca-k-means-and-gmm-clustering)，[介�性的笔记(introductory notebook)](https://www.kaggle.com/prashant111/k-means-clustering-with-python)，和 [�设�政府组织的例�(hypothetical NGO example)](https://www.kaggle.com/ankandash/pca-k-means-clustering-hierarchical-clustering)。
+

From 2e4cafe31604d04beaeae59aa2d73f6985a0cc40 Mon Sep 17 00:00:00 2001
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+# Técnicas de Machine Learning
+
+El proceso de creación, uso y mantenimiento de modelos de machine learning, y los datos que se utilizan, es un proceso muy diferente de muchos otros flujos de trabajo de desarrollo. En esta lección, demistificaremos el proceso, y describiremos las principales técnicas que necesita saber. Vas a:  
+
+- Comprender los procesos que sustentan el machine learning a un alto nivel.
+- Explorar conceptos básicos como 'modelos', 'predicciones', y 'datos de entrenamiento'
+
+ 
+## [Cuestionario previo a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## Introducción
+
+A un alto nivel, el arte de crear procesos de machine learning (ML) se compone de una serie de pasos:
+
+1. **Decidir sobre la pregunta**. La mayoría de los procesos de ML, comienzan por hacer una pregunta que no puede ser respondida por un simple programa condicional o un motor basado en reglas. Esas preguntas a menudo giran en torno a predicciones basadas en una recopilación de datos.
+2. **Recopile y prepare datos**. Para poder responder a su pregunta, necesita datos. La calidad y, a veces, cantidad de sus datos determinarán que tan bien puede responder a su pregunta inicial. La visualización de datos es un aspecto importante de esta fase. Esta fase también incluye dividir los datos en un grupo de entrenamiento y pruebas para construir un modelo.
+3. **Elige un método de entrenamiento**. Dependiendo de su pregunta y la naturaleza de sus datos, debe elegir cómo desea entrenar un modelo para reflejar mejor sus datos y hacer predicciones precisas contra ellos. Esta es la parte de su proceso de ML que requiere experiencia específica y, a menudo, una cantidad considerable de experimetación.
+4. **Entrena el model**. Usando sus datos de entrenamiento, usará varios algoritmos para entrenar un modelo para reconocer patrones en los datos. El modelo puede aprovechar las ponderaciones internas que se pueden ajustar para privilegiar ciertas partes de los datos sobre otras para construir un modelo mejor.
+5. **Evaluar el modelo**. Utiliza datos nunca antes vistos (sus datos de prueba) de su conjunto recopilado para ver cómo se está desempeñando el modelo.
+6. **Ajuste de parámetros**. Según el rendimiento de su modelo, puede rehacer el proceso utilizando diferentes parámetros, o variables, que controlan el comportamiento de los algoritmos utlizados para entrenarl el modelo.
+7. **Predecir**. Utilice nuevas entradas para probar la precisión de su modelo.
+
+## Que pregunta hacer
+
+Las computadoras son particularmente hábiles para descubrir patrones ocultos en los datos. Esta utlidad es muy útil para los investigadores que tienen preguntas sobre un dominio determinado que no pueden responderse fácilmente mediante la creación de un motor de reglas basado en condicionales. Dada una tarea actuarial, por ejemplo, un científico de datos podría construir reglas artesanales sobre la mortalidad de los fumadores frente a los no fumadores.
+
+Sin embargo, cuandos se incorporan muchas otras variables a la ecuación, un modelo de ML podría resultar más eficiente para predecir las tasas de mortalidad futuras en funciòn de los antecedentes de salud. Un ejemplo más alegre podría hacer predicciones meteorólogicas para el mes de abril en una ubicación determinada que incluya latitud, longitud, cambio climático, proximidad al océano, patrones de la corriente en chorro, y más. 
+
+✅ Esta [presentación de diapositivas](https://www2.cisl.ucar.edu/sites/default/files/0900%20June%2024%20Haupt_0.pdf) sobre modelos meteorológicos ofrece una perspectiva histórica del uso de ML en el análisis meteorológico.
+
+## Tarea previas a la construcción
+
+Antes de comenzar a construir su modelo, hay varias tareas que debe comletar. Para probar su pregunta y formar una hipótesis basada en las predicciones de su modelo, debe identificar y configurar varios elementos.
+
+### Datos
+
+Para poder responder su pregunta con cualquier tipo de certeza, necesita una buena cantidad de datos del tipo correcto.
+Hay dos cosas que debe hacer en este punto:
+
+- **Recolectar datos**. Teniendo en cuenta la lección anterior sobre la equidad en el análisis de datos, recopile sus datos con cuidado. Tenga en cuenta la fuente de estos datos, cualquier sesgo inherente que pueda tener y documente su origen.
+- **Preparar datos**. Hay varios pasos en el proceso de preparación de datos. Podría necesitar recopilar datos y normalizarlos si provienen de diversas fuentes. Puede mejorar la calidad y cantidad de los datos mediante varios métodos, como convertir strings en números (como hacemos en [Clustering](../../5-Clustering/1-Visualize/README.md)). También puede generar nuevos datos, basados en los originales (como hacemos en [Clasificación](../../4-Classification/1-Introduction/README.md)). Puede limpiar y editar los datos (como lo haremos antes de la lección [Web App](../../3-Web-App/README.md)). Por último, es posible que también deba aleotizarlo y mezclarlo, según sus técnicas de entrenamiento.
+
+✅ Despúes de recopilar y procesar sus datos, tómese un momento para ver si su forma le permitirá responder a su pregunta. ¡Puede ser que los datos no funcionen bien en su tarea dada, como descubriremos en nuestras lecciones de[Clustering](../../5-Clustering/1-Visualize/README.md)!
+
+### Seleccionando su variable característica
+
+Una [característica](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) es una propiedad medible de sus datos. En muchos conjuntos de datos, se expresa como un encabezado de columna como 'fecha', 'tamaño' o 'color'. Su variable característica, generalmente representada como `y` en el código, representa la respuesta a la pregunta que está tratando de hacer a sus datos: en diciembre, ¿qué calabazas de **color** serán las más baratas?, en San Francisco, ¿que vecinadarios tendrán el mejor **precio** de bienes raíces?
+
+🎓 **Selección y extracción de características** ¿ Cómo sabe que variable elegir al construir un modelo? Probablemente pasará por un proceso de selección o extracción de características para elegir las variables correctas para mayor un mayor rendimiento del modelo. Sin embargo, no son lo mismo: "La extracción de características crea nuevas características a partir de funciones de las características originales, mientras que la selección de características devuelve un subconjunto de las características." ([fuente](https://wikipedia.org/wiki/Feature_selection))
+
+### Visualiza tus datos
+
+Un aspecto importante del conjunto de herramientas del científico de datos es el poder de visualizar datos utilizando varias bibliotecas excelentes como Seaborn o MatPlotLib. Representar sus datos visualmente puede permitirle descubrir correlaciones ocultas que puede aprovechar. Sus visualizaciones también pueden ayudarlo a descubrir sesgos o datos desequilibrados. (como descubrimos en [Clasificación](../../4-Classification/2-Classifiers-1/README.md)).
+
+### Divide tu conjunto de datos
+
+Antes del entrenamiento, debe dividir su conjunto de datos en dos o más partes de tamaño desigual que aún represente bien los datos.
+
+- **Entrenamiento**. Esta parte del conjunto de datos se ajusta a su modelo para entrenarlo. Este conjunto constituye la mayor parte del conjunto de datos original.
+- **Pruebas**. Un conjunto de datos de pruebas es un grupo independiente de datos, a menudo recopilado a partir de los datos originales, que se utiliza para confirmar el rendimiento del modelo construido.
+- **Validación**. Un conjunto de validación es un pequeño grupo independiente de ejemplos que se usa para ajustar los hiperparámetros o la arquitectura del modelo para mejorar el modelo. Dependiendo del tamaño de de su conjunto de datos y de la pregunta que se está haciendo, es posible que no necesite crear este tercer conjunto (como notamos en [Pronóstico se series de tiempo](../../7-TimeSeries/1-Introduction/README.md)).
+
+## Contruye un modelo
+
+Usando sus datos de entrenamiento, su objetivo es construir un modelo, o una representación estadística de sus datos, usando varios algoritmos para **entrenarlo**. El entrenamiento de un modelo lo expone a los datos y le permite hacer suposiciones sobre los patrones percibidos que descubre, valida y rechaza.
+
+### Decide un método de entrenamiento
+
+Dependiendo de su pregunta y la naturaleza de sus datos, elegirá un método para entrenarlos. Pasando por la [documentación de Scikit-learn ](https://scikit-learn.org/stable/user_guide.html) - que usamos en este curso - puede explorar muchas formas de entrenar un modelo. Dependiendo de su experiencia, es posible que deba probar varios métodos diferentes para construir el mejor modelo. Es probable que pase por un proceso en el que los científicos de datos evalúan el rendimiento de un modelo alimentándolo con datos no vistos anteriormente por el modelo, verificando la precisión, el sesgo, y otros problemas que degradan la calidad, y seleccionando el método de entrenamieto más apropiado para la tarea en custión.
+### Entrena un modelo
+
+Armado con sus datos de entrenamiento, está listo para 'fit'(ajustarlos/entrenarlos) para crear un modelo. Notará que en muchas bibliotecas de ML, encontrará el código 'model.fit' - es en este momento cuando envías sus datos como una matriz de valores (generalmente 'X') y una variable característica (generalmente 'Y').
+### Evaluar el modelo
+
+Una vez que se completa el proceso de entrenamiento (puede tomar muchas iteraciones, o 'épocas', entrenar un modelo de gran tamaño), podrá evaluar la calidad del modelo utilizando datos de prueba para medir su rendimiento. Estos datos son un subconjunto de los datos originales que el modelo no ha analizado previamente. Puede imprimir una tabla de métricas sobre la calidad de su modelo.
+
+🎓 **Ajuste del modelo (Model fitting)**
+
+En el contexto del machine learning, el ajuste del modelo se refiere a la precisión de la función subyacente del modelo cuando intenta analizar datos con los que no está familiarizado.
+
+🎓 **Ajuste insuficiente (Underfitting)** y **sobreajuste (overfitting)** son problemas comunes que degradan la calidad del modelo, ya que el modelo no encaja suficientemente bien, o encaja demasiado bien. Esto hace que el modelo haga predicciones demasiado estrechamente alineadas o demasiado poco alineadas con sus datos de entrenamiento. Un modelo sobreajustadoo (overfitting) predice demasiado bien los datos de entrenamiento porque ha aprendido demasiado bien los detalles de los datos y el ruido. Un modelo insuficentemente ajustado (Underfitting) es es preciso, ya que ni puede analizar con precisión sus datos de entrenamiento ni los datos que aún no ha 'visto'.
+
+![overfitting model](images/overfitting.png)
+> Infografía de  [Jen Looper](https://twitter.com/jenlooper)
+
+## Ajuste de parámetros
+
+Una vez que haya completado su entrenamiento inicial, observe la calidad del modelo y considere mejorarlo ajustando sus 'hiperparámetros'. Lea más sobre el proceso [en la documentación](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?WT.mc_id=academic-15963-cxa).
+
+## Predicción
+
+Este es el momento en el que puede usar datos completamente nuevos para probar la precisión de su modelo. En una configuración de ML aplicada, donde está creando activos web para usar el modelo en producción, este proceo puede implicar la recopilación de la entrada del usuario (presionar un botón, por ejemplo) para establecer una variable y enviarla al modelo para la inferencia, o evaluación.
+En estas lecciones, descubrirá cómo utilizar estos pasos para preparar, construir, probar, evaluar, y predecir - todos los gestos de un científico de datos y más, a medida que avanza en su viaje para convertirse en un ingeniero de machine learning 'full stack'.
+---
+
+## 🚀Desafío
+
+Dibuje un diagrama de flujos que refleje los pasos de practicante de ML. ¿Dónde te ves ahora mismo en el proceso? ¿Dónde predice que encontrará dificultades? ¿Qué te parece fácil? 
+
+## [Cuestionario previo a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+
+## Revisión & Autoestudio
+
+Busque en línea entrevistas con científicos de datos que analicen su trabajo diario. Aquí está [uno](https://www.youtube.com/watch?v=Z3IjgbbCEfs).
+
+## Asignación
+
+[Entrevistar a un científico de datos](assignment.md)
\ No newline at end of file

From 102bf8a09151923a8d38a2dc631f9958ffc5c9db Mon Sep 17 00:00:00 2001
From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com>
Date: Mon, 2 Aug 2021 11:39:28 +0800
Subject: [PATCH 315/368] Update README.zh-cn.md

Update Introdcution Translation Link and Tabels Format Correction
---
 .../translations/README.zh-cn.md              | 40 +++++++++----------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
index 36273b91bb..83aa4fc4c2 100644
--- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -2,12 +2,12 @@
 
 本节课程将使用你在上一个课程中所�存的全部�过�衡和清洗的��数�。
 
-你将使用这份数�集，并通过多�分类器 _在给出了���料�预测这是那一个国家的��_。在此过程中，你将学到更多能够用�调试分类任务算法的方法。   
+你将使用此数�集和��分类器，_根�一组�料预测这是哪一国家的美食_。在此过程中，你将学到更多用��衡分类任务算法的方法  
 
 ## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
 # 准备工作
 
-�如你已�完�了[课程1](../1-Introduction/README.md), 确�在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件�进行接下�的四节课程。
+�如你已�完�了[课程1](../../1-Introduction/translations/README.zh-cn.md), 确�在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件�进行接下�的四节课程。
 
 ## 练习 - 预测�国的��
 
@@ -68,7 +68,7 @@
 
     你的特�集看上去将会是这样:
 
-    | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     |
+    | | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     
     | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
     |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
     |      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
@@ -200,13 +200,13 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
 
     �行�的输出如下———�以�现这是一��度�的�能性最大，是最��的猜测:
 
-    |          |        0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-    |   indian | 0.715851 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |  chinese | 0.229475 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | japanese | 0.029763 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |   korean | 0.017277 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    |     thai | 0.007634 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    |          |        0 |
+    | -------: | -------: | 
+    |   indian | 0.715851 |
+    |  chinese | 0.229475 |
+    | japanese | 0.029763 |
+    |   korean | 0.017277 | 
+    |     thai | 0.007634 |
 
     ✅ 你能解释下为什么模型会如此确定这是一��度�么？
 
@@ -217,16 +217,16 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
     print(classification_report(y_test,y_pred))
     ```
 
-    | precision    | recall | f1-score | support |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-    | chinese      | 0.73   | 0.71     | 0.72    | 229  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | indian       | 0.91   | 0.93     | 0.92    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | japanese     | 0.70   | 0.75     | 0.72    | 220  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | korean       | 0.86   | 0.76     | 0.81    | 242  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | thai         | 0.79   | 0.85     | 0.82    | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | accuracy     | 0.80   | 1199     |         |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+    | precision    | recall | f1-score | support |      |
+    | ------------ | ------ | -------- | ------- | ---- | 
+    | chinese      | 0.73   | 0.71     | 0.72    | 229  | 
+    | indian       | 0.91   | 0.93     | 0.92    | 254  |
+    | japanese     | 0.70   | 0.75     | 0.72    | 220  |
+    | korean       | 0.86   | 0.76     | 0.81    | 242  |
+    | thai         | 0.79   | 0.85     | 0.82    | 254  |
+    | accuracy     | 0.80   | 1199     |         |      |
+    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |
+    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |
 
 ## 挑战
 

From d2750d3979fcaae1cb7b2eac580fc28c7f244b36 Mon Sep 17 00:00:00 2001
From: Ravindranath Sawane
 <65583665+ravindranath-sawane@users.noreply.github.com>
Date: Mon, 2 Aug 2021 21:21:02 +0530
Subject: [PATCH 316/368] notebook.ipynb

Cell Deleted
---
 2-Regression/1-Tools/solution/notebook.ipynb | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/2-Regression/1-Tools/solution/notebook.ipynb b/2-Regression/1-Tools/solution/notebook.ipynb
index e7d80492aa..b70176240f 100644
--- a/2-Regression/1-Tools/solution/notebook.ipynb
+++ b/2-Regression/1-Tools/solution/notebook.ipynb
@@ -182,13 +182,6 @@
     "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ]
-}
\ No newline at end of file
+}

From cc50d72f6a5d8661270f3620955c9914f4ee2fe2 Mon Sep 17 00:00:00 2001
From: KAN <private@mars.com>
Date: Tue, 3 Aug 2021 02:10:40 +0800
Subject: [PATCH 317/368] Refine the translation

---
 5-Clustering/1-Visualize/translations/README.zh-cn.md | 11 ++++++-----
 5-Clustering/translations/README.zh-cn.md             |  2 +-
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md
index 1697e9c491..ef6c25fe24 100644
--- a/5-Clustering/1-Visualize/translations/README.zh-cn.md
+++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md
@@ -9,7 +9,7 @@
 
 [�类](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)对于数�探索�常有用。让我们看看它是�有助于�现尼日利亚观众消费音�的趋势和模�。
 
-✅花一点时间�考�类的用途。在现实生活中，�当你有一堆衣�需�整�家人的衣�时，就会�生�类🧦👕👖🩲. 在数�科学中，�类用于在�试分�用户的�好或确定任何未标记数�集的特�。在��程度上，�类有助于�解�乱的状�，就�一个袜�抽屉。
+✅花一点时间�考�类的用途。在现实生活中，�当你有一堆衣�需�整�家人的衣�时，就会�生�类🧦👕👖🩲. 在数�科学中，�类用于在�试分�用户的�好或确定任何未标记数�集的特�。在��程度上，�类有助于�解�乱的状�，就�是一个袜�抽屉。
 
 [![Introduction to ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduction to Clustering")
 
@@ -29,7 +29,7 @@
 
 ## �类入门
 
-[Scikit-learn �供了大�](https://scikit-learn.org/stable/modules/clustering.html)的方法�执行�类。您选择的类型将�决于您的用例。根�文档，��方法都有��的好处。以下是 Scikit-learn 支�的方法�其适当用例的简化表：
+[Scikit-learn ](https://scikit-learn.org/stable/modules/clustering.html)�供了大�的方法�执行�类。您选择的类型将�决于您的用例。根�文档，��方法都有��的好处。以下是 Scikit-learn 支�的方法�其适当用例的简化表：
 
 | 方法�称                     | 用例                                               |
 | ---------------------------- | -------------------------------------------------- |
@@ -41,7 +41,7 @@
 | Agglomerative clustering     | 许多，�约�的，�欧几里得�离，转导的             |
 | DBSCAN                       | �平�几何，��匀�类，转导的                     |
 | OPTICS                       | �平�的几何形状，具有��密度的��匀�类，转导的 |
-| Gaussian mixtures            | 平�几何，感应的                                   |
+| Gaussian mixtures            | 平�几何，归纳的                                   |
 | BIRCH                        | 具有异常值的大型数�集，归纳的                     |
 
 > 🎓我们如何创建�类与我们如何将数�点收集到组中有很大关系。让我们分�一些�汇：
@@ -63,7 +63,7 @@
 >
 > 🎓 ['�离'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)
 >
-> �类由它们的�离矩阵定义，例如点之间的�离。这个�离�以通过几�方��测�。欧几里得�类由点值的平�值定义，并包�“质心�或中心点。因此，�离是通过到该质心的�离�测�的。�欧��离指的是“�类心�，�离其他点最近的点。�类心��以用��方�定义。
+> �类由它们的�离矩阵定义，例如点之间的�离。这个�离�以通过几�方��测�。欧几里得�类由点值的平�值定义，并包�“质心�或中心点。因此，�离是通过到该质心的�离�测�的。�欧��离指的是“�类中心�，�离其他点最近的点。�类中心��以用��方�定义。
 >
 > 🎓 ['约�'](https://wikipedia.org/wiki/Constrained_clustering)
 >
@@ -232,7 +232,7 @@
 
 请注�，当顶级�派被�述为“缺失�时，这�味� Spotify 没有对其进行分类，所以让我们��它。
 
-1. 通过过滤掉丢失的数���
+1. 通过过滤掉丢失的数����
 
     ```python
     df = df[df['artist_top_genre'] != 'Missing']
@@ -335,3 +335,4 @@
 
 [研究用于�类的其他�视化](./assignment.zh-cn.md)
 
+转导
diff --git a/5-Clustering/translations/README.zh-cn.md b/5-Clustering/translations/README.zh-cn.md
index 88d29623dc..7f05082b5d 100644
--- a/5-Clustering/translations/README.zh-cn.md
+++ b/5-Clustering/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
 
 �类（clustering)是一项机器学习任务，用于寻找类似对象并将他们分���的组（这些组称�“�类�(cluster)）。�类与其它机器学习方法的��之处在于�类是自动进行的。事实上，我们�以说它是监�学习的对立�。
 
-## 区域性�题: 尼日利亚观众音��味的�类模型🎧
+## 本节主题: 尼日利亚观众音��味的�类模型🎧
 
 尼日利亚多样化的观众有�多样化的音��味。使用从Spotify上抓�的数�（�到[本文](https://towardsdatascience.com/country-wise-visual-analysis-of-music-taste-using-spotify-api-seaborn-in-python-77f5b749b421)的��），让我们看看尼日利亚�行的一些音�。这个数�集包括关于��歌曲的舞蹈性�声学��度�言语��行度和活力的分数。从这些数�中�现一些模�（pattern）会是很有趣的事情!
 

From 0c4db64fc84becc69073f40c72f8065817ac788a Mon Sep 17 00:00:00 2001
From: Ravindranath Sawane
 <65583665+ravindranath-sawane@users.noreply.github.com>
Date: Tue, 3 Aug 2021 09:32:29 +0530
Subject: [PATCH 318/368] New Scatterplot adding according to new code

Changes made in the code for new Scatterplot please check
---
 2-Regression/1-Tools/images/scatterplot.png | Bin 289640 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 2-Regression/1-Tools/images/scatterplot.png

diff --git a/2-Regression/1-Tools/images/scatterplot.png b/2-Regression/1-Tools/images/scatterplot.png
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From be808db39223590720dfb9dd2776fc8efd897306 Mon Sep 17 00:00:00 2001
From: Ravindranath Sawane
 <65583665+ravindranath-sawane@users.noreply.github.com>
Date: Tue, 3 Aug 2021 09:32:54 +0530
Subject: [PATCH 319/368] Add files via upload

---
 2-Regression/1-Tools/images/scatterplot.png | Bin 0 -> 16143 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 2-Regression/1-Tools/images/scatterplot.png

diff --git a/2-Regression/1-Tools/images/scatterplot.png b/2-Regression/1-Tools/images/scatterplot.png
new file mode 100644
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From 9d26c547f8e58f027b1d85b43be8057e9d53d021 Mon Sep 17 00:00:00 2001
From: Ravindranath Sawane
 <65583665+ravindranath-sawane@users.noreply.github.com>
Date: Tue, 3 Aug 2021 09:33:15 +0530
Subject: [PATCH 320/368] Update README.md

---
 2-Regression/1-Tools/README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index 2bd3f7f2d7..69986fba29 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -183,6 +183,9 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The
     ```python
     plt.scatter(X_test, y_test,  color='black')
     plt.plot(X_test, y_pred, color='blue', linewidth=3)
+    plt.xlabel('Scaled BMIs')
+    plt.ylabel('Disease Progression')
+    plt.title('A Graph Plot Showing Diabetes Progression Against BMI')
     plt.show()
     ```
 

From d5a71438efdb1895bc13c15ae5267b7b5e12ab3e Mon Sep 17 00:00:00 2001
From: RyanXin <xyz@ryanxin.cn>
Date: Tue, 3 Aug 2021 16:02:52 +0800
Subject: [PATCH 321/368] Fix minor typos.

---
 2-Regression/4-Logistic/README.md | 2 +-
 3-Web-App/1-Web-App/README.md     | 2 +-
 4-Classification/README.md        | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 62697103cc..a2a938a718 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -140,7 +140,7 @@ Now that we have an idea of the relationship between the binary categories of co
 
 > **🧮 Show Me The Math**
 >
-> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus:
+> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this:
 >
 > ![logistic function](images/sigmoid.png)
 >
diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index c4b406fa73..2e409e78d5 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -199,7 +199,7 @@ Now you can build a Flask app to call your model and return similar results, but
     2. Create **index.html** in _templates_ directory.
     3. Create **styles.css** in _static/css_ directory.
 
-1. Build out the _styles.css__ file with a few styles:
+1. Build out the _styles.css_ file with a few styles:
 
     ```css
     body {
diff --git a/4-Classification/README.md b/4-Classification/README.md
index f6133aa13e..73d83beb3e 100644
--- a/4-Classification/README.md
+++ b/4-Classification/README.md
@@ -8,7 +8,7 @@ In Asia and India, food traditions are extremely diverse, and very delicious! Le
   
 ## What you will learn
 
-In this section, you will build on the skills you learned in the first part of this curriculum all about regressionn to learn about other classifiers you can use that will help you learn about your data.
+In this section, you will build on the skills you learned in the first part of this curriculum all about regression to learn about other classifiers you can use that will help you learn about your data.
 
 > There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
 

From 0ca90cc26988282b72c60043d9aacab2ec53393c Mon Sep 17 00:00:00 2001
From: Jay Patel <59785863+jaypatel31@users.noreply.github.com>
Date: Tue, 3 Aug 2021 19:10:11 +0530
Subject: [PATCH 322/368] Feature Explanation Rectified

---
 1-Introduction/4-techniques-of-ML/README.md | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index b29c7b1882..70b9600021 100644
--- a/1-Introduction/4-techniques-of-ML/README.md
+++ b/1-Introduction/4-techniques-of-ML/README.md
@@ -40,9 +40,13 @@ To be able to answer your question with any kind of certainty, you need a good a
 
 ✅ After collecting and processing your data, take a moment to see if its shape will allow you to address your intended question. It may be that the data will not perform well in your given task, as we discover in our [Clustering](../../5-Clustering/1-Visualize/README.md) lessons!
 
-### Selecting your feature variable
+### Features and Target
+
+A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `X` in code, represent the input variable which will be used to train model.
 
-A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**?
+A target is a thing you are trying to predict. Target usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**? Sometimes target is also referred as label attribute.
+
+### Selecting your feature variable
 
 🎓 **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." ([source](https://wikipedia.org/wiki/Feature_selection))
 
@@ -68,7 +72,7 @@ Depending on your question and the nature of your data, you will choose a method
 
 ### Train a model
 
-Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your data as an array of values (usually 'X') and a feature variable (usually 'y').
+Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your feature variable as an array of values (usually 'X') and a target variable (usually 'y').
 
 ### Evaluate the model
 

From 2cb01b28428f4bf1c1e76ce18323662dbb1f47a7 Mon Sep 17 00:00:00 2001
From: LAKSHAY AGGARWAL <48948478+LakshayAggarwal25@users.noreply.github.com>
Date: Tue, 3 Aug 2021 19:36:40 +0530
Subject: [PATCH 323/368] Updated table visualization

Updated table visualization for output table  on line number 298
---
 7-TimeSeries/2-ARIMA/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md
index d54a781be2..97d0c49ada 100644
--- a/7-TimeSeries/2-ARIMA/README.md
+++ b/7-TimeSeries/2-ARIMA/README.md
@@ -295,7 +295,7 @@ Walk-forward validation is the gold standard of time series model evaluation and
     eval_df.head()
     ```
 
-    ```output
+    Output
     |     |            | timestamp | h   | prediction | actual   |
     | --- | ---------- | --------- | --- | ---------- | -------- |
     | 0   | 2014-12-30 | 00:00:00  | t+1 | 3,008.74   | 3,023.00 |
@@ -303,7 +303,7 @@ Walk-forward validation is the gold standard of time series model evaluation and
     | 2   | 2014-12-30 | 02:00:00  | t+1 | 2,900.17   | 2,899.00 |
     | 3   | 2014-12-30 | 03:00:00  | t+1 | 2,917.69   | 2,886.00 |
     | 4   | 2014-12-30 | 04:00:00  | t+1 | 2,946.99   | 2,963.00 |
-    ```
+    
 
     Observe the hourly data's prediction, compared to the actual load. How accurate is this?
 

From b3e586ca809c6dcdeb8ad4ee9e69ba772527c4a0 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Tue, 3 Aug 2021 10:55:02 -0400
Subject: [PATCH 324/368] adding Learn path for intro mod

---
 1-Introduction/1-intro-to-ML/README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md
index e18a003617..db1c72b114 100644
--- a/1-Introduction/1-intro-to-ML/README.md
+++ b/1-Introduction/1-intro-to-ML/README.md
@@ -102,6 +102,8 @@ Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com
 
 To learn more about how you can work with ML algorithms in the cloud, follow this [Learning Path](https://docs.microsoft.com/learn/paths/create-no-code-predictive-models-azure-machine-learning/?WT.mc_id=academic-15963-cxa).
 
+Take a [Learning Path](https://docs.microsoft.com/learn/modules/introduction-to-machine-learning/?WT.mc_id=academic-15963-cxa) about the basics of ML.
+
 ## Assignment
 
 [Get up and running](assignment.md)

From 986d4df906ee7c272d3999b6674f05588172b71d Mon Sep 17 00:00:00 2001
From: KAN <private@mars.com>
Date: Tue, 3 Aug 2021 23:19:03 +0800
Subject: [PATCH 325/368] Remove unnecessary word

---
 5-Clustering/1-Visualize/translations/README.zh-cn.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md
index ef6c25fe24..5f41ffa7e6 100644
--- a/5-Clustering/1-Visualize/translations/README.zh-cn.md
+++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md
@@ -334,5 +334,3 @@
 ## 作业
 
 [研究用于�类的其他�视化](./assignment.zh-cn.md)
-
-转导

From 067bcf54ff61f569636ed0ce54e128ef6f9b9d3b Mon Sep 17 00:00:00 2001
From: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
Date: Tue, 3 Aug 2021 14:19:44 -0400
Subject: [PATCH 326/368] add 1 to HORIZON to prevent looping errors

---
 7-TimeSeries/2-ARIMA/README.md | 56 +++++++++++++++++-----------------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md
index 97d0c49ada..3134d9392f 100644
--- a/7-TimeSeries/2-ARIMA/README.md
+++ b/7-TimeSeries/2-ARIMA/README.md
@@ -50,13 +50,13 @@ Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.
     import pandas as pd
     import datetime as dt
     import math
-    
+
     from pandas.plotting import autocorrelation_plot
     from statsmodels.tsa.statespace.sarimax import SARIMAX
     from sklearn.preprocessing import MinMaxScaler
     from common.utils import load_data, mape
     from IPython.display import Image
-    
+
     %matplotlib inline
     pd.options.display.float_format = '{:,.2f}'.format
     np.set_printoptions(precision=2)
@@ -83,16 +83,16 @@ Open the _/working_ folder in this lesson and find the _notebook.ipynb_ file.
 
 ### Create training and testing datasets
 
-Now your data is loaded, so you can separate it into train and test sets. You'll train your model on the train set. As usual, after the model has finished training, you'll evaluate its accuracy using the test set. You need to ensure that the test set covers a later period in time from the training set to ensure that the model does not gain information from future time periods. 
+Now your data is loaded, so you can separate it into train and test sets. You'll train your model on the train set. As usual, after the model has finished training, you'll evaluate its accuracy using the test set. You need to ensure that the test set covers a later period in time from the training set to ensure that the model does not gain information from future time periods.
 
 1. Allocate a two-month period from September 1 to October 31, 2014 to the training set. The test set will include the two-month period of November 1 to December 31, 2014:
 
     ```python
     train_start_dt = '2014-11-01 00:00:00'
-    test_start_dt = '2014-12-30 00:00:00'    
+    test_start_dt = '2014-12-30 00:00:00'
     ```
 
-    Since this data reflects the daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in more recent days. 
+    Since this data reflects the daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in more recent days.
 
 1. Visualize the differences:
 
@@ -120,11 +120,11 @@ Now, you need to prepare the data for training by performing filtering and scali
     ```python
     train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]
     test = energy.copy()[energy.index >= test_start_dt][['load']]
-    
+
     print('Training data shape: ', train.shape)
     print('Test data shape: ', test.shape)
     ```
- 
+
     You can see the shape of the data:
 
     ```output
@@ -189,17 +189,17 @@ Now you need to follow several steps
     print('Forecasting horizon:', HORIZON, 'hours')
     ```
 
-    Selecting the best values for an ARIMA model's parameters can be challenging as it's somewhat subjective and time intensive. You might consider using an `auto_arima()` function from the [`pyramid` library](https://alkaline-ml.com/pmdarima/0.9.0/modules/generated/pyramid.arima.auto_arima.html), 
+    Selecting the best values for an ARIMA model's parameters can be challenging as it's somewhat subjective and time intensive. You might consider using an `auto_arima()` function from the [`pyramid` library](https://alkaline-ml.com/pmdarima/0.9.0/modules/generated/pyramid.arima.auto_arima.html),
 
 1. For now try some manual selections to find a good model.
 
     ```python
     order = (4, 1, 0)
     seasonal_order = (1, 1, 0, 24)
-    
+
     model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)
     results = model.fit()
-    
+
     print(results.summary())
     ```
 
@@ -223,10 +223,10 @@ Walk-forward validation is the gold standard of time series model evaluation and
 
     ```python
     test_shifted = test.copy()
-    
-    for t in range(1, HORIZON):
+
+    for t in range(1, HORIZON+1):
         test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')
-        
+
     test_shifted = test_shifted.dropna(how='any')
     test_shifted.head(5)
     ```
@@ -246,18 +246,18 @@ Walk-forward validation is the gold standard of time series model evaluation and
     ```python
     %%time
     training_window = 720 # dedicate 30 days (720 hours) for training
-    
+
     train_ts = train['load']
     test_ts = test_shifted
-    
+
     history = [x for x in train_ts]
     history = history[(-training_window):]
-    
+
     predictions = list()
-    
+
     order = (2, 1, 0)
     seasonal_order = (1, 1, 0, 24)
-    
+
     for t in range(test_ts.shape[0]):
         model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)
         model_fit = model.fit()
@@ -276,10 +276,10 @@ Walk-forward validation is the gold standard of time series model evaluation and
     ```output
     2014-12-30 00:00:00
     1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]
-    
+
     2014-12-30 01:00:00
     2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]
-    
+
     2014-12-30 02:00:00
     3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]
     ```
@@ -303,7 +303,7 @@ Walk-forward validation is the gold standard of time series model evaluation and
     | 2   | 2014-12-30 | 02:00:00  | t+1 | 2,900.17   | 2,899.00 |
     | 3   | 2014-12-30 | 03:00:00  | t+1 | 2,917.69   | 2,886.00 |
     | 4   | 2014-12-30 | 04:00:00  | t+1 | 2,946.99   | 2,963.00 |
-    
+
 
     Observe the hourly data's prediction, compared to the actual load. How accurate is this?
 
@@ -311,10 +311,10 @@ Walk-forward validation is the gold standard of time series model evaluation and
 
 Check the accuracy of your model by testing its mean absolute percentage error (MAPE) over all the predictions.
 
-> **🧮 Show me the math** 
+> **🧮 Show me the math**
 >
 > ![MAPE](images/mape.png)
-> 
+>
 >  [MAPE](https://www.linkedin.com/pulse/what-mape-mad-msd-time-series-allameh-statistics/) is used to show prediction accuracy as a ratio defined by the above formula. The difference between actual<sub>t</sub> and predicted<sub>t</sub> is divided by the actual<sub>t</sub>. "The absolute value in this calculation is summed for every forecasted point in time and divided by the number of fitted points n." [wikipedia](https://wikipedia.org/wiki/Mean_absolute_percentage_error)
 
 1. Express equation in code:
@@ -351,13 +351,13 @@ Check the accuracy of your model by testing its mean absolute percentage error (
      if(HORIZON == 1):
         ## Plotting single step forecast
         eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))
-    
+
     else:
         ## Plotting multi step forecast
         plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]
         for t in range(1, HORIZON+1):
             plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values
-    
+
         fig = plt.figure(figsize=(15, 8))
         ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)
         ax = fig.add_subplot(111)
@@ -365,9 +365,9 @@ Check the accuracy of your model by testing its mean absolute percentage error (
             x = plot_df['timestamp'][(t-1):]
             y = plot_df['t+'+str(t)][0:len(x)]
             ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))
-        
+
         ax.legend(loc='best')
-        
+
     plt.xlabel('timestamp', fontsize=12)
     plt.ylabel('load', fontsize=12)
     plt.show()
@@ -389,6 +389,6 @@ Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE
 
 This lesson touches on only the basics of Time Series Forecasting with ARIMA. Take some time to deepen your knowledge by digging into [this repository](https://microsoft.github.io/forecasting/) and its various model types to learn other ways to build Time Series models.
 
-## Assignment 
+## Assignment
 
 [A new ARIMA model](assignment.md)

From 84a5ff41586b690de3aa6d85a096745d90aa3e58 Mon Sep 17 00:00:00 2001
From: Eric <eric.wanjau@studentpartner.com>
Date: Wed, 4 Aug 2021 12:02:37 +0300
Subject: [PATCH 327/368] Add artwork for lesson 07

---
 2-Regression/3-Linear/images/janitor.jpg | Bin 0 -> 894138 bytes
 2-Regression/3-Linear/images/recipes.png | Bin 0 -> 6600506 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 2-Regression/3-Linear/images/janitor.jpg
 create mode 100644 2-Regression/3-Linear/images/recipes.png

diff --git a/2-Regression/3-Linear/images/janitor.jpg b/2-Regression/3-Linear/images/janitor.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..93e6f011c5fa0c0303dfc059cd2d87f3508c5b75
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HcmV?d00001


From 2521fc2ba408975ad614bf945b4e7b0afec7b05c Mon Sep 17 00:00:00 2001
From: Eric <eric.wanjau@studentpartner.com>
Date: Wed, 4 Aug 2021 12:10:46 +0300
Subject: [PATCH 328/368] Add R notebooks for lesson 07

---
 .../3-Linear/solution/lesson_3-R.ipynb        | 1082 +++++++++++++++++
 2-Regression/3-Linear/solution/lesson_3.Rmd   |  679 +++++++++++
 2 files changed, 1761 insertions(+)
 create mode 100644 2-Regression/3-Linear/solution/lesson_3-R.ipynb
 create mode 100644 2-Regression/3-Linear/solution/lesson_3.Rmd

diff --git a/2-Regression/3-Linear/solution/lesson_3-R.ipynb b/2-Regression/3-Linear/solution/lesson_3-R.ipynb
new file mode 100644
index 0000000000..0cc040bbdf
--- /dev/null
+++ b/2-Regression/3-Linear/solution/lesson_3-R.ipynb
@@ -0,0 +1,1082 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {
+    "colab": {
+      "name": "lesson_3-R.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Build a regression model: linear and polynomial regression models"
+      ],
+      "metadata": {
+        "id": "EgQw8osnsUV-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Linear and Polynomial Regression for Pumpkin Pricing - Lesson 3\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/linear-polynomial.png\"\r\n",
+        "   width=\"800\"/>\r\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+        "\r\n",
+        "\r\n",
+        "<!--![Infographic by Dasani Madipalli](images/linear-polynomial.png){width=\"800\"}-->\r\n",
+        "\r\n",
+        "#### Introduction\r\n",
+        "\r\n",
+        "So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using `ggplot2`.💪\r\n",
+        "\r\n",
+        "Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: *basic linear regression* and *polynomial regression*, along with some of the math underlying these techniques.\r\n",
+        "\r\n",
+        "> Throughout this curriculum, we assume minimal knowledge of math, and seek to make it accessible for students coming from other fields, so watch for notes, 🧮 callouts, diagrams, and other learning tools to aid in comprehension.\r\n",
+        "\r\n",
+        "#### Preparation\r\n",
+        "\r\n",
+        "As a reminder, you are loading this data so as to ask questions of it.\r\n",
+        "\r\n",
+        "-   When is the best time to buy pumpkins?\r\n",
+        "\r\n",
+        "-   What price can I expect of a case of miniature pumpkins?\r\n",
+        "\r\n",
+        "-   Should I buy them in half-bushel baskets or by the 1 1/9 bushel box? Let's keep digging into this data.\r\n",
+        "\r\n",
+        "In the previous lesson, you created a `tibble` (a modern reimagining of the data frame) and populated it with part of the original dataset, standardizing the pricing by the bushel. By doing that, however, you were only able to gather about 400 data points and only for the fall months. Maybe we can get a little more detail about the nature of the data by cleaning it more? We'll see... 🕵��♀�\r\n",
+        "\r\n",
+        "For this task, we'll require the following packages:\r\n",
+        "\r\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\r\n",
+        "\r\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\r\n",
+        "\r\n",
+        "-   `janitor`: The [janitor package](https://github.com/sfirke/janitor) provides simple little tools for examining and cleaning dirty data.\r\n",
+        "\r\n",
+        "-   `corrplot`: The [corrplot package](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.\r\n",
+        "\r\n",
+        "You can have them installed as:\r\n",
+        "\r\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\r\n",
+        "\r\n",
+        "The script below checks whether you have the packages required to complete this module and installs them for you in case they are missing."
+      ],
+      "metadata": {
+        "id": "WqQPS1OAsg3H"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+        "\r\n",
+        "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "tA4C2WN3skCf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We'll later load these awesome packages and make them available in our current R session. (This is for mere illustration, `pacman::p_load()` already did that for you)\r\n",
+        "\r\n",
+        "## 1. A linear regression line\r\n",
+        "\r\n",
+        "As you learned in Lesson 1, the goal of a linear regression exercise is to be able to plot a *line* *of* *best fit* to:\r\n",
+        "\r\n",
+        "-   **Show variable relationships**. Show the relationship between variables\r\n",
+        "\r\n",
+        "-   **Make predictions**. Make accurate predictions on where a new data point would fall in relationship to that line.\r\n",
+        "\r\n",
+        "To draw this type of line, we use a statistical technique called **Least-Squares Regression**. The term `least-squares` means that all the data points surrounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. As such, the line of best fit is the line that gives us the lowest value for the sum of the squared errors - hence the name *least squares regression*.\r\n",
+        "\r\n",
+        "We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.\r\n",
+        "\r\n",
+        "> **🧮 Show me the math**\r\n",
+        ">\r\n",
+        "> This line, called the *line of best fit* can be expressed by [an equation](https://en.wikipedia.org/wiki/Simple_linear_regression):\r\n",
+        ">\r\n",
+        ">     Y = a + bX\r\n",
+        ">\r\n",
+        "> `X` is the '`explanatory variable` or `predictor`'. `Y` is the '`dependent variable` or `outcome`'. The slope of the line is `b` and `a` is the y-intercept, which refers to the value of `Y` when `X = 0`.\r\n",
+        ">\r\n",
+        "\r\n",
+        "> ![](../images/slope.png \"slope = $y/x$\")\r\n",
+        "    Infographic by Jen Looper\r\n",
+        ">\r\n",
+        "> First, calculate the slope `b`.\r\n",
+        ">\r\n",
+        "> In other words, and referring to our pumpkin data's original question: \"predict the price of a pumpkin per bushel by month\", `X` would refer to the price and `Y` would refer to the month of sale.\r\n",
+        ">\r\n",
+        "> ![](../images/calculation.png)\r\n",
+        "    Infographic by Jen Looper\r\n",
+        "> \r\n",
+        "> Calculate the value of Y. If you're paying around \\$4, it must be April!\r\n",
+        ">\r\n",
+        "> The math that calculates the line must demonstrate the slope of the line, which is also dependent on the intercept, or where `Y` is situated when `X = 0`.\r\n",
+        ">\r\n",
+        "> You can observe the method of calculation for these values on the [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web site. Also visit [this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html) to watch how the numbers' values impact the line.\r\n",
+        "\r\n",
+        "Not so scary, right? 🤓\r\n",
+        "\r\n",
+        "#### Correlation\r\n",
+        "\r\n",
+        "One more term to understand is the **Correlation Coefficient** between given X and Y variables. Using a scatterplot, you can quickly visualize this coefficient. A plot with datapoints scattered in a neat line have high correlation, but a plot with datapoints scattered everywhere between X and Y have a low correlation.\r\n",
+        "\r\n",
+        "A good linear regression model will be one that has a high (nearer to 1 than 0) Correlation Coefficient using the Least-Squares Regression method with a line of regression.\r\n",
+        "\r\n"
+      ],
+      "metadata": {
+        "id": "cdX5FRpvsoP5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## **2. A dance with data: creating a data frame that will be used for modelling**\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/janitor.jpg\"\r\n",
+        "   width=\"700\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
+        "\r\n",
+        "\r\n",
+        "<!--![Artwork by \\@allison_horst](images/janitor.jpg){width=\"700\"}-->"
+      ],
+      "metadata": {
+        "id": "WdUKXk7Bs8-V"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Load up required libraries and dataset. Convert the data to a data frame containing a subset of the data:\n",
+        "\n",
+        "-   Only get pumpkins priced by the bushel\n",
+        "\n",
+        "-   Convert the date to a month\n",
+        "\n",
+        "-   Calculate the price to be an average of high and low prices\n",
+        "\n",
+        "-   Convert the price to reflect the pricing by bushel quantity\n",
+        "\n",
+        "> We covered these steps in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb)."
+      ],
+      "metadata": {
+        "id": "fMCtu2G2s-p8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load the core Tidyverse packages\r\n",
+        "library(tidyverse)\r\n",
+        "library(lubridate)\r\n",
+        "\r\n",
+        "# Import the pumpkins data\r\n",
+        "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Get a glimpse and dimensions of the data\r\n",
+        "glimpse(pumpkins)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print the first 50 rows of the data set\r\n",
+        "pumpkins %>% \r\n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "ryMVZEEPtERn"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "In the spirit of sheer adventure, let's explore the [`janitor package`](github.com/sfirke/janitor) that provides simple functions for examining and cleaning dirty data. For instance, let's take a look at the column names for our data:"
+      ],
+      "metadata": {
+        "id": "xcNxM70EtJjb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Return column names\r\n",
+        "pumpkins %>% \r\n",
+        "  names()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "5XtpaIigtPfW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🤔 We can do better. Let's make these column names `friendR` by converting them to the [snake_case](https://en.wikipedia.org/wiki/Snake_case) convention using `janitor::clean_names`. To find out more about this function: `?clean_names`"
+      ],
+      "metadata": {
+        "id": "IbIqrMINtSHe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Clean names to the snake_case convention\r\n",
+        "pumpkins <- pumpkins %>% \r\n",
+        "  clean_names(case = \"snake\")\r\n",
+        "\r\n",
+        "# Return column names\r\n",
+        "pumpkins %>% \r\n",
+        "  names()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "a2uYvclYtWvX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Much tidyR 🧹! Now, a dance with the data using `dplyr` as in the previous lesson! 💃\n"
+      ],
+      "metadata": {
+        "id": "HfhnuzDDtaDd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Select desired columns\r\n",
+        "pumpkins <- pumpkins %>% \r\n",
+        "  select(variety, city_name, package, low_price, high_price, date)\r\n",
+        "\r\n",
+        "\r\n",
+        "\r\n",
+        "# Extract the month from the dates to a new column\r\n",
+        "pumpkins <- pumpkins %>%\r\n",
+        "  mutate(date = mdy(date),\r\n",
+        "         month = month(date)) %>% \r\n",
+        "  select(-date)\r\n",
+        "\r\n",
+        "\r\n",
+        "\r\n",
+        "# Create a new column for average Price\r\n",
+        "pumpkins <- pumpkins %>% \r\n",
+        "  mutate(price = (low_price + high_price)/2)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Retain only pumpkins with the string \"bushel\"\r\n",
+        "new_pumpkins <- pumpkins %>% \r\n",
+        "  filter(str_detect(string = package, pattern = \"bushel\"))\r\n",
+        "\r\n",
+        "\r\n",
+        "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\r\n",
+        "new_pumpkins <- new_pumpkins %>% \r\n",
+        "  mutate(price = case_when(\r\n",
+        "    str_detect(package, \"1 1/9\") ~ price/(1.1),\r\n",
+        "    str_detect(package, \"1/2\") ~ price*2,\r\n",
+        "    TRUE ~ price))\r\n",
+        "\r\n",
+        "# Relocate column positions\r\n",
+        "new_pumpkins <- new_pumpkins %>% \r\n",
+        "  relocate(month, .before = variety)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Display the first 5 rows\r\n",
+        "new_pumpkins %>% \r\n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "X0wU3gQvtd9f"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Good job!👌 You now have a clean, tidy data set on which you can build your new regression model!\n",
+        "\n",
+        "Mind a scatter plot?\n"
+      ],
+      "metadata": {
+        "id": "UpaIwaxqth82"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Set theme\r\n",
+        "theme_set(theme_light())\r\n",
+        "\r\n",
+        "# Make a scatter plot of month and price\r\n",
+        "new_pumpkins %>% \r\n",
+        "  ggplot(mapping = aes(x = month, y = price)) +\r\n",
+        "  geom_point(size = 1.6)\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "DXgU-j37tl5K"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "A scatter plot reminds us that we only have month data from August through December. We probably need more data to be able to draw conclusions in a linear fashion.\n",
+        "\n",
+        "Let's take a look at our modelling data again:"
+      ],
+      "metadata": {
+        "id": "Ve64wVbwtobI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Display first 5 rows\r\n",
+        "new_pumpkins %>% \r\n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "HFQX2ng1tuSJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "What if we wanted to predict the `price` of a pumpkin based on the `city` or `package` columns which are of type character? Or even more simply, how could we find the correlation (which requires both of its inputs to be numeric) between, say, `package` and `price`? 🤷🤷\n",
+        "\n",
+        "Machine learning models work best with numeric features rather than text values, so you generally need to convert categorical features into numeric representations.\n",
+        "\n",
+        "This means that we have to find a way to reformat our predictors to make them easier for a model to use effectively, a process known as `feature engineering`."
+      ],
+      "metadata": {
+        "id": "7hsHoxsStyjJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Preprocessing data for modelling with recipes 👩��👨��\n",
+        "\n",
+        "Activities that reformat predictor values to make them easier for a model to use effectively has been termed `feature engineering`.\n",
+        "\n",
+        "Different models have different preprocessing requirements. For instance, least squares requires `encoding categorical variables` such as month, variety and city_name. This simply involves `translating` a column with `categorical values` into one or more `numeric columns` that take the place of the original.\n",
+        "\n",
+        "For example, suppose your data includes the following categorical feature:\n",
+        "\n",
+        "|  city   |\n",
+        "|:-------:|\n",
+        "| Denver  |\n",
+        "| Nairobi |\n",
+        "|  Tokyo  |\n",
+        "\n",
+        "You can apply *ordinal encoding* to substitute a unique integer value for each category, like this:\n",
+        "\n",
+        "| city |\n",
+        "|:----:|\n",
+        "|  0   |\n",
+        "|  1   |\n",
+        "|  2   |\n",
+        "\n",
+        "And that's what we'll do to our data!\n",
+        "\n",
+        "In this section, we'll explore another amazing Tidymodels package: [recipes](https://tidymodels.github.io/recipes/) - which is designed to help you preprocess your data **before** training your model. At its core, a recipe is an object that defines what steps should be applied to a data set in order to get it ready for modelling.\n",
+        "\n",
+        "Now, let's create a recipe that prepares our data for modelling by substituting a unique integer for all the observations in the predictor columns:"
+      ],
+      "metadata": {
+        "id": "AD5kQbcvt3Xl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Specify a recipe\r\n",
+        "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \r\n",
+        "  step_integer(all_predictors(), zero_based = TRUE)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print out the recipe\r\n",
+        "pumpkins_recipe"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "BNaFKXfRt9TU"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Awesome! � We just created our first recipe that specifies an outcome (price) and its corresponding predictors and that all the predictor columns should be encoded into a set of integers 🙌! Let's quickly break it down:\r\n",
+        "\r\n",
+        "-   The call to `recipe()` with a formula tells the recipe the *roles* of the variables using `new_pumpkins` data as the reference. For instance the `price` column has been assigned an `outcome` role while the rest of the columns have been assigned a `predictor` role.\r\n",
+        "\r\n",
+        "-   `step_integer(all_predictors(), zero_based = TRUE)` specifies that all the predictors should be converted into a set of integers with the numbering starting at 0.\r\n",
+        "\r\n",
+        "We are sure you may be having thoughts such as: \"This is so cool!! But what if I needed to confirm that the recipes are doing exactly what I expect them to do? 🤔\"\r\n",
+        "\r\n",
+        "That's an awesome thought! You see, once your recipe is defined, you can estimate the parameters required to actually preprocess the data, and then extract the processed data. You don't typically need to do this when you use Tidymodels (we'll see the normal convention in just a minute-\\> `workflows`) but it can come in handy when you want to do some kind of sanity check for confirming that recipes are doing what you expect.\r\n",
+        "\r\n",
+        "For that, you'll need two more verbs: `prep()` and `bake()` and as always, our little R friends by [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) help you in understanding this better!\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/recipes.png\"\r\n",
+        "   width=\"550\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
+        "\r\n",
+        "\r\n",
+        "<!--![Artwork by \\@allison_horst](images/recipes.png){width=\"550\"}-->"
+      ],
+      "metadata": {
+        "id": "KEiO0v7kuC9O"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): estimates the required parameters from a training set that can be later applied to other data sets. For instance, for a given predictor column, what observation will be assigned integer 0 or 1 or 2 etc\n",
+        "\n",
+        "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): takes a prepped recipe and applies the operations to any data set.\n",
+        "\n",
+        "That said, lets prep and bake our recipes to really confirm that under the hood, the predictor columns will be first encoded before a model is fit."
+      ],
+      "metadata": {
+        "id": "Q1xtzebuuTCP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Prep the recipe\r\n",
+        "pumpkins_prep <- prep(pumpkins_recipe)\r\n",
+        "\r\n",
+        "# Bake the recipe to extract a preprocessed new_pumpkins data\r\n",
+        "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\r\n",
+        "\r\n",
+        "# Print out the baked data set\r\n",
+        "baked_pumpkins %>% \r\n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "FGBbJbP_uUUn"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Woo-hoo!🥳 The processed data `baked_pumpkins` has all it's predictors encoded confirming that indeed the preprocessing steps defined as our recipe will work as expected. This makes it harder for you to read but much more intelligible for Tidymodels! Take some time to find out what observation has been mapped to a corresponding integer.\n",
+        "\n",
+        "It is also worth mentioning that `baked_pumpkins` is a data frame that we can perform computations on.\n",
+        "\n",
+        "For instance, let's try to find a good correlation between two points of your data to potentially build a good predictive model. We'll use the function `cor()` to do this. Type `?cor()` to find out more about the function."
+      ],
+      "metadata": {
+        "id": "1dvP0LBUueAW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Find the correlation between the city_name and the price\r\n",
+        "cor(baked_pumpkins$city_name, baked_pumpkins$price)\r\n",
+        "\r\n",
+        "# Find the correlation between the package and the price\r\n",
+        "cor(baked_pumpkins$package, baked_pumpkins$price)\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "3bQzXCjFuiSV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As it turns out, there's only weak correlation between the City and Price. However there's a bit better correlation between the Package and its Price. That makes sense, right? Normally, the bigger the produce box, the higher the price.\n",
+        "\n",
+        "While we are at it, let's also try and visualize a correlation matrix of all the columns using the `corrplot` package."
+      ],
+      "metadata": {
+        "id": "BToPWbgjuoZw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load the corrplot package\r\n",
+        "library(corrplot)\r\n",
+        "\r\n",
+        "# Obtain correlation matrix\r\n",
+        "corr_mat <- cor(baked_pumpkins %>% \r\n",
+        "                  # Drop columns that are not really informative\r\n",
+        "                  select(-c(low_price, high_price)))\r\n",
+        "\r\n",
+        "# Make a correlation plot between the variables\r\n",
+        "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "ZwAL3ksmutVR"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🤩🤩 Much better.\r\n",
+        "\r\n",
+        "A good question to now ask of this data will be: '`What price can I expect of a given pumpkin package?`' Let's get right into it!\r\n",
+        "\r\n",
+        "> Note: When you **`bake()`** the prepped recipe **`pumpkins_prep`** with **`new_data = NULL`**, you extract the processed (i.e. encoded) training data. If you had another data set for example a test set and would want to see how a recipe would pre-process it, you would simply bake **`pumpkins_prep`** with **`new_data = test_set`**\r\n",
+        "\r\n",
+        "## 4. Build a linear regression model\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/linear-polynomial.png\"\r\n",
+        "   width=\"800\"/>\r\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+        "\r\n",
+        "\r\n",
+        "<!--![Infographic by Dasani Madipalli](images/linear-polynomial.png){width=\"800\"}-->"
+      ],
+      "metadata": {
+        "id": "YqXjLuWavNxW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we have build a recipe, and actually confirmed that the data will be pre-processed appropriately, let's now build a regression model to answer the question: `What price can I expect of a given pumpkin package?`\n",
+        "\n",
+        "#### Train a linear regression model using the training set\n",
+        "\n",
+        "As you may have already figured out, the column *price* is the `outcome` variable while the *package* column is the `predictor` variable.\n",
+        "\n",
+        "To do this, we'll first split the data such that 80% goes into training and 20% into test set, then define a recipe that will encode the predictor column into a set of integers, then build a model specification. We won't prep and bake our recipe since we already know it will preprocess the data as expected."
+      ],
+      "metadata": {
+        "id": "Pq0bSzCevW-h"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "set.seed(2056)\r\n",
+        "# Split the data into training and test sets\r\n",
+        "pumpkins_split <- new_pumpkins %>% \r\n",
+        "  initial_split(prop = 0.8)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Extract training and test data\r\n",
+        "pumpkins_train <- training(pumpkins_split)\r\n",
+        "pumpkins_test <- testing(pumpkins_split)\r\n",
+        "\r\n",
+        "\r\n",
+        "\r\n",
+        "# Create a recipe for preprocessing the data\r\n",
+        "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \r\n",
+        "  step_integer(all_predictors(), zero_based = TRUE)\r\n",
+        "\r\n",
+        "\r\n",
+        "\r\n",
+        "# Create a linear model specification\r\n",
+        "lm_spec <- linear_reg() %>% \r\n",
+        "  set_engine(\"lm\") %>% \r\n",
+        "  set_mode(\"regression\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "CyoEh_wuvcLv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Good job! Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities. How's that for your peace of mind!🤩\n",
+        "\n",
+        "In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components! This is what we'd call *pipelines* in *Python*.\n",
+        "\n",
+        "So let's bundle everything up into a workflow!📦"
+      ],
+      "metadata": {
+        "id": "G3zF_3DqviFJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Hold modelling components in a workflow\r\n",
+        "lm_wf <- workflow() %>% \r\n",
+        "  add_recipe(lm_pumpkins_recipe) %>% \r\n",
+        "  add_model(lm_spec)\r\n",
+        "\r\n",
+        "# Print out the workflow\r\n",
+        "lm_wf"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "T3olroU3v-WX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "👌 Into the bargain, a workflow can be fit/trained in much the same way a model can."
+      ],
+      "metadata": {
+        "id": "zd1A5tgOwEPX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Train the model\r\n",
+        "lm_wf_fit <- lm_wf %>% \r\n",
+        "  fit(data = pumpkins_train)\r\n",
+        "\r\n",
+        "# Print the model coefficients learned \r\n",
+        "lm_wf_fit"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "NhJagFumwFHf"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.\n",
+        "\n",
+        "\n",
+        "#### Evaluate model performance using the test set\n",
+        "\n",
+        "It's time to see how the model performed �! How do we do this?\n",
+        "\n",
+        "Now that we've trained the model, we can use it to make predictions for the test_set using `parsnip::predict()`. Then we can compare these predictions to the actual label values to evaluate how well (or not!) the model is working.\n",
+        "\n",
+        "Let's start with making predictions for the test set then bind the columns to the test set."
+      ],
+      "metadata": {
+        "id": "_4QkGtBTwItF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make predictions for the test set\r\n",
+        "predictions <- lm_wf_fit %>% \r\n",
+        "  predict(new_data = pumpkins_test)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Bind predictions to the test set\r\n",
+        "lm_results <- pumpkins_test %>% \r\n",
+        "  select(c(package, price)) %>% \r\n",
+        "  bind_cols(predictions)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print the first ten rows of the tibble\r\n",
+        "lm_results %>% \r\n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "UFZzTG0gwTs9"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "Yes, you have just trained a model and used it to make predictions!🔮 Is it any good, let's evaluate the model's performance!\n",
+        "\n",
+        "In Tidymodels, we do this using `yardstick::metrics()`! For linear regression, let's focus on the following metrics:\n",
+        "\n",
+        "-   `Root Mean Square Error (RMSE)`: The square root of the [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). This yields an absolute metric in the same unit as the label (in this case, the price of a pumpkin). The smaller the value, the better the model (in a simplistic sense, it represents the average price by which the predictions are wrong!)\n",
+        "\n",
+        "-   `Coefficient of Determination (usually known as R-squared or R2)`: A relative metric in which the higher the value, the better the fit of the model. In essence, this metric represents how much of the variance between predicted and actual label values the model is able to explain."
+      ],
+      "metadata": {
+        "id": "0A5MjzM7wW9M"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Evaluate performance of linear regression\r\n",
+        "metrics(data = lm_results,\r\n",
+        "        truth = price,\r\n",
+        "        estimate = .pred)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "reJ0UIhQwcEH"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "There goes the model performance. Let's see if we can get a better indication by visualizing a scatter plot of the package and price then use the predictions made to overlay a line of best fit.\n",
+        "\n",
+        "This means we'll have to prep and bake the test set in order to encode the package column then bind this to the predictions made by our model."
+      ],
+      "metadata": {
+        "id": "fdgjzjkBwfWt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Encode package column\r\n",
+        "package_encode <- lm_pumpkins_recipe %>% \r\n",
+        "  prep() %>% \r\n",
+        "  bake(new_data = pumpkins_test) %>% \r\n",
+        "  select(package)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Bind encoded package column to the results\r\n",
+        "lm_results <- lm_results %>% \r\n",
+        "  bind_cols(package_encode %>% \r\n",
+        "              rename(package_integer = package)) %>% \r\n",
+        "  relocate(package_integer, .after = package)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print new results data frame\r\n",
+        "lm_results %>% \r\n",
+        "  slice_head(n = 5)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Make a scatter plot\r\n",
+        "lm_results %>% \r\n",
+        "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+        "  geom_point(size = 1.6) +\r\n",
+        "  # Overlay a line of best fit\r\n",
+        "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\r\n",
+        "  xlab(\"package\")\r\n",
+        "  \r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "R0nw719lwkHE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Great! As you can see, the linear regression model does not really well generalize the relationship between a package and its corresponding price.\r\n",
+        "\r\n",
+        "🎃 Congratulations, you just created a model that can help predict the price of a few varieties of pumpkins. Your holiday pumpkin patch will be beautiful. But you can probably create a better model!\r\n",
+        "\r\n",
+        "## 5. Build a polynomial regression model\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/linear-polynomial.png\"\r\n",
+        "   width=\"800\"/>\r\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+        "\r\n",
+        "\r\n",
+        "<!--![Infographic by Dasani Madipalli](images/linear-polynomial.png){width=\"800\"}-->"
+      ],
+      "metadata": {
+        "id": "HOCqJXLTwtWI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Sometimes our data may not have a linear relationship, but we still want to predict an outcome. Polynomial regression can help us make predictions for more complex non-linear relationships.\n",
+        "\n",
+        "Take for instance the relationship between the package and price for our pumpkins data set. While sometimes there's a linear relationship between variables - the bigger the pumpkin in volume, the higher the price - sometimes these relationships can't be plotted as a plane or straight line.\n",
+        "\n",
+        "> ✅ Here are [some more examples](https://online.stat.psu.edu/stat501/lesson/9/9.8) of data that could use polynomial regression\n",
+        ">\n",
+        "> Take another look at the relationship between Variety to Price in the previous plot. Does this scatterplot seem like it should necessarily be analyzed by a straight line? Perhaps not. In this case, you can try polynomial regression.\n",
+        ">\n",
+        "> ✅ Polynomials are mathematical expressions that might consist of one or more variables and coefficients\n",
+        "\n",
+        "#### Train a polynomial regression model using the training set\n",
+        "\n",
+        "Polynomial regression creates a *curved line* to better fit nonlinear data.\n",
+        "\n",
+        "Let's see whether a polynomial model will perform better in making predictions. We'll follow a somewhat similar procedure as we did before:\n",
+        "\n",
+        "-   Create a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding predictors and computing polynomials of degree *n*\n",
+        "\n",
+        "-   Build a model specification\n",
+        "\n",
+        "-   Bundle the recipe and model specification into a workflow\n",
+        "\n",
+        "-   Create a model by fitting the workflow\n",
+        "\n",
+        "-   Evaluate how well the model performs on the test data\n",
+        "\n",
+        "Let's get right into it!\n"
+      ],
+      "metadata": {
+        "id": "VcEIpRV9wzYr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Specify a recipe\r\n",
+        "poly_pumpkins_recipe <-\r\n",
+        "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+        "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+        "  step_poly(all_predictors(), degree = 4)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Create a model specification\r\n",
+        "poly_spec <- linear_reg() %>% \r\n",
+        "  set_engine(\"lm\") %>% \r\n",
+        "  set_mode(\"regression\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Bundle recipe and model spec into a workflow\r\n",
+        "poly_wf <- workflow() %>% \r\n",
+        "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+        "  add_model(poly_spec)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Create a model\r\n",
+        "poly_wf_fit <- poly_wf %>% \r\n",
+        "  fit(data = pumpkins_train)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print learned model coefficients\r\n",
+        "poly_wf_fit\r\n",
+        "\r\n",
+        "  "
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "63n_YyRXw3CC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### Evaluate model performance\n",
+        "\n",
+        "��You've built a polynomial model let's make predictions on the test set!"
+      ],
+      "metadata": {
+        "id": "-LHZtztSxDP0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make price predictions on test data\r\n",
+        "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+        "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+        "  relocate(.pred, .after = last_col())\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print the results\r\n",
+        "poly_results %>% \r\n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "YUFpQ_dKxJGx"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Woo-hoo, let's evaluate how the model performed on the test_set using `yardstick::metrics()`."
+      ],
+      "metadata": {
+        "id": "qxdyj86bxNGZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "metrics(data = poly_results, truth = price, estimate = .pred)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "8AW5ltkBxXDm"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🤩🤩 Much better performance.\n",
+        "\n",
+        "The `rmse` decreased from about 7. to about 3. an indication that of a reduced error between the actual price and the predicted price. You can *loosely* interpret this as meaning that on average, incorrect predictions are wrong by around \\$3. The `rsq` increased from about 0.4 to 0.8.\n",
+        "\n",
+        "All these metrics indicate that the polynomial model performs way better than the linear model. Good job!\n",
+        "\n",
+        "Let's see if we can visualize this!"
+      ],
+      "metadata": {
+        "id": "6gLHNZDwxYaS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Bind encoded package column to the results\r\n",
+        "poly_results <- poly_results %>% \r\n",
+        "  bind_cols(package_encode %>% \r\n",
+        "              rename(package_integer = package)) %>% \r\n",
+        "  relocate(package_integer, .after = package)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print new results data frame\r\n",
+        "poly_results %>% \r\n",
+        "  slice_head(n = 5)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Make a scatter plot\r\n",
+        "poly_results %>% \r\n",
+        "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+        "  geom_point(size = 1.6) +\r\n",
+        "  # Overlay a line of best fit\r\n",
+        "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+        "  xlab(\"package\")\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "A83U16frxdF1"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can see a curved line that fits your data better! 🤩\n",
+        "\n",
+        "You can make this more smoother by passing a polynomial formula to `geom_smooth` like this:"
+      ],
+      "metadata": {
+        "id": "4U-7aHOVxlGU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make a scatter plot\r\n",
+        "poly_results %>% \r\n",
+        "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+        "  geom_point(size = 1.6) +\r\n",
+        "  # Overlay a line of best fit\r\n",
+        "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+        "  xlab(\"package\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "5vzNT0Uexm-w"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Much like a smooth curve!🤩\n",
+        "\n",
+        "Here's how you would make a new prediction:"
+      ],
+      "metadata": {
+        "id": "v9u-wwyLxq4G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make a hypothetical data frame\r\n",
+        "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+        "\r\n",
+        "# Make predictions using linear model\r\n",
+        "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+        "\r\n",
+        "# Make predictions using polynomial model\r\n",
+        "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+        "\r\n",
+        "# Return predictions in a list\r\n",
+        "list(\"linear model prediction\" = lm_pred, \r\n",
+        "     \"polynomial model prediction\" = poly_pred)\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "jRPSyfQGxuQv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The `polynomial model` prediction does make sense, given the scatter plots of `price` and `package`! And, if this is a better model than the previous one, looking at the same data, you need to budget for these more expensive pumpkins!\n",
+        "\n",
+        "� Well done! You created two regression models in one lesson. In the final section on regression, you will learn about logistic regression to determine categories.\n",
+        "\n",
+        "## **🚀Challenge**\n",
+        "\n",
+        "Test several different variables in this notebook to see how correlation corresponds to model accuracy.\n",
+        "\n",
+        "## [**Post-lecture quiz**](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)\n",
+        "\n",
+        "## **Review & Self Study**\n",
+        "\n",
+        "In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)\n",
+        "\n",
+        "If you want to learn more about how to use the amazing Tidymodels framework, please check out the following resources:\n",
+        "\n",
+        "-   Tidymodels website: [Get started with Tidymodels](https://www.tidymodels.org/start/)\n",
+        "\n",
+        "-   Max Kuhn and Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+        "\n",
+        "###### **THANK YOU TO:**\n",
+        "\n",
+        "[Allison Horst](https://twitter.com/allison_horst?lang=en) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n"
+      ],
+      "metadata": {
+        "id": "8zOLOWqMxzk5"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/2-Regression/3-Linear/solution/lesson_3.Rmd b/2-Regression/3-Linear/solution/lesson_3.Rmd
new file mode 100644
index 0000000000..a1b81780d5
--- /dev/null
+++ b/2-Regression/3-Linear/solution/lesson_3.Rmd
@@ -0,0 +1,679 @@
+---
+title: 'Build a regression model: linear and polynomial regression models'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Linear and Polynomial Regression for Pumpkin Pricing - Lesson 3
+
+![Infographic by Dasani Madipalli](../images/linear-polynomial.png){width="800"}
+
+#### Introduction
+
+So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using `ggplot2`.💪
+
+Now you are ready to dive deeper into regression for ML. In this lesson, you will learn more about two types of regression: *basic linear regression* and *polynomial regression*, along with some of the math underlying these techniques.
+
+> Throughout this curriculum, we assume minimal knowledge of math, and seek to make it accessible for students coming from other fields, so watch for notes, 🧮 callouts, diagrams, and other learning tools to aid in comprehension.
+
+#### Preparation
+
+As a reminder, you are loading this data so as to ask questions of it.
+
+-   When is the best time to buy pumpkins?
+
+-   What price can I expect of a case of miniature pumpkins?
+
+-   Should I buy them in half-bushel baskets or by the 1 1/9 bushel box? Let's keep digging into this data.
+
+In the previous lesson, you created a `tibble` (a modern reimagining of the data frame) and populated it with part of the original dataset, standardizing the pricing by the bushel. By doing that, however, you were only able to gather about 400 data points and only for the fall months. Maybe we can get a little more detail about the nature of the data by cleaning it more? We'll see... 🕵��♀�
+
+For this task, we'll require the following packages:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+-   `janitor`: The [janitor package](https://github.com/sfirke/janitor) provides simple little tools for examining and cleaning dirty data.
+
+-   `corrplot`: The [corrplot package](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels", "janitor", "corrplot"))`
+
+The script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+suppressWarnings(if (!require("pacman")) install.packages("pacman"))
+
+pacman::p_load(tidyverse, tidymodels, janitor, corrplot)
+```
+
+We'll later load these awesome packages and make them available in our current R session. (This is for mere illustration, `pacman::p_load()` already did that for you)
+
+## 1. A linear regression line
+
+As you learned in Lesson 1, the goal of a linear regression exercise is to be able to plot a *line* *of* *best fit* to:
+
+-   **Show variable relationships**. Show the relationship between variables
+
+-   **Make predictions**. Make accurate predictions on where a new data point would fall in relationship to that line.
+
+To draw this type of line, we use a statistical technique called **Least-Squares Regression**. The term `least-squares` means that all the data points surrounding the regression line are squared and then added up. Ideally, that final sum is as small as possible, because we want a low number of errors, or `least-squares`. As such, the line of best fit is the line that gives us the lowest value for the sum of the squared errors - hence the name *least squares regression*.
+
+We do so since we want to model a line that has the least cumulative distance from all of our data points. We also square the terms before adding them since we are concerned with its magnitude rather than its direction.
+
+> **🧮 Show me the math**
+>
+> This line, called the *line of best fit* can be expressed by [an equation](https://en.wikipedia.org/wiki/Simple_linear_regression):
+>
+>     Y = a + bX
+>
+> `X` is the '`explanatory variable` or `predictor`'. `Y` is the '`dependent variable` or `outcome`'. The slope of the line is `b` and `a` is the y-intercept, which refers to the value of `Y` when `X = 0`.
+>
+> ![Infographic by Jen Looper](../images/slope.png){width="400"}
+>
+> First, calculate the slope `b`.
+>
+> In other words, and referring to our pumpkin data's original question: "predict the price of a pumpkin per bushel by month", `X` would refer to the price and `Y` would refer to the month of sale.
+>
+> ![Infographic by Jen Looper](../images/calculation.png)
+>
+> Calculate the value of Y. If you're paying around \$4, it must be April!
+>
+> The math that calculates the line must demonstrate the slope of the line, which is also dependent on the intercept, or where `Y` is situated when `X = 0`.
+>
+> You can observe the method of calculation for these values on the [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web site. Also visit [this Least-squares calculator](https://www.mathsisfun.com/data/least-squares-calculator.html) to watch how the numbers' values impact the line.
+
+Not so scary, right? 🤓
+
+#### Correlation
+
+One more term to understand is the **Correlation Coefficient** between given X and Y variables. Using a scatterplot, you can quickly visualize this coefficient. A plot with datapoints scattered in a neat line have high correlation, but a plot with datapoints scattered everywhere between X and Y have a low correlation.
+
+A good linear regression model will be one that has a high (nearer to 1 than 0) Correlation Coefficient using the Least-Squares Regression method with a line of regression.
+
+## **2. A dance with data: creating a data frame that will be used for modelling**
+
+![Artwork by \@allison_horst](../images/janitor.jpg){width="700"}
+
+Load up required libraries and dataset. Convert the data to a data frame containing a subset of the data:
+
+-   Only get pumpkins priced by the bushel
+
+-   Convert the date to a month
+
+-   Calculate the price to be an average of high and low prices
+
+-   Convert the price to reflect the pricing by bushel quantity
+
+> We covered these steps in the [previous lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).
+
+```{r load_tidy_verse_models, message=F, warning=F}
+# Load the core Tidyverse packages
+library(tidyverse)
+library(lubridate)
+
+# Import the pumpkins data
+pumpkins <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv")
+
+
+# Get a glimpse and dimensions of the data
+glimpse(pumpkins)
+
+
+# Print the first 50 rows of the data set
+pumpkins %>% 
+  slice_head(n = 5)
+
+
+```
+
+In the spirit of sheer adventure, let's explore the [`janitor package`](github.com/sfirke/janitor) that provides simple functions for examining and cleaning dirty data. For instance, let's take a look at the column names for our data:
+
+```{r col_names}
+# Return column names
+pumpkins %>% 
+  names()
+
+```
+
+🤔 We can do better. Let's make these column names `friendR` by converting them to the [snake_case](https://en.wikipedia.org/wiki/Snake_case) convention using `janitor::clean_names`. To find out more about this function: `?clean_names`
+
+```{r friendR}
+# Clean names to the snake_case convention
+pumpkins <- pumpkins %>% 
+  clean_names(case = "snake")
+
+# Return column names
+pumpkins %>% 
+  names()
+
+```
+
+Much tidyR 🧹! Now, a dance with the data using `dplyr` as in the previous lesson! 💃
+
+```{r prep_data, message=F, warning=F}
+# Select desired columns
+pumpkins <- pumpkins %>% 
+  select(variety, city_name, package, low_price, high_price, date)
+
+
+
+# Extract the month from the dates to a new column
+pumpkins <- pumpkins %>%
+  mutate(date = mdy(date),
+         month = month(date)) %>% 
+  select(-date)
+
+
+
+# Create a new column for average Price
+pumpkins <- pumpkins %>% 
+  mutate(price = (low_price + high_price)/2)
+
+
+# Retain only pumpkins with the string "bushel"
+new_pumpkins <- pumpkins %>% 
+  filter(str_detect(string = package, pattern = "bushel"))
+
+
+# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel
+new_pumpkins <- new_pumpkins %>% 
+  mutate(price = case_when(
+    str_detect(package, "1 1/9") ~ price/(1.1),
+    str_detect(package, "1/2") ~ price*2,
+    TRUE ~ price))
+
+# Relocate column positions
+new_pumpkins <- new_pumpkins %>% 
+  relocate(month, .before = variety)
+
+
+# Display the first 5 rows
+new_pumpkins %>% 
+  slice_head(n = 5)
+```
+
+Good job!👌 You now have a clean, tidy data set on which you can build your new regression model!
+
+Mind a scatter plot?
+
+```{r scatter_price_month}
+# Set theme
+theme_set(theme_light())
+
+# Make a scatter plot of month and price
+new_pumpkins %>% 
+  ggplot(mapping = aes(x = month, y = price)) +
+  geom_point(size = 1.6)
+
+```
+
+A scatter plot reminds us that we only have month data from August through December. We probably need more data to be able to draw conclusions in a linear fashion.
+
+Let's take a look at our modelling data again:
+
+```{r modelling data}
+# Display first 5 rows
+new_pumpkins %>% 
+  slice_head(n = 5)
+
+```
+
+What if we wanted to predict the `price` of a pumpkin based on the `city` or `package` columns which are of type character? Or even more simply, how could we find the correlation (which requires both of its inputs to be numeric) between, say, `package` and `price`? 🤷🤷
+
+Machine learning models work best with numeric features rather than text values, so you generally need to convert categorical features into numeric representations.
+
+This means that we have to find a way to reformat our predictors to make them easier for a model to use effectively, a process known as `feature engineering`.
+
+## 3. Preprocessing data for modelling with recipes 👩��👨��
+
+Activities that reformat predictor values to make them easier for a model to use effectively has been termed `feature engineering`.
+
+Different models have different preprocessing requirements. For instance, least squares requires `encoding categorical variables` such as month, variety and city_name. This simply involves `translating` a column with `categorical values` into one or more `numeric columns` that take the place of the original.
+
+For example, suppose your data includes the following categorical feature:
+
+|  city   |
+|:-------:|
+| Denver  |
+| Nairobi |
+|  Tokyo  |
+
+You can apply *ordinal encoding* to substitute a unique integer value for each category, like this:
+
+| city |
+|:----:|
+|  0   |
+|  1   |
+|  2   |
+
+And that's what we'll do to our data!
+
+In this section, we'll explore another amazing Tidymodels package: [recipes](https://tidymodels.github.io/recipes/) - which is designed to help you preprocess your data **before** training your model. At its core, a recipe is an object that defines what steps should be applied to a data set in order to get it ready for modelling.
+
+Now, let's create a recipe that prepares our data for modelling by substituting a unique integer for all the observations in the predictor columns:
+
+```{r pumpkins_recipe}
+# Specify a recipe
+pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% 
+  step_integer(all_predictors(), zero_based = TRUE)
+
+
+# Print out the recipe
+pumpkins_recipe
+
+```
+
+Awesome! � We just created our first recipe that specifies an outcome (price) and its corresponding predictors and that all the predictor columns should be encoded into a set of integers 🙌! Let's quickly break it down:
+
+-   The call to `recipe()` with a formula tells the recipe the *roles* of the variables using `new_pumpkins` data as the reference. For instance the `price` column has been assigned an `outcome` role while the rest of the columns have been assigned a `predictor` role.
+
+-   `step_integer(all_predictors(), zero_based = TRUE)` specifies that all the predictors should be converted into a set of integers with the numbering starting at 0.
+
+We are sure you may be having thoughts such as: "This is so cool!! But what if I needed to confirm that the recipes are doing exactly what I expect them to do? 🤔"
+
+That's an awesome thought! You see, once your recipe is defined, you can estimate the parameters required to actually preprocess the data, and then extract the processed data. You don't typically need to do this when you use Tidymodels (we'll see the normal convention in just a minute-\> `workflows`) but it can come in handy when you want to do some kind of sanity check for confirming that recipes are doing what you expect.
+
+For that, you'll need two more verbs: `prep()` and `bake()` and as always, our little R friends by [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) help you in understanding this better!
+
+![Artwork by \@allison_horst](../images/recipes.png){width="550"}
+
+[`prep()`](https://recipes.tidymodels.org/reference/prep.html): estimates the required parameters from a training set that can be later applied to other data sets. For instance, for a given predictor column, what observation will be assigned integer 0 or 1 or 2 etc
+
+[`bake()`](https://recipes.tidymodels.org/reference/bake.html): takes a prepped recipe and applies the operations to any data set.
+
+That said, lets prep and bake our recipes to really confirm that under the hood, the predictor columns will be first encoded before a model is fit.
+
+```{r prep_bake}
+# Prep the recipe
+pumpkins_prep <- prep(pumpkins_recipe)
+
+# Bake the recipe to extract a preprocessed new_pumpkins data
+baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)
+
+# Print out the baked data set
+baked_pumpkins %>% 
+  slice_head(n = 10)
+```
+
+Woo-hoo!🥳 The processed data `baked_pumpkins` has all it's predictors encoded confirming that indeed the preprocessing steps defined as our recipe will work as expected. This makes it harder for you to read but much more intelligible for Tidymodels! Take some time to find out what observation has been mapped to a corresponding integer.
+
+It is also worth mentioning that `baked_pumpkins` is a data frame that we can perform computations on.
+
+For instance, let's try to find a good correlation between two points of your data to potentially build a good predictive model. We'll use the function `cor()` to do this. Type `?cor()` to find out more about the function.
+
+```{r corr}
+# Find the correlation between the city_name and the price
+cor(baked_pumpkins$city_name, baked_pumpkins$price)
+
+# Find the correlation between the package and the price
+cor(baked_pumpkins$package, baked_pumpkins$price)
+
+```
+
+As it turns out, there's only weak correlation between the City and Price. However there's a bit better correlation between the Package and its Price. That makes sense, right? Normally, the bigger the produce box, the higher the price.
+
+While we are at it, let's also try and visualize a correlation matrix of all the columns using the `corrplot` package.
+
+```{r corrplot}
+# Load the corrplot package
+library(corrplot)
+
+# Obtain correlation matrix
+corr_mat <- cor(baked_pumpkins %>% 
+                  # Drop columns that are not really informative
+                  select(-c(low_price, high_price)))
+
+# Make a correlation plot between the variables
+corrplot(corr_mat, method = "shade", shade.col = NA, tl.col = "black", tl.srt = 45, addCoef.col = "black", cl.pos = "n", order = "original")
+
+```
+
+🤩🤩 Much better.
+
+A good question to now ask of this data will be: '`What price can I expect of a given pumpkin package?`' Let's get right into it!
+
+> Note: When you **`bake()`** the prepped recipe **`pumpkins_prep`** with **`new_data = NULL`**, you extract the processed (i.e. encoded) training data. If you had another data set for example a test set and would want to see how a recipe would pre-process it, you would simply bake **`pumpkins_prep`** with **`new_data = test_set`**
+
+## 4. Build a linear regression model
+
+![Infographic by Dasani Madipalli](../images/linear-polynomial.png){width="800"}
+
+Now that we have build a recipe, and actually confirmed that the data will be pre-processed appropriately, let's now build a regression model to answer the question: `What price can I expect of a given pumpkin package?`
+
+#### Train a linear regression model using the training set
+
+As you may have already figured out, the column *price* is the `outcome` variable while the *package* column is the `predictor` variable.
+
+To do this, we'll first split the data such that 80% goes into training and 20% into test set, then define a recipe that will encode the predictor column into a set of integers, then build a model specification. We won't prep and bake our recipe since we already know it will preprocess the data as expected.
+
+```{r lm_rec_spec}
+set.seed(2056)
+# Split the data into training and test sets
+pumpkins_split <- new_pumpkins %>% 
+  initial_split(prop = 0.8)
+
+
+# Extract training and test data
+pumpkins_train <- training(pumpkins_split)
+pumpkins_test <- testing(pumpkins_split)
+
+
+
+# Create a recipe for preprocessing the data
+lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% 
+  step_integer(all_predictors(), zero_based = TRUE)
+
+
+
+# Create a linear model specification
+lm_spec <- linear_reg() %>% 
+  set_engine("lm") %>% 
+  set_mode("regression")
+
+
+```
+
+Good job! Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities. How's that for your peace of mind!🤩
+
+In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components! This is what we'd call *pipelines* in *Python*.
+
+So let's bundle everything up into a workflow!📦
+
+```{r lm_workflow}
+# Hold modelling components in a workflow
+lm_wf <- workflow() %>% 
+  add_recipe(lm_pumpkins_recipe) %>% 
+  add_model(lm_spec)
+
+# Print out the workflow
+lm_wf
+
+```
+
+👌 Into the bargain, a workflow can be fit/trained in much the same way a model can.
+
+```{r lm_wf_fit}
+# Train the model
+lm_wf_fit <- lm_wf %>% 
+  fit(data = pumpkins_train)
+
+# Print the model coefficients learned 
+lm_wf_fit
+
+```
+
+From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.
+
+#### Evaluate model performance using the test set
+
+It's time to see how the model performed �! How do we do this?
+
+Now that we've trained the model, we can use it to make predictions for the test_set using `parsnip::predict()`. Then we can compare these predictions to the actual label values to evaluate how well (or not!) the model is working.
+
+Let's start with making predictions for the test set then bind the columns to the test set.
+
+```{r lm_pred}
+# Make predictions for the test set
+predictions <- lm_wf_fit %>% 
+  predict(new_data = pumpkins_test)
+
+
+# Bind predictions to the test set
+lm_results <- pumpkins_test %>% 
+  select(c(package, price)) %>% 
+  bind_cols(predictions)
+
+
+# Print the first ten rows of the tibble
+lm_results %>% 
+  slice_head(n = 10)
+```
+
+Yes, you have just trained a model and used it to make predictions!🔮 Is it any good, let's evaluate the model's performance!
+
+In Tidymodels, we do this using `yardstick::metrics()`! For linear regression, let's focus on the following metrics:
+
+-   `Root Mean Square Error (RMSE)`: The square root of the [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). This yields an absolute metric in the same unit as the label (in this case, the price of a pumpkin). The smaller the value, the better the model (in a simplistic sense, it represents the average price by which the predictions are wrong!)
+
+-   `Coefficient of Determination (usually known as R-squared or R2)`: A relative metric in which the higher the value, the better the fit of the model. In essence, this metric represents how much of the variance between predicted and actual label values the model is able to explain.
+
+```{r lm_yardstick}
+# Evaluate performance of linear regression
+metrics(data = lm_results,
+        truth = price,
+        estimate = .pred)
+
+
+```
+
+There goes the model performance. Let's see if we can get a better indication by visualizing a scatter plot of the package and price then use the predictions made to overlay a line of best fit.
+
+This means we'll have to prep and bake the test set in order to encode the package column then bind this to the predictions made by our model.
+
+```{r lm_plot}
+# Encode package column
+package_encode <- lm_pumpkins_recipe %>% 
+  prep() %>% 
+  bake(new_data = pumpkins_test) %>% 
+  select(package)
+
+
+# Bind encoded package column to the results
+lm_results <- lm_results %>% 
+  bind_cols(package_encode %>% 
+              rename(package_integer = package)) %>% 
+  relocate(package_integer, .after = package)
+
+
+# Print new results data frame
+lm_results %>% 
+  slice_head(n = 5)
+
+
+# Make a scatter plot
+lm_results %>% 
+  ggplot(mapping = aes(x = package_integer, y = price)) +
+  geom_point(size = 1.6) +
+  # Overlay a line of best fit
+  geom_line(aes(y = .pred), color = "orange", size = 1.2) +
+  xlab("package")
+  
+
+
+```
+
+Great! As you can see, the linear regression model does not really well generalize the relationship between a package and its corresponding price.
+
+🎃 Congratulations, you just created a model that can help predict the price of a few varieties of pumpkins. Your holiday pumpkin patch will be beautiful. But you can probably create a better model!
+
+## 5. Build a polynomial regression model
+
+![Infographic by Dasani Madipalli](../images/linear-polynomial.png){width="800"}
+
+Sometimes our data may not have a linear relationship, but we still want to predict an outcome. Polynomial regression can help us make predictions for more complex non-linear relationships.
+
+Take for instance the relationship between the package and price for our pumpkins data set. While sometimes there's a linear relationship between variables - the bigger the pumpkin in volume, the higher the price - sometimes these relationships can't be plotted as a plane or straight line.
+
+> ✅ Here are [some more examples](https://online.stat.psu.edu/stat501/lesson/9/9.8) of data that could use polynomial regression
+>
+> Take another look at the relationship between Variety to Price in the previous plot. Does this scatterplot seem like it should necessarily be analyzed by a straight line? Perhaps not. In this case, you can try polynomial regression.
+>
+> ✅ Polynomials are mathematical expressions that might consist of one or more variables and coefficients
+
+#### Train a polynomial regression model using the training set
+
+Polynomial regression creates a *curved line* to better fit nonlinear data.
+
+Let's see whether a polynomial model will perform better in making predictions. We'll follow a somewhat similar procedure as we did before:
+
+-   Create a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding predictors and computing polynomials of degree *n*
+
+-   Build a model specification
+
+-   Bundle the recipe and model specification into a workflow
+
+-   Create a model by fitting the workflow
+
+-   Evaluate how well the model performs on the test data
+
+Let's get right into it!
+
+```{r polynomial_reg}
+# Specify a recipe
+poly_pumpkins_recipe <-
+  recipe(price ~ package, data = pumpkins_train) %>%
+  step_integer(all_predictors(), zero_based = TRUE) %>% 
+  step_poly(all_predictors(), degree = 4)
+
+
+# Create a model specification
+poly_spec <- linear_reg() %>% 
+  set_engine("lm") %>% 
+  set_mode("regression")
+
+
+# Bundle recipe and model spec into a workflow
+poly_wf <- workflow() %>% 
+  add_recipe(poly_pumpkins_recipe) %>% 
+  add_model(poly_spec)
+
+
+# Create a model
+poly_wf_fit <- poly_wf %>% 
+  fit(data = pumpkins_train)
+
+
+# Print learned model coefficients
+poly_wf_fit
+
+  
+
+```
+
+#### Evaluate model performance
+
+��You've built a polynomial model let's make predictions on the test set!
+
+```{r poly_predict}
+# Make price predictions on test data
+poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% 
+  bind_cols(pumpkins_test %>% select(c(package, price))) %>% 
+  relocate(.pred, .after = last_col())
+
+
+# Print the results
+poly_results %>% 
+  slice_head(n = 10)
+```
+
+Woo-hoo , let's evaluate how the model performed on the test_set using `yardstick::metrics()`.
+
+```{r poly_eval}
+metrics(data = poly_results, truth = price, estimate = .pred)
+```
+
+🤩🤩 Much better performance.
+
+The `rmse` decreased from about 7. to about 3. an indication that of a reduced error between the actual price and the predicted price. You can *loosely* interpret this as meaning that on average, incorrect predictions are wrong by around \$3. The `rsq` increased from about 0.4 to 0.8.
+
+All these metrics indicate that the polynomial model performs way better than the linear model. Good job!
+
+Let's see if we can visualize this!
+
+```{r poly_viz}
+# Bind encoded package column to the results
+poly_results <- poly_results %>% 
+  bind_cols(package_encode %>% 
+              rename(package_integer = package)) %>% 
+  relocate(package_integer, .after = package)
+
+
+# Print new results data frame
+poly_results %>% 
+  slice_head(n = 5)
+
+
+# Make a scatter plot
+poly_results %>% 
+  ggplot(mapping = aes(x = package_integer, y = price)) +
+  geom_point(size = 1.6) +
+  # Overlay a line of best fit
+  geom_line(aes(y = .pred), color = "midnightblue", size = 1.2) +
+  xlab("package")
+
+
+
+```
+
+You can see a curved line that fits your data better! 🤩
+
+You can make this more smoother by passing a polynomial formula to `geom_smooth` like this:
+
+```{r smooth curve}
+# Make a scatter plot
+poly_results %>% 
+  ggplot(mapping = aes(x = package_integer, y = price)) +
+  geom_point(size = 1.6) +
+  # Overlay a line of best fit
+  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = "midnightblue", size = 1.2, se = FALSE) +
+  xlab("package")
+
+
+
+
+```
+
+Much like a smooth curve!🤩
+
+Here's how you would make a new prediction:
+
+```{r predict}
+# Make a hypothetical data frame
+hypo_tibble <- tibble(package = "bushel baskets")
+
+# Make predictions using linear model
+lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)
+
+# Make predictions using polynomial model
+poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)
+
+# Return predictions in a list
+list("linear model prediction" = lm_pred, 
+     "polynomial model prediction" = poly_pred)
+
+
+```
+
+The `polynomial model` prediction does make sense, given the scatter plots of `price` and `package`! And, if this is a better model than the previous one, looking at the same data, you need to budget for these more expensive pumpkins!
+
+� Well done! You created two regression models in one lesson. In the final section on regression, you will learn about logistic regression to determine categories.
+
+## **🚀Challenge**
+
+Test several different variables in this notebook to see how correlation corresponds to model accuracy.
+
+## [**Post-lecture quiz**](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+
+## **Review & Self Study**
+
+In this lesson we learned about Linear Regression. There are other important types of Regression. Read about Stepwise, Ridge, Lasso and Elasticnet techniques. A good course to study to learn more is the [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)
+
+If you want to learn more about how to use the amazing Tidymodels framework, please check out the following resources:
+
+-   Tidymodels website: [Get started with Tidymodels](https://www.tidymodels.org/start/)
+
+-   Max Kuhn and Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*
+
+###### **THANK YOU TO:**
+
+[Allison Horst](https://twitter.com/allison_horst?lang=en) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).

From f5a5f12e838894212a6e69defad61515a4a8fa2d Mon Sep 17 00:00:00 2001
From: NihadGurbanov <82742813+NihadGurbanov@users.noreply.github.com>
Date: Wed, 4 Aug 2021 13:32:56 +0400
Subject: [PATCH 329/368] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 64ab0a82e2..01a7c598f0 100644
--- a/README.md
+++ b/README.md
@@ -81,7 +81,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 |      02       |                The History of machine learning                 |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
 |      03       |                 Fairness and machine learning                  |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
 |      04       |                Techniques for machine learning                 |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
+|      05       |                   Intro to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
 |      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
 |      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
 |      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |

From be3ea4d0283903945c9f84fd302dd45724b197df Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 4 Aug 2021 06:03:33 -0400
Subject: [PATCH 330/368] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 01a7c598f0..64ab0a82e2 100644
--- a/README.md
+++ b/README.md
@@ -81,7 +81,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 |      02       |                The History of machine learning                 |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
 |      03       |                 Fairness and machine learning                  |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
 |      04       |                Techniques for machine learning                 |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                   Intro to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
+|      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
 |      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
 |      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
 |      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |

From a6d3547d3dff08790104418f4207e6d680760480 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Wed, 4 Aug 2021 20:01:17 +0900
Subject: [PATCH 331/368] add japanese translations (2-Regression/4-Logistic)

---
 2-Regression/2-Data/translations/README.ja.md |   4 +-
 .../4-Logistic/translations/README.ja.md      | 310 ++++++++++++++++++
 .../4-Logistic/translations/assignment.ja.md  |  11 +
 3 files changed, 323 insertions(+), 2 deletions(-)
 create mode 100644 2-Regression/4-Logistic/translations/README.ja.md
 create mode 100644 2-Regression/4-Logistic/translations/assignment.ja.md

diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index d10c620dfe..8a9f4d5522 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -61,7 +61,7 @@ Visual Studio Code� _notebook.ipynb_ ファイルを開��スプレッド
     ✅ 最後�5行を表示�る�����よ��関数を使用����？
 
 
-1. �在�データフレーム�欠�データ��る����を�ェック���。
+2. �在�データフレーム�欠�データ��る����を�ェック���。
 
     ```python
     pumpkins.isnull().sum()
@@ -70,7 +70,7 @@ Visual Studio Code� _notebook.ipynb_ ファイルを開��スプレッド
     欠�データ��り�����今回�タスク��影響�������。
 
 
-1. データフレームを扱�や���る����`drop()` 関数を使�������列を削除��必��列��を残�よ�����。
+3. データフレームを扱�や���る����`drop()` 関数を使�������列を削除��必��列��を残�よ�����。
 
     ```python
     new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']
diff --git a/2-Regression/4-Logistic/translations/README.ja.md b/2-Regression/4-Logistic/translations/README.ja.md
new file mode 100644
index 0000000000..060209fbf2
--- /dev/null
+++ b/2-Regression/4-Logistic/translations/README.ja.md
@@ -0,0 +1,310 @@
+# カテゴリ予測����ロジスティック回帰
+
+![ロジスティク回帰 vs 線形回帰�インフォグラフィック](../images/logistic-linear.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
+## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+
+## イントロダクション
+
+回帰�最後�レッスン����典的�機械学習手法�一���る�「ロジスティック回帰�を見�����。��手法��2値�カテゴリを予測�る���パターンを発見�る���使���。例���「��キャンディ���ョコレート����？��「��病気��染�る����？��「��顧客����商�を������？�����。
+
+��レッスン��以下�内容を扱���。
+
+- データを�視化�る���新��ライブラリ
+- ロジスティック回帰����
+
+✅ ��[モジュール](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa) ���今回�タイプ�よ��回帰�����解を深�る�������。
+
+## ���件
+
+カボ�ャ�データを触������データ�扱����り慣れ�����。��際��イナリカテゴリ�一��る���気�����。「`Color`���。
+
+�����変数�与�られ������るカボ�ャ���よ��色��る�能性�高�� (オレンジ🎃���白👻)を予測�るロジスティック回帰モデルを構築�����ょ�。
+
+> ���回帰�����レッスン�二値分類�話を���る���ょ��？ロジスティック回帰��線形ベース�も����り����[実際��分類法](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) ��る���言語的�便宜上��。次�レッスングループ���データを分類�る他�方法����学���。
+
+## 質��定義
+
+�����「Orange��「Not Orange���二値�表������。データセット��「striped����カテゴリーも�り������ん�例����������使���ん。データセット�らnull値を削除�る����カテゴリー�消�������。
+
+> 🎃 �白�����白�カボ�ャを「�化�カボ�ャ��呼�����り��。彫る��簡��������オレンジ色�カボ�ャ��人気��り��ん��見�目�クール��よ��
+
+## ロジスティック回帰����
+
+ロジスティック回帰���回学ん�線形回帰�����������点�異�り��。
+
+### 2値分類
+
+ロジスティック回帰��線形回帰��異�る特徴を������。ロジスティック回帰��二値�カテゴリー（「オレンジ色�オレンジ色�����）�����予測を�供�る��対��線形回帰��連続的�値を予測�る�������。例���カボ�ャ�産地��穫時期�与�られれ����価格��れ��上昇�る�を予測�る�������。
+
+![カボ�ャ分類モデル](../images/pumpkin-classifier.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
+### ��他�分類
+
+ロジスティック回帰��他�もMultinomialやOrdinal���種類��り��。
+
+- **Multinomial**: �れ�2�以上�カテゴリーを����場���。 (オレンジ�白�ストライプ)
+- **Ordinal**: �れ��順�付�られ�カテゴリを�むも���有��数�サイズ（mini�sm�med�lg�xl�xxl）�並�られ�カボ�ャ�よ����果を論�的�並���場��便利��。
+
+![Multinomial vs ordinal 回帰](../images/multinomial-ordinal.png)
+> [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
+
+### 線形����
+
+��タイプ�回帰��「カテゴリー�予測��目的����従属変数（色）�他�独立変数（都市�やサイズ���データセット�残り�部分）�間�明確�線形関係��る場��最も効果的��。�れら�変数を分�る線形性��る����を把��る��良�����。
+
+### 変数�相関���る必����
+
+線形回帰��相関性�高�変数��よ�����を覚������？ロジスティック回帰�������り��ん。相関関係�やや弱���データ��有効���。
+
+### 大���れ��データ�必���
+
+ロジスティック回帰��より多��データを使用�れ��より正確��果�得られ��。�������データセット����タスク��最����り��ん�����点�注�������。
+
+✅ ロジスティック回帰����データ�種類を考��������。
+
+## エクササイズ - データ�整�
+
+���NULL値を削除��り�一部�列��を�択��り���データを少�綺麗����。
+
+1. 以下�コードを追加:
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    new_columns = ['Color','Origin','Item Size','Variety','City Name','Package']
+    
+    new_pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)
+    
+    new_pumpkins.dropna(inplace=True)
+    
+    new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
+    ```
+
+    新��データフレームを���も覗��������。
+
+    ```python
+    new_pumpkins.info
+    ```
+
+### �視化 - グリッド状�並�る
+
+������[スターターノートブック](../notebook.ipynb) �パンプキンデータを�度読�込��`Color`を�む�����変数を�むデータセットを���るよ��整形����。別�ライブラリを使���ノートブック内�データフレームを�視化�����ょ�。[Seaborn](https://seaborn.pydata.org/index.html) ���ライブラリを使���ノートブック内�データフレームを�視化�����ょ�。��ライブラリ��先��使��`Matplotlib`をベース������。
+
+Seaborn���データを�視化�る��������優れ�方法��り��。例����データ�分布を横並��グリッド�比較�る�������。
+
+1. ���ゃ�データ`new_pumpkins`を使���`PairGrid`をインスタンス化��`map()`メソッドを呼�出���以下�よ��グリッドを作����。
+
+    ```python
+    import seaborn as sns
+    
+    g = sns.PairGrid(new_pumpkins)
+    g.map(sns.scatterplot)
+    ```
+
+    ![グリッド状��視化](../images/grid.png)
+
+    データを並��観察�る����Color�データ�他�列���よ��関連���る��を知る�������。
+
+    ✅ ��散布図をも�����よ���白�試��考�られる��ょ��？
+
+### swarm plot
+
+Color�2��カテゴリー（Orange or Not）��る���「カテゴリカルデータ��呼�れ�「�視化��より[専門的�アプロー�](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar) ��必���り��。��カテゴリ�他�変数��関係を�視化�る方法�他�も�り��。
+
+Seabornプロット���変数を並��表示�る�������。
+
+1. 値�分布を示��'swarm' plotを試�����。 
+
+    ```python
+    sns.swarmplot(x="Color", y="Item Size", data=new_pumpkins)
+    ```
+
+    ![swarm plot�よる�視化](../images/swarm.png)
+
+### Violin plot
+
+'violin' タイプ�プロット��2��カテゴリー�データ���よ��分布���る�を簡��視覚化��る��便利��。Violin plot��分布�より「滑ら���表示�れる���データセット����場����り�������ん。
+
+1. パラメータ���`x=Color`�`kind="violin"` をセット�� `catplot()`メソッドを呼���。
+
+    ```python
+    sns.catplot(x="Color", y="Item Size",
+                kind="violin", data=new_pumpkins)
+    ```
+
+    ![�イオリンタイプ��ャート](../images/violin.png)
+
+    ✅ 他�変数を使�����プロットや他�Seaborn�プロットを作���������。
+
+���`Color`�二値カテゴリ��より大��サイズ�グループ��関係����������ロジスティック回帰を使����るカボ�ャ�色����調�����ょ�。
+
+> **🧮 数学�確�**
+>
+> 線形回帰���通常�最�二乗法を用��値を求�る���多�����を覚������？ロジスティック回帰��[シグモイド関数](https://wikipedia.org/wiki/Sigmoid_function) を使��「最尤��概念��存�����。シグモイド関数���プロット上��「S�字�よ��見���。��曲線�「ロジスティック曲線��も呼�れ��。数��次�よ���り��。
+>
+> ![ロジスティック関数](../images/sigmoid.png)
+>
+> ����シグモイド�中点�x=0�点�L�曲線�最大値�k�曲線�急峻�を表���。��関数��果�0.5以上��れ����ラベル�二値�択�クラス「1���り��。�����場���「0��分類�れ��。
+
+## モデル�構築
+
+�れら�二値分類を行����モデル�構築��Scikit-learn��驚���簡������。
+
+1. 分類モデル�使用���変数を�択��`train_test_split()`メソッド�トレーニングセット�テストセットを分割���。
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Origin','Item Size','Variety','City Name','Package']
+    
+    X = new_pumpkins[Selected_features]
+    y = new_pumpkins['Color']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    
+    ```
+
+2. �れ��学習データを使��`fit()`メソッドを呼�出�����果を出力�る����モデルを訓練�る�������。
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.linear_model import LogisticRegression
+    
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+    自分�モデル�スコアボードを見����ょ�。1000行程度�データ������を考�る��悪��������。
+
+    ```output
+                       precision    recall  f1-score   support
+    
+               0       0.85      0.95      0.90       166
+               1       0.38      0.15      0.22        33
+    
+        accuracy                           0.82       199
+       macro avg       0.62      0.55      0.56       199
+    weighted avg       0.77      0.82      0.78       199
+    
+    Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
+     0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 1 0 1 0 0 1 0 0 0 1 0]
+    ```
+
+## 混�行列�よる�解度��上
+
+
+上記�項目を出力�る���[スコアボードレ�ート](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html?highlight=classification_report#sklearn.metrics.classification_report) を得る���������[混�行列](https://scikit-learn.org/stable/modules/model_evaluation.html#confusion-matrix) を使�����より簡��モデルを�解�る�����る�も�れ��ん。
+
+
+> 🎓 [混�行列](https://wikipedia.org/wiki/Confusion_matrix) ���モデル�真�陽性�陰性を表�表��予測�正確�を測る�������。
+
+1. `confusion_matrix()`メソッドを呼ん��混�行列を作����。
+
+    ```python
+    from sklearn.metrics import confusion_matrix
+    confusion_matrix(y_test, predictions)
+    ```
+
+    T作���モデル�混�行列を��������。
+
+    ```output
+    array([[162,   4],
+           [ 33,   0]])
+    ```
+
+Scikit-learn���混�行列�行 (axis=0)�実際�ラベル�列 (axis=1)�予測ラベル��り��。
+
+|  |0|1|
+|:-:|:-:|:-:|
+|0|TN|FP|
+|1|FN|TP|
+
+���何�起����る��？例���カボ�ャを「オレンジ色��「オレンジ色�������2��カテゴリー�分類�るよ��求�られ�����ょ�。
+
+- モデル��オレンジ色�����予測�れ�カボ�ャ��実際��「オレンジ色��������カテゴリー�属����場��「true negative��呼�れ�左上�数字�示�れ��。
+- モデル��オレンジ色�予測�れ�カボ�ャ��実際��「オレンジ色�����カテゴリー�属����場��「false negative��呼�れ�左下�数字�示�れ��。
+- モデル�オレンジ�����予測�����ゃ��実際��カテゴリー「オレンジ��属����場��「false positive��呼�れ��上�数字�示�れ��。
+- モデル�カボ�ャをオレンジ色�予測���れ�実際�カテゴリ「オレンジ��属�る場��「true positive��呼�れ��下�数字�示�れ��。
+
+�気���通り�true positive�true negative�数�多��false positive�false negative�数�少�����好�����れ�モデル�性能�高���を�味���。
+
+混�行列��precision�recall���よ��関係�る���ょ��？上記�分類レ�ート���precision（0.83）�recall（0.98）�示�れ����。
+
+Precision = tp / (tp + fp) = 162 / (162 + 33) = 0.8307692307692308
+
+Recall = tp / (tp + fn) = 162 / (162 + 4) = 0.9759036144578314
+
+✅ Q: 混�行列�よる��モデル�出��������？ A: 悪��り��ん。true negative���り�数�り����false negativeも�����り��。
+
+先���用語を�混�行列�TP/TN�FP/FN�マッピングを�考����確������ょ�。
+
+🎓 Precision: TP/(TP + FP) 探索�れ�インスタンス����関連性��るインスタンス�割�（��ラベル�よ�ラベル付��れ������）。
+
+🎓 Recall: TP/(TP + FN) ラベリング�れ��る�����関�ら��探索�れ�関連インスタンス�割���。
+
+🎓 f1-score: (2 * precision * recall)/(precision + recall) precision�recall�加�平���最高�1�最低�0��る。
+
+🎓 Support: �得���ラベル�出�回数��。
+
+🎓 Accuracy: (TP + TN)/(TP + TN + FP + FN) サンプル�対��正確�予測�れ�ラベル�割���。
+
+🎓 Macro Avg: �ラベル��加�平�指標�計算��ラベル���衡を考慮���算出�れる。
+
+🎓 Weighted Avg: �ラベル�サ�ート数（�ラベル�真�インスタンス数）���付��る���より�ラベル���衡を考慮����ラベル�平�指標を算出�る。
+
+✅ 自分�モデル�false negative�数を減ら���場����指標�注目����考�られ���？
+
+## モデル�ROC曲線を�視化�る
+
+�れ�悪�モデル���り��ん。精度�80%�範囲���想的���一連�変数�与�られ����カボ�ャ�色を予測�る��使��������。
+
+��ゆる「ROC�スコアを見る����も�一���視化を行�����ょ�。
+
+```python
+from sklearn.metrics import roc_curve, roc_auc_score
+
+y_scores = model.predict_proba(X_test)
+# calculate ROC curve
+fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])
+sns.lineplot([0, 1], [0, 1])
+sns.lineplot(fpr, tpr)
+```
+Seaborn を�度使用���モデル� [�信者�作特性 (Receiving Operating Characteristic)](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html?highlight=roc) ���ROCをプロット���。ROC曲線��分類器�出力を�true positive�false positive�観点�ら見る���よ�使�れ��。ROC曲線�通常�true positive rateをY軸��false positive rateをX軸�������。������曲線�急峻���真ん中�線形�線�曲線�間�スペース��������頭を上��中線を超�るよ��曲線を求���。今回�ケース���最��false positive�出����後�ライン���ん�上�����超������。
+
+![ROC](../images/ROC.png)
+
+最後��Scikit-learn�[`roc_auc_score` API](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html?highlight=roc_auc#sklearn.metrics.roc_auc_score) を使���実際�「Area Under the Curve�(AUC)を計算���。
+
+```python
+auc = roc_auc_score(y_test,y_scores[:,1])
+print(auc)
+```
+�果�`0.6976998904709748`��り���。AUC�範囲�0�ら1��る��を考�る��大��スコア�欲�������。���ら�予測�100%正��モデル�AUC�1��る�ら��。
+
+今後�分類�レッスン���モデル�スコアを�上��る����復処��方法を学���。一旦����������。������回帰�レッスンを完了����。
+
+---
+## 🚀�ャレンジ
+
+ロジスティック回帰����������解�明����������ん�り��。����学����最良�方法��実験�る����。��種�分�����データセットを見�����れを使��モデルを構築�����ょ�。ヒント：�白�データセットを探����[Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) を試��������。
+
+## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+
+## レビュー & 自主学習
+
+ロジスティック回帰�実用的�使�方�����[Stanford�ら���論文](https://web.stanford.edu/~jurafsky/slp3/5.pdf) �最��数ページを読ん�������。�れ��学ん���回帰タスク������ら�一方�タイプ����タスク����考��������。何�一番�������ょ��？
+
+## 課題 
+
+[回帰��挑戦�る](./assignment.ja.md)
diff --git a/2-Regression/4-Logistic/translations/assignment.ja.md b/2-Regression/4-Logistic/translations/assignment.ja.md
new file mode 100644
index 0000000000..6c838173bb
--- /dev/null
+++ b/2-Regression/4-Logistic/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# 回帰��挑戦�る
+
+## 課題�指示
+
+レッスン���カボ�ャ�データ�サブセットを使用����。今度��元�データ�戻���ロジスティック回帰モデルを構築�る����整形��標準化��データを���使�����ょ�。
+
+## ルーブリック
+
+| 指標 | 模範的                                                               | �切                                                     | �改善                                           |
+| -------- | ----------------------------------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------------- |
+|          | 説明���りや���性能�良�モデル���れ�ノートブック| 最���性能��発�����モデル���れ�ノートブック | 性能�劣るモデルや�何も��モデル���れ�ノートブック |

From 97042aae1d73f0d0b5ec5c0aa25d5fe2efbb33d4 Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Wed, 4 Aug 2021 20:13:02 +0900
Subject: [PATCH 332/368] add japanese translations (2-Regression/4-Logistic)

---
 .../4-Logistic/translations/README.ja.md      | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/2-Regression/4-Logistic/translations/README.ja.md b/2-Regression/4-Logistic/translations/README.ja.md
index 060209fbf2..3e384824b2 100644
--- a/2-Regression/4-Logistic/translations/README.ja.md
+++ b/2-Regression/4-Logistic/translations/README.ja.md
@@ -6,7 +6,7 @@
 
 ## イントロダクション
 
-回帰�最後�レッスン����典的�機械学習手法�一���る�「ロジスティック回帰�を見�����。��手法��2値�カテゴリを予測�る���パターンを発見�る���使���。例���「��キャンディ���ョコレート����？��「��病気��染�る����？��「��顧客����商�を������？�����。
+回帰�最後�レッスン����典的�機械学習手法�一���る�「ロジスティック回帰�を見�����。��手法��2値�カテゴリを予測�る���パターンを発見�る���使���。例���「��������ョコレート����？��「��病気��染�る����？��「��顧客����商�を������？�����。
 
 ��レッスン��以下�内容を扱���。
 
@@ -35,7 +35,7 @@
 
 ### 2値分類
 
-ロジスティック回帰��線形回帰��異�る特徴を������。ロジスティック回帰��二値�カテゴリー（「オレンジ色�オレンジ色�����）�����予測を�供�る��対��線形回帰��連続的�値を予測�る�������。例���カボ�ャ�産地��穫時期�与�られれ����価格��れ��上昇�る�を予測�る�������。
+ロジスティック回帰��線形回帰��異�る特徴を������。ロジスティック回帰��二値�カテゴリー（「オレンジ色�オレンジ色�����）�����予測を行���対��線形回帰�連続的�値を予測���。例���カボ�ャ�産地��穫時期�与�られれ����価格��れ��上昇�る�を予測�る�������。
 
 ![カボ�ャ分類モデル](../images/pumpkin-classifier.png)
 > [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
@@ -43,7 +43,7 @@
 
 ロジスティック回帰��他�もMultinomialやOrdinal���種類��り��。
 
-- **Multinomial**: �れ�2�以上�カテゴリーを����場���。 (オレンジ�白�ストライプ)
+- **Multinomial**: �れ�2�以上�カテゴリーを��場���。 (オレンジ�白�ストライプ)
 - **Ordinal**: �れ��順�付�られ�カテゴリを�むも���有��数�サイズ（mini�sm�med�lg�xl�xxl）�並�られ�カボ�ャ�よ����果を論�的�並���場��便利��。
 
 ![Multinomial vs ordinal 回帰](../images/multinomial-ordinal.png)
@@ -55,15 +55,15 @@
 
 ### 変数�相関���る必����
 
-線形回帰��相関性�高�変数��よ�����を覚������？ロジスティック回帰�������り��ん。相関関係�やや弱���データ��有効���。
+線形回帰��相関性�高�変数��よ�����を覚������？ロジスティック回帰�������り��ん。相関関係�やや弱���データ��有効���。
 
 ### 大���れ��データ�必���
 
-ロジスティック回帰��より多��データを使用�れ��より正確��果�得られ��。�������データセット����タスク��最����り��ん�����点�注�������。
+一般的�ロジスティック回帰��より多��データを使用�れ��より正確��果�得られ��。�������データセット����タスク��最����り��ん�����点�注�������。
 
 ✅ ロジスティック回帰����データ�種類を考��������。
 
-## エクササイズ - データ�整�
+## エクササイズ - データ�整形
 
 ���NULL値を削除��り�一部�列��を�択��り���データを少�綺麗����。
 
@@ -81,7 +81,7 @@
     new_pumpkins = new_pumpkins.apply(LabelEncoder().fit_transform)
     ```
 
-    新��データフレームを���も覗��������。
+    新��データフレーム����も確��る�������。
 
     ```python
     new_pumpkins.info
@@ -89,7 +89,7 @@
 
 ### �視化 - グリッド状�並�る
 
-������[スターターノートブック](../notebook.ipynb) �パンプキンデータを�度読�込��`Color`を�む�����変数を�むデータセットを���るよ��整形����。別�ライブラリを使���ノートブック内�データフレームを�視化�����ょ�。[Seaborn](https://seaborn.pydata.org/index.html) ���ライブラリを使���ノートブック内�データフレームを�視化�����ょ�。��ライブラリ��先��使��`Matplotlib`をベース������。
+������[スターターノートブック](../notebook.ipynb) �パンプキンデータを�度読�込��`Color`を�む�����変数を�むデータセットを���るよ��整形����。別�ライブラリを使���ノートブック内�データフレームを�視化�����ょ�。[Seaborn](https://seaborn.pydata.org/index.html) ���ライブラリを使���ノートブック内�データフレームを�視化�����ょ�。��ライブラリ��今��使����`Matplotlib`をベース������。
 
 Seaborn���データを�視化�る��������優れ�方法��り��。例����データ�分布を横並��グリッド�比較�る�������。
 
@@ -141,7 +141,7 @@ Seabornプロット���変数を並��表示�る�������
 
 > **🧮 数学�確�**
 >
-> 線形回帰���通常�最�二乗法を用��値を求�る���多�����を覚������？ロジスティック回帰��[シグモイド関数](https://wikipedia.org/wiki/Sigmoid_function) を使��「最尤��概念��存�����。シグモイド関数���プロット上��「S�字�よ��見���。��曲線�「ロジスティック曲線��も呼�れ��。数��次�よ���り��。
+> 線形回帰���通常�最�二乗法を用��値を求�る���多�����を覚������？ロジスティック回帰��[シグモイド関数](https://wikipedia.org/wiki/Sigmoid_function) を使��「最尤��概念��存�����。シグモイド関数��プロット上��「S�字�よ��見���。��曲線�「ロジスティック曲線��も呼�れ��。数��次�よ���り��。
 >
 > ![ロジスティック関数](../images/sigmoid.png)
 >
@@ -165,7 +165,7 @@ Seabornプロット���変数を並��表示�る�������
     
     ```
 
-2. �れ��学習データを使��`fit()`メソッドを呼�出�����果を出力�る����モデルを訓練�る�������。
+2. �れ��学習データを使��`fit()`メソッドを呼�出��モデルを訓練�����果を出力�る�������。
 
     ```python
     from sklearn.model_selection import train_test_split
@@ -181,7 +181,7 @@ Seabornプロット���変数を並��表示�る�������
     print('Accuracy: ', accuracy_score(y_test, predictions))
     ```
 
-    自分�モデル�スコアボードを見����ょ�。1000行程度�データ������を考�る��悪��������。
+    モデル�スコアボードを見����ょ�。1000行程度�データ������を考�る��悪��������。
 
     ```output
                        precision    recall  f1-score   support

From 241e69f16152b3dbba6b6eb411ce99a4d749033a Mon Sep 17 00:00:00 2001
From: kenya-sk <ksakka0309@gmail.com>
Date: Wed, 4 Aug 2021 20:17:35 +0900
Subject: [PATCH 333/368] update each section README path

---
 2-Regression/translations/README.ja.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/2-Regression/translations/README.ja.md b/2-Regression/translations/README.ja.md
index 2ff2336638..50d8294b45 100644
--- a/2-Regression/translations/README.ja.md
+++ b/2-Regression/translations/README.ja.md
@@ -17,10 +17,10 @@
 
 ### レッスン
 
-1. [商売�具](../1-Tools/README.ja.md)
-2. [データ管�](../2-Data/README.ja.md)
-3. [線形回帰�多項�回帰](../3-Linear/README.ja.md)
-4. [ロジスティック回帰](../4-Logistic/README.ja.md)
+1. [商売�具](../1-Tools/translations/README.ja.md)
+2. [データ管�](../2-Data/translations/README.ja.md)
+3. [線形回帰�多項�回帰](../3-Linear/translations/README.ja.md)
+4. [ロジスティック回帰](../4-Logistic/translations/README.ja.md)
 
 ---
 ### クレジット

From 2bb8bd1d22e2a943e3726d890a91e421f4468a8d Mon Sep 17 00:00:00 2001
From: Jay Patel <59785863+jaypatel31@users.noreply.github.com>
Date: Wed, 4 Aug 2021 19:15:58 +0530
Subject: [PATCH 334/368] Update README.es.md

---
 .../4-techniques-of-ML/translations/README.es.md    | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.es.md b/1-Introduction/4-techniques-of-ML/translations/README.es.md
index 8105202115..824a7eda79 100755
--- a/1-Introduction/4-techniques-of-ML/translations/README.es.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.es.md
@@ -41,9 +41,13 @@ Hay dos cosas que debe hacer en este punto:
 
 ✅ Despúes de recopilar y procesar sus datos, tómese un momento para ver si su forma le permitirá responder a su pregunta. ¡Puede ser que los datos no funcionen bien en su tarea dada, como descubriremos en nuestras lecciones de[Clustering](../../5-Clustering/1-Visualize/README.md)!
 
-### Seleccionando su variable característica
+### Características y destino
+
+Una característica es una propiedad medible de los datos.En muchos conjuntos de datos se expresa como un encabezado de columna como 'date' 'size' o 'color'. La variable de entidad, normalmente representada como `X` en el código, representa la variable de entrada que se utilizará para entrenar el modelo.
 
-Una [característica](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) es una propiedad medible de sus datos. En muchos conjuntos de datos, se expresa como un encabezado de columna como 'fecha', 'tamaño' o 'color'. Su variable característica, generalmente representada como `y` en el código, representa la respuesta a la pregunta que está tratando de hacer a sus datos: en diciembre, ¿qué calabazas de **color** serán las más baratas?, en San Francisco, ¿que vecinadarios tendrán el mejor **precio** de bienes raíces?
+Un objetivo es una cosa que está tratando de predecir. Target generalmente representado como `y` en el código, representa la respuesta a la pregunta que está tratando de hacer de sus datos: en diciembre, ¿qué color de calabazas serán más baratas? en San Francisco, ¿qué barrios tendrán el mejor precio de bienes raíces? A veces, target también se conoce como atributo label.
+
+### Seleccionando su variable característica
 
 🎓 **Selección y extracción de características** ¿ Cómo sabe que variable elegir al construir un modelo? Probablemente pasará por un proceso de selección o extracción de características para elegir las variables correctas para mayor un mayor rendimiento del modelo. Sin embargo, no son lo mismo: "La extracción de características crea nuevas características a partir de funciones de las características originales, mientras que la selección de características devuelve un subconjunto de las características." ([fuente](https://wikipedia.org/wiki/Feature_selection))
 
@@ -68,7 +72,8 @@ Usando sus datos de entrenamiento, su objetivo es construir un modelo, o una rep
 Dependiendo de su pregunta y la naturaleza de sus datos, elegirá un método para entrenarlos. Pasando por la [documentación de Scikit-learn ](https://scikit-learn.org/stable/user_guide.html) - que usamos en este curso - puede explorar muchas formas de entrenar un modelo. Dependiendo de su experiencia, es posible que deba probar varios métodos diferentes para construir el mejor modelo. Es probable que pase por un proceso en el que los científicos de datos evalúan el rendimiento de un modelo alimentándolo con datos no vistos anteriormente por el modelo, verificando la precisión, el sesgo, y otros problemas que degradan la calidad, y seleccionando el método de entrenamieto más apropiado para la tarea en custión.
 ### Entrena un modelo
 
-Armado con sus datos de entrenamiento, está listo para 'fit'(ajustarlos/entrenarlos) para crear un modelo. Notará que en muchas bibliotecas de ML, encontrará el código 'model.fit' - es en este momento cuando envías sus datos como una matriz de valores (generalmente 'X') y una variable característica (generalmente 'Y').
+Armado con sus datos de entrenamiento, está listo para "ajustarlo" para crear un modelo. Notará que en muchas bibliotecas de ML encontrará el código 'model.fit' - es en este momento que envía su variable de característica como una matriz de valores (generalmente `X`) y una variable de destino (generalmente `y`).
+
 ### Evaluar el modelo
 
 Una vez que se completa el proceso de entrenamiento (puede tomar muchas iteraciones, o 'épocas', entrenar un modelo de gran tamaño), podrá evaluar la calidad del modelo utilizando datos de prueba para medir su rendimiento. Estos datos son un subconjunto de los datos originales que el modelo no ha analizado previamente. Puede imprimir una tabla de métricas sobre la calidad de su modelo.
@@ -104,4 +109,4 @@ Busque en línea entrevistas con científicos de datos que analicen su trabajo d
 
 ## Asignación
 
-[Entrevistar a un científico de datos](assignment.md)
\ No newline at end of file
+[Entrevistar a un científico de datos](assignment.md)

From 79da1edbd9862b0712bcaf234e41f418dc51b375 Mon Sep 17 00:00:00 2001
From: Jay Patel <59785863+jaypatel31@users.noreply.github.com>
Date: Wed, 4 Aug 2021 19:22:51 +0530
Subject: [PATCH 335/368] Update README.id.md

---
 .../4-techniques-of-ML/translations/README.id.md   | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
index 6fee4f14a0..8e6fac43ca 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.id.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -39,9 +39,13 @@ Untuk dapat menjawab pertanyaan kamu dengan kepastian, Kamu memerlukan sejumlah
 
 ✅ Setelah mengumpulkan dan memproses data kamu, luangkan waktu sejenak untuk melihat apakah bentuknya memungkinkan kamu untuk menjawab pertanyaan yang kamu maksudkan. Mungkin data tidak akan berkinerja baik dalam tugas yang kamu berikan, seperti yang kita temukan dalam pelajaran [Clustering](../../5-Clustering/1-Visualize/translations/README.id.md).
 
-### Memilih variabel fiturmu
+### Fitur dan Target
+
+Fitur adalah properti terukur dari data Anda. Dalam banyak set data, data tersebut dinyatakan sebagai judul kolom seperti 'date' 'size' atau 'color'. Variabel fitur Anda, biasanya direpresentasikan sebagai `X` dalam kode, mewakili variabel input yang akan digunakan untuk melatih model.
+
+A target is a thing you are trying to predict. Target usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what color pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate price? Sometimes target is also referred as label attribute.
 
-Sebuah [fitur](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) adalah sebuah properti yang dapat diukur dalam data kamu. Dalam banyak dataset, properti dinyatakan sebagai sebuah heading kolom seperti 'date' 'size' atau 'color'. Variabel fitur kamu yang biasanya direpresentasikan sebagai `y` dalam kode, mewakili jawaban atas pertanyaan yang kamu coba tanyakan tentang data kamu: pada bulan Desember, labu dengan **warna** apa yang akan paling murah? di San Francisco, lingkungan mana yang menawarkan **harga** real estate terbaik?
+### Memilih variabel fiturmu
 
 🎓 **Feature Selection dan Feature Extraction** Bagaimana kamu tahu variabel mana yang harus dipilih saat membangun model? Kamu mungkin akan melalui proses pemilihan fitur (*Feature Selection*) atau ekstraksi fitur (*Feature Extraction*) untuk memilih variabel yang tepat untuk membuat model yang berkinerja paling baik. Namun, keduanya tidak sama: "Ekstraksi fitur membuat fitur baru dari fungsi fitur asli, sedangkan pemilihan fitur mengembalikan subset fitur." ([sumber](https://wikipedia.org/wiki/Feature_selection))
 ### Visualisasikan datamu
@@ -61,10 +65,12 @@ Dengan menggunakan data *training*, tujuan kamu adalah membuat model atau repres
 
 ### Tentukan metode training
 
-Tergantung dari pertanyaan dan sifat datamu, Kamu akan memilih metode untuk melatihnya. Buka dokumentasi [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) yang kita gunakan dalam pelajaran ini, kamu bisa menjelajahi banyak cara untuk melatih sebuah model. Tergantung dari pengalamanmu, kamu mungkin perlu mencoba beberapa metode yang berbeda untuk membuat model yang terbaik. Kemungkinan kamu akan melalui proses di mana data scientist mengevaluasi kinerja model dengan memasukkan data yang belum pernah dilihat, memeriksa akurasi, bias, dan masalah penurunan kualitas lainnya, dan memilih metode training yang paling tepat untuk tugas yang ada. 
+Tergantung dari pertanyaan dan sifat datamu, Kamu akan memilih metode untuk melatihnya. Buka dokumentasi [Scikit-learn](https://scikit-learn.org/stable/user_guide.html) yang kita gunakan dalam pelajaran ini, kamu bisa menjelajahi banyak cara untuk melatih sebuah model. Tergantung dari pengalamanmu, kamu mungkin perlu mencoba beberapa metode yang berbeda untuk membuat model yang terbaik. Kemungkinan kamu akan melalui proses di mana data scientist mengevaluasi kinerja model dengan memasukkan data yang belum pernah dilihat, memeriksa akurasi, bias, dan masalah penurunan kualitas lainnya, dan memilih metode training yang paling tepat untuk tugas yang ada.
+
 ### Melatih sebuah model
 
-Berbekal data *training*, Kamu siap untuk menggunakannya untuk membuat model. Kamu akan melihat di banyak *library* ML mengenai kode 'model.fit' - pada saat inilah kamu mengirimkan data kamu sebagai *array* nilai (biasanya 'X') dan variabel fitur (biasanya 'y' ). 
+Berbekan dengan data pelatihan Anda, Anda siap untuk 'menyesuaikan' untuk membuat model. Anda akan melihat bahwa di banyak perpustakaan ML Anda akan menemukan kode 'model.fit' - saat inilah Anda mengirim variabel fitur Anda sebagai array nilai (biasanya `X`) dan variabel target (biasanya `y`).
+
 ### Mengevaluasi model
 
 Setelah proses *training* selesai (ini mungkin membutuhkan banyak iterasi, atau 'epoch', untuk melatih model besar), Kamu akan dapat mengevaluasi kualitas model dengan menggunakan data tes untuk mengukur kinerjanya. Data ini merupakan subset dari data asli yang modelnya belum pernah dianalisis sebelumnya. Kamu dapat mencetak tabel metrik tentang kualitas model kamu. 

From b110ed12c0ed40cd1b2ddd3634092e0a0ce59045 Mon Sep 17 00:00:00 2001
From: Jay Patel <59785863+jaypatel31@users.noreply.github.com>
Date: Wed, 4 Aug 2021 19:25:51 +0530
Subject: [PATCH 336/368] Update README.it.md

---
 .../4-techniques-of-ML/translations/README.it.md       | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.it.md b/1-Introduction/4-techniques-of-ML/translations/README.it.md
index 2fe5794b70..4e36c9cc54 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.it.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md
@@ -40,9 +40,13 @@ Per poter rispondere con sicurezza alla domanda, serve una buona quantità di da
 
 ✅ Dopo aver raccolto ed elaborato i propri dati, si prenda un momento per vedere se la loro forma consentirà di rispondere alla domanda prevista. Potrebbe essere che i dati non funzionino bene nello svolgere il compito assegnato, come si scopre nelle lezioni di [Clustering](../../../5-Clustering/1-Visualize/translations/README.it.md)!
 
-### Selezione della variabile caratteristica
+### Caratteristiche e destinazione
+
+Una funzionalità è una proprietà misurabile dei dati. In molti set di dati è espresso come intestazione di colonna come 'date' 'size' o 'color'. La variabile di funzionalità, solitamente rappresentata come `X` nel codice, rappresenta la variabile di input che verrà utilizzata per il training del modello.
 
-Una [caratteristica](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) è una proprietà misurabile dei propri dati. In molti insiemi di dati è espressa come un'intestazione di colonna come "data", "dimensione" o "colore". La propria variabile caratteristica, solitamente rappresentata come `y` nel codice, rappresenta la risposta alla domanda che si sta cercando di porre ai propri dati: a dicembre, di che **colore** saranno le zucche più economiche? A San Francisco, quali quartieri avranno il miglior **prezzo** immobiliare?
+Un obiettivo è una cosa che stai cercando di prevedere. Target solitamente rappresentato come `y` nel codice, rappresenta la risposta alla domanda che stai cercando di porre dei tuoi dati: a dicembre, di che colore saranno le zucche più economiche? a San Francisco, quali quartieri avranno il miglior prezzo immobiliare? A volte la destinazione viene anche definita attributo label.
+
+### Selezione della variabile caratteristica
 
 🎓 **Selezione ed estrazione della caratteristica** Come si fa a sapere quale variabile scegliere quando si costruisce un modello? Probabilmente si dovrà passare attraverso un processo di selezione o estrazione delle caratteristiche per scegliere le variabili giuste per il modello più efficace. Tuttavia, non è la stessa cosa: "L'estrazione delle caratteristiche crea nuove caratteristiche dalle funzioni delle caratteristiche originali, mentre la selezione delle caratteristiche restituisce un sottoinsieme delle caratteristiche". ([fonte](https://it.wikipedia.org/wiki/Selezione_delle_caratteristiche))
 
@@ -68,7 +72,7 @@ A seconda della domanda e della natura dei dati, si sceglierà un metodo per add
 
 ### Allenare un modello
 
-Occorre armarsi dei propri dati di allenamento, per essere pronti per "adattarli" per creare un modello. Si noterà che in molte librerie ML si trova il codice "model.fit" - è in questo momento che si inviano i propri dati come un vettore di valori (di solito "X") e una variabile di caratteristica (di solito "y" ).
+Armati dei tuoi dati di allenamento, sei pronto a "adattarlo" per creare un modello. Noterai che in molte librerie ML troverai il codice "model.fit" - è in questo momento che invii la tua variabile di funzionalità come matrice di valori (in genere `X`) e una variabile di destinazione (di solito `y`).
 
 ### Valutare il modello
 

From 22b756e56e9accb2fe90c46134afd5a38e72c327 Mon Sep 17 00:00:00 2001
From: Jay Patel <59785863+jaypatel31@users.noreply.github.com>
Date: Wed, 4 Aug 2021 19:29:00 +0530
Subject: [PATCH 337/368] Update README.ja.md

---
 .../4-techniques-of-ML/translations/README.ja.md       | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ja.md b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
index 0e0a1bca90..b964781de2 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.ja.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
@@ -40,9 +40,13 @@
 
 ✅ データを集��処���後����形��図��質��対応��る����を確������ょ�。[クラスタリング](../../../5-Clustering/1-Visualize/README.md) �講義���るよ���データ�与�られ�タスク�対��上手�機能����も�れ��ん�
 
-### 特徴���択
+### 機能�ターゲット
+
+フィー�ャ��データ�測定�能�プロパティ��。多��データセット���'日付' 'サイズ' や '色' �よ��列見出����表��れ��。通常�コード�� `X` ���表�れるフィー�ャ変数��モデル�トレーニング�使用�れる入力変数を表���
 
-[特徴](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) ���測定�能�データ�特性�����。多��データセット���「日付�「大���「色����列�見出����表�れ����。コード上�� `y` �表�れる���多�特徴���データ�関�る次�よ��質���回答を�味���。「12月��ん� **色** �カボ�ャ�一番安��？�「サンフランシスコ�����地域�最も�動産� **価格** �高��？�
+ターゲット��予測�よ�����るも���。ターゲット�通常�コード�`y`���表�れ�����データを尋�よ�����る質��対�る答�を表���:12月����色�カボ�ャ�最も安��り���?サンフランシスコ�����地域�最高��動産価格を����ょ��?ターゲット�ラベル属性�も呼�れる��も�り��。
+
+### 特徴���択
 
 🎓 **特徴�択�特徴抽出** モデルを構築�る際���変数を����������れ���る��ょ��？最も性能�高�モデル���������変数を�択�る特徴�択や特徴抽出�プロセスを��る����る��ょ�。�����れら���も����り��ん。「特徴抽出�元�特徴�機能�ら新��特徴を作��る��対��特徴�択�特徴�一部を返�も���。� ([出典](https://wikipedia.org/wiki/Feature_selection))
 
@@ -68,7 +72,7 @@
 
 ### モデルを学習�る
 
-訓練データを用������モデルを作��る����れを「�����る準備�整����。多��機械学習ライブラリ�� 'model.fit' ���コード��る���気����ょ�。データを（通常� 'X' �表�）値��列�（通常� 'y' �表�）特徴����渡�����。
+トレーニングデータを使用���モデルを作��る���「フィット��る準備�整����。多�� ML ライブラリ���コード 'model.fit' �見��り�� - ��時点��値��列 (通常� `X`) �ターゲット変数 (通常� `y`) ���機能変数を�信���。
 
 ### モデルを評価�る
 

From 4566d412e23400d3569270243334fe87f54b48aa Mon Sep 17 00:00:00 2001
From: Jay Patel <59785863+jaypatel31@users.noreply.github.com>
Date: Wed, 4 Aug 2021 19:31:13 +0530
Subject: [PATCH 338/368] Update README.zh-cn.md

---
 .../4-techniques-of-ML/translations/README.zh-cn.md    | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index 318876bd4d..c7b86c8e2a 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -40,9 +40,13 @@
 
 ✅ 在收集和处�你的数��，花点时间看看它的形状是�能让你解决你的预期问题。正如我们在[�类](../../../5-Clustering/1-Visualize/README.md)课程中�现的那样，数��能在你的给定任务中表现�佳�
 
-### 选择特���
+### 功能和目标
+
+功能是数�的�测�属性。在许多数�集中，它表示为标题为"日期""大�"或"颜色"的列。您的功能��（通常在代�中表示为 `X`）表示用于训练模型的输入��。
 
-[特�](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection)是数�的�衡�属性。在许多数�集中，它表示为列标题，如“日期��“大��或“颜色�。你的特���（通常在代�中表示为`y`）代表你试图对数��出的问题的答案：在12月，哪�**颜色**的�瓜最便宜？在旧金山，哪些街区的房地产**价格**最好？
+目标就是你试图预测的事情。目标通常表示为代�中的 `y`，代表您试图询问数�的问题的答案：在 12 月，什么颜色的�瓜最便宜？在旧金山，哪些街区的房地产价格最好？有时目标也称为标签属性。
+
+### 选择特���
 
 🎓 **特�选择和特���** 构建模型时如何知�选择哪个��？你�能会�历一个特�选择或特���的过程，以便为性能最好的模型选择正确的��。然而，它们�是一回事：“特���是从基于原始特�的函数中创建新特�，而特�选择返回特�的一个�集。�（[��](https://wikipedia.org/wiki/Feature_selection)）
 ### �视化数�
@@ -66,7 +70,7 @@
 
 ### 训练模型
 
-有了你的训练数�，你就�以“拟��它以创建模型。你会注�到，在许多ML库中，你会找到代�'model.fit'——此时你将数�作为值数组（通常为'X'）和特���（通常为'y'）��）。 
+有了您的培训数�，您就�以"适应"它�创建模型。您会注�到，在许多 ML 库中，您会�现代�"model.fit"-此时，您将功能��作为一系列值（通常是`X`）和目标��（通常是`y`）��。 
 
 ### 评估模型 
 

From 948a1a85659791733f6e737f4304ff02d6b3db57 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 4 Aug 2021 10:18:02 -0400
Subject: [PATCH 339/368] editing solution notebook supporting edit for HORIZON
 loop

---
 7-TimeSeries/2-ARIMA/solution/notebook.ipynb | 745 ++++++++++++++-----
 1 file changed, 543 insertions(+), 202 deletions(-)

diff --git a/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
index 4914ab705f..3e6307d21f 100644
--- a/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
+++ b/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
    "source": [
     "# Time series forecasting with ARIMA\n",
     "\n",
@@ -14,13 +15,14 @@
     "\n",
     "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
    ],
-   "cell_type": "markdown",
    "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "source": [
+    "!pip install statsmodels"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -40,15 +42,11 @@
      ]
     }
    ],
-   "source": [
-    "!pip install statsmodels"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "import os\n",
     "import warnings\n",
@@ -68,12 +66,17 @@
     "pd.options.display.float_format = '{:,.2f}'.format\n",
     "np.set_printoptions(precision=2)\n",
     "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -91,116 +94,188 @@
        "2012-01-01 08:00:00 2,916.00\n",
        "2012-01-01 09:00:00 3,105.00"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2,698.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2,558.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2,444.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2,402.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2,403.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 05:00:00</th>\n      <td>2,453.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 06:00:00</th>\n      <td>2,560.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 07:00:00</th>\n      <td>2,719.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 08:00:00</th>\n      <td>2,916.00</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 09:00:00</th>\n      <td>3,105.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 3
     }
    ],
-   "source": [
-    "energy = load_data('./data')[['load']]\n",
-    "energy.head(10)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Plot all available load data (January 2012 to Dec 2014)"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
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83.742527 195.962437 \nL 83.837991 148.230038 \nL 83.869813 161.617056 \nL 84.092563 314.141275 \nL 84.156206 304.046147 \nL 84.378956 188.061902 \nL 84.506242 207.92297 \nL 84.601707 158.873814 \nL 84.633528 171.382995 \nL 84.856278 323.687755 \nL 84.919921 312.60506 \nL 85.11085 190.805144 \nL 85.142671 192.121899 \nL 85.238136 198.595949 \nL 85.269957 197.937571 \nL 85.365422 150.095442 \nL 85.397243 159.641922 \nL 85.619993 299.437501 \nL 85.683636 287.806158 \nL 85.810922 179.39326 \nL 85.874565 183.233797 \nL 86.033672 208.691077 \nL 86.065494 196.291626 \nL 86.129137 164.140838 \nL 86.160958 174.784614 \nL 86.41553 312.166141 \nL 86.479173 297.133178 \nL 86.63828 206.606214 \nL 86.670102 211.763507 \nL 86.765566 233.599709 \nL 86.797388 232.831601 \nL 86.892852 171.382995 \nL 86.956495 197.388923 \nL 87.179245 301.412635 \nL 87.242888 288.354806 \nL 87.401995 205.728377 \nL 87.465638 212.641345 \nL 87.529281 226.577011 \nL 87.561103 223.394851 \nL 87.624746 169.956509 \nL 87.688389 203.753243 \nL 87.94296 312.49533 \nL 87.974782 303.497498 \nL 88.102068 192.670548 \nL 88.165711 195.742978 \nL 88.324818 223.72404 \nL 88.356639 210.666211 \nL 88.420282 165.457594 \nL 88.452104 179.832178 \nL 88.674854 324.565592 \nL 88.738497 313.373167 \nL 88.929426 202.875406 \nL 88.961247 205.508917 \nL 89.056712 216.372153 \nL 89.088533 214.397019 \nL 89.183998 154.155439 \nL 89.24764 180.490556 \nL 89.438569 296.035882 \nL 89.470391 295.048315 \nL 89.534034 251.485642 \nL 89.597677 175.333262 \nL 89.661319 181.148934 \nL 89.820427 213.080263 \nL 89.852248 211.10513 \nL 89.947713 153.716521 \nL 90.011356 182.46569 \nL 90.202284 308.435333 \nL 90.234106 308.215873 \nL 90.297749 265.640768 \nL 90.361392 191.682981 \nL 90.425035 198.815408 \nL 90.615963 224.711607 \nL 90.711428 166.884079 \nL 90.775071 192.560818 \nL 90.966 312.60506 \nL 90.997821 311.398033 \nL 91.061464 268.384009 \nL 91.125107 195.962437 \nL 91.18875 202.875406 \nL 91.284214 219.005665 \nL 91.379679 231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 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322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 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348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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292.853722 \nL 363.675957 281.002919 \nL 363.866885 170.505158 \nL 363.96235 181.478123 \nL 364.025993 187.952173 \nL 364.121457 139.451665 \nL 364.153279 150.095442 \nL 364.40785 275.187248 \nL 364.471493 258.61807 \nL 364.630601 179.064071 \nL 364.662422 182.026771 \nL 364.757886 197.827841 \nL 364.789708 195.633248 \nL 364.853351 150.534361 \nL 364.916994 166.774349 \nL 365.171565 292.524533 \nL 365.235208 280.125082 \nL 365.426137 192.560818 \nL 365.48978 199.802975 \nL 365.521602 202.436487 \nL 365.553423 196.511086 \nL 365.617066 144.060311 \nL 365.680709 175.772181 \nL 365.903459 291.427236 \nL 365.967102 276.613733 \nL 366.158031 168.420294 \nL 366.189852 168.091105 \nL 366.285317 178.954341 \nL 366.317138 176.979207 \nL 366.412603 119.480868 \nL 366.476246 149.107875 \nL 366.667174 268.493739 \nL 366.698996 267.835361 \nL 366.762639 222.626743 \nL 366.826282 150.973279 \nL 366.889925 159.971111 \nL 367.01721 180.161367 \nL 367.049032 186.635417 \nL 367.080853 185.318661 \nL 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 399.920605 208.361888 \nL 399.952427 206.386754 \nL 400.047891 171.163536 \nL 400.079713 179.1738 \nL 400.270642 302.290472 \nL 400.302463 301.302905 \nL 400.366106 256.204017 \nL 400.429749 181.697582 \nL 400.493392 188.83001 \nL 400.588856 206.277025 \nL 400.684321 222.846203 \nL 400.747964 212.531615 \nL 400.811607 182.46569 \nL 400.843428 189.159199 \nL 401.066178 308.654792 \nL 401.129821 269.042387 \nL 401.225286 191.353792 \nL 401.257107 192.341359 \nL 401.448036 222.517014 \nL 401.479857 222.407284 \nL 401.575322 194.426222 \nL 401.607143 199.144597 \nL 401.829893 312.714789 \nL 401.861715 306.679658 \nL 402.052644 210.9954 \nL 402.148108 233.928898 \nL 402.211751 240.841866 \nL 402.243572 237.330517 \nL 402.339037 207.045132 \nL 402.370858 213.189993 \nL 402.593609 325.662888 \nL 402.62543 322.700188 \nL 402.720894 275.735896 \nL 402.816359 230.966197 \nL 402.84818 234.148357 \nL 402.975466 254.448343 \nL 403.007288 249.839697 \nL 403.102752 207.374321 \nL 403.134573 214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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228.991063 \nL 545.853845 225.479714 \nL 545.917488 211.434319 \nL 545.949309 227.015929 \nL 546.17206 353.973139 \nL 546.203881 351.668816 \nL 546.39481 248.193753 \nL 546.522096 261.471041 \nL 546.553917 261.141852 \nL 546.681203 229.100793 \nL 546.713025 245.88943 \nL 546.935775 361.325026 \nL 546.967596 358.910974 \nL 547.063061 308.215873 \nL 547.190347 255.545639 \nL 547.222168 257.740233 \nL 547.285811 265.201849 \nL 547.317632 262.458608 \nL 547.444918 213.189993 \nL 547.47674 231.844034 \nL 547.667669 352.985572 \nL 547.69949 352.327194 \nL 547.763133 311.398033 \nL 547.92224 218.676476 \nL 548.017705 215.933234 \nL 548.081348 223.72404 \nL 548.113169 220.761339 \nL 548.208633 186.086768 \nL 548.240455 207.593781 \nL 548.463205 347.169901 \nL 548.526848 306.679658 \nL 548.685955 214.616478 \nL 548.78142 209.129996 \nL 548.813241 210.9954 \nL 548.845063 211.982967 \nL 548.876884 209.678644 \nL 548.972349 183.782446 \nL 549.22692 344.865578 \nL 549.258742 335.867746 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 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588.52643 200.351623 \nL 588.558251 198.37649 \nL 588.621894 165.896512 \nL 588.653716 173.797047 \nL 588.940109 325.22397 \nL 589.003752 304.704524 \nL 589.162859 221.090528 \nL 589.194681 223.065662 \nL 589.258324 230.527278 \nL 589.290145 230.198089 \nL 589.353788 186.745146 \nL 589.385609 165.238134 \nL 589.449252 187.732713 \nL 589.703824 296.145611 \nL 589.767467 280.78346 \nL 589.958396 181.478123 \nL 590.022039 191.024603 \nL 590.05386 194.206763 \nL 590.085682 186.196498 \nL 590.149325 127.930052 \nL 590.212968 148.559227 \nL 590.435718 272.992654 \nL 590.499361 258.069421 \nL 590.626647 153.387332 \nL 590.69029 158.325166 \nL 590.817575 178.515422 \nL 590.849397 172.480291 \nL 590.91304 119.15168 \nL 590.976683 132.319238 \nL 591.199433 289.122914 \nL 591.263076 278.259678 \nL 591.454005 168.530024 \nL 591.581291 183.562986 \nL 591.613112 180.929475 \nL 591.676755 142.194907 \nL 591.740398 162.604622 \nL 591.99497 315.56776 \nL 592.058613 281.332108 \nL 592.21772 177.418126 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\nL 595.495331 155.252736 \nL 595.558974 179.28353 \nL 595.781724 316.555327 \nL 595.845367 303.05858 \nL 596.036296 183.672716 \nL 596.068117 183.892175 \nL 596.163582 190.256495 \nL 596.195403 185.099202 \nL 596.259046 138.903017 \nL 596.322689 158.105707 \nL 596.577261 316.774786 \nL 596.609082 306.350469 \nL 596.736368 196.840275 \nL 596.800011 199.693246 \nL 596.927297 210.556481 \nL 596.959118 205.947836 \nL 597.022761 145.815985 \nL 597.086404 157.557058 \nL 597.309154 306.899118 \nL 597.372797 294.060748 \nL 597.468262 183.014338 \nL 597.563726 194.426222 \nL 597.595548 194.097033 \nL 597.691012 204.411621 \nL 597.722833 201.668379 \nL 597.786476 142.633825 \nL 597.850119 153.716521 \nL 598.072869 307.008847 \nL 598.168334 263.446175 \nL 598.327441 177.198666 \nL 598.359263 177.527855 \nL 598.454727 184.550553 \nL 598.550191 139.890584 \nL 598.613834 158.215436 \nL 598.836585 319.847217 \nL 598.900228 310.081277 \nL 599.091156 187.842443 \nL 599.122978 189.268928 \nL 599.218442 199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 653.155826 278.369407 \nL 653.283112 194.316493 \nL 653.346755 198.815408 \nL 653.442219 210.337022 \nL 653.47404 208.032699 \nL 653.569505 162.824082 \nL 653.601326 175.99164 \nL 653.855898 294.938585 \nL 653.919541 283.416972 \nL 654.078648 200.241894 \nL 654.142291 206.277025 \nL 654.205934 217.908368 \nL 654.237756 213.299723 \nL 654.33322 143.401933 \nL 654.396863 174.894344 \nL 654.587792 277.162381 \nL 654.619613 275.077518 \nL 654.683256 237.988895 \nL 654.810542 157.447329 \nL 654.842363 157.995977 \nL 654.969649 182.794879 \nL 655.001471 181.368393 \nL 655.096935 123.101947 \nL 655.160578 148.998145 \nL 655.351507 262.348878 \nL 655.383328 260.154285 \nL 655.446971 218.895935 \nL 655.606079 143.840852 \nL 655.765186 156.350032 \nL 655.797007 149.875983 \nL 655.86065 118.603031 \nL 655.892472 134.623561 \nL 656.115222 283.746161 \nL 656.178865 270.688332 \nL 656.369794 172.480291 \nL 656.528901 175.113803 \nL 656.592544 145.157608 \nL 656.624366 136.598694 \nL 656.656187 150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 211.434319 \nL 680.554107 315.56776 \nL 680.61775 312.934248 \nL 680.713215 263.885093 \nL 680.808679 219.554313 \nL 680.840501 221.419717 \nL 680.935965 240.402947 \nL 680.999608 233.928898 \nL 681.095072 181.258664 \nL 681.126894 185.867309 \nL 681.286001 288.025617 \nL 681.317823 286.489402 \nL 681.381465 272.224547 \nL 681.47693 164.689486 \nL 681.572394 173.02894 \nL 681.731502 204.960269 \nL 681.795145 191.682981 \nL 681.858787 160.958678 \nL 681.890609 167.981376 \nL 682.081538 281.880756 \nL 682.113359 278.808326 \nL 682.177002 231.734305 \nL 682.240645 161.726785 \nL 682.304288 173.797047 \nL 682.495217 211.873237 \nL 682.527038 207.154862 \nL 682.622503 166.335431 \nL 682.654324 173.577588 \nL 682.845253 297.023449 \nL 682.877074 295.267774 \nL 682.940717 253.680235 \nL 683.099825 166.55489 \nL 683.195289 175.333262 \nL 683.258932 182.685149 \nL 683.290753 181.039204 \nL 683.386218 153.277602 \nL 683.418039 162.275434 \nL 683.608968 278.918056 \nL 683.64079 276.504003 \nL 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205.179728 \nL 690.005083 213.189993 \nL 690.132368 229.649441 \nL 690.259654 203.094865 \nL 690.291476 210.446752 \nL 690.514226 315.348301 \nL 690.546047 312.056411 \nL 690.736976 209.129996 \nL 690.832441 232.282953 \nL 690.896084 240.073758 \nL 690.927905 237.549976 \nL 691.023369 214.506749 \nL 691.055191 219.883502 \nL 691.277941 322.480728 \nL 691.309763 319.188839 \nL 691.405227 272.334277 \nL 691.500691 235.684572 \nL 691.532513 238.427813 \nL 691.659799 261.58077 \nL 691.69162 255.874828 \nL 691.818906 217.35972 \nL 692.009835 320.725054 \nL 692.041656 317.872083 \nL 692.105299 273.980221 \nL 692.168942 206.386754 \nL 692.232585 213.628912 \nL 692.264407 212.970534 \nL 692.423514 225.589444 \nL 692.455335 220.43215 \nL 692.5508 196.511086 \nL 692.582621 204.74081 \nL 692.77355 327.199104 \nL 692.805371 325.662888 \nL 692.869014 285.172646 \nL 693.028122 203.643513 \nL 693.187229 223.175392 \nL 693.219051 217.798638 \nL 693.314515 197.718112 \nL 693.346336 206.606214 \nL 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697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 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236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 \nL 802.01664 323.358566 \nL 802.048462 317.542894 \nL 802.334855 186.086768 \nL 802.589427 225.479714 \nL 802.780355 336.855313 \nL 802.843998 325.00451 \nL 803.034927 226.357551 \nL 803.162213 232.282953 \nL 803.289499 213.189993 \nL 803.32132 213.958101 \nL 803.54407 331.807749 \nL 803.607713 324.455862 \nL 803.703178 257.301314 \nL 803.894107 182.794879 \nL 804.053214 162.714352 \nL 804.085035 173.687318 \nL 804.307786 304.046147 \nL 804.339607 303.05858 \nL 804.435072 261.58077 \nL 804.626 146.474363 \nL 804.657822 146.584093 \nL 804.753286 125.625729 \nL 804.816929 127.710593 \nL 804.880572 177.088937 \nL 805.071501 302.180742 \nL 805.135144 265.531038 \nL 805.357894 106.42304 \nL 805.389715 99.071153 \nL 805.421537 104.228447 \nL 805.453358 112.019252 \nL 805.517001 103.02142 \nL 805.580644 102.801961 \nL 805.612466 114.872223 \nL 805.835216 284.733727 \nL 805.898859 254.777532 \nL 806.089788 149.437064 \nL 806.248895 139.890584 \nL 806.280717 140.878151 \nL 806.376181 158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 \nL 821.077698 342.561255 \nL 821.109519 342.122336 \nL 821.173162 304.814254 \nL 821.33227 222.846203 \nL 821.364091 220.980798 \nL 821.427734 221.858636 \nL 821.491377 226.79647 \nL 821.523198 224.162958 \nL 821.618663 192.999737 \nL 821.650484 212.751074 \nL 821.841413 346.511523 \nL 821.873234 344.975307 \nL 821.936877 303.607228 \nL 822.064163 223.943499 \nL 822.095985 225.699174 \nL 822.127806 217.140261 \nL 822.191449 220.761339 \nL 822.255092 228.552145 \nL 822.286914 226.138092 \nL 822.382378 196.181897 \nL 822.414199 214.06783 \nL 822.63695 344.09747 \nL 822.700593 305.911551 \nL 822.8597 220.43215 \nL 823.018807 227.784037 \nL 823.050629 226.68674 \nL 823.146093 193.877574 \nL 823.177915 212.202426 \nL 823.368843 336.745583 \nL 823.400665 336.635854 \nL 823.464308 299.437501 \nL 823.623415 215.494316 \nL 823.718879 212.531615 \nL 823.782522 218.127827 \nL 823.846165 215.604045 \nL 823.909808 202.765676 \nL 823.94163 217.140261 \nL 824.16438 342.451525 \nL 824.196201 339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 \nL 831.419674 224.711607 \nL 831.451496 223.50458 \nL 831.54696 189.817577 \nL 831.578781 212.531615 \nL 831.76971 339.159636 \nL 831.801532 337.623421 \nL 831.865175 299.327771 \nL 832.024282 203.753243 \nL 832.087925 208.581348 \nL 832.119746 208.142429 \nL 832.183389 217.46945 \nL 832.215211 214.17756 \nL 832.278854 188.83001 \nL 832.342497 209.020266 \nL 832.565247 336.965043 \nL 832.62889 296.035882 \nL 832.787997 204.63108 \nL 832.819819 204.082432 \nL 832.85164 205.399188 \nL 832.947104 216.701342 \nL 833.010747 209.349455 \nL 833.042569 190.695414 \nL 833.106212 210.88567 \nL 833.328962 337.074772 \nL 833.392605 298.230475 \nL 833.551712 221.968365 \nL 833.583534 221.639176 \nL 833.71082 232.392682 \nL 833.742641 227.345118 \nL 833.838105 194.865141 \nL 833.869927 211.214859 \nL 834.060856 337.513691 \nL 834.092677 337.074772 \nL 834.15632 300.973716 \nL 834.251784 218.786205 \nL 834.315427 219.883502 \nL 834.37907 224.053229 \nL 834.474535 234.806735 \nL 834.506356 234.148357 \nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 \nL 845.198368 217.24999 \nL 845.262011 196.730545 \nL 845.293833 206.057565 \nL 845.389297 271.23698 \nL 845.548405 343.109903 \nL 845.612048 326.869915 \nL 845.739333 250.388346 \nL 845.802976 251.485642 \nL 845.898441 269.920224 \nL 845.962084 260.483474 \nL 846.025727 225.260255 \nL 846.08937 249.620238 \nL 846.31212 348.376927 \nL 846.343941 347.169901 \nL 846.439406 300.754257 \nL 846.566691 244.462944 \nL 846.598513 245.011593 \nL 846.662156 257.411044 \nL 846.693977 254.667802 \nL 846.789442 200.461353 \nL 846.853085 229.320252 \nL 847.044013 342.561255 \nL 847.075835 340.91531 \nL 847.139478 298.998582 \nL 847.298585 211.324589 \nL 847.457693 233.489979 \nL 847.489514 225.260255 \nL 847.553157 183.562986 \nL 847.6168 210.9954 \nL 847.807729 337.294232 \nL 847.83955 337.074772 \nL 847.903193 295.816422 \nL 848.030479 208.361888 \nL 848.0623 211.544048 \nL 848.125943 217.24999 \nL 848.221408 225.918633 \nL 848.253229 222.626743 \nL 848.316872 181.258664 \nL 848.380515 210.337022 \nL 848.571444 338.172069 \nL 848.603265 337.294232 \nL 848.666908 297.791556 \nL 848.826016 207.045132 \nL 848.985123 219.225124 \nL 849.080587 184.770013 \nL 849.112409 194.645681 \nL 849.335159 337.513691 \nL 849.398802 325.443429 \nL 849.557909 210.337022 \nL 849.621552 219.993232 \nL 849.748838 230.527278 \nL 849.812481 207.92297 \nL 849.844302 188.83001 \nL 849.907945 206.825673 \nL 850.098874 331.36883 \nL 850.130696 330.600723 \nL 850.194339 289.561832 \nL 850.289803 209.788374 \nL 850.321624 212.092696 \nL 850.480732 237.549976 \nL 850.512553 236.891598 \nL 850.608018 217.908368 \nL 850.639839 228.003496 \nL 850.703482 253.460776 \nL 850.894411 336.855313 \nL 850.958054 321.712621 \nL 851.148982 218.127827 \nL 851.212625 224.382418 \nL 851.244447 226.357551 \nL 851.276268 224.931066 \nL 851.371733 199.144597 \nL 851.403554 213.738641 \nL 851.626304 333.673153 \nL 851.721769 315.128842 \nL 851.912698 206.606214 \nL 851.944519 211.873237 \nL 852.008162 224.821336 \nL 852.039984 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\n"
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ],
+      "image/png": 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83.742527 195.962437 \nL 83.837991 148.230038 \nL 83.869813 161.617056 \nL 84.092563 314.141275 \nL 84.156206 304.046147 \nL 84.378956 188.061902 \nL 84.506242 207.92297 \nL 84.601707 158.873814 \nL 84.633528 171.382995 \nL 84.856278 323.687755 \nL 84.919921 312.60506 \nL 85.11085 190.805144 \nL 85.142671 192.121899 \nL 85.238136 198.595949 \nL 85.269957 197.937571 \nL 85.365422 150.095442 \nL 85.397243 159.641922 \nL 85.619993 299.437501 \nL 85.683636 287.806158 \nL 85.810922 179.39326 \nL 85.874565 183.233797 \nL 86.033672 208.691077 \nL 86.065494 196.291626 \nL 86.129137 164.140838 \nL 86.160958 174.784614 \nL 86.41553 312.166141 \nL 86.479173 297.133178 \nL 86.63828 206.606214 \nL 86.670102 211.763507 \nL 86.765566 233.599709 \nL 86.797388 232.831601 \nL 86.892852 171.382995 \nL 86.956495 197.388923 \nL 87.179245 301.412635 \nL 87.242888 288.354806 \nL 87.401995 205.728377 \nL 87.465638 212.641345 \nL 87.529281 226.577011 \nL 87.561103 223.394851 \nL 87.624746 169.956509 \nL 87.688389 203.753243 \nL 87.94296 312.49533 \nL 87.974782 303.497498 \nL 88.102068 192.670548 \nL 88.165711 195.742978 \nL 88.324818 223.72404 \nL 88.356639 210.666211 \nL 88.420282 165.457594 \nL 88.452104 179.832178 \nL 88.674854 324.565592 \nL 88.738497 313.373167 \nL 88.929426 202.875406 \nL 88.961247 205.508917 \nL 89.056712 216.372153 \nL 89.088533 214.397019 \nL 89.183998 154.155439 \nL 89.24764 180.490556 \nL 89.438569 296.035882 \nL 89.470391 295.048315 \nL 89.534034 251.485642 \nL 89.597677 175.333262 \nL 89.661319 181.148934 \nL 89.820427 213.080263 \nL 89.852248 211.10513 \nL 89.947713 153.716521 \nL 90.011356 182.46569 \nL 90.202284 308.435333 \nL 90.234106 308.215873 \nL 90.297749 265.640768 \nL 90.361392 191.682981 \nL 90.425035 198.815408 \nL 90.615963 224.711607 \nL 90.711428 166.884079 \nL 90.775071 192.560818 \nL 90.966 312.60506 \nL 90.997821 311.398033 \nL 91.061464 268.384009 \nL 91.125107 195.962437 \nL 91.18875 202.875406 \nL 91.284214 219.005665 \nL 91.379679 231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 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322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 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348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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292.853722 \nL 363.675957 281.002919 \nL 363.866885 170.505158 \nL 363.96235 181.478123 \nL 364.025993 187.952173 \nL 364.121457 139.451665 \nL 364.153279 150.095442 \nL 364.40785 275.187248 \nL 364.471493 258.61807 \nL 364.630601 179.064071 \nL 364.662422 182.026771 \nL 364.757886 197.827841 \nL 364.789708 195.633248 \nL 364.853351 150.534361 \nL 364.916994 166.774349 \nL 365.171565 292.524533 \nL 365.235208 280.125082 \nL 365.426137 192.560818 \nL 365.48978 199.802975 \nL 365.521602 202.436487 \nL 365.553423 196.511086 \nL 365.617066 144.060311 \nL 365.680709 175.772181 \nL 365.903459 291.427236 \nL 365.967102 276.613733 \nL 366.158031 168.420294 \nL 366.189852 168.091105 \nL 366.285317 178.954341 \nL 366.317138 176.979207 \nL 366.412603 119.480868 \nL 366.476246 149.107875 \nL 366.667174 268.493739 \nL 366.698996 267.835361 \nL 366.762639 222.626743 \nL 366.826282 150.973279 \nL 366.889925 159.971111 \nL 367.01721 180.161367 \nL 367.049032 186.635417 \nL 367.080853 185.318661 \nL 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 399.920605 208.361888 \nL 399.952427 206.386754 \nL 400.047891 171.163536 \nL 400.079713 179.1738 \nL 400.270642 302.290472 \nL 400.302463 301.302905 \nL 400.366106 256.204017 \nL 400.429749 181.697582 \nL 400.493392 188.83001 \nL 400.588856 206.277025 \nL 400.684321 222.846203 \nL 400.747964 212.531615 \nL 400.811607 182.46569 \nL 400.843428 189.159199 \nL 401.066178 308.654792 \nL 401.129821 269.042387 \nL 401.225286 191.353792 \nL 401.257107 192.341359 \nL 401.448036 222.517014 \nL 401.479857 222.407284 \nL 401.575322 194.426222 \nL 401.607143 199.144597 \nL 401.829893 312.714789 \nL 401.861715 306.679658 \nL 402.052644 210.9954 \nL 402.148108 233.928898 \nL 402.211751 240.841866 \nL 402.243572 237.330517 \nL 402.339037 207.045132 \nL 402.370858 213.189993 \nL 402.593609 325.662888 \nL 402.62543 322.700188 \nL 402.720894 275.735896 \nL 402.816359 230.966197 \nL 402.84818 234.148357 \nL 402.975466 254.448343 \nL 403.007288 249.839697 \nL 403.102752 207.374321 \nL 403.134573 214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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228.991063 \nL 545.853845 225.479714 \nL 545.917488 211.434319 \nL 545.949309 227.015929 \nL 546.17206 353.973139 \nL 546.203881 351.668816 \nL 546.39481 248.193753 \nL 546.522096 261.471041 \nL 546.553917 261.141852 \nL 546.681203 229.100793 \nL 546.713025 245.88943 \nL 546.935775 361.325026 \nL 546.967596 358.910974 \nL 547.063061 308.215873 \nL 547.190347 255.545639 \nL 547.222168 257.740233 \nL 547.285811 265.201849 \nL 547.317632 262.458608 \nL 547.444918 213.189993 \nL 547.47674 231.844034 \nL 547.667669 352.985572 \nL 547.69949 352.327194 \nL 547.763133 311.398033 \nL 547.92224 218.676476 \nL 548.017705 215.933234 \nL 548.081348 223.72404 \nL 548.113169 220.761339 \nL 548.208633 186.086768 \nL 548.240455 207.593781 \nL 548.463205 347.169901 \nL 548.526848 306.679658 \nL 548.685955 214.616478 \nL 548.78142 209.129996 \nL 548.813241 210.9954 \nL 548.845063 211.982967 \nL 548.876884 209.678644 \nL 548.972349 183.782446 \nL 549.22692 344.865578 \nL 549.258742 335.867746 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 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588.52643 200.351623 \nL 588.558251 198.37649 \nL 588.621894 165.896512 \nL 588.653716 173.797047 \nL 588.940109 325.22397 \nL 589.003752 304.704524 \nL 589.162859 221.090528 \nL 589.194681 223.065662 \nL 589.258324 230.527278 \nL 589.290145 230.198089 \nL 589.353788 186.745146 \nL 589.385609 165.238134 \nL 589.449252 187.732713 \nL 589.703824 296.145611 \nL 589.767467 280.78346 \nL 589.958396 181.478123 \nL 590.022039 191.024603 \nL 590.05386 194.206763 \nL 590.085682 186.196498 \nL 590.149325 127.930052 \nL 590.212968 148.559227 \nL 590.435718 272.992654 \nL 590.499361 258.069421 \nL 590.626647 153.387332 \nL 590.69029 158.325166 \nL 590.817575 178.515422 \nL 590.849397 172.480291 \nL 590.91304 119.15168 \nL 590.976683 132.319238 \nL 591.199433 289.122914 \nL 591.263076 278.259678 \nL 591.454005 168.530024 \nL 591.581291 183.562986 \nL 591.613112 180.929475 \nL 591.676755 142.194907 \nL 591.740398 162.604622 \nL 591.99497 315.56776 \nL 592.058613 281.332108 \nL 592.21772 177.418126 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\nL 595.495331 155.252736 \nL 595.558974 179.28353 \nL 595.781724 316.555327 \nL 595.845367 303.05858 \nL 596.036296 183.672716 \nL 596.068117 183.892175 \nL 596.163582 190.256495 \nL 596.195403 185.099202 \nL 596.259046 138.903017 \nL 596.322689 158.105707 \nL 596.577261 316.774786 \nL 596.609082 306.350469 \nL 596.736368 196.840275 \nL 596.800011 199.693246 \nL 596.927297 210.556481 \nL 596.959118 205.947836 \nL 597.022761 145.815985 \nL 597.086404 157.557058 \nL 597.309154 306.899118 \nL 597.372797 294.060748 \nL 597.468262 183.014338 \nL 597.563726 194.426222 \nL 597.595548 194.097033 \nL 597.691012 204.411621 \nL 597.722833 201.668379 \nL 597.786476 142.633825 \nL 597.850119 153.716521 \nL 598.072869 307.008847 \nL 598.168334 263.446175 \nL 598.327441 177.198666 \nL 598.359263 177.527855 \nL 598.454727 184.550553 \nL 598.550191 139.890584 \nL 598.613834 158.215436 \nL 598.836585 319.847217 \nL 598.900228 310.081277 \nL 599.091156 187.842443 \nL 599.122978 189.268928 \nL 599.218442 199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 653.155826 278.369407 \nL 653.283112 194.316493 \nL 653.346755 198.815408 \nL 653.442219 210.337022 \nL 653.47404 208.032699 \nL 653.569505 162.824082 \nL 653.601326 175.99164 \nL 653.855898 294.938585 \nL 653.919541 283.416972 \nL 654.078648 200.241894 \nL 654.142291 206.277025 \nL 654.205934 217.908368 \nL 654.237756 213.299723 \nL 654.33322 143.401933 \nL 654.396863 174.894344 \nL 654.587792 277.162381 \nL 654.619613 275.077518 \nL 654.683256 237.988895 \nL 654.810542 157.447329 \nL 654.842363 157.995977 \nL 654.969649 182.794879 \nL 655.001471 181.368393 \nL 655.096935 123.101947 \nL 655.160578 148.998145 \nL 655.351507 262.348878 \nL 655.383328 260.154285 \nL 655.446971 218.895935 \nL 655.606079 143.840852 \nL 655.765186 156.350032 \nL 655.797007 149.875983 \nL 655.86065 118.603031 \nL 655.892472 134.623561 \nL 656.115222 283.746161 \nL 656.178865 270.688332 \nL 656.369794 172.480291 \nL 656.528901 175.113803 \nL 656.592544 145.157608 \nL 656.624366 136.598694 \nL 656.656187 150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 211.434319 \nL 680.554107 315.56776 \nL 680.61775 312.934248 \nL 680.713215 263.885093 \nL 680.808679 219.554313 \nL 680.840501 221.419717 \nL 680.935965 240.402947 \nL 680.999608 233.928898 \nL 681.095072 181.258664 \nL 681.126894 185.867309 \nL 681.286001 288.025617 \nL 681.317823 286.489402 \nL 681.381465 272.224547 \nL 681.47693 164.689486 \nL 681.572394 173.02894 \nL 681.731502 204.960269 \nL 681.795145 191.682981 \nL 681.858787 160.958678 \nL 681.890609 167.981376 \nL 682.081538 281.880756 \nL 682.113359 278.808326 \nL 682.177002 231.734305 \nL 682.240645 161.726785 \nL 682.304288 173.797047 \nL 682.495217 211.873237 \nL 682.527038 207.154862 \nL 682.622503 166.335431 \nL 682.654324 173.577588 \nL 682.845253 297.023449 \nL 682.877074 295.267774 \nL 682.940717 253.680235 \nL 683.099825 166.55489 \nL 683.195289 175.333262 \nL 683.258932 182.685149 \nL 683.290753 181.039204 \nL 683.386218 153.277602 \nL 683.418039 162.275434 \nL 683.608968 278.918056 \nL 683.64079 276.504003 \nL 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205.179728 \nL 690.005083 213.189993 \nL 690.132368 229.649441 \nL 690.259654 203.094865 \nL 690.291476 210.446752 \nL 690.514226 315.348301 \nL 690.546047 312.056411 \nL 690.736976 209.129996 \nL 690.832441 232.282953 \nL 690.896084 240.073758 \nL 690.927905 237.549976 \nL 691.023369 214.506749 \nL 691.055191 219.883502 \nL 691.277941 322.480728 \nL 691.309763 319.188839 \nL 691.405227 272.334277 \nL 691.500691 235.684572 \nL 691.532513 238.427813 \nL 691.659799 261.58077 \nL 691.69162 255.874828 \nL 691.818906 217.35972 \nL 692.009835 320.725054 \nL 692.041656 317.872083 \nL 692.105299 273.980221 \nL 692.168942 206.386754 \nL 692.232585 213.628912 \nL 692.264407 212.970534 \nL 692.423514 225.589444 \nL 692.455335 220.43215 \nL 692.5508 196.511086 \nL 692.582621 204.74081 \nL 692.77355 327.199104 \nL 692.805371 325.662888 \nL 692.869014 285.172646 \nL 693.028122 203.643513 \nL 693.187229 223.175392 \nL 693.219051 217.798638 \nL 693.314515 197.718112 \nL 693.346336 206.606214 \nL 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697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 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236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 \nL 802.01664 323.358566 \nL 802.048462 317.542894 \nL 802.334855 186.086768 \nL 802.589427 225.479714 \nL 802.780355 336.855313 \nL 802.843998 325.00451 \nL 803.034927 226.357551 \nL 803.162213 232.282953 \nL 803.289499 213.189993 \nL 803.32132 213.958101 \nL 803.54407 331.807749 \nL 803.607713 324.455862 \nL 803.703178 257.301314 \nL 803.894107 182.794879 \nL 804.053214 162.714352 \nL 804.085035 173.687318 \nL 804.307786 304.046147 \nL 804.339607 303.05858 \nL 804.435072 261.58077 \nL 804.626 146.474363 \nL 804.657822 146.584093 \nL 804.753286 125.625729 \nL 804.816929 127.710593 \nL 804.880572 177.088937 \nL 805.071501 302.180742 \nL 805.135144 265.531038 \nL 805.357894 106.42304 \nL 805.389715 99.071153 \nL 805.421537 104.228447 \nL 805.453358 112.019252 \nL 805.517001 103.02142 \nL 805.580644 102.801961 \nL 805.612466 114.872223 \nL 805.835216 284.733727 \nL 805.898859 254.777532 \nL 806.089788 149.437064 \nL 806.248895 139.890584 \nL 806.280717 140.878151 \nL 806.376181 158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 \nL 821.077698 342.561255 \nL 821.109519 342.122336 \nL 821.173162 304.814254 \nL 821.33227 222.846203 \nL 821.364091 220.980798 \nL 821.427734 221.858636 \nL 821.491377 226.79647 \nL 821.523198 224.162958 \nL 821.618663 192.999737 \nL 821.650484 212.751074 \nL 821.841413 346.511523 \nL 821.873234 344.975307 \nL 821.936877 303.607228 \nL 822.064163 223.943499 \nL 822.095985 225.699174 \nL 822.127806 217.140261 \nL 822.191449 220.761339 \nL 822.255092 228.552145 \nL 822.286914 226.138092 \nL 822.382378 196.181897 \nL 822.414199 214.06783 \nL 822.63695 344.09747 \nL 822.700593 305.911551 \nL 822.8597 220.43215 \nL 823.018807 227.784037 \nL 823.050629 226.68674 \nL 823.146093 193.877574 \nL 823.177915 212.202426 \nL 823.368843 336.745583 \nL 823.400665 336.635854 \nL 823.464308 299.437501 \nL 823.623415 215.494316 \nL 823.718879 212.531615 \nL 823.782522 218.127827 \nL 823.846165 215.604045 \nL 823.909808 202.765676 \nL 823.94163 217.140261 \nL 824.16438 342.451525 \nL 824.196201 339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 \nL 831.419674 224.711607 \nL 831.451496 223.50458 \nL 831.54696 189.817577 \nL 831.578781 212.531615 \nL 831.76971 339.159636 \nL 831.801532 337.623421 \nL 831.865175 299.327771 \nL 832.024282 203.753243 \nL 832.087925 208.581348 \nL 832.119746 208.142429 \nL 832.183389 217.46945 \nL 832.215211 214.17756 \nL 832.278854 188.83001 \nL 832.342497 209.020266 \nL 832.565247 336.965043 \nL 832.62889 296.035882 \nL 832.787997 204.63108 \nL 832.819819 204.082432 \nL 832.85164 205.399188 \nL 832.947104 216.701342 \nL 833.010747 209.349455 \nL 833.042569 190.695414 \nL 833.106212 210.88567 \nL 833.328962 337.074772 \nL 833.392605 298.230475 \nL 833.551712 221.968365 \nL 833.583534 221.639176 \nL 833.71082 232.392682 \nL 833.742641 227.345118 \nL 833.838105 194.865141 \nL 833.869927 211.214859 \nL 834.060856 337.513691 \nL 834.092677 337.074772 \nL 834.15632 300.973716 \nL 834.251784 218.786205 \nL 834.315427 219.883502 \nL 834.37907 224.053229 \nL 834.474535 234.806735 \nL 834.506356 234.148357 \nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 \nL 845.198368 217.24999 \nL 845.262011 196.730545 \nL 845.293833 206.057565 \nL 845.389297 271.23698 \nL 845.548405 343.109903 \nL 845.612048 326.869915 \nL 845.739333 250.388346 \nL 845.802976 251.485642 \nL 845.898441 269.920224 \nL 845.962084 260.483474 \nL 846.025727 225.260255 \nL 846.08937 249.620238 \nL 846.31212 348.376927 \nL 846.343941 347.169901 \nL 846.439406 300.754257 \nL 846.566691 244.462944 \nL 846.598513 245.011593 \nL 846.662156 257.411044 \nL 846.693977 254.667802 \nL 846.789442 200.461353 \nL 846.853085 229.320252 \nL 847.044013 342.561255 \nL 847.075835 340.91531 \nL 847.139478 298.998582 \nL 847.298585 211.324589 \nL 847.457693 233.489979 \nL 847.489514 225.260255 \nL 847.553157 183.562986 \nL 847.6168 210.9954 \nL 847.807729 337.294232 \nL 847.83955 337.074772 \nL 847.903193 295.816422 \nL 848.030479 208.361888 \nL 848.0623 211.544048 \nL 848.125943 217.24999 \nL 848.221408 225.918633 \nL 848.253229 222.626743 \nL 848.316872 181.258664 \nL 848.380515 210.337022 \nL 848.571444 338.172069 \nL 848.603265 337.294232 \nL 848.666908 297.791556 \nL 848.826016 207.045132 \nL 848.985123 219.225124 \nL 849.080587 184.770013 \nL 849.112409 194.645681 \nL 849.335159 337.513691 \nL 849.398802 325.443429 \nL 849.557909 210.337022 \nL 849.621552 219.993232 \nL 849.748838 230.527278 \nL 849.812481 207.92297 \nL 849.844302 188.83001 \nL 849.907945 206.825673 \nL 850.098874 331.36883 \nL 850.130696 330.600723 \nL 850.194339 289.561832 \nL 850.289803 209.788374 \nL 850.321624 212.092696 \nL 850.480732 237.549976 \nL 850.512553 236.891598 \nL 850.608018 217.908368 \nL 850.639839 228.003496 \nL 850.703482 253.460776 \nL 850.894411 336.855313 \nL 850.958054 321.712621 \nL 851.148982 218.127827 \nL 851.212625 224.382418 \nL 851.244447 226.357551 \nL 851.276268 224.931066 \nL 851.371733 199.144597 \nL 851.403554 213.738641 \nL 851.626304 333.673153 \nL 851.721769 315.128842 \nL 851.912698 206.606214 \nL 851.944519 211.873237 \nL 852.008162 224.821336 \nL 852.039984 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       "needs_background": "light"
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    ],
-   "source": [
-    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
-    "plt.xlabel('timestamp', fontsize=12)\n",
-    "plt.ylabel('load', fontsize=12)\n",
-    "plt.show()"
-   ]
+   "metadata": {}
   },
   {
+   "cell_type": "markdown",
    "source": [
     "## Create training and testing data sets\n"
    ],
-   "cell_type": "markdown",
    "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "train_start_dt = '2014-11-01 00:00:00'\n",
     "test_start_dt = '2014-12-30 00:00:00'    "
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
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200.041514 \nL 275.467899 153.145119 \nL 276.040011 129.254503 \nL 277.184235 167.833424 \nL 277.756347 198.625774 \nL 278.900572 299.851199 \nL 279.472684 335.775607 \nL 280.044796 361.435898 \nL 280.616908 373.46969 \nL 281.18902 377.893878 \nL 281.761132 375.416333 \nL 282.333244 354.534165 \nL 282.905356 300.913004 \nL 283.477468 215.083754 \nL 284.04958 177.920573 \nL 285.193805 157.923242 \nL 285.765917 146.066418 \nL 286.338029 144.650678 \nL 288.054365 153.145119 \nL 288.626477 147.659126 \nL 289.198589 121.821867 \nL 289.770701 107.31053 \nL 290.342814 121.467932 \nL 291.487038 175.97393 \nL 293.203374 328.166003 \nL 293.775486 355.772937 \nL 294.91971 372.407885 \nL 295.491822 367.275826 \nL 296.063935 345.862756 \nL 296.636047 294.01127 \nL 297.208159 207.47415 \nL 297.780271 180.929021 \nL 298.352383 180.221151 \nL 298.924495 177.566638 \nL 299.496607 176.858768 \nL 300.068719 182.167794 \nL 300.640831 186.238047 \nL 301.212943 185.707144 \nL 301.785056 184.645339 \nL 302.357168 169.780067 \nL 303.501392 80.411466 \nL 304.073504 85.897459 \nL 305.217728 127.484828 \nL 305.78984 173.85032 \nL 306.361952 231.895669 \nL 306.934064 273.660005 \nL 307.506177 296.134881 \nL 308.078289 309.407445 \nL 308.650401 314.185568 \nL 309.222513 312.238926 \nL 309.794625 293.834303 \nL 310.366737 242.51372 \nL 310.938849 157.39234 \nL 311.510961 130.670243 \nL 312.083073 136.510172 \nL 312.655185 147.128223 \nL 313.227298 152.437249 \nL 314.371522 167.833424 \nL 314.943634 170.310969 \nL 315.515746 174.735157 \nL 316.087858 163.055301 \nL 316.65997 115.451036 \nL 317.232082 83.242946 \nL 317.804194 87.667134 \nL 318.376306 99.700926 \nL 318.948418 123.237607 \nL 320.664755 276.491485 \nL 321.236867 307.460802 \nL 321.808979 323.918782 \nL 322.381091 329.404776 \nL 322.953203 326.750263 \nL 323.525315 307.106867 \nL 324.097427 255.963252 \nL 324.669539 172.43458 \nL 325.241652 141.465262 \nL 325.813764 145.181581 \nL 326.385876 152.260282 \nL 326.957988 161.285625 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\n"
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ],
+      "image/png": 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403.048897 333.298061 \nL 403.621009 362.497703 \nL 404.193121 377.362976 \nL 404.765233 384.264709 \nL 405.337345 373.823625 \nL 405.909457 357.71958 \nL 406.48157 314.185568 \nL 407.053682 248.530616 \nL 407.625794 203.580864 \nL 408.197906 178.451476 \nL 409.914242 134.740496 \nL 410.486354 132.793854 \nL 411.058466 128.723601 \nL 411.630578 130.316308 \nL 412.202691 135.625334 \nL 412.774803 114.743166 \nL 413.346915 119.344322 \nL 413.919027 142.173133 \nL 415.635363 274.72181 \nL 416.779587 367.098859 \nL 417.351699 395.236696 \nL 417.923812 408.686228 \nL 418.495924 417.357637 \nL 419.068036 421.073955 \nL 419.640148 417.003701 \nL 420.21226 399.483916 \nL 421.356484 340.55373 \nL 422.500708 269.589752 \nL 423.07282 250.477259 \nL 423.644933 237.912564 \nL 424.217045 249.769389 \nL 424.789157 271.89033 \nL 425.361269 285.516829 \nL 425.933381 292.064628 \nL 426.505493 270.297622 \nL 427.077605 260.741375 \nL 427.649717 261.09531 \nL 428.221829 262.511051 \nL 428.793941 267.466141 \nL 429.366053 286.932569 \nL 430.510278 340.730697 \nL 431.08239 358.781385 \nL 431.654502 369.576404 \nL 432.226614 372.407885 \nL 432.798726 370.107307 \nL 433.370838 358.604418 \nL 433.94295 334.359867 \nL 434.515062 293.657335 \nL 436.231399 212.429241 \nL 436.803511 210.836533 \nL 437.375623 208.005052 \nL 437.947735 211.367436 \nL 438.519847 212.075306 \nL 439.091959 222.693357 \nL 439.664071 210.128663 \nL 440.236183 162.170463 \nL 440.808295 137.748944 \nL 441.380408 145.004613 \nL 441.95252 153.852989 \nL 442.524632 171.903677 \nL 443.096744 203.049962 \nL 444.240968 281.977479 \nL 444.81308 313.831633 \nL 445.385192 328.519938 \nL 445.957304 335.598639 \nL 446.529416 336.129542 \nL 447.101529 329.050841 \nL 447.673641 308.699575 \nL 448.817865 235.611987 \nL 449.389977 198.802741 \nL 449.962089 184.645339 \nL 450.534201 184.291404 \nL 451.106313 191.01617 \nL 451.678425 200.749384 \nL 452.250537 206.76628 \nL 452.82265 209.774728 \nL 453.394762 197.563968 \nL 453.966874 144.119775 \nL 454.538986 118.105549 \nL 455.111098 125.18425 \nL 456.255322 159.692918 \nL 456.827434 195.794293 \nL 457.971658 282.685349 \nL 458.543771 315.247374 \nL 459.115883 334.890769 \nL 459.687995 348.517269 \nL 460.260107 355.419002 \nL 460.832219 353.118424 \nL 461.404331 339.668892 \nL 461.976443 320.733367 \nL 462.548555 285.693797 \nL 463.120667 240.036175 \nL 463.692779 208.712923 \nL 464.264891 197.917903 \nL 464.837004 196.325196 \nL 465.409116 204.288734 \nL 465.981228 213.844981 \nL 466.55334 217.738266 \nL 467.125452 203.049962 \nL 467.697564 151.552411 \nL 468.269676 134.209594 \nL 468.841788 146.951256 \nL 469.4139 171.195807 \nL 469.986012 201.457254 \nL 471.130237 304.275387 \nL 471.702349 345.154886 \nL 472.274461 370.461242 \nL 472.846573 383.733806 \nL 473.418685 387.80406 \nL 473.990797 387.80406 \nL 474.562909 369.399437 \nL 475.135021 320.379432 \nL 475.707133 237.381662 \nL 476.279246 200.926351 \nL 476.851358 195.794293 \nL 477.42347 195.617326 \nL 477.995582 186.591982 \nL 479.139806 196.856098 \nL 479.711918 196.148228 \nL 480.28403 198.094871 \nL 480.856142 191.72404 \nL 481.428254 143.588873 \nL 482.000367 107.133562 \nL 482.572479 108.549303 \nL 483.144591 126.423023 \nL 483.716703 149.251834 \nL 485.433039 294.188238 \nL 486.005151 320.910334 \nL 486.577263 334.182899 \nL 487.149375 338.076185 \nL 487.721488 333.651996 \nL 488.2936 315.778276 \nL 488.865712 259.502603 \nL 489.437824 171.72671 \nL 490.009936 141.111327 \nL 490.582048 140.757392 \nL 491.726272 146.597321 \nL 492.298384 155.091762 \nL 492.870496 159.162015 \nL 493.442609 153.499054 \nL 494.014721 154.383892 \nL 494.586833 140.93436 \nL 495.158945 89.43681 \nL 495.731057 62.537746 \nL 496.303169 69.085544 \nL 496.875281 89.082875 \nL 497.447393 116.689809 \nL 498.019505 168.541294 \nL 498.591617 233.134441 \nL 499.163729 280.384771 \nL 499.735842 306.398997 \nL 500.307954 325.334523 \nL 500.880066 336.837412 \nL 501.452178 338.43012 \nL 502.02429 322.149107 \nL 502.596402 277.376323 \nL 503.168514 204.819637 \nL 503.740626 163.409236 \nL 504.312738 152.083314 \nL 504.88485 153.852989 \nL 505.456963 150.667574 \nL 506.029075 149.782736 \nL 506.601187 154.737827 \nL 507.173299 158.10021 \nL 507.745411 164.117106 \nL 508.317523 155.091762 \nL 508.889635 115.981939 \nL 509.461747 95.276738 \nL 510.033859 102.178472 \nL 511.178084 151.021509 \nL 512.89442 304.452355 \nL 513.466532 330.997484 \nL 514.038644 348.340301 \nL 514.610756 352.056619 \nL 515.182868 348.871204 \nL 515.75498 329.404776 \nL 516.327092 277.022388 \nL 516.899205 184.822307 \nL 517.471317 155.9766 \nL 518.615541 158.277178 \nL 519.187653 155.799632 \nL 519.759765 160.754723 \nL 520.331877 170.310969 \nL 520.903989 171.549742 \nL 521.476101 170.664904 \nL 522.048213 155.622665 \nL 522.620326 108.195367 \nL 523.192438 76.872115 \nL 523.76455 80.234498 \nL 524.336662 95.276738 \nL 524.908774 117.220711 \nL 526.62511 261.449246 \nL 527.197222 289.941017 \nL 527.769334 305.337192 \nL 528.341447 311.531056 \nL 528.913559 307.991705 \nL 529.485671 288.879212 \nL 530.057783 235.965922 \nL 530.629895 149.251834 \nL 531.202007 116.689809 \nL 531.774119 118.813419 \nL 532.918343 134.032626 \nL 534.062567 154.914794 \nL 534.63468 156.330535 \nL 535.206792 156.507502 \nL 535.778904 146.597321 \nL 536.351016 101.647569 \nL 536.923128 88.551972 \nL 537.49524 103.771179 \nL 538.639464 143.411905 \nL 539.211576 180.752053 \nL 540.355801 282.685349 \nL 540.927913 318.432789 \nL 541.500025 338.43012 \nL 542.072137 350.286944 \nL 542.644249 355.772937 \nL 543.216361 351.879652 \nL 543.788473 332.767159 \nL 544.932697 256.14022 \nL 545.504809 213.668013 \nL 546.076922 183.052631 \nL 546.649034 170.487937 \nL 547.221146 169.957034 \nL 547.793258 175.089093 \nL 548.36537 184.468371 \nL 548.937482 189.069527 \nL 549.509594 182.875664 \nL 550.081706 139.872555 \nL 550.653818 121.821867 \nL 551.22593 140.403457 \nL 552.370155 190.3083 \nL 553.514379 270.474589 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      },
      "metadata": {
       "needs_background": "light"
      }
     }
    ],
-   "source": [
-    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
-    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
-    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
-    "plt.xlabel('timestamp', fontsize=12)\n",
-    "plt.ylabel('load', fontsize=12)\n",
-    "plt.show()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "Training data shape:  (1416, 1)\nTest data shape:  (48, 1)\n"
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
      ]
     }
    ],
-   "source": [
-    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
-    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
-    "\n",
-    "print('Training data shape: ', train.shape)\n",
-    "print('Test data shape: ', test.shape)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -218,36 +293,104 @@
        "2014-11-01 08:00:00  0.40\n",
        "2014-11-01 09:00:00  0.48"
       ],
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+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 8
     }
    ],
-   "source": [
-    "scaler = MinMaxScaler()\n",
-    "train['load'] = scaler.fit_transform(train)\n",
-    "train.head(10)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Original vs scaled data:"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
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-   "source": [
-    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
-    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
-    "plt.show()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Let's also scale the test data"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -294,28 +438,75 @@
        "2014-12-30 03:00:00  0.27\n",
        "2014-12-30 04:00:00  0.30"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-12-30 00:00:00</th>\n      <td>0.33</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 01:00:00</th>\n      <td>0.29</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 02:00:00</th>\n      <td>0.27</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 03:00:00</th>\n      <td>0.27</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 04:00:00</th>\n      <td>0.30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 10
     }
    ],
-   "source": [
-    "test['load'] = scaler.transform(test)\n",
-    "test.head()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Implement ARIMA method"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -325,25 +516,11 @@
      ]
     }
    ],
-   "source": [
-    "# Specify the number of steps to forecast ahead\n",
-    "HORIZON = 3\n",
-    "print('Forecasting horizon:', HORIZON, 'hours')"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "                                     SARIMAX Results                                      \n==========================================================================================\nDep. Variable:                               load   No. Observations:                 1416\nModel:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\nDate:                            Fri, 14 May 2021   AIC                          -6942.477\nTime:                                    17:05:41   BIC                          -6911.050\nSample:                                11-01-2014   HQIC                         -6930.725\n                                     - 12-29-2014                                         \nCovariance Type:                              opg                                         \n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\nar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\nar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\nar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\nar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\nsigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n===================================================================================\nLjung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\nProb(Q):                              0.83   Prob(JB):                         0.00\nHeteroskedasticity (H):               0.84   Skew:                             0.14\nProb(H) (two-sided):                  0.07   Kurtosis:                         8.02\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
-     ]
-    }
-   ],
    "source": [
     "order = (4, 1, 0)\n",
     "seasonal_order = (1, 1, 0, 24)\n",
@@ -352,26 +529,70 @@
     "results = model.fit()\n",
     "\n",
     "print(results.summary())\n"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Fri, 14 May 2021   AIC                          -6942.477\n",
+      "Time:                                    17:05:41   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Evaluate the model"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Create a test data point for each HORIZON step."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON+1):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -384,35 +605,110 @@
        "2014-12-30 03:00:00  0.27    0.30    0.41\n",
        "2014-12-30 04:00:00  0.30    0.41    0.57"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n      <th>load+1</th>\n      <th>load+2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-12-30 00:00:00</th>\n      <td>0.33</td>\n      <td>0.29</td>\n      <td>0.27</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 01:00:00</th>\n      <td>0.29</td>\n      <td>0.27</td>\n      <td>0.27</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 02:00:00</th>\n      <td>0.27</td>\n      <td>0.27</td>\n      <td>0.30</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 03:00:00</th>\n      <td>0.27</td>\n      <td>0.30</td>\n      <td>0.41</td>\n    </tr>\n    <tr>\n      <th>2014-12-30 04:00:00</th>\n      <td>0.30</td>\n      <td>0.41</td>\n      <td>0.57</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 13
     }
    ],
-   "source": [
-    "test_shifted = test.copy()\n",
-    "\n",
-    "for t in range(1, HORIZON):\n",
-    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
-    "    \n",
-    "test_shifted = test_shifted.dropna(how='any')\n",
-    "test_shifted.head(5)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Make predictions on the test data"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -515,46 +811,28 @@
      ]
     }
    ],
-   "source": [
-    "%%time\n",
-    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
-    "\n",
-    "train_ts = train['load']\n",
-    "test_ts = test_shifted\n",
-    "\n",
-    "history = [x for x in train_ts]\n",
-    "history = history[(-training_window):]\n",
-    "\n",
-    "predictions = list()\n",
-    "\n",
-    "# let's user simpler model for demonstration\n",
-    "order = (2, 1, 0)\n",
-    "seasonal_order = (1, 1, 0, 24)\n",
-    "\n",
-    "for t in range(test_ts.shape[0]):\n",
-    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
-    "    model_fit = model.fit()\n",
-    "    yhat = model_fit.forecast(steps = HORIZON)\n",
-    "    predictions.append(yhat)\n",
-    "    obs = list(test_ts.iloc[t])\n",
-    "    # move the training window\n",
-    "    history.append(obs[0])\n",
-    "    history.pop(0)\n",
-    "    print(test_ts.index[t])\n",
-    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
-   ]
+   "metadata": {
+    "scrolled": true
+   }
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Compare predictions to actual load"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
    "outputs": [
     {
      "output_type": "execute_result",
@@ -567,53 +845,116 @@
        "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
        "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
       ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>timestamp</th>\n      <th>h</th>\n      <th>prediction</th>\n      <th>actual</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2014-12-30 00:00:00</td>\n      <td>t+1</td>\n      <td>3,008.74</td>\n      <td>3,023.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2014-12-30 01:00:00</td>\n      <td>t+1</td>\n      <td>2,955.53</td>\n      <td>2,935.00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2014-12-30 02:00:00</td>\n      <td>t+1</td>\n      <td>2,900.17</td>\n      <td>2,899.00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2014-12-30 03:00:00</td>\n      <td>t+1</td>\n      <td>2,917.69</td>\n      <td>2,886.00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2014-12-30 04:00:00</td>\n      <td>t+1</td>\n      <td>2,946.99</td>\n      <td>2,963.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "metadata": {},
      "execution_count": 15
     }
    ],
-   "source": [
-    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
-    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
-    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
-    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
-    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
-    "eval_df.head()"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Compute the **mean absolute percentage error (MAPE)** over all predictions\n",
     "\n",
     "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "h\nt+1   0.01\nt+2   0.01\nt+3   0.02\nName: APE, dtype: float64\n"
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
      ]
     }
    ],
-   "source": [
-    "if(HORIZON > 1):\n",
-    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
-    "    print(eval_df.groupby('h')['APE'].mean())"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -623,14 +964,14 @@
      ]
     }
    ],
-   "source": [
-    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
    "outputs": [
     {
      "output_type": "stream",
@@ -640,41 +981,18 @@
      ]
     }
    ],
-   "source": [
-    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Plot the predictions vs the actuals for the first week of the test set"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "No handles with labels found to put in legend.\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
    "source": [
     "if(HORIZON == 1):\n",
     "    ## Plotting single step forecast\n",
@@ -699,14 +1017,37 @@
     "plt.xlabel('timestamp', fontsize=12)\n",
     "plt.ylabel('load', fontsize=12)\n",
     "plt.show()"
-   ]
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ],
+      "image/png": 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "source": [],
    "outputs": [],
-   "source": []
+   "metadata": {}
   }
  ],
  "metadata": {

From 6ebd1b499f552e0a230eaa7b8d9c5687d89c2918 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 4 Aug 2021 21:34:22 -0400
Subject: [PATCH 340/368] fixing a missing comma

---
 4-Classification/1-Introduction/translations/README.zh-cn.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
index ae8d123b1e..df585e3f5b 100644
--- a/4-Classification/1-Introduction/translations/README.zh-cn.md
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -158,7 +158,7 @@ Scikit-learn项目�供多�对数�进行分类的算法，你需�根�
     def create_ingredient_df(df):
         ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
         ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
-        ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+        ingredient_df = ingredient_df.sort_values(by='value', ascending=False,
         inplace=False)
         return ingredient_df
     ```

From 601c0f817f9489f0ae17e0ac875ea869419cc455 Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Thu, 5 Aug 2021 20:33:23 +0900
Subject: [PATCH 341/368] feat: add ja 3.1 assignment

Refs #149
---
 3-Web-App/1-Web-App/translations/assignment.ja.md | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 3-Web-App/1-Web-App/translations/assignment.ja.md

diff --git a/3-Web-App/1-Web-App/translations/assignment.ja.md b/3-Web-App/1-Web-App/translations/assignment.ja.md
new file mode 100644
index 0000000000..2151a7c243
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/assignment.ja.md
@@ -0,0 +1,11 @@
+# ��モデルを試�
+
+## 指示
+
+訓練�れ�回帰モデルを使用��1��Webアプリを構築������回�回帰�講義�使用��モデル�1�を使用�����Webアプリを�実行������。スタイルを�������も����ゃ�データを�映�る���別�デザイン���も構���ん。モデル�学習方法�����入力を変更�るよ��注�������。
+
+## 評価基準
+
+| 指標 | 模範的                                                      | �切                                                              | �改善                          |
+| ---- | ----------------------------------------------------------- | ----------------------------------------------------------------- | ------------------------------- |
+|      | Webアプリ�期待通り�動作��クラウド上�デプロイ�れ��る | Webアプリ�欠陥���れ��る��期待������果を表示���る | Webアプリ�正��機能����� |

From c03fc21073bb3b8d2c3cc4cfd6faa3dd35e63cb2 Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Thu, 5 Aug 2021 09:59:18 -0300
Subject: [PATCH 342/368] 
 1-Introduction/4-techniques-of-ML/translations/README.es.md

---
 .../2-history-of-ML/translations/README.es.md | 118 ++++++++++++++++++
 .../translations/README.es.md                 |   2 +-
 2 files changed, 119 insertions(+), 1 deletion(-)
 mode change 100644 => 100755 1-Introduction/2-history-of-ML/translations/README.es.md

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
old mode 100644
new mode 100755
index e69de29bb2..4297f4d6ba
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -0,0 +1,118 @@
+# Historia del machine learning
+
+![Resumen de la historoia del machine learning en un boceto](../../sketchnotes/ml-history.png)
+> Boceto por [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
+## [Cuestionario previo a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+
+En esta lección, analizaremos los principales hitos en la historia del machine learning y la inteligencia artificial.
+
+La historia de la inteligencia artificial, AI, como campo está entrelazada con la historia del machine learning, ya que los algoritmos y avances computacionales que sustentan el ML se incorporaron al desarrollo de la inteligencia artificial. Es útil recordar que, si bien, estos campos como áreas distintas de investigación comenzaron a cristalizar en la década de 1950, importantes [desubrimientos algorítmicos, estadísticos, matemáticos, computacionales y técnicos](https://wikipedia.org/wiki/Timeline_of_machine_learning) predecieronn y superpusieron a esta era. De hecho, las personas han estado pensando en estas preguntas durante [cientos de años](https://wikipedia.org/wiki/History_of_artificial_intelligence): este artículo analiza los fundamentos intelectuales históricos de la idea de una 'máquina pensante.'
+
+## Descubrimientos notables
+
+- 1763, 1812 [Teorema de Bayes](https://wikipedia.org/wiki/Bayes%27_theorem) y sus predecesores. Este teorema y sus aplicaciones son la base de la inferencia, describiendo la probabilidad de que ocurra un evento basado en el concimiento previo.
+- 1805 [Teoría de mínimos cuadrados](https://wikipedia.org/wiki/Least_squares) por el matemático francés Adrien-Marie Legendre. Esta teoría, que aprenderá en nuestra unidad de Regresión, ayuda en el data fitting.
+- 1913 [Cadenas de Markov](https://wikipedia.org/wiki/Markov_chain) el nombre del matemático ruso Andrey Markov es utilizado para describir una secuencia de eventos basados en su estado anterior.
+- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) es un tipo de clasificador lineal inventado por el psicólogo Frank Rosenblatt que subyace a los avances en el deep learning.
+- 1967 [Nearest Neighbor (Vecino más cercano)](https://wikipedia.org/wiki/Nearest_neighbor) es un algoritmo diseñado originalmente para trazar rutas. En un contexto de ML, se utiliza para detectar patrones.
+- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) es usado para entrenar [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network).
+- 1982 [Recurrent Neural Networks](https://wikipedia.org/wiki/Recurrent_neural_network) son redes neuronales artificiales derivadas de redes neuronales feedforward que crean grafos temporales.
+
+✅ Investigue un poco. ¿Qué otras fechas se destacan como fundamentales en la historia del machine learning (ML) y la inteligencia artificial (AI)?
+## 1950: Máquinas que piensan
+
+Alan Turing, una persona verdaderamente notable que fue votada [por el público en 2019](https://wikipedia.org/wiki/Icons:_The_Greatest_Person_of_the_20th_Century) como el científico más grande del siglo XX, se le atribuye haber ayudado a sentar las bases del concepto de una 'máquina que puede pensar.' Lidió con los detractores y su propia necesidad de evidencia empírica de este concepto en parte mediante la creación de la [prueba de Turing](https://www.bbc.com/news/technology-18475646, que explorarás en nuestras lecciones de NLP.
+
+## 1956: Dartmouth Summer Research Project
+
+"The Dartmouth Summer Research Project sobre inteligencia artificial fuer un evento fundamental para la inteligencia artificial como campo," y fue aquí donde el se acuñó el término 'inteligencia artificial' ([fuente](https://250.dartmouth.edu/highlights/artificial-intelligence-ai-coined-dartmouth)).
+
+
+> Todos los aspectos del aprendizaje y cualquier otra característica de la inteligencia pueden, en principio, describirse con tanta precisión que se puede hacer una máquina para simularlos.
+
+El investigador principal, el profesor de matemáticas John McCarthy, esperaba "proceder sobre las bases de la conjetura que cada aspecto del aprendizaje o cualquier otra característica de la inteligencia pueden, en principio, describirse con tanta precición que se se puede hacer una máquina para simularlos." Los participantes, incluyeron otra luminaria en el campo, Marvin Minsky.
+
+El taller tiene el mérito de haber iniciado y alentado varias discusiones que incluyen "el surgimiento de métodos simbólicos, systemas en dominios limitados (primeros sistemas expertos), y sistemas deductivos versus sistemas inductivos."  ([fuente](https://wikipedia.org/wiki/Dartmouth_workshop)).
+
+## 1956 - 1974: "Los años dorados"
+
+Desde la década de 1950, hasta mediados de la de 1970, el optimismo se elevó con la esperanza de que la AI pudiera resolver muchos problemas. En 1967, Marvin Minsky declaró con seguridad que "dentro de una generación ... el problema de crear 'inteligencia artificial' se resolverá sustancialemte."  (Minsky, Marvin (1967), Computation: Finite and Infinite Machines, Englewood Cliffs, N.J.: Prentice-Hall)
+
+La investigación del procesamiento del lenguaje natural floreció, la búsqueda se refinó y se hizo más poderosa, y el concepto de 'micro-worlds' fue creado, donde se completaban tareas simples utilizando instrucciones en lenguaje sencillo.
+
+La investigación estuvo bien financiado por agencias gubernamentales, se realizaron avances en computación y algoritmos, y se construyeron prototipos de máquinas inteligentes.Algunas de esta máquinas incluyen:
+
+* [Shakey la robot](https://wikipedia.org/wiki/Shakey_the_robot), que podría maniobrar y decidir cómo realizar las tares de forma 'inteligente'.
+
+    ![Shakey, un robot inteligente](images/shakey.jpg)
+    > Shakey en 1972
+
+* Eliza, unas de las primeras 'chatterbot', podía conversar con las personas y actuar como un 'terapeuta' primitivo. Aprenderá más sobre ELiza en las lecciones de NLP.
+
+    ![Eliza, un bot](images/eliza.png)
+    > Una versión de Eliza, un chatbot
+
+* "Blocks world" era un ejemplo de micro-world donde los bloques se podían apilar y ordenar, y se podían probar experimentos en máquinas de enseñanza para tomar decisiones. Los avances creados con librerías como [SHRDLU](https://wikipedia.org/wiki/SHRDLU) ayudaron a inpulsar el procesamiento del lenguaje natural.
+
+    [![blocks world con SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world con SHRDLU")
+    
+    > 🎥 Haga click en la imagen de arriba para ver un video: Blocks world con SHRDLU
+
+## 1974 - 1980: "Invierno de la AI"
+
+A mediados de la década de 1970, se hizo evidente que la complejidad de la fabricación de 'máquinas inteligentes' se había subestimado y que su promesa, dado la potencia computacional disponible, había sido exagerada. La financiación se agotó y la confianza en el campo se ralentizó. Algunos problemas que impactaron la confianza incluyeron:
+
+- **Limitaciones**. La potencia computacional era demasiado limitada.
+- **Explosión combinatoria**. La cantidad de parámetros necesitados para entrenar creció exponencialmente a medida que se pedía más a las computadoras sin una evolución paralela de la potencia y la capacidad de cómputo.
+- **Escasez de datos**. Hubo una escasez de datos que obstaculizó el proceso de pruebas, desarrollo y refinamiento de algoritmos.
+- **¿Estamos haciendo las preguntas correctas?**. Las mismas preguntas que se estaban formulando comenzaron a cuestionarse. Los investigadores comenzaron a criticar sus aproches:
+  - Las pruebas de Turing se cuestionaron por medio, entre otras ideas, de la 'teoría de la habitación china' que postulaba que "progrmar una computadora digital puede hacerse que parezca que entiende el lenguaje, pero no puede producir una comprensión real" ([fuente](https://plato.stanford.edu/entries/chinese-room/))
+  - Se cuestionó la ética de introducir inteligencias artificiales como la "terapeuta" Eliza en la sociedad.
+
+Al mismo tiempo, comenzaron a formarse varia escuelas de pensamiento de AI. Se estableció una dicotomía entre las prácticas ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). 
+At the same time, various AI schools of thought began to form. A dichotomy was established between ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) practices. _Scruffy_ labs tweaked programs for hours until they had the desired results. _Neat_ labs "focused on logic and formal problem solving". ELIZA and SHRDLU were well-known _scruffy_ systems. In the 1980s, as demand emerged to make ML systems reproducible, the _neat_ approach gradually took the forefront as its results are more explainable.
+
+## 1980s Expert systems
+
+As the field grew, its benefit to business became clearer, and in the 1980s so did the proliferation of 'expert systems'. "Expert systems were among the first truly successful forms of artificial intelligence (AI) software." ([source](https://wikipedia.org/wiki/Expert_system)).
+
+This type of system is actually _hybrid_, consisting partially of a rules engine defining business requirements, and an inference engine that leveraged the rules system to deduce new facts.
+
+This era also saw increasing attention paid to neural networks.
+
+## 1987 - 1993: AI 'Chill'
+
+The proliferation of specialized expert systems hardware had the unfortunate effect of becoming too specialized. The rise of personal computers also competed with these large, specialized, centralized systems. The democratization of computing had begun, and it eventually paved the way for the modern explosion of big data.
+
+## 1993 - 2011
+
+This epoch saw a new era for ML and AI to be able to solve some of the problems that had been caused earlier by the lack of data and compute power. The amount of data began to rapidly increase and become more widely available, for better and for worse, especially with the advent of the smartphone around 2007. Compute power expanded exponentially, and algorithms evolved alongside. The field began to gain maturity as the freewheeling days of the past began to crystallize into a true discipline.
+
+## Now
+
+Today, machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+
+It remains to be seen what the future holds, but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
+
+[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
+> 🎥 Click the image above for a video: Yann LeCun discusses the history of deep learning in this lecture
+
+---
+## 🚀Challenge
+
+Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
+
+## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+
+## Review & Self Study
+
+Here are items to watch and listen to:
+
+[This podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739)
+
+[![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
+
+## Assignment
+
+[Create a timeline](assignment.md)
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.es.md b/1-Introduction/4-techniques-of-ML/translations/README.es.md
index 8105202115..f89c14b3b9 100755
--- a/1-Introduction/4-techniques-of-ML/translations/README.es.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.es.md
@@ -96,7 +96,7 @@ En estas lecciones, descubrirá cómo utilizar estos pasos para preparar, constr
 
 Dibuje un diagrama de flujos que refleje los pasos de practicante de ML. ¿Dónde te ves ahora mismo en el proceso? ¿Dónde predice que encontrará dificultades? ¿Qué te parece fácil? 
 
-## [Cuestionario previo a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
 
 ## Revisión & Autoestudio
 

From d166585d6015cef3a886dd2a3fb31d67f9a6e235 Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Thu, 5 Aug 2021 11:37:00 -0300
Subject: [PATCH 343/368] 
 1-Introduction/2-history-of-ML/translations/README.es.md

---
 .../2-history-of-ML/translations/README.es.md | 43 +++++++++----------
 1 file changed, 21 insertions(+), 22 deletions(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index 4297f4d6ba..0528e27ad6 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -70,49 +70,48 @@ A mediados de la década de 1970, se hizo evidente que la complejidad de la fabr
   - Las pruebas de Turing se cuestionaron por medio, entre otras ideas, de la 'teoría de la habitación china' que postulaba que "progrmar una computadora digital puede hacerse que parezca que entiende el lenguaje, pero no puede producir una comprensión real" ([fuente](https://plato.stanford.edu/entries/chinese-room/))
   - Se cuestionó la ética de introducir inteligencias artificiales como la "terapeuta" Eliza en la sociedad.
 
-Al mismo tiempo, comenzaron a formarse varia escuelas de pensamiento de AI. Se estableció una dicotomía entre las prácticas ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). 
-At the same time, various AI schools of thought began to form. A dichotomy was established between ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies) practices. _Scruffy_ labs tweaked programs for hours until they had the desired results. _Neat_ labs "focused on logic and formal problem solving". ELIZA and SHRDLU were well-known _scruffy_ systems. In the 1980s, as demand emerged to make ML systems reproducible, the _neat_ approach gradually took the forefront as its results are more explainable.
+Al mismo tiempo, comenzaron a formarse varia escuelas de pensamiento de AI. Se estableció una dicotomía entre las prácticas ["scruffy" vs. "neat AI"](https://wikipedia.org/wiki/Neats_and_scruffies). _Scruffy_ labs modificó los programas durante horas hasta que obtuvieron los objetivos deseados. _Neat_ labs "centrados en la lógica y la resolución de problemas formales". ELIZA y SHRDLU eran systemas _scruffy_ bien conocidos. En la década de 1980, cuando surgió la demanda para hacer que los sistemas de aprendizaje fueran reproducibles, el enfoque _neat_ gradualmente tomó la vanguardia a medidad que sus resultados eran más explicables.
 
-## 1980s Expert systems
+## Systemas expertos de la década de 1980
 
-As the field grew, its benefit to business became clearer, and in the 1980s so did the proliferation of 'expert systems'. "Expert systems were among the first truly successful forms of artificial intelligence (AI) software." ([source](https://wikipedia.org/wiki/Expert_system)).
+A medida que el campo creció, su beneficio para las empresas se hizo más claro, y en la década de 1980 también lo hizo la proliferación de 'sistemas expertos'. "Los sistemas expertos estuvieron entre las primeras formas verdaderamente exitosas de software de inteligencia artificial (AI)." ([fuente](https://wikipedia.org/wiki/Expert_system)).
 
-This type of system is actually _hybrid_, consisting partially of a rules engine defining business requirements, and an inference engine that leveraged the rules system to deduce new facts.
+Este tipo de sistemas es en realidad _híbrido_, que consta parcialmente de un motor de reglas que define los requisitos comerciales, y un motor de inferencia que aprovechó el sistema de reglas para deducir nuevos hechos.
 
-This era also saw increasing attention paid to neural networks.
+En esta era también se prestó mayor atención a las redes neuronales.
 
 ## 1987 - 1993: AI 'Chill'
 
-The proliferation of specialized expert systems hardware had the unfortunate effect of becoming too specialized. The rise of personal computers also competed with these large, specialized, centralized systems. The democratization of computing had begun, and it eventually paved the way for the modern explosion of big data.
+La prolifercaión de hardware de sistemas expertos especializados tuvo el desafortunado efecto de volverse demasiado especializado. El auge de las computadoras personales también compitió con estos grandes sistemas centralizados especializados. La democratización de la informática había comenzado, y finalmente, allanó el camino para la explosión moderna del big data.
 
 ## 1993 - 2011
 
-This epoch saw a new era for ML and AI to be able to solve some of the problems that had been caused earlier by the lack of data and compute power. The amount of data began to rapidly increase and become more widely available, for better and for worse, especially with the advent of the smartphone around 2007. Compute power expanded exponentially, and algorithms evolved alongside. The field began to gain maturity as the freewheeling days of the past began to crystallize into a true discipline.
+Esta época vió una nueva era para el ML y la AI para poder resolver problemas que habían sido causados anteriormente for la falta de datos y poder de cómputo. La cantidad de datos comenzó a aumentar rápidamente y a estar más disponible, para bien o para mal, especialmente con la llegada del smartphone alrededor del 2007. El poder computacional se expandió exponencialmentem y los algoritmos evolucionaron al mismo tiempo. El campo comenzó a ganar madurez a medida que los días libres del pasado comenzaron a cristalizar en un verdadera disciplina.
 
-## Now
+## Ahora
 
-Today, machine learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses" ([source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
+Hoy en día, machine learning y la inteligencia artificial tocan casi todos los aspectos de nuestras vidas. Esta era requiere una comprensión cuidadosa de los riesgos y los efectos potenciales de estos algoritmos en las vidas humanas. Como ha dicho Brad Smith de Microsoft, "La tecnología de la información plantea problemas que van al corazón de las protecciones fundamentales de los derechos humanos, como la privacidad y la libertad de expresión. Esos problemas aumentan las responsabilidades de las empresas de tecnología que crean estos productos. En nuestra opinión, también exige regulación gubernamental reflexiva y para el desarrollo de normas sobre usos aceptables" ([fuente](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/)).
 
-It remains to be seen what the future holds, but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
+Queda por ver qué depara el futuro, pero es importante entender estos sistemas informáticos y el software y algortimos que ejecutan. Esperamos que este plan de estudios le ayude a comprender mejor para que pueda decidir por si mismo.
 
-[![The history of deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
-> 🎥 Click the image above for a video: Yann LeCun discusses the history of deep learning in this lecture
+[![La historia del deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
+> 🎥 Haga Click en la imagen de arriba para ver un video: Yann LeCun analiza la historia del deep learning es esta conferencia
 
 ---
-## 🚀Challenge
+## 🚀Desafío
 
-Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
+Sumérjas dentro de unos de estos momentos históricos y aprenda más sobre las personas detrás de ellos. Hay personajes fascinantes y nunca se se creó ningún descubrimientos científico en un vacío cultural. ¿Qué descubres?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
 
-## Review & Self Study
+## Revisión y autoestudio
 
-Here are items to watch and listen to:
+Aquí hay elementos para ver y escuchar:
 
-[This podcast where Amy Boyd discusses the evolution of AI](http://runasradio.com/Shows/Show/739)
+[Este podcast donde Amy Boyd habla sobre la evolución de la AI](http://runasradio.com/Shows/Show/739)
 
-[![The history of AI by Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
+[![La historia de la AI por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
 
-## Assignment
+## Asignación
 
-[Create a timeline](assignment.md)
+[Crea un timeline](assignment.md)

From a0954fbdbb7b115b8fa0e7a824704695e45ee400 Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Thu, 5 Aug 2021 14:37:55 -0300
Subject: [PATCH 344/368] Update
 1-Introduction/2-history-of-ML/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 1-Introduction/2-history-of-ML/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index 0528e27ad6..4aa540750a 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -74,7 +74,7 @@ Al mismo tiempo, comenzaron a formarse varia escuelas de pensamiento de AI. Se e
 
 ## Systemas expertos de la década de 1980
 
-A medida que el campo creció, su beneficio para las empresas se hizo más claro, y en la década de 1980 también lo hizo la proliferación de 'sistemas expertos'. "Los sistemas expertos estuvieron entre las primeras formas verdaderamente exitosas de software de inteligencia artificial (AI)." ([fuente](https://wikipedia.org/wiki/Expert_system)).
+A medida que el campo creció, su beneficio para las empresas se hizo más claro, y en la década de 1980 también lo hizo la proliferación de 'sistemas expertos'. "Los sistemas expertos estuvieron entre las primeras formas verdaderamente exitosas de software de inteligencia artificial (IA)." ([fuente](https://wikipedia.org/wiki/Expert_system)).
 
 Este tipo de sistemas es en realidad _híbrido_, que consta parcialmente de un motor de reglas que define los requisitos comerciales, y un motor de inferencia que aprovechó el sistema de reglas para deducir nuevos hechos.
 

From 79b9b7881d58aa4b364f6956227bbe0781ef7790 Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Thu, 5 Aug 2021 14:38:20 -0300
Subject: [PATCH 345/368] Update
 1-Introduction/2-history-of-ML/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 1-Introduction/2-history-of-ML/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index 4aa540750a..6cc065d5ec 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -86,7 +86,7 @@ La prolifercaión de hardware de sistemas expertos especializados tuvo el desafo
 
 ## 1993 - 2011
 
-Esta época vió una nueva era para el ML y la AI para poder resolver problemas que habían sido causados anteriormente for la falta de datos y poder de cómputo. La cantidad de datos comenzó a aumentar rápidamente y a estar más disponible, para bien o para mal, especialmente con la llegada del smartphone alrededor del 2007. El poder computacional se expandió exponencialmentem y los algoritmos evolucionaron al mismo tiempo. El campo comenzó a ganar madurez a medida que los días libres del pasado comenzaron a cristalizar en un verdadera disciplina.
+Esta época vió una nueva era para el ML y la IA para poder resolver problemas que habían sido causados anteriormente for la falta de datos y poder de cómputo. La cantidad de datos comenzó a aumentar rápidamente y a estar más disponible, para bien o para mal, especialmente con la llegada del smartphone alrededor del 2007. El poder computacional se expandió exponencialmente y los algoritmos evolucionaron al mismo tiempo. El campo comenzó a ganar madurez a medida que los días libres del pasado comenzaron a cristalizar en un verdadera disciplina.
 
 ## Ahora
 

From da23dd9a80611ef62fbcb32caaf0ab977e384702 Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Thu, 5 Aug 2021 14:38:42 -0300
Subject: [PATCH 346/368] Update
 1-Introduction/2-history-of-ML/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 1-Introduction/2-history-of-ML/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index 6cc065d5ec..1450b29b67 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -110,7 +110,7 @@ Aquí hay elementos para ver y escuchar:
 
 [Este podcast donde Amy Boyd habla sobre la evolución de la AI](http://runasradio.com/Shows/Show/739)
 
-[![La historia de la AI por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "The history of AI by Amy Boyd")
+[![La historia de la AI por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La historia de la AI por Amy Boyd")
 
 ## Asignación
 

From e58610e8d1fc8aa10909231ac6b45ea4139c3d61 Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Thu, 5 Aug 2021 14:39:04 -0300
Subject: [PATCH 347/368] Update
 1-Introduction/2-history-of-ML/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 1-Introduction/2-history-of-ML/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index 1450b29b67..d3f13bd4a1 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -108,7 +108,7 @@ Sumérjas dentro de unos de estos momentos históricos y aprenda más sobre las
 
 Aquí hay elementos para ver y escuchar:
 
-[Este podcast donde Amy Boyd habla sobre la evolución de la AI](http://runasradio.com/Shows/Show/739)
+[Este podcast donde Amy Boyd habla sobre la evolución de la IA](http://runasradio.com/Shows/Show/739)
 
 [![La historia de la AI por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La historia de la AI por Amy Boyd")
 

From c2a3c81f2a64c4c71e312acf6427a4597cf99f7e Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Thu, 5 Aug 2021 14:39:18 -0300
Subject: [PATCH 348/368] Update
 1-Introduction/2-history-of-ML/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 1-Introduction/2-history-of-ML/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index d3f13bd4a1..d90e49e99b 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -100,7 +100,7 @@ Queda por ver qué depara el futuro, pero es importante entender estos sistemas
 ---
 ## 🚀Desafío
 
-Sumérjas dentro de unos de estos momentos históricos y aprenda más sobre las personas detrás de ellos. Hay personajes fascinantes y nunca se se creó ningún descubrimientos científico en un vacío cultural. ¿Qué descubres?
+Sumérjase dentro de unos de estos momentos históricos y aprenda más sobre las personas detrás de ellos. Hay personajes fascinantes y nunca se creó ningún descubrimiento científico en un vacío cultural. ¿Qué descubres?
 
 ## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
 

From fce9ba2b148357bcfe41c194b40a451bc4e50d24 Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Thu, 5 Aug 2021 14:39:28 -0300
Subject: [PATCH 349/368] Update
 1-Introduction/2-history-of-ML/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 1-Introduction/2-history-of-ML/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index d90e49e99b..184978bb52 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -95,7 +95,7 @@ Hoy en día, machine learning y la inteligencia artificial tocan casi todos los
 Queda por ver qué depara el futuro, pero es importante entender estos sistemas informáticos y el software y algortimos que ejecutan. Esperamos que este plan de estudios le ayude a comprender mejor para que pueda decidir por si mismo.
 
 [![La historia del deep learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of deep learning")
-> 🎥 Haga Click en la imagen de arriba para ver un video: Yann LeCun analiza la historia del deep learning es esta conferencia
+> 🎥 Haga Click en la imagen de arriba para ver un video: Yann LeCun analiza la historia del deep learning en esta conferencia
 
 ---
 ## 🚀Desafío

From 819c0e4730365a0ed23e4d4174cf0ac4e8ca7000 Mon Sep 17 00:00:00 2001
From: Roberto Pauletto <roberto.pauletto@gmail.com>
Date: Fri, 6 Aug 2021 17:59:21 +0200
Subject: [PATCH 350/368] Italian translation - Chapter 6 complete

---
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 6-NLP/2-Tasks/translations/README.it.md       | 214 +++++++++
 6-NLP/2-Tasks/translations/assignment.it.md   |  11 +
 .../translations/README.it.md                 | 187 ++++++++
 .../translations/assignment.it.md             |  11 +
 .../translations/README.it.md                 | 412 ++++++++++++++++++
 .../translations/assignment.it.md             |   5 +
 .../translations/README.it.md                 | 376 ++++++++++++++++
 .../translations/assignment.it.md             |  11 +
 6-NLP/translations/README.it.md               |  24 +
 11 files changed, 1427 insertions(+)
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 create mode 100644 6-NLP/5-Hotel-Reviews-2/translations/assignment.it.md
 create mode 100644 6-NLP/translations/README.it.md

diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.it.md b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
new file mode 100644
index 0000000000..e85bd13611
--- /dev/null
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
@@ -0,0 +1,165 @@
+# Introduzione all'elaborazione del linguaggio naturale
+
+Questa lezione copre una breve storia e concetti importanti dell' *elaborazione del linguaggio naturale*, un sottocampo della *linguistica computazionale*.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+
+## Introduzione
+
+NLP, come è comunemente conosciuto, è una delle aree più note in cui machine learning è stato applicato e utilizzato nei software di produzione.
+
+✅ Si riesce a pensare a un software che si usa tutti i giorni che probabilmente ha NLP incorporato? Che dire dei programmi di elaborazione testi o le app mobili che si usano regolarmente?
+
+Si imparerà a conoscere:
+
+- **L'idea delle lingue**. Come si sono sviluppate le lingue e quali sono state le principali aree di studio.
+- **Definizione e concetti**. Si impareranno anche definizioni e concetti su come i computer elaborano il testo, inclusa l'analisi, la grammatica e l'identificazione di nomi e verbi. Ci sono alcune attività di codifica in questa lezione e vengono introdotti diversi concetti importanti che si imparerà a codificare più avanti nelle lezioni successive.
+
+## Linguistica computazionale
+
+La linguistica computazionale è un'area di ricerca e sviluppo che da molti decenni studia come i computer possono lavorare e persino capire, tradurre e comunicare con le lingue. L'elaborazione del linguaggio naturale (NLP) è un campo correlato incentrato su come i computer possono elaborare le lingue "naturali" o umane.
+
+### Esempio: dettatura telefonica
+
+Se si è mai dettato al telefono invece di digitare o posto una domanda a un assistente virtuale, il proprio discorso è stato convertito in formato testuale e quindi elaborato o *analizzato* dalla lingua con la quale si è parlato. Le parole chiave rilevate sono state quindi elaborate in un formato che il telefono o l'assistente possono comprendere e utilizzare.
+
+![comprensione](../images/comprehension.png)
+> La vera comprensione linguistica è difficile! Immagine di [Jen Looper](https://twitter.com/jenlooper)
+
+### Come è resa possibile questa tecnologia?
+
+Questo è possibile perché qualcuno ha scritto un programma per computer per farlo. Alcuni decenni fa, alcuni scrittori di fantascienza prevedevano che le persone avrebbero parlato principalmente con i loro computer e che i computer avrebbero sempre capito esattamente cosa intendevano. Purtroppo, si è rivelato essere un problema più difficile di quanto molti immaginavano, e sebbene sia un problema molto meglio compreso oggi, ci sono sfide significative nel raggiungere un'elaborazione del linguaggio naturale "perfetta" quando si tratta di comprendere il significato di una frase. Questo è un problema particolarmente difficile quando si tratta di comprendere l'umore o rilevare emozioni come il sarcasmo in una frase.
+
+A questo punto, si potrebbero ricordare le lezioni scolastiche in cui l'insegnante ha coperto le parti della grammatica in una frase. In alcuni paesi, agli studenti viene insegnata la grammatica e la linguistica come materie dedicate, ma in molti questi argomenti sono inclusi nell'apprendimento di una lingua: o la prima lingua nella scuola primaria (imparare a leggere e scrivere) e forse una seconda lingua in post-primario o liceo. Non occorre preoccuparsi se non si è esperti nel distinguere i nomi dai verbi o gli avverbi dagli aggettivi!
+
+Se si fa fatica a comprendere la differenza tra il *presente semplice* e il *presente progressivo*, non si è soli. Questa è una cosa impegnativa per molte persone, anche madrelingua di una lingua. La buona notizia è che i computer sono davvero bravi ad applicare regole formali e si imparerà a scrivere codice in grado di *analizzare* una frase così come un essere umano. La sfida più grande che si esaminerà in seguito è capire il *significato* e il *sentimento* di una frase.
+
+## Prerequisiti
+
+Per questa lezione, il prerequisito principale è essere in grado di leggere e comprendere la lingua di questa lezione. Non ci sono problemi di matematica o equazioni da risolvere. Sebbene l'autore originale abbia scritto questa lezione in inglese, è anche tradotta in altre lingue, quindi si potrebbe leggere una traduzione. Ci sono esempi in cui vengono utilizzati un numero di lingue diverse (per confrontare le diverse regole grammaticali di lingue diverse). Questi *non* sono tradotti, ma il testo esplicativo lo è, quindi il significato dovrebbe essere chiaro.
+
+Per le attività di codifica, si utilizzerà Python e gli esempi utilizzano Python 3.8.
+
+In questa sezione servirà e si utilizzerà:
+
+- **Comprensione del linguaggio Python 3**. Questa lezione utilizza input, loop, lettura di file, array.
+- **Visual Studio Code + estensione**. Si userà Visual Studio Code e la sua estensione Python. Si può anche usare un IDE Python a propria scelta.
+- **TextBlob**. [TextBlob](https://github.com/sloria/TextBlob) è una libreria di elaborazione del testo semplificata per Python. Seguire le istruzioni sul sito TextBlob per installarlo sul proprio sistema (installare anche i corpora, come mostrato di seguito):
+
+   ```bash
+   pip install -U textblob
+   python -m textblob.download_corpora
+   ```
+
+> 💡 Suggerimento: si può eseguire Python direttamente negli ambienti VS Code. Controllare la [documentazione](https://code.visualstudio.com/docs/languages/python?WT.mc_id=academic-15963-cxa) per ulteriori informazioni.
+
+## Parlare con le macchine
+
+La storia del tentativo di far capire ai computer il linguaggio umano risale a decenni fa e uno dei primi scienziati a considerare l'elaborazione del linguaggio naturale è stato *Alan Turing*.
+
+### Il Test di Turing.
+
+Quando Turing stava facendo ricerche sull'*intelligenza artificiale* negli anni '50, considerò se un test di conversazione potesse essere somministrato a un essere umano e a un computer (tramite corrispondenza digitata) in cui l'essere umano nella conversazione non era sicuro se stesse conversando con un altro umano o un computer.
+
+Se, dopo una certa durata di conversazione, l'essere umano non è riuscito a determinare se le risposte provenivano da un computer o meno, allora si potrebbe dire che il computer *sta pensando*?
+
+### L'ispirazione - 'il gioco dell'imitazione'
+
+L'idea è nata da un gioco di società chiamato *The Imitation Game* in cui un interrogatore è da solo in una stanza e ha il compito di determinare quale delle due persone (in un'altra stanza) sono rispettivamente maschio e femmina. L'interrogatore può inviare note e deve cercare di pensare a domande in cui le risposte scritte rivelano il sesso della persona misteriosa. Ovviamente, i giocatori nell'altra stanza stanno cercando di ingannare l'interrogatore rispondendo alle domande in modo tale da fuorviare o confondere l'interrogatore, dando anche l'impressione di rispondere onestamente.
+
+### Lo sviluppo di Eliza
+
+Negli anni '60 uno scienziato del MIT chiamato *Joseph* Weizenbaum sviluppò [*Eliza*](https:/wikipedia.org/wiki/ELIZA), un "terapista" informatico che poneva domande a un umano e dava l'impressione di comprendere le loro risposte. Tuttavia, mentre Eliza poteva analizzare una frase e identificare alcuni costrutti grammaticali e parole chiave in modo da dare una risposta ragionevole, non si poteva dire *che capisse* la frase. Se a Eliza viene presentata una frase che segue il formato "**Sono** _triste_", potrebbe riorganizzare e sostituire le parole nella frase per formare la risposta "Da quanto tempo **sei** _triste_".
+
+Questo dava l'impressione che Eliza avesse capito la frase e stesse facendo una domanda successiva, mentre in realtà stava cambiando il tempo e aggiungendo alcune parole. Se Eliza non fosse stata in grado di identificare una parola chiave per la quale aveva una risposta, avrebbe dato invece una risposta casuale che potrebbe essere applicabile a molte frasi diverse. Eliza avrebbe potuto essere facilmente ingannata, ad esempio se un utente avesse scritto "**Sei** una _bicicletta_" avrebbe potuto rispondere con "Da quanto tempo **sono** una _bicicletta_?", invece di una risposta più ragionata.
+
+[![Chiacchierare conEliza](https://img.youtube.com/vi/RMK9AphfLco/0.jpg)](https://youtu.be/RMK9AphfLco " Chiaccherare con Eliza")
+
+> 🎥 Fare clic sull'immagine sopra per un video sul programma ELIZA originale
+
+> Nota: si può leggere la descrizione originale di [Eliza](https://cacm.acm.org/magazines/1966/1/13317-elizaa-computer-program-for-the-study-of-natural-language-communication-between-man-and-machine/abstract) pubblicata nel 1966 se si dispone di un account ACM. In alternativa, leggere di Eliza su [wikipedia](https://it.wikipedia.org/wiki/ELIZA_(chatterbot))
+
+## Esercizio: codificare un bot conversazionale di base
+
+Un bot conversazionale, come Eliza, è un programma che sollecita l'input dell'utente e sembra capire e rispondere in modo intelligente. A differenza di Eliza, questo bot non avrà diverse regole che gli danno l'impressione di avere una conversazione intelligente. Invece, il bot avrà una sola capacità, per mantenere viva la conversazione con risposte casuali che potrebbero funzionare in quasi tutte le conversazioni banali.
+
+### Il piano
+
+I passaggi durante la creazione di un bot conversazionale:
+
+1. Stampare le istruzioni che consigliano all'utente come interagire con il bot
+2. Iniziare un ciclo
+   1. Accettare l'input dell'utente
+   2. Se l'utente ha chiesto di uscire, allora si esce
+   3. Elaborare l'input dell'utente e determinare la risposta (in questo caso, la risposta è una scelta casuale da un elenco di possibili risposte generiche)
+   4. Stampare la risposta
+3. Riprendere il ciclo dal passaggio 2
+
+### Costruire il bot
+
+Si crea il bot. Si inizia definendo alcune frasi.
+
+1. Creare questo bot in Python con le seguenti risposte casuali:
+
+   ```python
+   random_responses = ["That is quite interesting, please tell me more.",
+                       "I see. Do go on.",
+                       "Why do you say that?",
+                       "Funny weather we've been having, isn't it?",
+                       "Let's change the subject.",
+                       "Did you catch the game last night?"]
+   ```
+
+   Ecco un esempio di output come guida (l'input dell'utente è sulle righe che iniziano con `>`):
+
+   ```output
+   Hello, I am Marvin, the simple robot.
+   You can end this conversation at any time by typing 'bye'
+   After typing each answer, press 'enter'
+   How are you today?
+   > I am good thanks
+   That is quite interesting, please tell me more.
+   > today I went for a walk     
+   Did you catch the game last night?
+   > I did, but my team lost
+   Funny weather we've been having, isn't it?
+   > yes but I hope next week is better
+   Let's change the subject.
+   > ok, lets talk about music
+   Why do you say that?
+   > because I like music!
+   Why do you say that?
+   > bye
+   It was nice talking to you, goodbye!
+   ```
+
+   Una possibile soluzione al compito è [qui](../solution/bot.py)
+
+   ✅ Fermarsi e riflettere
+
+   1. Si ritiene che le risposte casuali "ingannerebbero" qualcuno facendogli pensare che il bot le abbia effettivamente capite?
+   2. Di quali caratteristiche avrebbe bisogno il bot per essere più efficace?
+   3. Se un bot potesse davvero "capire" il significato di una frase, avrebbe bisogno di "ricordare" anche il significato delle frasi precedenti in una conversazione?
+
+---
+
+## 🚀 Sfida
+
+Scegliere uno degli elementi "fermarsi e riflettere" qui sopra e provare a implementarli nel codice o scrivere una soluzione su carta usando pseudocodice.
+
+Nella prossima lezione si impareranno una serie di altri approcci all'analisi del linguaggio naturale e dell'machine learning.
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+
+## Revisione e Auto Apprendimento
+
+Dare un'occhiata ai riferimenti di seguito come ulteriori opportunità di lettura.
+
+### Bibliografia
+
+1. Schubert, Lenhart, "Computational Linguistics", *The Stanford Encyclopedia of Philosophy* (Edizione primavera 2020), Edward N. Zalta (a cura di), URL = <https://plato.stanford.edu/archives/spr2020/entries/computational-linguistics/>.
+2. Università di Princeton "About WordNet". [WordNet](https://wordnet.princeton.edu/). Princeton University 2010.
+
+## Compito
+
+[Cercare un bot](assignment.it.md)
diff --git a/6-NLP/1-Introduction-to-NLP/translations/assignment.it.md b/6-NLP/1-Introduction-to-NLP/translations/assignment.it.md
new file mode 100644
index 0000000000..02150752bd
--- /dev/null
+++ b/6-NLP/1-Introduction-to-NLP/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Cercare un bot
+
+## Istruzioni
+
+I bot sono ovunque. Il compito: trovarne uno e adottarlo! È possibile trovarli sui siti web, nelle applicazioni bancarie e al telefono, ad esempio quando si chiamano società di servizi finanziari per consigli o informazioni sull'account. Analizzare il bot e vedire se si riesce a confonderlo. Se si riesce a confondere il bot, perché si pensa sia successo? Scrivere un breve articolo sulla propria esperienza.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ------------------------------------------------------------------------------------------------------------- | -------------------------------------------- | --------------------- |
+|          | Viene scritto un documento a pagina intera, che spiega la presunta architettura del bot e delinea l'esperienza con esso | Un documento è incompleto o non ben concepito | Nessun documento inviato |
diff --git a/6-NLP/2-Tasks/translations/README.it.md b/6-NLP/2-Tasks/translations/README.it.md
new file mode 100644
index 0000000000..afe455e737
--- /dev/null
+++ b/6-NLP/2-Tasks/translations/README.it.md
@@ -0,0 +1,214 @@
+# Compiti e tecniche comuni di elaborazione del linguaggio naturale
+
+Per la maggior parte delle attività di *elaborazione del linguaggio naturale* , il testo da elaborare deve essere suddiviso, esaminato e i risultati archiviati o incrociati con regole e insiemi di dati. Queste attività consentono al programmatore di derivare il _significato_ o l'_intento_ o solo la _frequenza_ di termini e parole in un testo.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/)
+
+Si esaminano le comuni tecniche utilizzate nell'elaborazione del testo. Combinate con machine learning, queste tecniche aiutano ad analizzare grandi quantità di testo in modo efficiente. Prima di applicare machine learning a queste attività, tuttavia, occorre cercare di comprendere i problemi incontrati da uno specialista in NLP.
+
+## Compiti comuni per NLP
+
+Esistono diversi modi per analizzare un testo su cui si sta lavorando. Ci sono attività che si possono eseguire e attraverso le quali si è in grado di valutare la comprensione del testo e trarre conclusioni. Di solito si eseguono queste attività in sequenza.
+
+### Tokenizzazione
+
+Probabilmente la prima cosa che la maggior parte degli algoritmi di NLP deve fare è dividere il testo in token o parole. Anche se questo sembra semplice, dover tenere conto della punteggiatura e dei delimitatori di parole e frasi di lingue diverse può renderlo complicato. Potrebbe essere necessario utilizzare vari metodi per determinare le demarcazioni.
+
+![Tokenizzazione](../images/tokenization.png)
+> Tokenizzazione di una frase da **Orgoglio e Pregiudizio**. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+### Embedding
+
+I [word embeddings](https://it.wikipedia.org/wiki/Word_embedding) sono un modo per convertire numericamente i dati di testo. Gli embedding vengono eseguiti in modo tale che le parole con un significato simile o le parole usate insieme vengano raggruppate insieme.
+
+![word embeddings](../images/embedding.png)
+> "I have the highest respect for your nerves, they are my old friends." - Incorporazioni di parole per una frase in **Orgoglio e Pregiudizio**. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+✅ Provare [questo interessante strumento](https://projector.tensorflow.org/) per sperimentare i word embedding. Facendo clic su una parola vengono visualizzati gruppi di parole simili: gruppi di "toy" con "disney", "lego", "playstation" e "console".
+
+### Analisi e codifica di parti del discorso
+
+Ogni parola che è stata tokenizzata può essere etichettata come parte del discorso: un sostantivo, un verbo o un aggettivo. La frase `the quick red fox jumped over the lazy brown dog` potrebbe essere etichettata come fox = sostantivo, jumped = verbo.
+
+![elaborazione](../images/parse.png)
+
+> Analisi di una frase da **Orgoglio e Pregiudizio**. Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+L'analisi consiste nel riconoscere quali parole sono correlate tra loro in una frase - per esempio `the quick red fox jumped` è una sequenza aggettivo-sostantivo-verbo che è separata dalla sequenza `lazy brown dog` .
+
+### Frequenze di parole e frasi
+
+Una procedura utile quando si analizza un corpo di testo di grandi dimensioni è creare un dizionario di ogni parola o frase di interesse e con quale frequenza viene visualizzata. La frase `the quick red fox jumped over the lazy brown dog` ha una frequenza di parole di 2 per the.
+
+Si esamina un testo di esempio in cui si conta la frequenza delle parole. La poesia di Rudyard Kipling The Winners contiene i seguenti versi:
+
+```output
+What the moral? Who rides may read.
+When the night is thick and the tracks are blind
+A friend at a pinch is a friend, indeed,
+But a fool to wait for the laggard behind.
+Down to Gehenna or up to the Throne,
+He travels the fastest who travels alone.
+```
+
+Poiché le frequenze delle frasi possono essere o meno insensibili alle maiuscole o alle maiuscole, a seconda di quanto richiesto, la frase `a friend` ha una frequenza di 2, `the` ha una frequenza di 6 e `travels` è 2.
+
+### N-grammi
+
+Un testo può essere suddiviso in sequenze di parole di una lunghezza prestabilita, una parola singola (unigramma), due parole (bigrammi), tre parole (trigrammi) o un numero qualsiasi di parole (n-grammi).
+
+Ad esempio, `the quick red fox jumped over the lazy brown dog` con un punteggio n-grammo di 2 produce i seguenti n-grammi:
+
+1. the quick
+2. quick red
+3. red fox
+4. fox jumped
+5. jumped over
+6. over the
+7. the lazy
+8. lazy brown
+9. brown dog
+
+Potrebbe essere più facile visualizzarlo come una casella scorrevole per la frase. Qui è per n-grammi di 3 parole, l'n-grammo è in grassetto in ogni frase:
+
+1. **the quick red** fox jumped over the lazy brown dog
+2. the **quick red fox** jumped over the lazy brown dog
+3. the quick **red fox jumped** over the lazy brown dog
+4. the quick red **fox jumped over** the lazy brown dog
+5. the quick red fox **jumped over the** lazy brown dog
+6. the quick red fox jumped **over the lazy** brown dog
+7. the quick red fox jumped over **the lazy brown** dog
+8. the quick red fox jumped over the **lazy brown dog**
+
+![finestra scorrevole n-grammi](../images/n-grams.gif)
+
+> Valore N-gram di 3: Infografica di [Jen Looper](https://twitter.com/jenlooper)
+
+### Estrazione frase nominale
+
+Nella maggior parte delle frasi, c'è un sostantivo che è il soggetto o l'oggetto della frase. In inglese, è spesso identificabile con "a" o "an" o "the" che lo precede. Identificare il soggetto o l'oggetto di una frase "estraendo la frase nominale" è un compito comune in NLP quando si cerca di capire il significato di una frase.
+
+✅ Nella frase "I cannot fix on the hour, or the spot, or the look or the words, which laid the foundation. It is too long ago. I was in the middle before I knew that I had begun.", si possono identificare i nomi nelle frasi?
+
+Nella frase `the quick red fox jumped over the lazy brown dog` ci sono 2 frasi nominali: **quick red fox** e **lazy brown dog**.
+
+### Analisi del sentiment
+
+Una frase o un testo può essere analizzato per il sentimento, o quanto *positivo* o *negativo* esso sia. Il sentimento si misura in *polarità* e *oggettività/soggettività*. La polarità è misurata da -1,0 a 1,0 (da negativo a positivo) e da 0,0 a 1,0 (dal più oggettivo al più soggettivo).
+
+✅ In seguito si imparerà che ci sono diversi modi per determinare il sentimento usando machine learning ma un modo è avere un elenco di parole e frasi che sono classificate come positive o negative da un esperto umano e applicare quel modello al testo per calcolare un punteggio di polarità. Si riesce a vedere come funzionerebbe in alcune circostanze e meno bene in altre?
+
+### Inflessione
+
+L'inflessione consente di prendere una parola e ottenere il singolare o il plurale della parola.
+
+### Lemmatizzazione
+
+Un *lemma* è la radice o il lemma per un insieme di parole, ad esempio *volò*, *vola*, *volando* ha un lemma del verbo *volare*.
+
+Ci sono anche utili database disponibili per il ricercatore NPL, in particolare:
+
+### WordNet
+
+[WordNet](https://wordnet.princeton.edu/) è un database di parole, sinonimi, contari e molti altri dettagli per ogni parola in molte lingue diverse. È incredibilmente utile quando si tenta di costruire traduzioni, correttori ortografici o strumenti di lingua di qualsiasi tipo.
+
+## Librerie NPL
+
+Fortunatamente, non è necessario creare tutte queste tecniche da soli, poiché sono disponibili eccellenti librerie Python che le rendono molto più accessibili agli sviluppatori che non sono specializzati nell'elaborazione del linguaggio naturale o in machine learning. Le prossime lezioni includono altri esempi di queste, ma qui si impareranno alcuni esempi utili che aiuteranno con il prossimo compito.
+
+### Esercizio: utilizzo della libreria `TextBlob`
+
+Si usa una libreria chiamata TextBlob in quanto contiene API utili per affrontare questi tipi di attività. TextBlob "sta sulle spalle giganti di [NLTK](https://nltk.org) e [pattern](https://github.com/clips/pattern), e si sposa bene con entrambi". Ha una notevole quantità di ML incorporato nella sua API.
+
+> Nota: per TextBlob è disponibile un'utile [guida rapida](https://textblob.readthedocs.io/en/dev/quickstart.html#quickstart), consigliata per sviluppatori Python esperti
+
+Quando si tenta di identificare *le frasi nominali*, TextBlob offre diverse opzioni di estrattori per trovarle.
+
+1. Dare un'occhiata a `ConllExtractor`.
+
+   ```python
+   from textblob import TextBlob
+   from textblob.np_extractors import ConllExtractor
+   # importa e crea un extrattore Conll da usare successivamente
+   extractor = ConllExtractor()
+
+   # quando serve un estrattore di frasi nominali:
+   user_input = input("> ")
+   user_input_blob = TextBlob(user_input, np_extractor=extractor)  # notare specificato estrattore non predefinito
+   np = user_input_blob.noun_phrases                                    
+   ```
+
+   > Cosa sta succedendo qui? [ConllExtractor](https://textblob.readthedocs.io/en/dev/api_reference.html?highlight=Conll#textblob.en.np_extractors.ConllExtractor) è "Un estrattore di frasi nominali che utilizza l'analisi dei blocchi addestrata con il corpus di formazione ConLL-2000". ConLL-2000 si riferisce alla Conferenza del 2000 sull'apprendimento computazionale del linguaggio naturale. Ogni anno la conferenza ha ospitato un workshop per affrontare uno spinoso problema della NPL, e nel 2000 è stato lo spezzettamento dei sostantivi. Un modello è stato addestrato sul Wall Street Journal, con "sezioni 15-18 come dati di addestramento (211727 token) e sezione 20 come dati di test (47377 token)". Si possono guardare le procedure utilizzate [qui](https://www.clips.uantwerpen.be/conll2000/chunking/) e i [risultati](https://ifarm.nl/erikt/research/np-chunking.html).
+
+### Sfida: migliorare il bot con NPL
+
+Nella lezione precedente si è creato un bot di domande e risposte molto semplice. Ora si renderà Marvin un po' più comprensivo analizzando l'input per il sentimento e stampando una risposta che corrisponda al sentimento. Si dovrà anche identificare una frase nominale `noun_phrase` e chiedere informazioni su di essa.
+
+I passaggi durante la creazione di un bot conversazionale:
+
+1. Stampare le istruzioni che consigliano all'utente come interagire con il bot
+2. Avviare il ciclo
+   1. Accettare l'input dell'utente
+   2. Se l'utente ha chiesto di uscire, allora si esce
+   3. Elaborare l'input dell'utente e determinare la risposta di sentimento appropriata
+   4. Se viene rilevata una frase nominale nel sentimento, pluralizzala e chiedere ulteriori input su quell'argomento
+   5. Stampare la risposta
+3. Riprendere il ciclo dal passo 2
+
+Ecco il frammento di codice per determinare il sentimento usando TextBlob. Si noti che ci sono solo quattro *gradienti* di risposta al sentimento (se ne potrebbero avere di più se lo si desidera):
+
+```python
+if user_input_blob.polarity <= -0.5:
+  response = "Oh dear, that sounds bad. "  # Oh caro, è terribile
+elif user_input_blob.polarity <= 0: 
+  response = "Hmm, that's not great. "  # Mmm, non è eccezionale
+elif user_input_blob.polarity <= 0.5:
+  response = "Well, that sounds positive. "  # Bene, questo è positivo
+elif user_input_blob.polarity <= 1:
+  response = "Wow, that sounds great. "  # Wow, sembra eccezionale
+```
+
+Ecco un risultato di esempio a scopo di guida (l'input utente è sulle righe che iniziano per >):
+
+```output
+Hello, I am Marvin, the friendly robot.
+You can end this conversation at any time by typing 'bye'
+After typing each answer, press 'enter'
+How are you today?
+> I am ok
+Well, that sounds positive. Can you tell me more?
+> I went for a walk and saw a lovely cat
+Well, that sounds positive. Can you tell me more about lovely cats?
+> cats are the best. But I also have a cool dog
+Wow, that sounds great. Can you tell me more about cool dogs?
+> I have an old hounddog but he is sick
+Hmm, that's not great. Can you tell me more about old hounddogs?
+> bye
+It was nice talking to you, goodbye!
+```
+
+Una possibile soluzione al compito è [qui](../solution/bot.py)
+
+Verifica delle conoscenze
+
+1. Si ritiene che le risposte casuali "ingannerebbero" qualcuno facendogli pensare che il bot le abbia effettivamente capite?
+2. Identificare la frase nominale rende il bot più 'credibile'?
+3. Perché estrarre una "frase nominale" da una frase sarebbe una cosa utile da fare?
+
+---
+
+Implementare il bot nel controllo delle conoscenze precedenti e testarlo su un amico. Può ingannarlo? Si può rendere il bot più 'credibile?'
+
+## 🚀 Sfida
+
+Prendere un'attività dalla verifica delle conoscenze qui sopra e provare a implementarla. Provare il bot su un amico. Può ingannarlo? Si può rendere il bot più 'credibile?'
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/)
+
+## Revisione e Auto Apprendimento
+
+Nelle prossime lezioni si imparerà di più sull'analisi del sentiment. Fare ricerche su questa interessante tecnica in articoli come questi su [KDNuggets](https://www.kdnuggets.com/tag/nlp)
+
+## Compito
+
+[Fare rispondere un bot](assignment.it.md)
diff --git a/6-NLP/2-Tasks/translations/assignment.it.md b/6-NLP/2-Tasks/translations/assignment.it.md
new file mode 100644
index 0000000000..fddf44bc0d
--- /dev/null
+++ b/6-NLP/2-Tasks/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Fare rispondere un bot
+
+## Istruzioni
+
+Nelle ultime lezioni, si è programmato un bot di base con cui chattare. Questo bot fornisce risposte casuali finché non si dice ciao ("bye"). Si possono rendere le risposte un po' meno casuali e attivare le risposte se si dicono cose specifiche, come "perché" o "come"? Si pensi a come machine learning potrebbe rendere questo tipo di lavoro meno manuale mentre si estende il bot. Si possono utilizzare le librerie NLTK o TextBlob per semplificare le attività.
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | --------------------------------------------- | ------------------------------------------------ | ----------------------- |
+|          | Viene presentato e documentato un nuovo file bot.py | Viene presentato un nuovo file bot ma contiene bug | Non viene presentato un file |
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md
new file mode 100644
index 0000000000..32e52922f8
--- /dev/null
+++ b/6-NLP/3-Translation-Sentiment/translations/README.it.md
@@ -0,0 +1,187 @@
+# Traduzione e analisi del sentiment con ML
+
+Nelle lezioni precedenti si è imparato come creare un bot di base utilizzando `TextBlob`, una libreria che incorpora machine learning dietro le quinte per eseguire attività di base di NPL come l'estrazione di frasi nominali. Un'altra sfida importante nella linguistica computazionale è _la traduzione_ accurata di una frase da una lingua parlata o scritta a un'altra.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/35/)
+
+La traduzione è un problema molto difficile, aggravato dal fatto che ci sono migliaia di lingue e ognuna può avere regole grammaticali molto diverse. Un approccio consiste nel convertire le regole grammaticali formali per una lingua, come l'inglese, in una struttura non dipendente dalla lingua e quindi tradurla convertendola in un'altra lingua. Questo approccio significa che si dovrebbero eseguire i seguenti passaggi:
+
+1. **Identificazione**. Identificare o taggare le parole nella lingua di input in sostantivi, verbi, ecc.
+2. **Creare la traduzione**. Produrre una traduzione diretta di ogni parola nel formato della lingua di destinazione.
+
+### Frase di esempio, dall'inglese all'irlandese
+
+In inglese, la frase _I feel happy_ (sono felice) è composta da tre parole nell'ordine:
+
+- **soggetto** (I)
+- **verbo** (feel)
+- **aggettivo** (happy)
+
+Tuttavia, nella lingua irlandese, la stessa frase ha una struttura grammaticale molto diversa - emozioni come "*felice*" o "*triste*" sono espresse come se fossero *su se stessi*.
+
+La frase inglese `I feel happy` in irlandese sarebbe `Tá athas orm`. Una traduzione *letterale* sarebbe `Happy is upon me` (felicità su di me).
+
+Un oratore irlandese che traduce in inglese direbbe `I feel happy`, non `Happy is upon me`, perché capirebbe il significato della frase, anche se le parole e la struttura della frase sono diverse.
+
+L'ordine formale per la frase in irlandese sono:
+
+- **verbo** (Tá o is)
+- **aggettivo** (athas, o happy)
+- **soggetto** (orm, o upon me)
+
+## Traduzione
+
+Un programma di traduzione ingenuo potrebbe tradurre solo parole, ignorando la struttura della frase.
+
+✅ Se si è imparato una seconda (o terza o più) lingua da adulto, si potrebbe aver iniziato pensando nella propria lingua madre, traducendo un concetto parola per parola nella propria testa nella seconda lingua, e poi pronunciando la traduzione. Questo è simile a quello che stanno facendo i programmi per computer di traduzione ingenui. È importante superare questa fase per raggiungere la fluidità!
+
+La traduzione ingenua porta a cattive (e talvolta esilaranti) traduzioni errate: `I feel happy` si traduce letteralmente in `Mise bhraitheann athas` in irlandese. Ciò significa (letteralmente) `me feel happy` e non è una frase irlandese valida. Anche se l'inglese e l'irlandese sono lingue parlate su due isole vicine, sono lingue molto diverse con strutture grammaticali diverse.
+
+> E' possibile guardare alcuni video sulle tradizioni linguistiche irlandesi come [questo](https://www.youtube.com/watch?v=mRIaLSdRMMs)
+
+### Approcci di machine learning
+
+Finora, si è imparato a conoscere l'approccio delle regole formali all'elaborazione del linguaggio naturale. Un altro approccio consiste nell'ignorare il significato delle parole e _utilizzare invece machine learning per rilevare i modelli_. Questo può funzionare nella traduzione se si ha molto testo (un *corpus*) o testi (*corpora*) sia nella lingua di origine che in quella di destinazione.
+
+Si prenda ad esempio il caso di *Pride and Prejudice (Orgoglio* e pregiudizio),un noto romanzo inglese scritto da Jane Austen nel 1813. Se si consulta il libro in inglese e una traduzione umana del libro in *francese*, si potrebberoi rilevare frasi in uno che sono tradotte *idiomaticamente* nell'altro. Si farà fra un minuto.
+
+Ad esempio, quando una frase inglese come `I have no money` (non ho denaro) viene tradotta letteralmente in francese, potrebbe diventare `Je n'ai pas de monnaie`. "Monnaie" è un complicato "falso affine" francese, poiché "money" e "monnaie" non sono sinonimi. Una traduzione migliore che un essere umano potrebbe fare sarebbe `Je n'ai pas d'argent`, perché trasmette meglio il significato che non si hanno soldi (piuttosto che "moneta spicciola" che è il significato di "monnaie").
+
+![monnaie](../images/monnaie.png)
+
+> Immagine di [Jen Looper](https://twitter.com/jenlooper)
+
+Se un modello ML ha abbastanza traduzioni umane su cui costruire un modello, può migliorare l'accuratezza delle traduzioni identificando modelli comuni in testi che sono stati precedentemente tradotti da umani esperti parlanti di entrambe le lingue.
+
+### Esercizio - traduzione
+
+Si può usare `TextBlob` per tradurre le frasi. Provare la famosa prima riga di **Orgoglio e Pregiudizio**:
+
+```python
+from textblob import TextBlob
+
+blob = TextBlob(
+    "It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife!"
+)
+print(blob.translate(to="fr"))
+
+```
+
+`TextBlob` fa un buon lavoro con la traduzione: "C'est une vérité universalllement reconnue, qu'un homme célibataire en possession d'une bonne fortune doit avoir besoin d'une femme!".
+
+Si può sostenere che la traduzione di TextBlob è molto più esatta, infatti, della traduzione francese del 1932 del libro di V. Leconte e Ch. Pressoir:
+
+"C'est une vérité universelle qu'un celibataire pourvu d'une belle fortune doit avoir envie de se marier, et, si peu que l'on sache de son sentiment à cet egard, lorsqu'il arrive dans une nouvelle residence, cette idée est si bien fixée dans l'esprit de ses voisins qu'ils le considèrent sur-le-champ comme la propriété légitime de l'une ou l'autre de leurs filles."
+
+In questo caso, la traduzione informata da ML fa un lavoro migliore del traduttore umano che mette inutilmente parole nella bocca dell'autore originale per "chiarezza".
+
+> Cosa sta succedendo qui? e perché TextBlob è così bravo a tradurre? Ebbene, dietro le quinte, utilizza Google translate, una sofisticata intelligenza artificiale in grado di analizzare milioni di frasi per prevedere le migliori stringhe per il compito da svolgere. Non c'è niente di manuale in corso qui e serve una connessione Internet per usare `blob.translate`.
+
+✅ Provare altre frasi. Qual'è migliore, ML o traduzione umana? In quali casi?
+
+## Analisi del sentiment
+
+Un'altra area in cui l'apprendimento automatico può funzionare molto bene è l'analisi del sentiment. Un approccio non ML al sentiment consiste nell'identificare parole e frasi che sono "positive" e "negative". Quindi, dato un nuovo pezzo di testo, calcolare il valore totale delle parole positive, negative e neutre per identificare il sentimento generale.
+
+Questo approccio è facilmente ingannabile come si potrebbe aver visto nel compito di Marvin: la frase `Great, that was a wonderful waste of time, I'm glad we are lost on this dark road` (Grande, è stata una meravigliosa perdita di tempo, sono contento che ci siamo persi su questa strada oscura) è una frase sarcastica e negativa, ma il semplice algoritmo rileva 'great' (grande), 'wonderful' (meraviglioso), 'glad' (contento) come positivo e 'waste' (spreco), 'lost' (perso) e 'dark' (oscuro) come negativo. Il sentimento generale è influenzato da queste parole contrastanti.
+
+✅ Si rifletta un momento su come si trasmette il sarcasmo come oratori umani. L'inflessione del tono gioca un ruolo importante. Provare a dire la frase "Beh, quel film è stato fantastico" in modi diversi per scoprire come la propria voce trasmette significato.
+
+### Approcci ML
+
+L'approccio ML sarebbe quello di raccogliere manualmente corpi di testo negativi e positivi: tweet, recensioni di film o qualsiasi cosa in cui l'essere umano abbia assegnato un punteggio *e* un parere scritto. Quindi le tecniche di NPL possono essere applicate alle opinioni e ai punteggi, in modo che emergano modelli (ad esempio, le recensioni positive di film tendono ad avere la frase "degno di un Oscar" più delle recensioni di film negative, o le recensioni positive di ristoranti dicono "gourmet" molto più di "disgustoso").
+
+> ⚖� **Esempio**: se si è lavorato nell'ufficio di un politico e c'era qualche nuova legge in discussione, gli elettori potrebbero scrivere all'ufficio con e-mail a sostegno o e-mail contro la nuova legge specifica. Si supponga che si abbia il compito di leggere le e-mail e ordinarle in 2 pile, *pro* e *contro*. Se ci fossero molte e-mail, si potrebbe essere sopraffatti dal tentativo di leggerle tutte. Non sarebbe bello se un bot potesse leggerle tutte, capirle e dire a quale pila apparteneva ogni email?
+>
+> Un modo per raggiungere questo obiettivo è utilizzare machine learning. Si addestrerebbe il modello con una parte delle email *contro* e una parte delle email *per* . Il modello tenderebbe ad associare frasi e parole con il lato contro o il lato per, *ma non capirebbe alcun contenuto*, solo che è più probabile che alcune parole e modelli in una email appaiano in un *contro* o in un *pro*. Si potrebbe fare una prova con alcune e-mail non usate per addestrare il modello e vedere se si arriva alla stessa conclusione tratta da un umano. Quindi, una volta soddisfatti dell'accuratezza del modello, si potrebbero elaborare le email future senza doverle leggere tutte.
+
+✅ Questo processo ricorda processi usati nelle lezioni precedenti?
+
+## Esercizio - frasi sentimentali
+
+Il sentimento viene misurato con una *polarità* da -1 a 1, il che significa che -1 è il sentimento più negativo e 1 è il più positivo. Il sentimento viene anche misurato con un punteggio 0 - 1 per oggettività (0) e soggettività (1).
+
+Si dia un'altra occhiata a *Orgoglio e pregiudizio* di Jane Austen. Il testo è disponibile qui su [Project Gutenberg](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm). L'esempio seguente mostra un breve programma che analizza il sentimento della prima e dell'ultima frase del libro e ne mostra la polarità del sentimento e il punteggio di soggettività/oggettività.
+
+Si dovrebbe utilizzare la libreria `TextBlob` (descritta sopra) per determinare il `sentiment` (non si deve scrivere il proprio calcolatore del sentimento) nella seguente attività.
+
+```python
+from textblob import TextBlob
+
+quote1 = """It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife."""
+
+quote2 = """Darcy, as well as Elizabeth, really loved them; and they were both ever sensible of the warmest gratitude towards the persons who, by bringing her into Derbyshire, had been the means of uniting them."""
+
+sentiment1 = TextBlob(quote1).sentiment
+sentiment2 = TextBlob(quote2).sentiment
+
+print(quote1 + " has a sentiment of " + str(sentiment1))
+print(quote2 + " has a sentiment of " + str(sentiment2))
+```
+
+Si dovrebbe ottenere il seguente risultato:
+
+```output
+It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want # of a wife. has a sentiment of Sentiment(polarity=0.20952380952380953, subjectivity=0.27142857142857146)
+
+Darcy, as well as Elizabeth, really loved them; and they were
+     both ever sensible of the warmest gratitude towards the persons
+      who, by bringing her into Derbyshire, had been the means of
+      uniting them. has a sentiment of Sentiment(polarity=0.7, subjectivity=0.8)
+```
+
+## Sfida: controllare la polarità del sentimento
+
+Il compito è determinare, usando la polarità del sentiment, se *Orgoglio e Pregiudizio* ha più frasi assolutamente positive di quelle assolutamente negative. Per questa attività, si può presumere che un punteggio di polarità di 1 o -1 sia rispettivamente assolutamente positivo o negativo.
+
+**Procedura:**
+
+1. Scaricare una [copia di Orgoglio e pregiudizio](https://www.gutenberg.org/files/1342/1342-h/1342-h.htm) dal Progetto Gutenberg come file .txt. Rimuovere i metadati all'inizio e alla fine del file, lasciando solo il testo originale
+2. Aprire il file in Python ed estrare il contenuto come una stringa
+3. Creare un TextBlob usando la stringa del libro
+4. Analizzare ogni frase del libro in un ciclo
+   1. Se la polarità è 1 o -1, memorizzare la frase in un array o in un elenco di messaggi positivi o negativi
+5. Alla fine, stampare tutte le frasi positive e negative (separatamente) e il numero di ciascuna.
+
+Ecco una [soluzione](../solution/notebook.ipynb) di esempio.
+
+✅ Verifica delle conoscenze
+
+1. Il sentimento si basa sulle parole usate nella frase, ma il codice *comprende* le parole?
+2. Si ritiene che la polarità del sentimento sia accurata o, in altre parole, si è *d'accordo* con i punteggi?
+   1. In particolare, si è d'accordo o in disaccordo con l'assoluta polarità **positiva** delle seguenti frasi?
+      * What an excellent father you have, girls! (Che padre eccellente avete, ragazze!) said she, when the door was shut. (disse lei, non appena si chiuse la porta).
+      * “Your examination of Mr. Darcy is over, I presume,� said Miss Bingley; “and pray what is the result?� ("Il vostro esame di Mr. Darcy è finito, presumo", disse Miss Bingley; "e vi prego qual è il risultato?") “I am perfectly convinced by it that Mr. Darcy has no defect. (Sono perfettamente convinta che il signor Darcy non abbia difetti).
+      * How wonderfully these sort of things occur! (Come accadono meravigliosamente questo genere di cose!).
+      * I have the greatest dislike in the world to that sort of thing. (Ho la più grande antipatia del mondo per quel genere di cose).
+      * Charlotte is an excellent manager, I dare say (Charlotte è un'eccellente manager, oserei dire).
+      * “This is delightful indeed! (“Questo è davvero delizioso!)
+      * I am so happy! (Che gioia!)
+      * Your idea of the ponies is delightful. (La vostra idea dei pony è deliziosa).
+   2. Le successive 3 frasi sono state valutate con un sentimento assolutamente positivo, ma a una lettura attenta, non sono frasi positive. Perché l'analisi del sentiment ha pensato che fossero frasi positive?
+      * Happy shall I be, when his stay at Netherfield is over!� “I wish I could say anything to comfort you,� replied Elizabeth; “but it is wholly out of my power. (Come sarò felice, quando il suo soggiorno a Netherfield sarà finito!» "Vorrei poter dire qualcosa per consolarti", rispose Elizabeth; “ma proprio non ci riesco).
+      * If I could but see you as happy! (Se solo potessi vederti felice!)
+      * Our distress, my dear Lizzy, is very great. (La nostra angoscia, mia cara Lizzy, è devvero grande).
+   3. Sei d'accordo o in disaccordo con la polarità **negativa** assoluta delle seguenti frasi?
+      - Everybody is disgusted with his pride. (Tutti sono disgustati dal suo orgoglio).
+      - “I should like to know how he behaves among strangers.� “You shall hear then—but prepare yourself for something very dreadful. ("Vorrei sapere come si comporta in mezzo agli estranei." "Allora sentirete, ma preparatevi a qualcosa di terribile).
+      - The pause was to Elizabeth’s feelings dreadful. (La pausa fu terribile per i sentimenti di Elizabeth).
+      - It would be dreadful! (Sarebbe terribile!)
+
+✅ Qualsiasi appassionato di Jane Austen capirebbe che usa spesso i suoi libri per criticare gli aspetti più ridicoli della società inglese Regency. Elizabeth Bennett, la protagonista di *Orgoglio e pregiudizio,* è un'attenta osservatrice sociale (come l'autrice) e il suo linguaggio è spesso pesantemente sfumato. Anche Mr. Darcy (l'interesse amoroso della storia) nota l'uso giocoso e canzonatorio del linguaggio di Elizabeth: "I have had the pleasure of your acquaintance long enough to know that you find great enjoyment in occasionally professing opinions which in fact are not your own." ("Ho il piacere di conoscervi da abbastanza tempo per sapere quanto vi divertiate a esprimere di tanto in tanto delle opinioni che in realtà non vi appartengono")
+
+---
+
+## 🚀 Sfida
+
+Si può rendere Marvin ancora migliore estraendo altre funzionalità dall'input dell'utente?
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/36/)
+
+## Revisione e Auto Apprendimento
+
+Esistono molti modi per estrarre il sentiment dal testo. Si pensi alle applicazioni aziendali che potrebbero utilizzare questa tecnica. Pensare a cosa potrebbe andare storto. Ulteriori informazioni sui sistemi sofisticati pronti per l'azienda che analizzano il sentiment come l'[analisi del testo di Azure](https://docs.microsoft.com/azure/cognitive-services/Text-Analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1?WT.mc_id=academic-15963-cxa). Provare alcune delle frasi di Orgoglio e Pregiudizio sopra e vedere se può rilevare sfumature.
+
+## Compito
+
+[Licenza poetica](assignment.it.md)
diff --git a/6-NLP/3-Translation-Sentiment/translations/assignment.it.md b/6-NLP/3-Translation-Sentiment/translations/assignment.it.md
new file mode 100644
index 0000000000..1b786ba267
--- /dev/null
+++ b/6-NLP/3-Translation-Sentiment/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Licenza poetica
+
+## Istruzioni
+
+In [questo notebook](https://www.kaggle.com/jenlooper/emily-dickinson-word-frequency) si possono trovare oltre 500 poesie di Emily Dickinson precedentemente analizzate per il sentiment utilizzando l'analisi del testo di Azure. Utilizzando questo insieme di dati, analizzarlo utilizzando le tecniche descritte nella lezione. Il sentimento suggerito di una poesia corrisponde alla decisione più sofisticata del servizio Azure? Perché o perché no, secondo il proprio parere? C’è qualcosa che sorprende?
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | -------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------ |
+|          | Un notebook viene presentato con una solida analisi del risultato da un campione di un autore | Il notebook è incompleto o non esegue l'analisi | Nessun notebook presentato |
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
new file mode 100644
index 0000000000..a541d0d6f4
--- /dev/null
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
@@ -0,0 +1,412 @@
+# Analisi del sentiment con le recensioni degli hotel - elaborazione dei dati
+
+In questa sezione si utilizzeranno le tecniche delle lezioni precedenti per eseguire alcune analisi esplorative dei dati di un grande insieme di dati. Una volta compresa bene l'utilità delle varie colonne, si imparerà:
+
+- come rimuovere le colonne non necessarie
+- come calcolare alcuni nuovi dati in base alle colonne esistenti
+- come salvare l'insieme di dati risultante per l'uso nella sfida finale
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/)
+
+### Introduzione
+
+Finora si è appreso come i dati di testo siano abbastanza diversi dai tipi di dati numerici. Se è un testo scritto o parlato da un essere umano, può essere analizzato per trovare schemi e frequenze, sentiment e significati. Questa lezione entra in un vero insieme di dati con una vera sfida: **[515K dati di recensioni di hotel in Europa](https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe)** e include una [licenza CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/). È stato ricavato da Booking.com da fonti pubbliche. Il creatore dell'insieme di dati è stato Jiashen Liu.
+
+### Preparazione
+
+Ecco l'occorrente:
+
+* La possibilità di eseguire notebook .ipynb utilizzando Python 3
+* pandas
+* NLTK, [che si dovrebbe installare localmente](https://www.nltk.org/install.html)
+* L'insieme di dati disponibile su Kaggle [515K Hotel Reviews Data in Europe](https://www.kaggle.com/jiashenliu/515k-hotel-reviews-data-in-europe). Sono circa 230 MB decompressi. Scaricarlo nella cartella radice `/data` associata a queste lezioni di NLP.
+
+## Analisi esplorativa dei dati
+
+Questa sfida presuppone che si stia creando un bot di raccomandazione di hotel utilizzando l'analisi del sentiment e i punteggi delle recensioni degli ospiti. L'insieme di dati da utilizzare include recensioni di 1493 hotel diversi in 6 città.
+
+Utilizzando Python, un insieme di dati di recensioni di hotel e l'analisi del sentiment di NLTK si potrebbe scoprire:
+
+* quali sono le parole e le frasi più usate nelle recensioni?
+* i *tag* ufficiali che descrivono un hotel correlato con punteggi di recensione (ad es. sono le più negative per un particolare hotel per *famiglia con bambini piccoli* rispetto a *viaggiatore singolo*, forse indicando che è meglio per i *viaggiatori sinogli*?)
+* i punteggi del sentiment NLTK "concordano" con il punteggio numerico del recensore dell'hotel?
+
+#### Insieme di dati
+
+Per esplorare l'insieme di dati scaricato e salvato localmente, aprire il file in un editor tipo VS Code o anche Excel.
+
+Le intestazioni nell'insieme di dati sono le seguenti:
+
+*Hotel_Address, Additional_Number_of_Scoring, Review_Date, Average_Score, Hotel_Name, Reviewer_Nationality, Negative_Review, Review_Total_Negative_Word_Counts, Total_Number_of_Reviews, Positive_Review, Review_Total_Positive_Word_Counts, Total_Number_of_Reviews_Reviewer_Has_Given, Reviewer_Score, Tags, days_since_review, lat, lng*
+
+Qui sono raggruppati in un modo che potrebbe essere più facile da esaminare:
+
+##### Colonne Hotel
+
+* `Hotel_Name` (nome hotel), `Hotel_Address` (indirizzo hotel), `lat` (latitudine)`,` lng (longitudine)
+   * Usando *lat* e *lng* si può tracciare una mappa con Python che mostra le posizioni degli hotel (forse codificate a colori per recensioni negative e positive)
+   * Hotel_Address non è ovviamente utile e probabilmente verrà sostituito con una nazione per semplificare l'ordinamento e la ricerca
+
+**Colonne di meta-recensione dell'hotel**
+
+* `Average_Score` (Punteggio medio)
+   * Secondo il creatore dell'insieme di dati, questa colonna è il *punteggio medio dell'hotel, calcolato in base all'ultimo commento dell'ultimo anno*. Questo sembra un modo insolito per calcolare il punteggio, ma sono i dati recuperati, quindi per ora si potrebbero prendere come valore nominale.
+
+   ✅ Sulla base delle altre colonne di questi dati, si riesce a pensare a un altro modo per calcolare il punteggio medio?
+
+* `Total_Number_of_Reviews` (Numero totale di recensioni)
+   * Il numero totale di recensioni ricevute da questo hotel - non è chiaro (senza scrivere del codice) se si riferisce alle recensioni nell'insieme di dati.
+* `Additional_Number_of_Scoring` (Numero aggiuntivo di punteggio
+   * Ciò significa che è stato assegnato un punteggio di recensione ma nessuna recensione positiva o negativa è stata scritta dal recensore
+
+**Colonne di recensione**
+
+- `Reviewer_Score` (Punteggio recensore)
+   - Questo è un valore numerico con al massimo 1 cifra decimale tra i valori minimo e massimo 2,5 e 10
+   - Non è spiegato perché 2,5 sia il punteggio più basso possibile
+- `Negative_Review` (Recensione Negativa)
+   - Se un recensore non ha scritto nulla, questo campo avrà "**No Negative" (Nessun negativo)**
+   - Si tenga presente che un recensore può scrivere una recensione positiva nella colonna delle recensioni negative (ad es. "non c'è niente di negativo in questo hotel")
+- `Review_Total_Negative_Word_Counts` (Conteggio parole negative totali per revisione)
+   - Conteggi di parole negative più alti indicano un punteggio più basso (senza controllare il sentiment)
+- `Positive_Review` (Recensioni positive)
+   - Se un recensore non ha scritto nulla, questo campo avrà "**No Positive" (Nessun positivo)**
+   - Si tenga presente che un recensore può scrivere una recensione negativa nella colonna delle recensioni positive (ad es. "non c'è niente di buono in questo hotel")
+- `Review_Total_Positive_Word_Counts` (Conteggio parole positive totali per revisione)
+   - Conteggi di parole positive più alti indicano un punteggio più alto (senza controllare il sentiment)
+- `Review_Date` e `days_since_review` (Data revisione e giorni trascorsi dalla revisione)
+   - Una misura di freschezza od obsolescenza potrebbe essere applicata a una recensione (le recensioni più vecchie potrebbero non essere accurate quanto quelle più recenti perché la gestione dell'hotel è cambiata, o sono stati effettuati lavori di ristrutturazione, o è stata aggiunta una piscina, ecc.)
+- `Tag`
+   - Questi sono brevi descrittori che un recensore può selezionare per descrivere il tipo di ospite in cui rientra (ad es. da soli o in famiglia), il tipo di camera che aveva, la durata del soggiorno e come è stata inviata la recensione.
+   - Sfortunatamente, l'uso di questi tag è problematico, controllare la sezione sottostante che discute la loro utilità
+
+**Colonne dei recensori**
+
+- `Total_Number_of_Reviews_Reviewer_Has_Given` (Numero totale di revisioni per recensore)
+   - Questo potrebbe essere un fattore in un modello di raccomandazione, ad esempio, se si potesse determinare che i recensori più prolifici con centinaia di recensioni avevano maggiori probabilità di essere negativi piuttosto che positivi. Tuttavia, il recensore di una particolare recensione non è identificato con un codice univoco e quindi non può essere collegato a un insieme di recensioni. Ci sono 30 recensori con 100 o più recensioni, ma è difficile vedere come questo possa aiutare il modello di raccomandazione.
+- `Reviewer_Nationality` (Nazionalità recensore)
+   - Alcune persone potrebbero pensare che alcune nazionalità abbiano maggiore propensione a dare una recensione positiva o negativa a causa di un'inclinazione nazionalista. Fare attenzione a creare tali punti di vista aneddotici nei propri modelli. Questi sono stereotipi nazionalisti (e talvolta razziali) e ogni recensore era un individuo che ha scritto una recensione in base alla propria esperienza. Potrebbe essere stata filtrata attraverso molte lenti come i loro precedenti soggiorni in hotel, la distanza percorsa e la loro indole. Pensare che la loro nazionalità sia stata la ragione per un punteggio di recensione è difficile da giustificare.
+
+##### Esempi
+
+| Average  Score | Total Number   Reviews | Reviewer   Score | Negative <br />Review                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | Positive   Review                 | Tags                                                                                      |
+| -------------- | ---------------------- | ---------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- | ----------------------------------------------------------------------------------------- |
+| 7.8            | 1945                   | 2.5              | This is  currently not a hotel but a construction site I was terroized from early  morning and all day with unacceptable building noise while resting after a  long trip and working in the room People were working all day i e with  jackhammers in the adjacent rooms I asked for a room change but no silent  room was available To make thinks worse I was overcharged I checked out in  the evening since I had to leave very early flight and received an appropiate  bill A day later the hotel made another charge without my concent in excess  of booked price It s a terrible place Don t punish yourself by booking  here | Nothing  Terrible place Stay away | Business trip                                Couple Standard Double  Room Stayed 2 nights |
+
+Come si può vedere, questo ospite non ha trascorso un soggiorno felice in questo hotel. L'hotel ha un buon punteggio medio di 7,8 e 1945 recensioni, ma questo recensore ha dato 2,5 e ha scritto 115 parole su quanto sia stato negativo il suo soggiorno. Se non è stato scritto nulla nella colonna Positive_Review, si potrebbe supporre che non ci sia stato nulla di positivo, ma purtroppo sono state scritte 7 parole di avvertimento. Se si contassero solo le parole invece del significato o del sentiment delle parole, si potrebbe avere una visione distorta dell'intento dei recensori. Stranamente, il punteggio di 2,5 è fonte di confusione, perché se il soggiorno in hotel è stato così negativo, perché dargli dei punti? Indagando da vicino l'insieme di dati, si vedrà che il punteggio più basso possibile è 2,5, non 0. Il punteggio più alto possibile è 10.
+
+##### Tag
+
+Come accennato in precedenza, a prima vista, l'idea di utilizzare i `tag` per classificare i dati ha senso. Sfortunatamente questi tag non sono standardizzati, il che significa che in un determinato hotel le opzioni potrebbero essere *Camera singola* , *Camera a due letti* e *Camera doppia*, ma nell'hotel successivo potrebbero essere *Camera singola deluxe*, *Camera matrimoniale classica* e *Camera king executive*. Potrebbero essere le stesse cose, ma ci sono così tante varianti che la scelta diventa:
+
+1. Tentare di modificare tutti i termini in un unico standard, il che è molto difficile, perché non è chiaro quale sarebbe il percorso di conversione in ciascun caso (ad es. *Camera singola classica* mappata in *camera singola*, ma *camera Queen Superior con cortile giardino o vista città* è molto più difficile da mappare)
+
+1. Si può adottare un approccio NLP e misurare la frequenza di determinati termini come *Solo*, *Business Traveller* (Viaggiatore d'affari) o *Family with young kids* (Famiglia con bambini piccoli) quando si applicano a ciascun hotel e tenerne conto nella raccomandazione
+
+I tag sono in genere (ma non sempre) un singolo campo contenente un elenco di valori separati da 5 a 6 virgole allineati per *Type of trip* (Tipo di viaggio), *Type of guests* (Tipo di ospiti), *Type of room* (Tipo di camera), *Number of nights* (Numero di notti) e *Type of device* (Tipo di dispositivo con il quale è stata inviata la recensione).Tuttavia, poiché alcuni recensori non compilano ogni campo (potrebbero lasciarne uno vuoto), i valori non sono sempre nello stesso ordine.
+
+Ad esempio, si prenda *Type of group*  (Tipo di gruppo). Ci sono 1025 possibilità uniche in questo campo nella colonna `Tag`, e purtroppo solo alcune di esse fanno riferimento a un gruppo (alcune sono il tipo di stanza ecc.). Se si filtra solo quelli che menzionano la famiglia, saranno ricavati molti risultati relativi al tipo di *Family room* . Se si include il termine *with* (con), cioè si conta i valori per *Family with*, i risultati sono migliori, con oltre 80.000 dei 515.000 risultati che contengono la frase "Family with young children" (Famiglia con figli piccoli) o "Family with older children" (Famiglia con figli grandi).
+
+Ciò significa che la colonna dei tag non è completamente inutile allo scopo, ma richiederà del lavoro per renderla utile.
+
+##### Punteggio medio dell'hotel
+
+Ci sono una serie di stranezze o discrepanze con l'insieme di dati che non si riesce a capire, ma sono illustrate qui in modo che ci siano note quando si costruiscono i propri modelli. Se ci si capisce qualcosa, renderlo noto nella sezione discussione!
+
+L'insieme di dati ha le seguenti colonne relative al punteggio medio e al numero di recensioni:
+
+1. Hotel_Name (Nome Hotel)
+2. Additional_Number_of_Scoring (Numero aggiuntivo di punteggio
+3. Average_Score (Punteggio medio)
+4. Total_Number_of_Reviews (Numero totale di recensioni)
+5. Reviewer_Score (Punteggio recensore)
+
+L'hotel con il maggior numero di recensioni in questo insieme di dati è *il Britannia International Hotel Canary Wharf* con 4789 recensioni su 515.000. Ma se si guarda al valore `Total_Number_of_Reviews` per questo hotel, è 9086. Si potrebbe supporre che ci siano molti più punteggi senza recensioni, quindi forse si dovrebbe aggiungere il valore della colonna `Additional_Number_of_Scoring` . Quel valore è 2682 e aggiungendolo a 4789 si ottiene 7.471 che è ancora 1615 in meno del valore di `Total_Number_of_Reviews`.
+
+Se si prende la colonna `Average_Score`, si potrebbe supporre che sia la media delle recensioni nell'insieme di dati, ma la descrizione di Kaggle è "*Punteggio medio dell’hotel, calcolato in base all’ultimo commento nell’ultimo anno*". Non sembra così utile, ma si può calcolare la media in base ai punteggi delle recensioni nell'insieme di dati. Utilizzando lo stesso hotel come esempio, il punteggio medio dell'hotel è 7,1 ma il punteggio calcolato (punteggio medio del recensore nell'insieme di dati) è 6,8. Questo è vicino, ma non lo stesso valore, e si può solo supporre che i punteggi dati nelle recensioni `Additional_Number_of_Scoring` hanno aumentato la media a 7,1. Sfortunatamente, senza alcun modo per testare o dimostrare tale affermazione, è difficile utilizzare o fidarsi di `Average_Score`, `Additional_Number_of_Scoring` e `Total_Number_of_Reviews` quando si basano su o fanno riferimento a dati che non sono presenti.
+
+Per complicare ulteriormente le cose, l'hotel con il secondo numero più alto di recensioni ha un punteggio medio calcolato di 8,12 e l'insieme di dati `Average_Score` è 8,1. Questo punteggio corretto è una coincidenza o il primo hotel è una discrepanza?
+
+Sulla possibilità che questi hotel possano essere un valore anomalo e che forse la maggior parte dei valori coincidano (ma alcuni non lo fanno per qualche motivo)  si scriverà un breve programma per esplorare i valori nell'insieme di dati e determinare l'utilizzo corretto (o mancato utilizzo) dei valori.
+
+> 🚨 Una nota di cautela
+>
+> Quando si lavora con questo insieme di dati, si scriverà un codice che calcola qualcosa dal testo senza dover leggere o analizzare il testo da soli. Questa è l'essenza di NLP, interpretare il significato o il sentiment senza che lo faccia un essere umano. Tuttavia, è possibile che si leggano alcune delle recensioni negative. Non è raccomandabile farlo. Alcune di esse sono recensioni negative sciocche o irrilevanti, come "Il tempo non era eccezionale", qualcosa al di fuori del controllo dell'hotel, o di chiunque, in effetti. Ma c'è anche un lato oscuro in alcune recensioni. A volte le recensioni negative sono razziste, sessiste o antietà. Questo è un peccato, ma è prevedibile in un insieme di dati recuperato da un sito web pubblico. Alcuni recensori lasciano recensioni che si potrebbe trovare sgradevoli, scomode o sconvolgenti. Meglio lasciare che il codice misuri il sentiment piuttosto che leggerle da soli e arrabbiarsi. Detto questo, è una minoranza che scrive queste cose, ma esistono lo stesso.
+
+## Esercizio - Esplorazione dei dati
+
+### Caricare i dati
+
+Per ora l'esame visivo dei dati è sufficiente, adesso si scriverà del codice e si otterranno alcune risposte! Questa sezione utilizza la libreria pandas. Il primo compito è assicurarsi di poter caricare e leggere i dati CSV. La libreria pandas ha un veloce caricatore CSV e il risultato viene inserito in un dataframe, come nelle lezioni precedenti. Il CSV che si sta caricando ha oltre mezzo milione di righe, ma solo 17 colonne. Pandas offre molti modi potenti per interagire con un dataframe, inclusa la possibilità di eseguire operazioni su ogni riga.
+
+Da qui in poi in questa lezione, ci saranno frammenti di codice e alcune spiegazioni del codice e alcune discussioni su cosa significano i risultati. Usare il _notebook.ipynb_ incluso per il proprio codice.
+
+Si inizia con il caricamento del file di dati da utilizzare:
+
+```python
+# Carica il CSV con le recensioni degli hotel
+import pandas as pd
+import time
+# importa time per determinare orario di inizio e fine caricamento per poterne calcolare la durata
+print("Loading data file now, this could take a while depending on file size")
+start = time.time()
+# df è un 'DataFrame' - assicurarsi di aver scaricato il file nella cartelle data
+df = pd.read_csv('../../data/Hotel_Reviews.csv')
+end = time.time()
+print("Loading took " + str(round(end - start, 2)) + " seconds")
+```
+
+Ora che i dati sono stati caricati, si possono eseguire alcune operazioni su di essi. Tenere questo codice nella parte superiore del programma per la parte successiva.
+
+## Esplorare i dati
+
+In questo caso, i dati sono già *puliti*, il che significa che sono pronti per essere lavorati e non ci sono caratteri in altre lingue che potrebbero far scattare algoritmi che si aspettano solo caratteri inglesi.
+
+✅ Potrebbe essere necessario lavorare con dati che richiedono un'elaborazione iniziale per formattarli prima di applicare le tecniche di NLP, ma non questa volta. Se si dovesse, come si gestirebero i caratteri non inglesi?
+
+Si prenda un momento per assicurarsi che una volta caricati, i dati possano essere esplorarati con il codice. È molto facile volersi concentrare sulle colonne `Negative_Review` e `Positive_Review` . Sono pieni di testo naturale per essere elaborato dagli algoritmi di NLP. Ma non è finita qui! Prima di entrare nel NLP e nel sentiment, si dovrebbe seguire il codice seguente per accertarsi se i valori forniti nell'insieme di dati corrispondono ai valori calcolati con pandas.
+
+## Operazioni con dataframe
+
+Il primo compito di questa lezione è verificare se le seguenti asserzioni sono corrette scrivendo del codice che esamini il dataframe (senza modificarlo).
+
+> Come molte attività di programmazione, ci sono diversi modi per completarla, ma un buon consiglio è farlo nel modo più semplice e facile possibile, soprattutto se sarà più facile da capire quando si riesaminerà questo codice in futuro. Con i dataframe, esiste un'API completa che spesso avrà un modo per fare ciò che serve in modo efficiente.
+
+Trattare le seguenti domande come attività di codifica e provare a rispondere senza guardare la soluzione.
+
+1. Stampare la *forma* del dataframe appena caricato (la forma è il numero di righe e colonne)
+2. Calcolare il conteggio della frequenza per le nazionalità dei recensori:
+   1. Quanti valori distinti ci sono per la colonna `Reviewer_Nationality` e quali sono?
+   2. Quale nazionalità del recensore è la più comune nell'insieme di dati (stampare nazione e numero di recensioni)?
+   3. Quali sono le prossime 10 nazionalità più frequenti e la loro frequenza?
+3. Qual è stato l'hotel più recensito per ciascuna delle 10 nazionalità più recensite?
+4. Quante recensioni ci sono per hotel (conteggio della frequenza dell'hotel) nell'insieme di dati?
+5. Sebbene sia presente una colonna `Average_Score` per ogni hotel nell'insieme di dati, si può anche calcolare un punteggio medio (ottenendo la media di tutti i punteggi dei recensori nell'insieme di dati per ogni hotel). Aggiungere una nuova colonna al dataframe con l'intestazione della colonna `Calc_Average_Score` che contiene quella media calcolata.
+6. Ci sono hotel che hanno lo stesso (arrotondato a 1 decimale) `Average_Score` e `Calc_Average_Score`?
+   1. Provare a scrivere una funzione Python che accetta una serie (riga) come argomento e confronta i valori, stampando un messaggio quando i valori non sono uguali. Quindi usare il metodo `.apply()` per elaborare ogni riga con la funzione.
+7. Calcolare e stampare quante righe contengono valori di "No Negative" nella colonna `Negative_Review` "
+8. Calcolare e stampare quante righe contengono valori di "No Positive" nella colonna `Positive_Review` "
+9. Calcolare e stampare quante righe contengono valori di "No Positive" nella colonna `Positive_Review` " **e** valori di "No Negative" nella colonna `Negative_Review`
+
+### Risposte
+
+1. Stampare la *forma* del dataframei appena caricato (la forma è il numero di righe e colonne)
+
+   ```python
+   print("The shape of the data (rows, cols) is " + str(df.shape))
+   > The shape of the data (rows, cols) is (515738, 17)
+   ```
+
+2. Calcolare il conteggio della frequenza per le nazionalità dei recensori:
+
+   1. Quanti valori distinti ci sono per la colonna `Reviewer_Nationality` e quali sono?
+   2. Quale nazionalità del recensore è la più comune nell'insieme di dati (paese di stampa e numero di recensioni)?
+
+   ```python
+   # value_counts() crea un oggetto Series con indice e valori, in questo caso la nazione 
+   # e la frequenza con la quale si manifestano nella nazionalità del recensore
+   nationality_freq = df["Reviewer_Nationality"].value_counts()
+   print("There are " + str(nationality_freq.size) + " different nationalities")
+   # stampa la prima e ultima riga della Series. Modificare in nationality_freq.to_string() per stampare tutti i dati
+   print(nationality_freq)
+
+   There are 227 different nationalities
+    United Kingdom               245246
+    United States of America      35437
+    Australia                     21686
+    Ireland                       14827
+    United Arab Emirates          10235
+                                  ...  
+    Comoros                           1
+    Palau                             1
+    Northern Mariana Islands          1
+    Cape Verde                        1
+    Guinea                            1
+   Name: Reviewer_Nationality, Length: 227, dtype: int64
+   ```
+
+   3. Quali sono le prossime 10 nazionalità più frequenti e la loro frequenza?
+
+      ```python
+      print("The highest frequency reviewer nationality is " + str(nationality_freq.index[0]).strip() + " with " + str(nationality_freq[0]) + " reviews.")
+      # Notare che c'è uno spazio davanti ai valori, strip() lo rimuove per la stampa
+      # Quale sono le 10 nazionalità più comuni e la loro frequenza?
+      print("The next 10 highest frequency reviewer nationalities are:")
+      print(nationality_freq[1:11].to_string())
+
+      The highest frequency reviewer nationality is United Kingdom with 245246 reviews.
+      The next 10 highest frequency reviewer nationalities are:
+       United States of America     35437
+       Australia                    21686
+       Ireland                      14827
+       United Arab Emirates         10235
+       Saudi Arabia                  8951
+       Netherlands                   8772
+       Switzerland                   8678
+       Germany                       7941
+       Canada                        7894
+       France                        7296
+      ```
+
+3. Qual è stato l'hotel più recensito per ciascuna delle 10 nazionalità più recensite?
+
+   ```python
+   # Qual è stato l'hotel più recensito per ciascuna delle 10 nazionalità più recensite
+   # In genere con pandas si cerca di evitare un ciclo esplicito, ma si vuole mostrare come si crea un
+   # nuovo dataframe usando criteri (non fare questo con un grande volume di dati in quanto potrebbe essere molto lento)
+   for nat in nationality_freq[:10].index:
+      # Per prima cosa estrarre tutte le righe che corrispondono al criterio in un nuovo dataframe
+      nat_df = df[df["Reviewer_Nationality"] == nat]   
+      # Ora ottenere la frequenza per l'hotel
+      freq = nat_df["Hotel_Name"].value_counts()
+      print("The most reviewed hotel for " + str(nat).strip() + " was " + str(freq.index[0]) + " with " + str(freq[0]) + " reviews.")
+
+   The most reviewed hotel for United Kingdom was Britannia International Hotel Canary Wharf with 3833 reviews.
+   The most reviewed hotel for United States of America was Hotel Esther a with 423 reviews.
+   The most reviewed hotel for Australia was Park Plaza Westminster Bridge London with 167 reviews.
+   The most reviewed hotel for Ireland was Copthorne Tara Hotel London Kensington with 239 reviews.
+   The most reviewed hotel for United Arab Emirates was Millennium Hotel London Knightsbridge with 129 reviews.
+   The most reviewed hotel for Saudi Arabia was The Cumberland A Guoman Hotel with 142 reviews.
+   The most reviewed hotel for Netherlands was Jaz Amsterdam with 97 reviews.
+   The most reviewed hotel for Switzerland was Hotel Da Vinci with 97 reviews.
+   The most reviewed hotel for Germany was Hotel Da Vinci with 86 reviews.
+   The most reviewed hotel for Canada was St James Court A Taj Hotel London with 61 reviews.
+   ```
+
+4. Quante recensioni ci sono per hotel (conteggio della frequenza dell'hotel) nell'insieme di dati?
+
+   ```python
+   # Per prima cosa creare un nuovo dataframe in base a quello vecchio, togliendo le colonne non necessarie
+   hotel_freq_df = df.drop(["Hotel_Address", "Additional_Number_of_Scoring", "Review_Date", "Average_Score", "Reviewer_Nationality", "Negative_Review", "Review_Total_Negative_Word_Counts", "Positive_Review", "Review_Total_Positive_Word_Counts", "Total_Number_of_Reviews_Reviewer_Has_Given", "Reviewer_Score", "Tags", "days_since_review", "lat", "lng"], axis = 1)
+
+   # Raggruppre le righe per Hotel_Name, conteggiarle e inserire il risultato in una nuova colonna Total_Reviews_Found
+   hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')
+
+   # Eliminare tutte le righe duplicate
+   hotel_freq_df = hotel_freq_df.drop_duplicates(subset = ["Hotel_Name"])
+   display(hotel_freq_df)
+   ```
+   
+   | Hotel_Name (Nome Hotel) | Total_Number_of_Reviews (Numero totale di recensioni) | Total_Reviews_Found |
+   | :----------------------------------------: | :---------------------: | :-----------------: |
+   | Britannia International Hotel Canary Wharf | 9086 | 4789 |
+   | Park Plaza Westminster Bridge Londra | 12158 | 4169 |
+   | Copthorne Tara Hotel London Kensington | 7105 | 3578 |
+   | ... | ... | ... |
+   | Mercure Paris Porte d'Orléans | 110 | 10 |
+   | Hotel Wagner | 135 | 10 |
+   | Hotel Gallitzinberg | 173 | 8 |
+   
+
+   Si potrebbe notare che il *conteggio nell'insieme di dati* non corrisponde al valore in `Total_Number_of_Reviews`. Non è chiaro se questo valore nell'insieme di dati rappresentasse il numero totale di recensioni che l'hotel aveva, ma non tutte sono state recuperate o qualche altro calcolo. `Total_Number_of_Reviews` non viene utilizzato nel modello a causa di questa non chiarezza.
+
+5. Sebbene sia presente una colonna `Average_Score` per ogni hotel nell'insieme di dati, si può anche calcolare un punteggio medio (ottenendo la media di tutti i punteggi dei recensori nell'insieme di dati per ogni hotel). Aggiungere una nuova colonna al dataframe con l'intestazione della colonna `Calc_Average_Score` che contiene quella media calcolata. Stampare le colonne `Hotel_Name`, `Average_Score` e `Calc_Average_Score`.
+
+   ```python
+   # definisce una funzione che ottiene una riga ed esegue alcuni calcoli su di essa
+   def get_difference_review_avg(row):
+     return row["Average_Score"] - row["Calc_Average_Score"]
+
+   # 'mean' è la definizione matematica per 'average'
+   df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)
+
+   # Aggiunge una nuova colonna con la differenza tra le due medie di punteggio
+   df["Average_Score_Difference"] = df.apply(get_difference_review_avg, axis = 1)
+
+   # Crea un df senza tutti i duplicati di Hotel_Name (quindi una sola riga per hotel)
+   review_scores_df = df.drop_duplicates(subset = ["Hotel_Name"])
+
+   # Ordina il dataframe per trovare la differnza più bassa e più alta per il punteggio medio
+   review_scores_df = review_scores_df.sort_values(by=["Average_Score_Difference"])
+
+   display(review_scores_df[["Average_Score_Difference", "Average_Score", "Calc_Average_Score", "Hotel_Name"]])
+   ```
+
+   Ci si potrebbe anche chiedere del valore `Average_Score` e perché a volte è diverso dal punteggio medio calcolato. Poiché non è possibile sapere perché alcuni valori corrispondano, ma altri hanno una differenza, in questo caso è più sicuro utilizzare i punteggi delle recensioni a disposizione per calcolare autonomamente la media. Detto questo, le differenze sono solitamente molto piccole, ecco gli hotel con la maggiore deviazione dalla media dell'insieme di dati e dalla media calcolata:
+
+   | Average_Score_Difference | Average_Score | Calc_Average_Score | Hotel_Name (Nome Hotel) |
+   | :----------------------: | :-----------: | :----------------: | ------------------------------------------: |
+   | -0,8 | 7,7 | 8,5 | Best Western Hotel Astoria |
+   | -0,7 | 8,8 | 9,5 | Hotel Stendhal Place Vend me Parigi MGallery |
+   | -0,7 | 7,5 | 8.2 | Mercure Paris Porte d'Orléans |
+   | -0,7 | 7,9 | 8,6 | Renaissance Paris Vendome Hotel |
+   | -0,5 | 7,0 | 7,5 | Hotel Royal Elys es |
+   | ... | ... | ... | ... |
+   | 0.7 | 7,5 | 6.8 | Mercure Paris Op ra Faubourg Montmartre |
+   | 0,8 | 7,1 | 6.3 | Holiday Inn Paris Montparnasse Pasteur |
+   | 0,9 | 6.8 | 5,9 | Villa Eugenia |
+   | 0,9 | 8,6 | 7,7 | MARCHESE Faubourg St Honor Relais Ch teaux |
+   | 1,3 | 7,2 | 5,9 | Kube Hotel Ice Bar |
+
+   Con un solo hotel con una differenza di punteggio maggiore di 1, significa che probabilmente si può ignorare la differenza e utilizzare il punteggio medio calcolato.
+
+6. Calcolare e stampare quante righe hanno la colonna `Negative_Review` valori di "No Negative"
+
+7. Calcolare e stampare quante righe hanno la colonna `Positive_Review` valori di "No Positive"
+
+8. Calcolare e stampare quante righe hanno la colonna `Positive_Review` valori di "No Positive" **e** `Negative_Review` valori di "No Negative"
+
+   ```python
+   # con funzini lambda:
+   start = time.time()
+   no_negative_reviews = df.apply(lambda x: True if x['Negative_Review'] == "No Negative" else False , axis=1)
+   print("Number of No Negative reviews: " + str(len(no_negative_reviews[no_negative_reviews == True].index)))
+
+   no_positive_reviews = df.apply(lambda x: True if x['Positive_Review'] == "No Positive" else False , axis=1)
+   print("Number of No Positive reviews: " + str(len(no_positive_reviews[no_positive_reviews == True].index)))
+
+   both_no_reviews = df.apply(lambda x: True if x['Negative_Review'] == "No Negative" and x['Positive_Review'] == "No Positive" else False , axis=1)
+   print("Number of both No Negative and No Positive reviews: " + str(len(both_no_reviews[both_no_reviews == True].index)))
+   end = time.time()
+   print("Lambdas took " + str(round(end - start, 2)) + " seconds")
+
+   Number of No Negative reviews: 127890
+   Number of No Positive reviews: 35946
+   Number of both No Negative and No Positive reviews: 127
+   Lambdas took 9.64 seconds
+   ```
+
+## Un'altra strada
+
+Un altro modo per contare gli elementi senza Lambda e utilizzare sum per contare le righe:
+
+```python
+# senza funzioni lambda (usando un misto di notazioni per mostrare che si possono usare entrambi)
+start = time.time()
+no_negative_reviews = sum(df.Negative_Review == "No Negative")
+print("Number of No Negative reviews: " + str(no_negative_reviews))
+
+no_positive_reviews = sum(df["Positive_Review"] == "No Positive")
+print("Number of No Positive reviews: " + str(no_positive_reviews))
+
+both_no_reviews = sum((df.Negative_Review == "No Negative") & (df.Positive_Review == "No Positive"))
+print("Number of both No Negative and No Positive reviews: " + str(both_no_reviews))
+
+end = time.time()
+print("Sum took " + str(round(end - start, 2)) + " seconds")
+
+Number of No Negative reviews: 127890
+Number of No Positive reviews: 35946
+Number of both No Negative and No Positive reviews: 127
+Sum took 0.19 seconds
+```
+
+Si potrebbe aver notato che ci sono 127 righe che hanno entrambi i valori "No Negative" e "No Positive" rispettivamente per le colonne `Negative_Review` e `Positive_Review` . Ciò significa che il recensore ha assegnato all'hotel un punteggio numerico, ma ha rifiutato di scrivere una recensione positiva o negativa. Fortunatamente questa è una piccola quantità di righe (127 su 515738, o 0,02%), quindi probabilmente non distorcerà il modello o i risultati in una direzione particolare, ma si potrebbe non aspettarsi che un insieme di dati di recensioni abbia righe con nessuna recensione, quindi vale la pena esplorare i dati per scoprire righe come questa.
+
+Ora che si è esplorato l'insieme di dati, nella prossima lezione si filtreranno i dati e si aggiungerà un'analisi del sentiment.
+
+---
+
+## 🚀 Sfida
+
+Questa lezione dimostra, come visto nelle lezioni precedenti, quanto sia di fondamentale importanza comprendere i dati e le loro debolezze prima di eseguire operazioni su di essi. I dati basati su testo, in particolare, sono oggetto di un attento esame. Esaminare vari insiemi di dati contenenti principalmente testo e vedere se si riesce a scoprire aree che potrebbero introdurre pregiudizi o sentiment distorti in un modello.
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/)
+
+## Revisione e Auto Apprendimento
+
+Seguire [questo percorso di apprendimento su NLP](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-15963-cxa) per scoprire gli strumenti da provare durante la creazione di modelli vocali e di testo.
+
+## Compito
+
+[NLTK](assignment.it.md)
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/assignment.it.md b/6-NLP/4-Hotel-Reviews-1/translations/assignment.it.md
new file mode 100644
index 0000000000..5fd90e11e9
--- /dev/null
+++ b/6-NLP/4-Hotel-Reviews-1/translations/assignment.it.md
@@ -0,0 +1,5 @@
+# NLTK
+
+## Instructions
+
+NLTK is a well-known library for use in computational linguistics and NLP. Take this opportunity to read through the '[NLTK book](https://www.nltk.org/book/)' and try out its exercises. In this ungraded assignment, you will get to know this library more deeply.
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
new file mode 100644
index 0000000000..f6d22b563a
--- /dev/null
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
@@ -0,0 +1,376 @@
+# Analisi del sentiment con recensioni di hotel
+
+Ora che si è  esplorato in dettaglio l'insieme di dati, è il momento di filtrare le colonne e quindi utilizzare le tecniche NLP sull'insieme di dati per ottenere nuove informazioni sugli hotel.
+
+## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/)
+
+### Operazioni di Filtraggio e Analisi del Sentiment
+
+Come probabilmente notato, l'insieme di dati presenta alcuni problemi. Alcune colonne sono piene di informazioni inutili, altre sembrano errate. Se sono corrette, non è chiaro come sono state calcolate e le risposte non possono essere verificate in modo indipendente dai propri calcoli.
+
+## Esercizio: un po' più di elaborazione dei dati
+
+Occorre pulire un po' di più i dati. Si aggiungono colonne che saranno utili in seguito, si modificano i valori in altre colonne e si eliminano completamente determinate colonne.
+
+1. Elaborazione iniziale colonne
+
+   1. Scartare `lat` e `lng`
+
+   2. Sostituire i valori `Hotel_Address` con i seguenti valori (se l'indirizzo contiene lo stesso della città e del paese, si cambia solo con la città e la nazione).
+
+      Queste sono le uniche città e nazioni nell'insieme di dati:
+
+      Amsterdam, Netherlands
+
+      Barcelona, Spain
+
+      London, United Kingdom
+
+      Milan, Italy
+
+      Paris, France
+
+      Vienna, Austria
+
+      ```python
+      def replace_address(row):
+          if "Netherlands" in row["Hotel_Address"]:
+              return "Amsterdam, Netherlands"
+          elif "Barcelona" in row["Hotel_Address"]:
+              return "Barcelona, Spain"
+          elif "United Kingdom" in row["Hotel_Address"]:
+              return "London, United Kingdom"
+          elif "Milan" in row["Hotel_Address"]:        
+              return "Milan, Italy"
+          elif "France" in row["Hotel_Address"]:
+              return "Paris, France"
+          elif "Vienna" in row["Hotel_Address"]:
+              return "Vienna, Austria"
+
+      # Sostituisce tutti gli indirizzi con una forma ridotta più utile
+      df["Hotel_Address"] = df.apply(replace_address, axis = 1)
+      # La somma di value_counts() dovrebbe sommarsi al numero totale recensioni
+      print(df["Hotel_Address"].value_counts())
+      ```
+
+      Ora si possono interrogare i dati a livello di nazione:
+
+      ```python
+      display(df.groupby("Hotel_Address").agg({"Hotel_Name": "nunique"}))
+      ```
+
+      | Hotel_Address | Hotel_Name (Nome Hotel) |
+      | :--------------------- | :--------: |
+      | Amsterdam, Paesi Bassi | 105 |
+      | Barcellona, Spagna | 211 |
+      | Londra, Regno Unito | 400 |
+      | Milano, Italia | 162 |
+      | Parigi, Francia | 458 |
+      | Vienna, Austria | 158 |
+
+2. Elaborazione colonne di meta-recensione dell'hotel
+
+1. Eliminare `Additional_Number_of_Scoring`
+
+1. Sostituire `Total_Number_of_Reviews` con il numero totale di recensioni per quell'hotel che sono effettivamente nell'insieme di dati
+
+1. Sostituire `Average_Score` con il punteggio calcolato via codice
+
+```python
+# Elimina `Additional_Number_of_Scoring`
+df.drop(["Additional_Number_of_Scoring"], axis = 1, inplace=True)
+# Sostituisce `Total_Number_of_Reviews` e `Average_Score` con i propri valori calcolati
+df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')
+df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)
+```
+
+3. Elaborazione delle colonne di recensione
+
+   1. Eliminare `Review_Total_Negative_Word_Counts`, `Review_Total_Positive_Word_Counts`, `Review_Date` e `days_since_review`
+
+   2. Mantenere `Reviewer_Score`, `Negative_Review` e `Positive_Review` così come sono
+
+   3. Conservare i `Tags` per ora
+
+   - Si faranno alcune operazioni di filtraggio aggiuntive sui tag nella prossima sezione, successivamente i tag verranno eliminati
+
+4. Elaborazione delle colonne del recensore
+
+1. Scartare `Total_Number_of_Reviews_Reviewer_Has_Given`
+
+2. Mantenere `Reviewer_Nationality`
+
+### Colonne tag
+
+Le colonne `Tag` sono problematiche in quanto si tratta di un elenco (in formato testo) memorizzato nella colonna. Purtroppo l'ordine e il numero delle sottosezioni in questa colonna non sono sempre gli stessi. È difficile per un essere umano identificare le frasi corrette a cui essere interessato, perché ci sono 515.000 righe e 1427 hotel e ognuno ha opzioni leggermente diverse che un recensore potrebbe scegliere. È qui che la NLP brilla. Si può scansionare il testo, trovare le frasi più comuni e contarle.
+
+Purtroppo non interessano parole singole, ma frasi composte da più parole (es. *Viaggio di lavoro*). L'esecuzione di un algoritmo di distribuzione della frequenza a più parole su così tanti dati (6762646 parole) potrebbe richiedere una quantità straordinaria di tempo, ma senza guardare i dati, sembrerebbe che sia una spesa necessaria. È qui che l'analisi dei dati esplorativi diventa utile, perché si è visto un esempio di tag come `["Business trip", "Solo traveler", "Single Room", "Stayed 5 nights", "Submitted from a mobile device"]` , si può iniziare a chiedersi se è possibile ridurre notevolmente l'elaborazione da fare. Fortunatamente lo è, ma prima occorre seguire alcuni passaggi per accertare i tag di interesse.
+
+### Filtraggio tag
+
+Ricordare che l'obiettivo dell'insieme di dati è aggiungere il sentiment e le colonne che aiuteranno a scegliere l'hotel migliore (per se stessi o forse per un cliente che incarica di creare un bot di raccomandazione dell'hotel). Occorre chiedersi se i tag sono utili o meno nell'insieme di dati finale. Ecco un'interpretazione (se serve l'insieme di dati per altri motivi diversi tag potrebbero rimanere dentro/fuori dalla selezione):
+
+1. Il tipo di viaggio è rilevante e dovrebbe rimanere
+2. Il tipo di gruppo di ospiti è importante e dovrebbe rimanere
+3. Il tipo di camera, suite o monolocale in cui ha soggiornato l'ospite è irrilevante (tutti gli hotel hanno praticamente le stesse stanze)
+4. Il dispositivo su cui è stata inviata la recensione è irrilevante
+5. Il numero di notti in cui il recensore ha soggiornato *potrebbe* essere rilevante se si attribuisce a soggiorni più lunghi un gradimento maggiore per l'hotel, ma è una forzatura e probabilmente irrilevante
+
+In sintesi, si **mantengono 2 tipi di tag e si rimuove il resto**.
+
+Innanzitutto, non si vogliono contare i tag finché non sono in un formato migliore, quindi ciò significa rimuovere le parentesi quadre e le virgolette. Si può fare in diversi modi, ma serve il più veloce in quanto potrebbe richiedere molto tempo per elaborare molti dati. Fortunatamente, pandas ha un modo semplice per eseguire ciascuno di questi passaggi.
+
+```Python
+# Rimuove le parentesi quadre di apertura e chiusura
+df.Tags = df.Tags.str.strip("[']")
+# rimuove anche tutte le virgolette
+df.Tags = df.Tags.str.replace(" ', '", ",", regex = False)
+```
+
+Ogni tag diventa qualcosa come: `Business trip, Solo traveler, Single Room, Stayed 5 nights, Submitted from a mobile device`.
+
+Successivamente si manifesta un problema. Alcune recensioni, o righe, hanno 5 colonne, altre 3, altre 6. Questo è il risultato di come è stato creato l'insieme di dati ed è difficile da risolvere. Si vuole ottenere un conteggio della frequenza di ogni frase, ma sono in ordine diverso in ogni recensione, quindi il conteggio potrebbe essere disattivato e un hotel potrebbe non ricevere un tag assegnato per ciò che meritava.
+
+Si utilizzerà invece l'ordine diverso a proprio vantaggio, perché ogni tag è composto da più parole ma anche separato da una virgola! Il modo più semplice per farlo è creare 6 colonne temporanee con ogni tag inserito nella colonna corrispondente al suo ordine nel tag. Quindi si uniscono le 6 colonne in una grande colonna e si esegue il metodo `value_counts()` sulla colonna risultante. Stampandolo, si vedrà che c'erano 2428 tag univoci. Ecco un piccolo esempio:
+
+| Tag                            | Count  |
+| ------------------------------ | ------ |
+| Leisure trip                   | 417778 |
+| Submitted from a mobile device | 307640 |
+| Couple                         | 252294 |
+| Stayed 1 night                 | 193645 |
+| Stayed 2 nights                | 133937 |
+| Solo traveler                  | 108545 |
+| Stayed 3 nights                | 95821  |
+| Business trip                  | 82939  |
+| Group                          | 65392  |
+| Family with young children     | 61015  |
+| Stayed 4 nights                | 47817  |
+| Double Room                    | 35207  |
+| Standard Double Room           | 32248  |
+| Superior Double Room           | 31393  |
+| Family with older children     | 26349  |
+| Deluxe Double Room             | 24823  |
+| Double or Twin Room            | 22393  |
+| Stayed 5 nights                | 20845  |
+| Standard Double or Twin Room   | 17483  |
+| Classic Double Room            | 16989  |
+| Superior Double or Twin Room   | 13570  |
+| 2 rooms                        | 12393  |
+
+Alcuni dei tag comuni come `Submitted from a mobile device` non sono di alcuna utilità, quindi potrebbe essere una cosa intelligente rimuoverli prima di contare l'occorrenza della frase, ma è un'operazione così veloce che si possono lasciare e ignorare.
+
+### Rimozione della durata dai tag di soggiorno
+
+La rimozione di questi tag è il passaggio 1, riduce leggermente il numero totale di tag da considerare. Notare che non si rimuovono dall'insieme di dati, si sceglie semplicemente di rimuoverli dalla considerazione come valori da contare/mantenere nell'insieme di dati delle recensioni.
+
+| Length of stay   | Count  |
+| ---------------- | ------ |
+| Stayed 1 night   | 193645 |
+| Stayed  2 nights | 133937 |
+| Stayed 3 nights  | 95821  |
+| Stayed  4 nights | 47817  |
+| Stayed 5 nights  | 20845  |
+| Stayed  6 nights | 9776   |
+| Stayed 7 nights  | 7399   |
+| Stayed  8 nights | 2502   |
+| Stayed 9 nights  | 1293   |
+| ...              | ...    |
+
+C'è una grande varietà di camere, suite, monolocali, appartamenti e così via. Significano tutti più o meno la stessa cosa e non sono rilevanti allo scopo, quindi si rimuovono dalla considerazione.
+
+| Type of room                  | Count |
+| ----------------------------- | ----- |
+| Double Room                   | 35207 |
+| Standard  Double Room         | 32248 |
+| Superior Double Room          | 31393 |
+| Deluxe  Double Room           | 24823 |
+| Double or Twin Room           | 22393 |
+| Standard  Double or Twin Room | 17483 |
+| Classic Double Room           | 16989 |
+| Superior  Double or Twin Room | 13570 |
+
+Infine, e questo è delizioso (perché non ha richiesto molta elaborazione), rimarranno i seguenti tag *utili*:
+
+| Tag                                           | Count  |
+| --------------------------------------------- | ------ |
+| Leisure trip                                  | 417778 |
+| Couple                                        | 252294 |
+| Solo  traveler                                | 108545 |
+| Business trip                                 | 82939  |
+| Group (combined with Travellers with friends) | 67535  |
+| Family with young children                    | 61015  |
+| Family  with older children                   | 26349  |
+| With a  pet                                   | 1405   |
+
+Si potrebbe obiettare che `Travellers with friends` (Viaggiatori con amici) è più o meno lo stesso di `Group` (Gruppo), e sarebbe giusto combinare i due come fatto sopra. Il codice per identificare i tag corretti è [il notebook Tags](../solution/1-notebook.ipynb).
+
+Il passaggio finale consiste nel creare nuove colonne per ciascuno di questi tag. Quindi, per ogni riga di recensione, se la colonna `Tag` corrisponde a una delle nuove colonne, aggiungere 1, in caso contrario aggiungere 0. Il risultato finale sarà un conteggio di quanti recensori hanno scelto questo hotel (in aggregato) per, ad esempio, affari o piacere, o per portare un animale domestico, e questa è un'informazione utile quando consiglia un hotel.
+
+```python
+# Elabora Tags in nuove colonne
+# Il file Hotel_Reviews_Tags.py, identifica i tag più importanti
+# Leisure trip, Couple, Solo traveler, Business trip, Group combinato con Travelers with friends,
+# Family with young children, Family with older children, With a pet
+df["Leisure_trip"] = df.Tags.apply(lambda tag: 1 if "Leisure trip" in tag else 0)
+df["Couple"] = df.Tags.apply(lambda tag: 1 if "Couple" in tag else 0)
+df["Solo_traveler"] = df.Tags.apply(lambda tag: 1 if "Solo traveler" in tag else 0)
+df["Business_trip"] = df.Tags.apply(lambda tag: 1 if "Business trip" in tag else 0)
+df["Group"] = df.Tags.apply(lambda tag: 1 if "Group" in tag or "Travelers with friends" in tag else 0)
+df["Family_with_young_children"] = df.Tags.apply(lambda tag: 1 if "Family with young children" in tag else 0)
+df["Family_with_older_children"] = df.Tags.apply(lambda tag: 1 if "Family with older children" in tag else 0)
+df["With_a_pet"] = df.Tags.apply(lambda tag: 1 if "With a pet" in tag else 0)
+
+```
+
+### Salvare il file.
+
+Infine, salvare l'insieme di dati così com'è ora con un nuovo nome.
+
+```python
+df.drop(["Review_Total_Negative_Word_Counts", "Review_Total_Positive_Word_Counts", "days_since_review", "Total_Number_of_Reviews_Reviewer_Has_Given"], axis = 1, inplace=True)
+
+# Salvataggio del nuovo file dati con le colonne calcolate
+print("Saving results to Hotel_Reviews_Filtered.csv")
+df.to_csv(r'../data/Hotel_Reviews_Filtered.csv', index = False)
+```
+
+## Operazioni di Analisi del Sentiment
+
+In questa sezione finale, si applicherà l'analisi del sentiment alle colonne di recensione e si salveranno i risultati in un insieme di dati.
+
+## Esercizio: caricare e salvare i dati filtrati
+
+Tenere presente che ora si sta caricando l'insieme di dati filtrato che è stato salvato nella sezione precedente, **non** quello originale.
+
+```python
+import time
+import pandas as pd
+import nltk as nltk
+from nltk.corpus import stopwords
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+nltk.download('vader_lexicon')
+
+# Carica le recensioni di hotel filtrate dal CSV
+df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')
+
+# Il proprio codice andrà aggiunto qui
+
+
+# Infine ricordarsi di salvare le recensioni di hotel con i nuovi dati NLP aggiunti
+print("Saving results to Hotel_Reviews_NLP.csv")
+df.to_csv(r'../data/Hotel_Reviews_NLP.csv', index = False)
+```
+
+### Rimozione delle stop word
+
+Se si dovesse eseguire l'analisi del sentiment sulle colonne delle recensioni negative e positive, potrebbe volerci molto tempo. Testato su un potente laptop di prova con CPU veloce, ci sono voluti 12 - 14 minuti a seconda della libreria di sentiment utilizzata. È un tempo (relativamente) lungo, quindi vale la pena indagare se può essere accelerato.
+
+Il primo passo è rimuovere le stop word, o parole inglesi comuni che non cambiano il sentiment di una frase. Rimuovendole, l'analisi del sentiment dovrebbe essere eseguita più velocemente, ma non essere meno accurata (poiché le stop word non influiscono sul sentiment, ma rallentano l'analisi).
+
+La recensione negativa più lunga è stata di 395 parole, ma dopo aver rimosso le stop word, è di 195 parole.
+
+Anche la rimozione delle stop word è un'operazione rapida, poiché la rimozione di esse da 2 colonne di recensione su 515.000 righe ha richiesto 3,3 secondi sul dispositivo di test. Potrebbe volerci un po' più o meno tempo a seconda della velocità della CPU del proprio dispositivo, della RAM, del fatto che si abbia o meno un SSD e alcuni altri fattori. La relativa brevità dell'operazione significa che se migliora il tempo di analisi del sentiment, allora vale la pena farlo.
+
+```python
+from nltk.corpus import stopwords
+
+# Carica le recensioni di hotel da CSV
+df = pd.read_csv("../../data/Hotel_Reviews_Filtered.csv")
+
+# Rimuove le stop word - potrebbe essere lento quando c'è molto testo!
+# Ryan Han (ryanxjhan su Kaggle) ha un gran post riguardo al misurare le prestazioni di diversi approcci per la rimozione delle stop word
+# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # si usa l'approccio raccomandato da Ryan
+start = time.time()
+cache = set(stopwords.words("english"))
+def remove_stopwords(review):
+    text = " ".join([word for word in review.split() if word not in cache])
+    return text
+
+# Rimuove le stop word da entrambe le colonne
+df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   
+df.Positive_Review = df.Positive_Review.apply(remove_stopwords)
+```
+
+### Esecuzione dell'analisi del sentiment
+
+Ora si dovrebbe calcolare l'analisi del sentiment per le colonne di recensioni negative e positive e memorizzare il risultato in 2 nuove colonne. Il test del sentiment sarà quello di confrontarlo con il punteggio del recensore per la stessa recensione. Ad esempio, se il sentiment ritiene che la recensione negativa abbia avuto un sentiment pari a 1 (giudizio estremamente positivo) e un sentiment positivo della recensione pari a 1, ma il recensore ha assegnato all'hotel il punteggio più basso possibile, allora il testo della recensione non corrisponde al punteggio, oppure l'analizzatore del sentiment non è stato in grado di riconoscere correttamente il sentiment. Ci si dovrebbe aspettare che alcuni punteggi del sentiment siano completamente sbagliati, e spesso ciò sarà spiegabile, ad esempio la recensione potrebbe essere estremamente sarcastica "Certo che mi è piaciuto dormire in una stanza senza riscaldamento" e l'analizzatore del sentimento pensa che sia un sentimento positivo, anche se un un lettore umano avrebbe rilevato il sarcasmo.
+
+NLTK fornisce diversi analizzatori di sentiment con cui imparare e si possono sostituire e vedere se il sentiment è più o meno accurato. Qui viene utilizzata l'analisi del sentiment di VADER.
+
+> Hutto, CJ & Gilbert, EE (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Ottava Conferenza Internazionale su Weblog e Social Media (ICWSM-14). Ann Arbor, MI, giugno 2014.
+
+```python
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+# Crea l'analizzatore di sentiment vader (ce ne sono altri in NLTK che si possono provare)
+vader_sentiment = SentimentIntensityAnalyzer()
+# Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
+
+# Ci sono tre possibilità di input per un recensore:
+# Potrebbe essere "No Negative", nel qual caso ritorna 0
+# Potrebbe essere "No Positive", nel qual caso ritorna 0
+# Potrebbe essere una recensione, nel qual caso calcola il sentiment
+def calc_sentiment(review):    
+    if review == "No Negative" or review == "No Positive":
+        return 0
+    return vader_sentiment.polarity_scores(review)["compound"]    
+```
+
+Più avanti nel programma, quando si è pronti per calcolare il sentiment, lo si può applicare a ciascuna recensione come segue:
+
+```python
+# Aggiunge una colonna di sentiment negativa e positiva
+print("Calculating sentiment columns for both positive and negative reviews")
+start = time.time()
+df["Negative_Sentiment"] = df.Negative_Review.apply(calc_sentiment)
+df["Positive_Sentiment"] = df.Positive_Review.apply(calc_sentiment)
+end = time.time()
+print("Calculating sentiment took " + str(round(end - start, 2)) + " seconds")
+```
+
+Questo richiede circa 120 secondi sul computer utilizzato, ma varierà per ciascun computer. Se si vogliono stampare i risultati e vedere se il sentiment corrisponde alla recensione:
+
+```python
+df = df.sort_values(by=["Negative_Sentiment"], ascending=True)
+print(df[["Negative_Review", "Negative_Sentiment"]])
+df = df.sort_values(by=["Positive_Sentiment"], ascending=True)
+print(df[["Positive_Review", "Positive_Sentiment"]])
+```
+
+L'ultima cosa da fare con il file prima di utilizzarlo nella sfida è salvarlo! Si dovrrebbe anche considerare di riordinare tutte le nuove colonne in modo che sia facile lavorarci (per un essere umano, è un cambiamento estetico).
+
+```python
+# Riordina le colonne (E' un estetismo ma facilita l'esplorazione successiva dei dati)
+df = df.reindex(["Hotel_Name", "Hotel_Address", "Total_Number_of_Reviews", "Average_Score", "Reviewer_Score", "Negative_Sentiment", "Positive_Sentiment", "Reviewer_Nationality", "Leisure_trip", "Couple", "Solo_traveler", "Business_trip", "Group", "Family_with_young_children", "Family_with_older_children", "With_a_pet", "Negative_Review", "Positive_Review"], axis=1)
+
+print("Saving results to Hotel_Reviews_NLP.csv")
+df.to_csv(r"../data/Hotel_Reviews_NLP.csv", index = False)
+```
+
+Si dovrebbe eseguire l'intero codice per [il notebook di analisi](../solution/3-notebook.ipynb) (dopo aver eseguito [il notebook di filtraggio](../solution/1-notebook.ipynb) per generare il file Hotel_Reviews_Filtered.csv).
+
+Per riepilogare, i passaggi sono:
+
+1. Il file del'insieme di dati originale **Hotel_Reviews.csv** è stato esplorato nella lezione precedente con [il notebook explorer](../../4-Hotel-Reviews-1/solution/notebook.ipynb)
+2. Hotel_Reviews.csv viene filtrato [dal notebook di filtraggio](../solution/1-notebook.ipynb) risultante in **Hotel_Reviews_Filtered.csv**
+3. Hotel_Reviews_Filtered.csv viene elaborato dal [notebook di analisi del sentiment](../solution/3-notebook.ipynb) risultante in **Hotel_Reviews_NLP.csv**
+4. Usare Hotel_Reviews_NLP.csv nella Sfida NLP di seguito
+
+### Conclusione
+
+Quando si è iniziato, si disponeva di un insieme di dati con colonne e dati, ma non tutto poteva essere verificato o utilizzato. Si sono esplorati i dati, filtrato ciò che non serve, convertito i tag in qualcosa di utile, calcolato le proprie medie, aggiunto alcune colonne di sentiment e, si spera, imparato alcune cose interessanti sull'elaborazione del testo naturale.
+
+## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/)
+
+## Sfida
+
+Ora che si è analizzato il proprio insieme di dati per il sentiment, vedere se si possono usare le strategie apprese in questo programma di studi (clustering, forse?) per determinare modelli intorno al sentiment.
+
+## recensione e Auto Apprendimento
+
+Seguire [questo modulo di apprendimento](https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?WT.mc_id=academic-15963-cxa) per saperne di più e utilizzare diversi strumenti per esplorare il sentiment nel testo.
+
+## Compito
+
+[Provare un insieme di dati diverso](assignment.it.md)
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/assignment.it.md b/6-NLP/5-Hotel-Reviews-2/translations/assignment.it.md
new file mode 100644
index 0000000000..dae727b78f
--- /dev/null
+++ b/6-NLP/5-Hotel-Reviews-2/translations/assignment.it.md
@@ -0,0 +1,11 @@
+# Provare un insieme di dati diverso
+
+## Istruzioni
+
+Ora che si è imparato a usare NLTK per assegnare sentiment al testo, provare un insieme di dati diverso. Probabilmente si dovranno elaborare dei dati attorno ad esso, quindi creare un notebook e documentare il proprio processo di pensiero. Cosa si è scoperto?
+
+## Rubrica
+
+| Criteri | Ottimo | Adeguato | Necessita miglioramento |
+| -------- | ----------------------------------------------------------------------------------------------------------------- | ----------------------------------------- | ---------------------- |
+|          | Vengono presentati un notebook completo e un insieme di dati con celle ben documentate che spiegano come viene assegnato il sentiment | Il notebook manca di buone spiegazioni | Il notebook è difettoso |
diff --git a/6-NLP/translations/README.it.md b/6-NLP/translations/README.it.md
new file mode 100644
index 0000000000..13eb3226eb
--- /dev/null
+++ b/6-NLP/translations/README.it.md
@@ -0,0 +1,24 @@
+# Iniziare con l'elaborazione del linguaggio naturale
+
+L'elaborazione del linguaggio naturale, NLP, è un sottocampo dell'intelligenza artificiale. L'intero campo è volto ad aiutare le macchine a comprendere ed elaborare il linguaggio umano. Questo può quindi essere utilizzato per eseguire attività come il controllo ortografico o la traduzione automatica.
+
+## Argomento regionale: lingue e letterature europee e hotel romantici d'Europa ��
+
+In questa sezione del programma di studi, verrà presentato uno degli usi più diffusi di machine learning: l'elaborazione del linguaggio naturale (NLP). Derivato dalla linguistica computazionale, questa categoria di intelligenza artificiale è il ponte tra umani e macchine tramite la comunicazione vocale o testuale.
+
+In queste lezioni si impareranno le basi di NLP costruendo piccoli bot conversazionali per imparare come machine learning aiuti a rendere queste conversazioni sempre più "intelligenti". Si viaggerà indietro nel tempo, chiacchierando con Elizabeth Bennett e Mr. Darcy dal romanzo classico di Jane Austen, **Orgoglio e pregiudizio**, pubblicato nel 1813. Quindi, si approfondiranno le proprie conoscenze imparando l'analisi del sentiment tramite le recensioni di hotel in Europa.
+
+![Libro e tè di Orgoglio e Pregiudizio](../images/p&p.jpg)
+> Foto di <a href="https://unsplash.com/@elaineh?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Elaine Howlin</a> su <a href="https://unsplash.com/s/photos/pride-and-prejudice?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+
+## Lezioni
+
+1. [Introduzione all'elaborazione del linguaggio naturale](../1-Introduction-to-NLP/translations/README.it.md)
+2. [Compiti e tecniche comuni di NLP](../2-Tasks/translations/README.it.md)
+3. [Traduzione e analisi del sentiment con machine learning](../3-Translation-Sentiment/translations/README.it.md)
+4. [Preparazione dei dati](../4-Hotel-Reviews-1/translations/README.it.md)
+5. [NLTK per l'analisi del sentiment](../5-Hotel-Reviews-2/translations/README.it.md)
+
+## Crediti
+
+Queste lezioni sull'elaborazione del linguaggio naturale sono state scritte con ☕ da [Stephen Howell](https://twitter.com/Howell_MSFT)

From cf979a301bbce5199673748f8c39007e0f759c2d Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Fri, 6 Aug 2021 16:55:55 -0300
Subject: [PATCH 351/368] Update
 1-Introduction/2-history-of-ML/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 1-Introduction/2-history-of-ML/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index 184978bb52..c5154176b7 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -110,7 +110,7 @@ Aquí hay elementos para ver y escuchar:
 
 [Este podcast donde Amy Boyd habla sobre la evolución de la IA](http://runasradio.com/Shows/Show/739)
 
-[![La historia de la AI por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La historia de la AI por Amy Boyd")
+[![La historia de la IA por Amy Boyd](https://img.youtube.com/vi/EJt3_bFYKss/0.jpg)](https://www.youtube.com/watch?v=EJt3_bFYKss "La historia de la IA por Amy Boyd")
 
 ## Asignación
 

From a76aee53ff2b65d58174bc5cc363360339e7241f Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Sat, 7 Aug 2021 18:41:38 +0900
Subject: [PATCH 352/368] feat: add ja 3.1 README

Refs #149
---
 3-Web-App/1-Web-App/translations/README.ja.md | 345 ++++++++++++++++++
 1 file changed, 345 insertions(+)
 create mode 100644 3-Web-App/1-Web-App/translations/README.ja.md

diff --git a/3-Web-App/1-Web-App/translations/README.ja.md b/3-Web-App/1-Web-App/translations/README.ja.md
new file mode 100644
index 0000000000..edfaa34260
--- /dev/null
+++ b/3-Web-App/1-Web-App/translations/README.ja.md
@@ -0,0 +1,345 @@
+# 機械学習モデルを使����Webアプリを構築�る
+
+��講義�����世界�も�����データセットを使��機械学習モデルを学習����。NUFORC�データベース�登録�れ��る「�去100年�UFO目撃情報���。
+
+����学�内容�以下�通り��。
+
+- 学習��モデルを「塩漬����る方法
+- モデルをFlaskアプリ�使�方法
+
+引�続�ノートブックを使��データ�クリーニングやモデル�学習を行������ら�一歩進ん�モデルを「野生�����りWebアプリ�使��を検討�る��も�能��。
+
+�������Flaskを使��Webアプリを構築�る必���り��。
+
+## [講義���テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17?loc=ja)
+
+## アプリ�構築
+
+機械学習モデルを使����Webアプリを構築�る方法������り��。Webアーキテク�ャ�モデル�学習方法�影響を与�る�能性��り��。データサイエンスグループ�学習��モデルをアプリ�使用�る����業務�����任�れ��る状�をイメージ������。
+
+### 検討事項
+
+�������質�����ん�り��。
+
+- **Webアプリ���？�れ�もモ�イルアプリ���？** モ�イルアプリを構築���る場�や�IoT�環境�モデルを使�必���る場���[TensorFlow Lite](https://www.tensorflow.org/lite/) を使用���Android���iOSアプリ�モデルを使��������。
+- **モデル�����存����？** クラウド��ょ��？�れ�もローカル��ょ��？
+- **オフライン��サ�ート。** アプリ�オフライン�動作�る必���り���？
+- **モデル�学習����よ��技術�使�れ�����？** �択�れ�技術�使用���れ�����ツール�影響を与�る�能性��り��。
+    - **Tensor flow を使���る。** 例�� TensorFlow を使��モデルを学習���る場�� [TensorFlow.js](https://www.tensorflow.org/js/) を使���Webアプリ�使用��るよ�� TensorFlow モデルを変��る機能を��エコシステム��供�����。
+    - **PyTorchを使���る。** [PyTorch](https://pytorch.org/) ���ライブラリを使用��モデルを構築���る場��[ONNX](https://onnx.ai/) (Open Neural Network Exchange) 形��出力���JavaScript �Webアプリ� [Onnx Runtime](https://www.onnxruntime.ai/) を使用�る����択肢��り��。���択肢��Scikit-learn �学習��モデルを使�今後�講義�調���。
+    - **Lobe.ai ��� Azure Custom Vision を使���る。** [Lobe.ai](https://lobe.ai/) や [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) �よ��機械学習SaaS (Software as a Service) システムを使用��モデルを学習���る場����種�ソフトウェア�多��プラットフォーム���モデルを出力�る方法を用�������れ��クラウド上�オンラインアプリケーション�らリクエスト�れるよ��専用APIを構築�る��も��れ��。
+
+���ウェブブラウザ上�モデルを学習�る�����るFlask�Webアプリを構築�る��も����。JavaScript �場��も TensorFlow.js を使����実�����。
+
+����場��Pythonベース�ノートブックを今��使用�������学習��モデルを��よ��ノートブック�らPython�構築�れ�Webアプリ�読�る形��出力�る���必��手順を探�����ょ�。
+
+## ツール
+
+����作業��2��ツール�必���。Flask�Pickle����らもPython上�動作���。
+
+✅ [Flask](https://palletsprojects.com/p/flask/) ��？制作者�よ��「マイクロフレームワーク��定義�れ��るFlask��Pythonを使��Webフレームワーク�基本機能��Webページを構築�る���テンプレートエンジンを�供�����。Flask��構築を練習�る��� [��学習モジュール](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) を見�������。
+
+✅ [Pickle](https://docs.python.org/3/library/pickle.html) ��？Pickle 🥒 ��Python�オブジェクト構造をシリアライズ・デシリアライズ�るPythonモジュール��。モデルを「塩漬����る��Web�使用�る�����構造をシリアライズ��り平�化��り���。pickle�本質的�安全�������ファイル� 'un-pickle' を促�れ�際�注�������。塩漬��れ�ファイル�末尾� `.pkl` ��り��。
+
+## 演習 - データをクリーニング�る
+
+��講義���[NUFORC](https://nuforc.org) (The National UFO Reporting Center) �集��8万件�UFO目撃情報�データを使���。��データ���UFO�目撃情報�関�る興味深�記述��り��。例��以下�よ��も���。
+
+- **長�記述�例。** 「夜��原を照ら�光線�ら男��れ�Texas Instruments ��車場�����走���
+- **短�記述�例。** 「���を光�追�������
+
+[ufos.csv](../data/ufos.csv) �スプレッドシート���目撃�れ�場所�都市 (`city`)�州 (`state`)�国 (`country`)�物体�形状 (`shape`)�緯度 (`latitude`)�経度 (`longitude`) ���列���れ����。
+
+��講義��ん��る空� [ノートブック](../notebook.ipynb) ��以下�手順�従������。
+
+1. �回�講義�行��よ�� `pandas`�`matplotlib`�`numpy` をイン�ート��UFO�スプレッドシートをイン�ート������。サンプル�データセットを見る�������。
+
+    ```python
+    import pandas as pd
+    import numpy as np
+    
+    ufos = pd.read_csv('./data/ufos.csv')
+    ufos.head()
+    ```
+
+1. UFO�データを新��タイトル����データフレーム�変�������。���`Country` 属性�一��値を確�������。
+
+    ```python
+    ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})
+    
+    ufos.Country.unique()
+    ```
+
+1. ����null値を���削除��1~60秒�目撃情報��を読�込む���処����データ�を減ら��������。
+
+    ```python
+    ufos.dropna(inplace=True)
+    
+    ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]
+    
+    ufos.info()
+    ```
+
+1. Scikit-learn � `LabelEncoder` ライブラリをイン�ート���国�文字列値を数値�変�������。
+
+   ✅ LabelEncoder �データをアルファベット順�エンコード���。
+
+    ```python
+    from sklearn.preprocessing import LabelEncoder
+    
+    ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])
+    
+    ufos.head()
+    ```
+
+    データ�以下�よ���り��。
+
+    ```output
+    	Seconds	Country	Latitude	Longitude
+    2	20.0	3		53.200000	-2.916667
+    3	20.0	4		28.978333	-96.645833
+    14	30.0	4		35.823889	-80.253611
+    23	60.0	4		45.582778	-122.352222
+    24	3.0		3		51.783333	-0.783333
+    ```
+
+## 演習 - モデルを構築�る
+
+�れ�データを訓練グループ�テストグループ�分��モデルを学習�る準備������。
+
+1. Xベクトル���学習���3��特徴を�択��Yベクトル�� `Country` を指定���。`Seconds`�`Latitude`�`Longitude` を入力��国�IDを�得�る������。
+
+    ```python
+    from sklearn.model_selection import train_test_split
+    
+    Selected_features = ['Seconds','Latitude','Longitude']
+    
+    X = ufos[Selected_features]
+    y = ufos['Country']
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    ```
+
+1. ロジスティック回帰を使��モデルを学習������。
+
+    ```python
+    from sklearn.metrics import accuracy_score, classification_report
+    from sklearn.linear_model import LogisticRegression
+    model = LogisticRegression()
+    model.fit(X_train, y_train)
+    predictions = model.predict(X_test)
+    
+    print(classification_report(y_test, predictions))
+    print('Predicted labels: ', predictions)
+    print('Accuracy: ', accuracy_score(y_test, predictions))
+    ```
+
+国 (`Country`) �緯度・経度 (`Latitude/Longitude`) �相関���る��当然����精度�悪�����。**（約95%）**
+
+緯度 (`Latitude`) �経度 (`Longitude`) �ら国 (`Country`) を推測�る�����る���作���モデル�画期的�も����り��ん��クリーニング��出力��生�データ�ら学習を行����モデルをWebアプリ�使用���る良�練習���り��。
+
+## 演習 - モデルを「塩漬����る
+
+����よ�よモデルを「塩漬��������ょ���れ�数行�コード�実行����。「塩漬�����後����モデルを読�込ん��秒・緯度・経度を�むサンプルデータ��列�テスト������。
+
+```python
+import pickle
+model_filename = 'ufo-model.pkl'
+pickle.dump(model, open(model_filename,'wb'))
+
+model = pickle.load(open('ufo-model.pkl','rb'))
+print(model.predict([[50,44,-12]]))
+```
+
+モデル�イギリス�国番���る **「3�** を返���。��ら���👽
+
+## 演習 - Flaskアプリを構築�る
+
+�れ�Flaskアプリを構築��モデルを呼�出������るよ���り��れ���よ���果を返�����視覚的�より��りや��方法��。
+
+1. ���_ufo-model.pkl_ ファイル� _notebook.ipynb_ ファイル�存在�る場所� **web-app** ���フォルダを作�������。
+
+1. ��フォルダ�中���ら�3��フォルダを作�������。**css** ���フォルダを�む **static** ��**templates** ��。以下�よ��ファイル�ディレクトリ�����る����。
+
+    ```output
+    web-app/
+      static/
+        css/
+      templates/
+    notebook.ipynb
+    ufo-model.pkl
+    ```
+
+    ✅ 完���アプリを見る���solution フォルダを�照������。
+
+1. _web-app_ フォルダ�中�作��る最��ファイル� **requirements.txt** ��。JavaScript アプリ���る _package.json_ ��様����ファイル�アプリ�必���存関係をリスト���も���。**requirements.txt** �以下�行を追加������。
+
+    ```text
+    scikit-learn
+    pandas
+    numpy
+    flask
+    ```
+
+1. 次��_web-app_ �移動�����ファイルを実行���。
+
+    ```bash
+    cd web-app
+    ```
+
+1. _requirements.txt_ �記載�れ��るライブラリをインストール�る����ターミナル� `pip install` �入力������。
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+1. アプリを完���る�����ら�3��ファイルを作��る準備�整����。
+
+    1. ルート� **app.py** を作�������。
+    2. _templates_ ディレクトリ� **index.html** を作�������。
+    3. _static/css_ ディレクトリ� **styles.css** を作�������。
+
+1. 以下�スタイル� _styles.css_ ファイルを構築������。
+
+    ```css
+    body {
+    	width: 100%;
+    	height: 100%;
+    	font-family: 'Helvetica';
+    	background: black;
+    	color: #fff;
+    	text-align: center;
+    	letter-spacing: 1.4px;
+    	font-size: 30px;
+    }
+    
+    input {
+    	min-width: 150px;
+    }
+    
+    .grid {
+    	width: 300px;
+    	border: 1px solid #2d2d2d;
+    	display: grid;
+    	justify-content: center;
+    	margin: 20px auto;
+    }
+    
+    .box {
+    	color: #fff;
+    	background: #2d2d2d;
+    	padding: 12px;
+    	display: inline-block;
+    }
+    ```
+
+1. 次� _index.html_ を構築������。
+
+    ```html
+    <!DOCTYPE html>
+    <html>
+      <head>
+        <meta charset="UTF-8">
+        <title>🛸 UFO Appearance Prediction! 👽</title>
+        <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
+      </head>
+    
+      <body>
+        <div class="grid">
+    
+          <div class="box">
+    
+            <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
+    
+            <form action="{{ url_for('predict')}}" method="post">
+              <input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
+              <input type="text" name="latitude" placeholder="Latitude" required="required" />
+              <input type="text" name="longitude" placeholder="Longitude" required="required" />
+              <button type="submit" class="btn">Predict country where the UFO is seen</button>
+            </form>
+    
+            <p>{{ prediction_text }}</p>
+    
+          </div>
+    
+        </div>
+    
+      </body>
+    </html>
+    ```
+
+    ��ファイル�テンプレートを見����ょ�。予測テキスト�よ��アプリ�ら渡�れ�変数を� `{{}}` ���「マスタッシュ�構文�囲ん��る���注目������。���`/predict` �パス�対��予測を�信�るフォームも�り��。
+
+    ����モデルを使用��予測値を表示�るpythonファイルを構築�る準備������。
+
+1. `app.py` �以下を追加������。
+
+    ```python
+    import numpy as np
+    from flask import Flask, request, render_template
+    import pickle
+    
+    app = Flask(__name__)
+    
+    model = pickle.load(open("../ufo-model.pkl", "rb"))
+    
+    
+    @app.route("/")
+    def home():
+        return render_template("index.html")
+    
+    
+    @app.route("/predict", methods=["POST"])
+    def predict():
+    
+        int_features = [int(x) for x in request.form.values()]
+        final_features = [np.array(int_features)]
+        prediction = model.predict(final_features)
+    
+        output = prediction[0]
+    
+        countries = ["Australia", "Canada", "Germany", "UK", "US"]
+    
+        return render_template(
+            "index.html", prediction_text="Likely country: {}".format(countries[output])
+        )
+    
+    
+    if __name__ == "__main__":
+        app.run(debug=True)
+    ```
+
+    > 💡 ヒント: Flaskを使��Webアプリを実行�る際� [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) を加�る��サー�を�起動����もアプリ�加��変更�����映�れ��。注��本番アプリ����モードを有効���������。
+
+`python app.py` も��� `python3 app.py` を実行�る��Webサー��ローカル�起動��UFO����目撃�れ���������質��対�る答�を�短�フォーム�記入�る���得られ��。
+
+���� `app.py` を見����ょ�。
+
+1. 最����存関係�読�込�れ�アプリ�起動���。
+1. 次��モデル�読�込�れ��。
+1. 次��ホーム�パス� index.html �レンダリング�れ��。
+
+`/predict` �パス�フォームを�信�る���������起�り��。
+
+1. フォーム�変数�集�られ�numpy��列�変��れ��。�れら�モデル��られ�予測�返�れ��。
+2. 表示����国��予測�れ�コード�ら読�や��文字列��レンダリング�れ��index.html ��り返�れ�後�テンプレート�中�レンダリング�れ��。
+
+��よ���Flask�pickle�れ�モデルを使���比較的簡���。一番難�����予測を得る���モデル��ら��れ��ら��データ���よ��形を���る�を�解�る����。�れ�モデル���よ��学習�れ���より��。今回�場���予測を得る���入力���データ�3��り��。
+
+プロ��場���モデルを学習�る人���れをWebやモ�イルアプリ�使用�る人��間��良好�コミュニケーション�必���る�����り��。今回����一人�人間��り��れ�������
+
+---
+
+## 🚀 �ャレンジ
+
+ノートブック�作業��モデルをFlaskアプリ�イン�ート�る代�り��Flaskアプリ�中�モデルをトレーニング�る�������。��ら�データをクリーニング��後��り����ノートブック内�Pythonコードを変����アプリ内� `train` ���パス�モデルを学習��������。��方法を採用�る���長所�短所�何��ょ��？
+
+## [講義後��テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18?loc=ja)
+
+## 振り返り�自主学習
+
+機械学習モデルを使用�るWebアプリを構築�る方法����ん�り��。JavaScript やPythonを使��機械学習を活用�るWebアプリを構築�る方法を挙������。アーキテク�ャ�関�る検討: モデル�アプリ内�置�����ょ��？�れ�もクラウド�置�����ょ��？後者�場����よ��アクセス�る��ょ��？機械学習を使��Webソリューション�アーキテク�ャモデルを�������。
+
+## 課題
+
+[��モデルを試�](assignment.ja.md)

From 561f6bec009fb8e05a7731894516f13a04ea74de Mon Sep 17 00:00:00 2001
From: Foo-x <www.sand.x@gmail.com>
Date: Sun, 8 Aug 2021 18:14:59 +0900
Subject: [PATCH 353/368] style: clean 3.1 README

---
 3-Web-App/1-Web-App/README.md | 112 +++++++++++++++++-----------------
 1 file changed, 55 insertions(+), 57 deletions(-)

diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index 2e409e78d5..fae04026d5 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -19,15 +19,15 @@ There are several ways to build web apps to consume machine learning models. You
 
 ### Considerations
 
-There are many questions you need to ask: 
+There are many questions you need to ask:
 
 - **Is it a web app or a mobile app?** If you are building a mobile app or need to use the model in an IoT context, you could use [TensorFlow Lite](https://www.tensorflow.org/lite/) and use the model in an Android or iOS app.
-- **Where will the model reside**? In the cloud or locally?
-- **Offline support**. Does the app have to work offline?
+- **Where will the model reside?** In the cloud or locally?
+- **Offline support.** Does the app have to work offline?
 - **What technology was used to train the model?** The chosen technology may influence the tooling you need to use.
-   - **Using Tensor flow**. If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).  
-   - **Using PyTorch**.  If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model.
-   - **Using Lobe.ai or Azure Custom vision**. If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
+    - **Using Tensor flow.** If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).
+    - **Using PyTorch.** If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model.
+    - **Using Lobe.ai or Azure Custom Vision.** If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
 
 You also have the opportunity to build an entire Flask web app that would be able to train the model itself in a web browser. This can also be done using TensorFlow.js in a JavaScript context.
 
@@ -37,7 +37,7 @@ For our purposes, since we have been working with Python-based notebooks, let's
 
 For this task, you need two tools: Flask and Pickle, both of which run on Python.
 
-✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages.  Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) to practice building with Flask.
+✅ What's [Flask](https://palletsprojects.com/p/flask/)? Defined as a 'micro-framework' by its creators, Flask provides the basic features of web frameworks using Python and a templating engine to build web pages. Take a look at [this Learn module](https://docs.microsoft.com/learn/modules/python-flask-build-ai-web-app?WT.mc_id=academic-15963-cxa) to practice building with Flask.
 
 ✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`.
 
@@ -45,12 +45,12 @@ For this task, you need two tools: Flask and Pickle, both of which run on Python
 
 In this lesson you'll use data from 80,000 UFO sightings, gathered by [NUFORC](https://nuforc.org) (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:
 
-- **Long example description**. "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
-- **Short example description**. "the lights chased us".
+- **Long example description.** "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
+- **Short example description.** "the lights chased us".
 
-The [ufos.csv](./data/ufos.csv) spreadsheet includes columns about the `city`, `state` and `country` where the sighting occurred, the object's `shape` and its `latitude` and `longitude`. 
+The [ufos.csv](./data/ufos.csv) spreadsheet includes columns about the `city`, `state` and `country` where the sighting occurred, the object's `shape` and its `latitude` and `longitude`.
 
-In the blank [notebook](notebook.ipynb) included in this lesson: 
+In the blank [notebook](notebook.ipynb) included in this lesson:
 
 1. import `pandas`, `matplotlib`, and `numpy` as you did in previous lessons and import the ufos spreadsheet. You can take a look at a sample data set:
 
@@ -58,7 +58,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
     import pandas as pd
     import numpy as np
     
-    ufos = pd.read_csv('../data/ufos.csv')
+    ufos = pd.read_csv('./data/ufos.csv')
     ufos.head()
     ```
 
@@ -82,7 +82,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
 
 1. Import Scikit-learn's `LabelEncoder` library to convert the text values for countries to a number:
 
-   ✅ LabelEncoder encodes data alphabetically
+    ✅ LabelEncoder encodes data alphabetically
 
     ```python
     from sklearn.preprocessing import LabelEncoder
@@ -96,16 +96,16 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
 
     ```output
     	Seconds	Country	Latitude	Longitude
-    2	20.0	3	    53.200000	-2.916667
-    3	20.0	4	    28.978333	-96.645833
-    14	30.0	4	    35.823889	-80.253611
-    23	60.0	4	    45.582778	-122.352222
-    24	3.0	    3	    51.783333	-0.783333
+    2	20.0	3		53.200000	-2.916667
+    3	20.0	4		28.978333	-96.645833
+    14	30.0	4		35.823889	-80.253611
+    23	60.0	4		45.582778	-122.352222
+    24	3.0		3		51.783333	-0.783333
     ```
 
 ## Exercise - build your model
 
-Now you can get ready to train a model by diving the data into the training and testing group. 
+Now you can get ready to train a model by dividing the data into the training and testing group.
 
 1. Select the three features you want to train on as your X vector, and the y vector will be the `Country`. You want to be able to input `Seconds`, `Latitude` and `Longitude` and get a country id to return.
 
@@ -123,7 +123,7 @@ Now you can get ready to train a model by diving the data into the training and
 1. Train your model using logistic regression:
 
     ```python
-    from sklearn.metrics import accuracy_score, classification_report 
+    from sklearn.metrics import accuracy_score, classification_report
     from sklearn.linear_model import LogisticRegression
     model = LogisticRegression()
     model.fit(X_train, y_train)
@@ -159,7 +159,7 @@ Now you can build a Flask app to call your model and return similar results, but
 
 1. Start by creating a folder called **web-app** next to the _notebook.ipynb_ file where your _ufo-model.pkl_ file resides.
 
-1. In that folder create three more folders: **static**, with a folder **css** inside it, and **templates`**. You should now have the following files and directories:
+1. In that folder create three more folders: **static**, with a folder **css** inside it, and **templates**. You should now have the following files and directories:
 
     ```output
     web-app/
@@ -168,11 +168,11 @@ Now you can build a Flask app to call your model and return similar results, but
       templates/
     notebook.ipynb
     ufo-model.pkl
-    ``` 
+    ```
 
-   ✅ Refer to the solution folder for a view of the finished app
+    ✅ Refer to the solution folder for a view of the finished app
 
-1. The first file to create in _web-app_ folder is **requirements.txt** file. Like _package.json_ in a JavaScript app, this file lists dependencies required by the app. In **requirements.txt** add the lines: 
+1. The first file to create in _web-app_ folder is **requirements.txt** file. Like _package.json_ in a JavaScript app, this file lists dependencies required by the app. In **requirements.txt** add the lines:
 
     ```text
     scikit-learn
@@ -183,19 +183,19 @@ Now you can build a Flask app to call your model and return similar results, but
 
 1. Now, run this file by navigating to _web-app_:
 
-   ```bash
-   cd web-app
-   ```
+    ```bash
+    cd web-app
+    ```
 
 1. In your terminal type `pip install`, to install the libraries listed in _requirements.txt_:
 
-   ```bash
-   pip install -r requirements.txt
-   ```
+    ```bash
+    pip install -r requirements.txt
+    ```
 
 1. Now, you're ready to create three more files to finish the app:
 
-    1. Create **app.py** in the root
+    1. Create **app.py** in the root.
     2. Create **index.html** in _templates_ directory.
     3. Create **styles.css** in _static/css_ directory.
 
@@ -238,33 +238,33 @@ Now you can build a Flask app to call your model and return similar results, but
     ```html
     <!DOCTYPE html>
     <html>
-    <head>
-      <meta charset="UTF-8">
-      <title>🛸 UFO Appearance Prediction! 👽</title>
-      <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}"> 
-    </head>
+      <head>
+        <meta charset="UTF-8">
+        <title>🛸 UFO Appearance Prediction! 👽</title>
+        <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
+      </head>
     
-    <body>
-     <div class="grid">
+      <body>
+        <div class="grid">
     
-      <div class="box">
+          <div class="box">
     
-      <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
+            <p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
     
-        <form action="{{ url_for('predict')}}" method="post">
-        	<input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
-          <input type="text" name="latitude" placeholder="Latitude" required="required" />
-    		  <input type="text" name="longitude" placeholder="Longitude" required="required" />
-          <button type="submit" class="btn">Predict country where the UFO is seen</button>
-        </form>
+            <form action="{{ url_for('predict')}}" method="post">
+              <input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
+              <input type="text" name="latitude" placeholder="Latitude" required="required" />
+              <input type="text" name="longitude" placeholder="Longitude" required="required" />
+              <button type="submit" class="btn">Predict country where the UFO is seen</button>
+            </form>
     
-      
-       <p>{{ prediction_text }}</p>
+            <p>{{ prediction_text }}</p>
     
-     </div>
-    </div>
+          </div>
     
-    </body>
+        </div>
+    
+      </body>
     </html>
     ```
 
@@ -309,7 +309,7 @@ Now you can build a Flask app to call your model and return similar results, but
         app.run(debug=True)
     ```
 
-   > 💡 Tip: when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
+    > 💡 Tip: when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
 
 If you run `python app.py` or `python3 app.py` - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!
 
@@ -324,13 +324,13 @@ On the `/predict` route, several things happen when the form is posted:
 1. The form variables are gathered and converted to a numpy array. They are then sent to the model and a prediction is returned.
 2. The Countries that we want displayed are re-rendered as readable text from their predicted country code, and that value is sent back to index.html to be rendered in the template.
 
-Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction. 
+Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction.
 
 In a professional setting, you can see how good communication is necessary between the folks who train the model and those who consume it in a web or mobile app. In our case, it's only one person, you!
 
 ---
 
-## 🚀 Challenge: 
+## 🚀 Challenge:
 
 Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
 
@@ -340,8 +340,6 @@ Instead of working in a notebook and importing the model to the Flask app, you c
 
 There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.
 
-## Assignment 
+## Assignment
 
 [Try a different model](assignment.md)
-
-

From 95faf1b7cab3a9ea1d88cc784c817a2366dae810 Mon Sep 17 00:00:00 2001
From: R-icntay <63848664+R-icntay@users.noreply.github.com>
Date: Mon, 9 Aug 2021 16:42:11 +0300
Subject: [PATCH 354/368] Suppress warnings for pacman and resize images for
 lesson_1-R.ipynb

---
 .../1-Tools/solution/lesson_1-R.ipynb         | 395 +++++++++---------
 1 file changed, 200 insertions(+), 195 deletions(-)

diff --git a/2-Regression/1-Tools/solution/lesson_1-R.ipynb b/2-Regression/1-Tools/solution/lesson_1-R.ipynb
index cba2408151..ef28c30d16 100644
--- a/2-Regression/1-Tools/solution/lesson_1-R.ipynb
+++ b/2-Regression/1-Tools/solution/lesson_1-R.ipynb
@@ -1,6 +1,6 @@
 {
   "nbformat": 4,
-  "nbformat_minor": 0,
+  "nbformat_minor": 2,
   "metadata": {
     "colab": {
       "name": "lesson_1-R.ipynb",
@@ -19,37 +19,39 @@
   "cells": [
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "YJUHCXqK57yz"
-      },
       "source": [
         "#Build a regression model: Get started with R and Tidymodels for regression models"
-      ]
+      ],
+      "metadata": {
+        "id": "YJUHCXqK57yz"
+      }
     },
     {
       "cell_type": "markdown",
+      "source": [
+        "## Introduction to Regression - Lesson 1\r\n",
+        "\r\n",
+        "#### Putting it into perspective\r\n",
+        "\r\n",
+        "✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use `linear regression`, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use `logistic regression`. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.\r\n",
+        "\r\n",
+        "In this section, you will work with a [small dataset about diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.\r\n",
+        "\r\n",
+        "That said, let's get started on this task!\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/encouRage.jpg\"\r\n",
+        "   width=\"630\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
+        "\r\n",
+        "<!--![Artwork by \\@allison_horst](../images/encouRage.jpg)<br>Artwork by @allison_horst-->"
+      ],
       "metadata": {
         "id": "LWNNzfqd6feZ"
-      },
-      "source": [
-        "## Introduction to Regression - Lesson 1\n",
-        "\n",
-        "#### Putting it into perspective\n",
-        "\n",
-        "✅ There are many types of regression methods, and which one you pick depends on the answer you're looking for. If you want to predict the probable height for a person of a given age, you'd use `linear regression`, as you're seeking a **numeric value**. If you're interested in discovering whether a type of cuisine should be considered vegan or not, you're looking for a **category assignment** so you would use `logistic regression`. You'll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.\n",
-        "\n",
-        "In this section, you will work with a [small dataset about diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.\n",
-        "\n",
-        "That said, let's get started on this task!\n",
-        "\n",
-        "![Artwork by \\@allison_horst](../images/encouRage.jpg)<br>Artwork by @allison_horst"
-      ]
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "FIo2YhO26wI9"
-      },
       "source": [
         "## 1. Loading up our tool set\n",
         "\n",
@@ -64,62 +66,62 @@
         "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
         "\n",
         "The script below checks whether you have the packages required to complete this module and installs them for you in case some are missing."
-      ]
+      ],
+      "metadata": {
+        "id": "FIo2YhO26wI9"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "cIA9fz9v7Dss",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
-      },
+      "execution_count": 2,
       "source": [
-        "if (!require(\"pacman\")) install.packages(\"pacman\")\n",
+        "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
         "pacman::p_load(tidyverse, tidymodels)"
       ],
-      "execution_count": 2,
       "outputs": [
         {
           "output_type": "stream",
+          "name": "stderr",
           "text": [
             "Loading required package: pacman\n",
             "\n"
-          ],
-          "name": "stderr"
+          ]
         }
-      ]
+      ],
+      "metadata": {
+        "id": "cIA9fz9v7Dss",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "gpO_P_6f9WUG"
-      },
       "source": [
         "Now, let's load these awesome packages and make them available in our current R session.(This is for mere illustration, `pacman::p_load()` already did that for you)"
-      ]
+      ],
+      "metadata": {
+        "id": "gpO_P_6f9WUG"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "NLMycgG-9ezO"
-      },
+      "execution_count": null,
       "source": [
-        "# load the core Tidyverse packages\n",
-        "library(tidyverse)\n",
-        "\n",
-        "# load the core Tidymodels packages\n",
-        "library(tidymodels)\n"
+        "# load the core Tidyverse packages\r\n",
+        "library(tidyverse)\r\n",
+        "\r\n",
+        "# load the core Tidymodels packages\r\n",
+        "library(tidymodels)\r\n"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "NLMycgG-9ezO"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "KM6iXLH996Cl"
-      },
       "source": [
         "## 2. The diabetes dataset\n",
         "\n",
@@ -156,34 +158,34 @@
         "Before going any further, let's also introduce something you will encounter often in R code ��: the pipe operator `%>%`\n",
         "\n",
         "The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying \"and then\" in your code."
-      ]
+      ],
+      "metadata": {
+        "id": "KM6iXLH996Cl"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "Z1geAMhM-bSP"
-      },
+      "execution_count": null,
       "source": [
-        "# Import the data set\n",
-        "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\n",
-        "\n",
-        "\n",
-        "# Get a glimpse and dimensions of the data\n",
-        "glimpse(diabetes)\n",
-        "\n",
-        "\n",
-        "# Select the first 5 rows of the data\n",
-        "diabetes %>% \n",
+        "# Import the data set\r\n",
+        "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Get a glimpse and dimensions of the data\r\n",
+        "glimpse(diabetes)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Select the first 5 rows of the data\r\n",
+        "diabetes %>% \r\n",
         "  slice(1:5)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "Z1geAMhM-bSP"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "UwjVT1Hz-c3Z"
-      },
       "source": [
         "`glimpse()` shows us that this data has 442 rows and 11 columns with all the columns being of data type `double` \n",
         "\n",
@@ -198,65 +200,65 @@
         "Now that we have the data, let's narrow down to one feature (`bmi`) to target for this exercise. This will require us to select the desired columns. So, how do we do this?\n",
         "\n",
         "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) allows us to *select* (and optionally rename) columns in a data frame."
-      ]
+      ],
+      "metadata": {
+        "id": "UwjVT1Hz-c3Z"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "RDY1oAKI-m80"
-      },
+      "execution_count": null,
       "source": [
-        "# Select predictor feature `bmi` and outcome `y`\n",
-        "diabetes_select <- diabetes %>% \n",
-        "  select(c(bmi, y))\n",
-        "\n",
-        "# Print the first 5 rows\n",
-        "diabetes_select %>% \n",
+        "# Select predictor feature `bmi` and outcome `y`\r\n",
+        "diabetes_select <- diabetes %>% \r\n",
+        "  select(c(bmi, y))\r\n",
+        "\r\n",
+        "# Print the first 5 rows\r\n",
+        "diabetes_select %>% \r\n",
         "  slice(1:10)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "RDY1oAKI-m80"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "SDk668xK-tc3"
-      },
       "source": [
         "## 3. Training and Testing data\n",
         "\n",
         "It's common practice in supervised learning to *split* the data into two subsets; a (typically larger) set with which to train the model, and a smaller \"hold-back\" set with which to see how the model performed.\n",
         "\n",
         "Now that we have data ready, we can see if a machine can help determine a logical split between the numbers in this dataset. We can use the [rsample](https://tidymodels.github.io/rsample/) package, which is part of the Tidymodels framework, to create an object that contains the information on *how* to split the data, and then two more rsample functions to extract the created training and testing sets:\n"
-      ]
+      ],
+      "metadata": {
+        "id": "SDk668xK-tc3"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "EqtHx129-1h-"
-      },
+      "execution_count": null,
       "source": [
-        "set.seed(2056)\n",
-        "# Split 67% of the data for training and the rest for tesing\n",
-        "diabetes_split <- diabetes_select %>% \n",
-        "  initial_split(prop = 0.67)\n",
-        "\n",
-        "# Extract the resulting train and test sets\n",
-        "diabetes_train <- training(diabetes_split)\n",
-        "diabetes_test <- testing(diabetes_split)\n",
-        "\n",
-        "# Print the first 3 rows of the training set\n",
-        "diabetes_train %>% \n",
+        "set.seed(2056)\r\n",
+        "# Split 67% of the data for training and the rest for tesing\r\n",
+        "diabetes_split <- diabetes_select %>% \r\n",
+        "  initial_split(prop = 0.67)\r\n",
+        "\r\n",
+        "# Extract the resulting train and test sets\r\n",
+        "diabetes_train <- training(diabetes_split)\r\n",
+        "diabetes_test <- testing(diabetes_split)\r\n",
+        "\r\n",
+        "# Print the first 3 rows of the training set\r\n",
+        "diabetes_train %>% \r\n",
         "  slice(1:10)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "EqtHx129-1h-"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "sBOS-XhB-6v7"
-      },
       "source": [
         "## 4. Train a linear regression model with Tidymodels\n",
         "\n",
@@ -271,68 +273,68 @@
         "-   Model **engine** is the computational tool which will be used to fit the model. Often these are R packages, such as **`\"lm\"`** or **`\"ranger\"`**\n",
         "\n",
         "This modeling information is captured in a model specification, so let's build one!"
-      ]
+      ],
+      "metadata": {
+        "id": "sBOS-XhB-6v7"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "20OwEw20--t3"
-      },
+      "execution_count": null,
       "source": [
-        "# Build a linear model specification\n",
-        "lm_spec <- \n",
-        "  # Type\n",
-        "  linear_reg() %>% \n",
-        "  # Engine\n",
-        "  set_engine(\"lm\") %>% \n",
-        "  # Mode\n",
-        "  set_mode(\"regression\")\n",
-        "\n",
-        "\n",
-        "# Print the model specification\n",
+        "# Build a linear model specification\r\n",
+        "lm_spec <- \r\n",
+        "  # Type\r\n",
+        "  linear_reg() %>% \r\n",
+        "  # Engine\r\n",
+        "  set_engine(\"lm\") %>% \r\n",
+        "  # Mode\r\n",
+        "  set_mode(\"regression\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print the model specification\r\n",
         "lm_spec"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "20OwEw20--t3"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "_oDHs89k_CJj"
-      },
       "source": [
         "After a model has been *specified*, the model can be `estimated` or `trained` using the [`fit()`](https://parsnip.tidymodels.org/reference/fit.html) function, typically using a formula and some data.\n",
         "\n",
         "`y ~ .` means we'll fit `y` as the predicted quantity/target, explained by all the predictors/features ie, `.` (in this case, we only have one predictor: `bmi` )"
-      ]
+      ],
+      "metadata": {
+        "id": "_oDHs89k_CJj"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "YlsHqd-q_GJQ"
-      },
+      "execution_count": null,
       "source": [
-        "# Build a linear model specification\n",
-        "lm_spec <- linear_reg() %>% \n",
-        "  set_engine(\"lm\") %>%\n",
-        "  set_mode(\"regression\")\n",
-        "\n",
-        "\n",
-        "# Train a linear regression model\n",
-        "lm_mod <- lm_spec %>% \n",
-        "  fit(y ~ ., data = diabetes_train)\n",
-        "\n",
-        "# Print the model\n",
+        "# Build a linear model specification\r\n",
+        "lm_spec <- linear_reg() %>% \r\n",
+        "  set_engine(\"lm\") %>%\r\n",
+        "  set_mode(\"regression\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Train a linear regression model\r\n",
+        "lm_mod <- lm_spec %>% \r\n",
+        "  fit(y ~ ., data = diabetes_train)\r\n",
+        "\r\n",
+        "# Print the model\r\n",
         "lm_mod"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "YlsHqd-q_GJQ"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "kGZ22RQj_Olu"
-      },
       "source": [
         "From the model output, we can see the coefficients learned during training. They represent the coefficients of the line of best fit that gives us the lowest overall error between the actual and predicted variable.\n",
         "<br>\n",
@@ -340,97 +342,100 @@
         "## 5. Make predictions on the test set\n",
         "\n",
         "Now that we've trained a model, we can use it to predict the disease progression y for the test dataset using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). This will be used to draw the line between data groups."
-      ]
+      ],
+      "metadata": {
+        "id": "kGZ22RQj_Olu"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "nXHbY7M2_aao"
-      },
+      "execution_count": null,
       "source": [
-        "# Make predictions for the test set\n",
-        "predictions <- lm_mod %>% \n",
-        "  predict(new_data = diabetes_test)\n",
-        "\n",
-        "# Print out some of the predictions\n",
-        "predictions %>% \n",
+        "# Make predictions for the test set\r\n",
+        "predictions <- lm_mod %>% \r\n",
+        "  predict(new_data = diabetes_test)\r\n",
+        "\r\n",
+        "# Print out some of the predictions\r\n",
+        "predictions %>% \r\n",
         "  slice(1:5)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "nXHbY7M2_aao"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "R_JstwUY_bIs"
-      },
       "source": [
         "Woohoo! 💃🕺 We just trained a model and used it to make predictions!\n",
         "\n",
         "When making predictions, the tidymodels convention is to always produce a tibble/data frame of results with standardized column names. This makes it easy to combine the original data and the predictions in a usable format for subsequent operations such as plotting.\n",
         "\n",
         "`dplyr::bind_cols()` efficiently binds multiple data frames column."
-      ]
+      ],
+      "metadata": {
+        "id": "R_JstwUY_bIs"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "RybsMJR7_iI8"
-      },
+      "execution_count": null,
       "source": [
-        "# Combine the predictions and the original test set\n",
-        "results <- diabetes_test %>% \n",
-        "  bind_cols(predictions)\n",
-        "\n",
-        "\n",
-        "results %>% \n",
+        "# Combine the predictions and the original test set\r\n",
+        "results <- diabetes_test %>% \r\n",
+        "  bind_cols(predictions)\r\n",
+        "\r\n",
+        "\r\n",
+        "results %>% \r\n",
         "  slice(1:5)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "RybsMJR7_iI8"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "XJbYbMZW_n_s"
-      },
       "source": [
         "## 6. Plot modelling results\n",
         "\n",
         "Now, its time to see this visually 📈. We'll create a scatter plot of all the `y` and `bmi` values of the test set, then use the predictions to draw a line in the most appropriate place, between the model's data groupings.\n",
         "\n",
         "R has several systems for making graphs, but `ggplot2` is one of the most elegant and most versatile. This allows you to compose graphs by **combining independent components**."
-      ]
+      ],
+      "metadata": {
+        "id": "XJbYbMZW_n_s"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "R9tYp3VW_sTn"
-      },
+      "execution_count": null,
       "source": [
-        "# Set a theme for the plot\n",
-        "theme_set(theme_light())\n",
-        "# Create a scatter plot\n",
-        "results %>% \n",
-        "  ggplot(aes(x = bmi)) +\n",
-        "  # Add a scatter plot\n",
-        "  geom_point(aes(y = y), size = 1.6) +\n",
-        "  # Add a line plot\n",
+        "# Set a theme for the plot\r\n",
+        "theme_set(theme_light())\r\n",
+        "# Create a scatter plot\r\n",
+        "results %>% \r\n",
+        "  ggplot(aes(x = bmi)) +\r\n",
+        "  # Add a scatter plot\r\n",
+        "  geom_point(aes(y = y), size = 1.6) +\r\n",
+        "  # Add a line plot\r\n",
         "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "R9tYp3VW_sTn"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "zrPtHIxx_tNI"
-      },
       "source": [
         "> ✅ Think a bit about what's going on here. A straight line is running through many small dots of data, but what is it doing exactly? Can you see how you should be able to use this line to predict where a new, unseen data point should fit in relationship to the plot's y axis? Try to put into words the practical use of this model.\n",
         "\n",
         "Congratulations, you built your first linear regression model, created a prediction with it, and displayed it in a plot!\n"
-      ]
+      ],
+      "metadata": {
+        "id": "zrPtHIxx_tNI"
+      }
     }
   ]
-}
+}
\ No newline at end of file

From 43a44f6a283989d3839039722c11797c95393d41 Mon Sep 17 00:00:00 2001
From: R-icntay <63848664+R-icntay@users.noreply.github.com>
Date: Mon, 9 Aug 2021 16:45:21 +0300
Subject: [PATCH 355/368] Suppress warnings for pacman and resize images in
 lesson_2-R.ipynb

---
 2-Regression/2-Data/solution/lesson_2-R.ipynb | 622 +++++++++---------
 1 file changed, 321 insertions(+), 301 deletions(-)

diff --git a/2-Regression/2-Data/solution/lesson_2-R.ipynb b/2-Regression/2-Data/solution/lesson_2-R.ipynb
index adb3a50376..2409c37d82 100644
--- a/2-Regression/2-Data/solution/lesson_2-R.ipynb
+++ b/2-Regression/2-Data/solution/lesson_2-R.ipynb
@@ -1,6 +1,6 @@
 {
   "nbformat": 4,
-  "nbformat_minor": 0,
+  "nbformat_minor": 2,
   "metadata": {
     "colab": {
       "name": "lesson_2-R.ipynb",
@@ -19,35 +19,39 @@
   "cells": [
     {
       "cell_type": "markdown",
+      "source": [
+        "# Build a regression model: prepare and visualize data\r\n",
+        "\r\n",
+        "## **Linear Regression for Pumpkins - Lesson 2**\r\n",
+        "#### Introduction\r\n",
+        "\r\n",
+        "Now that you are set up with the tools you need to start tackling machine learning model building with Tidymodels and the Tidyverse, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.\r\n",
+        "\r\n",
+        "In this lesson, you will learn:\r\n",
+        "\r\n",
+        "-   How to prepare your data for model-building.\r\n",
+        "\r\n",
+        "-   How to use `ggplot2` for data visualization.\r\n",
+        "\r\n",
+        "The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.\r\n",
+        "\r\n",
+        "Let's see this by working through a practical exercise.\r\n",
+        "\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/unruly_data.jpg\"\r\n",
+        "   width=\"700\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
+        "\r\n",
+        "\r\n",
+        "<!--![Artwork by \\@allison_horst](../images/unruly_data.jpg)<br>Artwork by \\@allison_horst-->"
+      ],
       "metadata": {
         "id": "Pg5aexcOPqAZ"
-      },
-      "source": [
-        "# Build a regression model: prepare and visualize data\n",
-        "\n",
-        "## **Linear Regression for Pumpkins - Lesson 2**\n",
-        "#### Introduction\n",
-        "\n",
-        "Now that you are set up with the tools you need to start tackling machine learning model building with Tidymodels and the Tidyverse, you are ready to start asking questions of your data. As you work with data and apply ML solutions, it's very important to understand how to ask the right question to properly unlock the potentials of your dataset.\n",
-        "\n",
-        "In this lesson, you will learn:\n",
-        "\n",
-        "-   How to prepare your data for model-building.\n",
-        "\n",
-        "-   How to use `ggplot2` for data visualization.\n",
-        "\n",
-        "The question you need answered will determine what type of ML algorithms you will leverage. And the quality of the answer you get back will be heavily dependent on the nature of your data.\n",
-        "\n",
-        "Let's see this by working through a practical exercise.\n",
-        "\n",
-        "![Artwork by \\@allison_horst](../images/unruly_data.jpg)<br>Artwork by \\@allison_horst"
-      ]
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "dc5WhyVdXAjR"
-      },
       "source": [
         "## 1. Importing pumpkins data and summoning the Tidyverse\n",
         "\n",
@@ -60,58 +64,58 @@
         "`install.packages(c(\"tidyverse\"))`\n",
         "\n",
         "The script below checks whether you have the packages required to complete this module and installs them for you in case some are missing."
-      ]
+      ],
+      "metadata": {
+        "id": "dc5WhyVdXAjR"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "GqPYUZgfXOBt"
-      },
+      "execution_count": null,
       "source": [
-        "if (!require(\"pacman\")) install.packages(\"pacman\")\n",
+        "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
         "pacman::p_load(tidyverse)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "GqPYUZgfXOBt"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "kvjDTPDSXRr2"
-      },
       "source": [
         "Now, let's fire up some packages and load the [data](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) provided for this lesson!"
-      ]
+      ],
+      "metadata": {
+        "id": "kvjDTPDSXRr2"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "VMri-t2zXqgD"
-      },
+      "execution_count": null,
       "source": [
-        "# Load the core Tidyverse packages\n",
-        "library(tidyverse)\n",
-        "\n",
-        "# Import the pumpkins data\n",
-        "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
-        "\n",
-        "\n",
-        "# Get a glimpse and dimensions of the data\n",
-        "glimpse(pumpkins)\n",
-        "\n",
-        "\n",
-        "# Print the first 50 rows of the data set\n",
-        "pumpkins %>% \n",
+        "# Load the core Tidyverse packages\r\n",
+        "library(tidyverse)\r\n",
+        "\r\n",
+        "# Import the pumpkins data\r\n",
+        "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Get a glimpse and dimensions of the data\r\n",
+        "glimpse(pumpkins)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print the first 50 rows of the data set\r\n",
+        "pumpkins %>% \r\n",
         "  slice_head(n =50)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "VMri-t2zXqgD"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "REWcIv9yX29v"
-      },
       "source": [
         "A quick `glimpse()` immediately shows that there are blanks and a mix of strings (`chr`) and numeric data (`dbl`). The `Date` is of type character and there's also a strange column called `Package` where the data is a mix between `sacks`, `bins` and other values. The data, in fact, is a bit of a mess 😤.\n",
         "\n",
@@ -120,13 +124,13 @@
         "\n",
         "> A refresher: The pipe operator (`%>%`) performs operations in logical sequence by passing an object forward into a function or call expression. You can think of the pipe operator as saying \"and then\" in your code.\n",
         "\n"
-      ]
+      ],
+      "metadata": {
+        "id": "REWcIv9yX29v"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "Zxfb3AM5YbUe"
-      },
       "source": [
         "## 2. Check for missing data\n",
         "\n",
@@ -135,105 +139,112 @@
         "So how would we know that the data frame contains missing values?\n",
         "<br>\n",
         "-   One straight forward way would be to use the base R function `anyNA` which returns the logical objects `TRUE` or `FALSE`"
-      ]
+      ],
+      "metadata": {
+        "id": "Zxfb3AM5YbUe"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "G--DQutAYltj"
-      },
+      "execution_count": null,
       "source": [
-        "pumpkins %>% \n",
+        "pumpkins %>% \r\n",
         "  anyNA()"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "G--DQutAYltj"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "mU-7-SB6YokF"
-      },
       "source": [
         "Great, there seems to be some missing data! That's a good place to start.\n",
         "\n",
         "-   Another way would be to use the function `is.na()` that indicates which individual column elements are missing with a logical `TRUE`."
-      ]
+      ],
+      "metadata": {
+        "id": "mU-7-SB6YokF"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "W-DxDOR4YxSW"
-      },
+      "execution_count": null,
       "source": [
-        "pumpkins %>% \n",
-        "  is.na() %>% \n",
+        "pumpkins %>% \r\n",
+        "  is.na() %>% \r\n",
         "  head(n = 7)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "W-DxDOR4YxSW"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "xUWxipKYY0o7"
-      },
       "source": [
         "Okay, got the job done but with a large data frame such as this, it would be inefficient and practically impossible to review all of the rows and columns individually😴.\n",
         "\n",
         "-   A more intuitive way would be to calculate the sum of the missing values for each column:"
-      ]
+      ],
+      "metadata": {
+        "id": "xUWxipKYY0o7"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "ZRBWV6P9ZArL"
-      },
+      "execution_count": null,
       "source": [
-        "pumpkins %>% \n",
-        "  is.na() %>% \n",
+        "pumpkins %>% \r\n",
+        "  is.na() %>% \r\n",
         "  colSums()"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "ZRBWV6P9ZArL"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "9gv-crB6ZD1Y"
-      },
       "source": [
         "Much better! There is missing data, but maybe it won't matter for the task at hand. Let's see what further analysis brings forth.\n",
         "\n",
         "> Along with the awesome sets of packages and functions, R has a very good documentation. For instance, use `help(colSums)` or `?colSums` to find out more about the function."
-      ]
+      ],
+      "metadata": {
+        "id": "9gv-crB6ZD1Y"
+      }
     },
     {
       "cell_type": "markdown",
+      "source": [
+        "## 3. Dplyr: A Grammar of Data Manipulation\r\n",
+        "\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/dplyr_wrangling.png\"\r\n",
+        "   width=\"569\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
+        "\r\n",
+        "\r\n",
+        "<!--![Artwork by \\@allison_horst](../images/dplyr_wrangling.png)<br/>Artwork by \\@allison_horst-->"
+      ],
       "metadata": {
         "id": "o4jLY5-VZO2C"
-      },
-      "source": [
-        "## 3. Dplyr: A Grammar of Data Manipulation\n",
-        "\n",
-        "![Artwork by \\@allison_horst](../images/dplyr_wrangling.png)<br/>Artwork by \\@allison_horst"
-      ]
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "i5o33MQBZWWw"
-      },
       "source": [
         "[`dplyr`](https://dplyr.tidyverse.org/), a package in the Tidyverse, is a grammar of data manipulation that provides a consistent set of verbs that help you solve the most common data manipulation challenges. In this section, we'll explore some of dplyr's verbs!\n",
         "<br>\n"
-      ]
+      ],
+      "metadata": {
+        "id": "i5o33MQBZWWw"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "x3VGMAGBZiUr"
-      },
       "source": [
         "#### dplyr::select()\n",
         "\n",
@@ -242,31 +253,31 @@
         "To make your data frame easier to work with, drop several of its columns, using `select()`, keeping only the columns you need.\n",
         "\n",
         "For instance, in this exercise, our analysis will involve the columns `Package`, `Low Price`, `High Price` and `Date`. Let's select these columns."
-      ]
+      ],
+      "metadata": {
+        "id": "x3VGMAGBZiUr"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "F_FgxQnVZnM0"
-      },
+      "execution_count": null,
       "source": [
-        "# Select desired columns\n",
-        "pumpkins <- pumpkins %>% \n",
-        "  select(Package, `Low Price`, `High Price`, Date)\n",
-        "\n",
-        "\n",
-        "# Print data set\n",
-        "pumpkins %>% \n",
+        "# Select desired columns\r\n",
+        "pumpkins <- pumpkins %>% \r\n",
+        "  select(Package, `Low Price`, `High Price`, Date)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print data set\r\n",
+        "pumpkins %>% \r\n",
         "  slice_head(n = 5)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "F_FgxQnVZnM0"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "2KKo0Ed9Z1VB"
-      },
       "source": [
         "#### dplyr::mutate()\n",
         "\n",
@@ -283,66 +294,66 @@
         "2.  Extract the month from the dates to a new column.\n",
         "\n",
         "In R, the package [lubridate](https://lubridate.tidyverse.org/) makes it easier to work with Date-time data. So, let's use `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` and see how to achieve the above objectives. We can drop the Date column since we won't be needing it again in subsequent operations."
-      ]
+      ],
+      "metadata": {
+        "id": "2KKo0Ed9Z1VB"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "5joszIVSZ6xe"
-      },
+      "execution_count": null,
       "source": [
-        "# Load lubridate\n",
-        "library(lubridate)\n",
-        "\n",
-        "pumpkins <- pumpkins %>% \n",
-        "  # Convert the Date column to a date object\n",
-        "  mutate(Date = mdy(Date)) %>% \n",
-        "  # Extract month from Date\n",
-        "  mutate(Month = month(Date)) %>% \n",
-        "  # Drop Date column\n",
-        "  select(-Date)\n",
-        "\n",
-        "# View the first few rows\n",
-        "pumpkins %>% \n",
+        "# Load lubridate\r\n",
+        "library(lubridate)\r\n",
+        "\r\n",
+        "pumpkins <- pumpkins %>% \r\n",
+        "  # Convert the Date column to a date object\r\n",
+        "  mutate(Date = mdy(Date)) %>% \r\n",
+        "  # Extract month from Date\r\n",
+        "  mutate(Month = month(Date)) %>% \r\n",
+        "  # Drop Date column\r\n",
+        "  select(-Date)\r\n",
+        "\r\n",
+        "# View the first few rows\r\n",
+        "pumpkins %>% \r\n",
         "  slice_head(n = 7)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "5joszIVSZ6xe"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "nIgLjNMCZ-6Y"
-      },
       "source": [
         "Woohoo! 🤩\n",
         "\n",
         "Next, let's create a new column `Price`, which represents the average price of a pumpkin. Now, let's take the average of the `Low Price` and `High Price` columns to populate the new Price column.\n",
         "<br>"
-      ]
+      ],
+      "metadata": {
+        "id": "nIgLjNMCZ-6Y"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "Zo0BsqqtaJw2"
-      },
+      "execution_count": null,
       "source": [
-        "# Create a new column Price\n",
-        "pumpkins <- pumpkins %>% \n",
-        "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
-        "\n",
-        "# View the first few rows of the data\n",
-        "pumpkins %>% \n",
+        "# Create a new column Price\r\n",
+        "pumpkins <- pumpkins %>% \r\n",
+        "  mutate(Price = (`Low Price` + `High Price`)/2)\r\n",
+        "\r\n",
+        "# View the first few rows of the data\r\n",
+        "pumpkins %>% \r\n",
         "  slice_head(n = 5)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "Zo0BsqqtaJw2"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "p77WZr-9aQAR"
-      },
       "source": [
         "Yeees!💪\n",
         "\n",
@@ -351,38 +362,38 @@
         "If you look at the `Package` column, pumpkins are sold in many different configurations. Some are sold in `1 1/9 bushel` measures, and some in `1/2 bushel` measures, some per pumpkin, some per pound, and some in big boxes with varying widths.\n",
         "\n",
         "Let's verify this:"
-      ]
+      ],
+      "metadata": {
+        "id": "p77WZr-9aQAR"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "XISGfh0IaUy6"
-      },
+      "execution_count": null,
       "source": [
-        "# Verify the distinct observations in Package column\n",
-        "pumpkins %>% \n",
+        "# Verify the distinct observations in Package column\r\n",
+        "pumpkins %>% \r\n",
         "  distinct(Package)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "XISGfh0IaUy6"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "7sMjiVujaZxY"
-      },
       "source": [
         "Amazing!�\n",
         "\n",
         "Pumpkins seem to be very hard to weigh consistently, so let's filter them by selecting only pumpkins with the string *bushel* in the `Package` column and put this in a new data frame `new_pumpkins`.\n",
         "<br>"
-      ]
+      ],
+      "metadata": {
+        "id": "7sMjiVujaZxY"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "L8Qfcs92ageF"
-      },
       "source": [
         "#### dplyr::filter() and stringr::str_detect()\n",
         "\n",
@@ -391,43 +402,43 @@
         "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): detects the presence or absence of a pattern in a string.\n",
         "\n",
         "The [`stringr`](https://github.com/tidyverse/stringr) package provides simple functions for common string operations."
-      ]
+      ],
+      "metadata": {
+        "id": "L8Qfcs92ageF"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "hy_SGYREampd"
-      },
+      "execution_count": null,
       "source": [
-        "# Retain only pumpkins with \"bushel\"\n",
-        "new_pumpkins <- pumpkins %>% \n",
-        "       filter(str_detect(Package, \"bushel\"))\n",
-        "\n",
-        "# Get the dimensions of the new data\n",
-        "dim(new_pumpkins)\n",
-        "\n",
-        "# View a few rows of the new data\n",
-        "new_pumpkins %>% \n",
+        "# Retain only pumpkins with \"bushel\"\r\n",
+        "new_pumpkins <- pumpkins %>% \r\n",
+        "       filter(str_detect(Package, \"bushel\"))\r\n",
+        "\r\n",
+        "# Get the dimensions of the new data\r\n",
+        "dim(new_pumpkins)\r\n",
+        "\r\n",
+        "# View a few rows of the new data\r\n",
+        "new_pumpkins %>% \r\n",
         "  slice_head(n = 5)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "hy_SGYREampd"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "VrDwF031avlR"
-      },
       "source": [
         "You can see that we have narrowed down to 415 or so rows of data containing pumpkins by the bushel.🤩\n",
         "<br>"
-      ]
+      ],
+      "metadata": {
+        "id": "VrDwF031avlR"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "mLpw2jH4a0tx"
-      },
       "source": [
         "#### dplyr::case_when()\n",
         "\n",
@@ -436,33 +447,33 @@
         "Did you notice that the bushel amount varies per row? You need to normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel. Time to do some math to standardize it.\n",
         "\n",
         "We'll use the function [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) to *mutate* the Price column depending on some conditions. `case_when` allows you to vectorise multiple `if_else()`statements.\n"
-      ]
+      ],
+      "metadata": {
+        "id": "mLpw2jH4a0tx"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "P68kLVQmbM6I"
-      },
+      "execution_count": null,
       "source": [
-        "# Convert the price if the Package contains fractional bushel values\n",
-        "new_pumpkins <- new_pumpkins %>% \n",
-        "  mutate(Price = case_when(\n",
-        "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
-        "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
-        "    TRUE ~ Price))\n",
-        "\n",
-        "# View the first few rows of the data\n",
-        "new_pumpkins %>% \n",
+        "# Convert the price if the Package contains fractional bushel values\r\n",
+        "new_pumpkins <- new_pumpkins %>% \r\n",
+        "  mutate(Price = case_when(\r\n",
+        "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\r\n",
+        "    str_detect(Package, \"1/2\") ~ Price/(1/2),\r\n",
+        "    TRUE ~ Price))\r\n",
+        "\r\n",
+        "# View the first few rows of the data\r\n",
+        "new_pumpkins %>% \r\n",
         "  slice_head(n = 30)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "P68kLVQmbM6I"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "pS2GNPagbSdb"
-      },
       "source": [
         "Now, we can analyze the pricing per unit based on their bushel measurement. All this study of bushels of pumpkins, however, goes to show how very `important` it is to `understand the nature of your data`!\n",
         "\n",
@@ -470,103 +481,109 @@
         ">\n",
         "> ✅ Did you notice that pumpkins sold by the half-bushel are very expensive? Can you figure out why? Hint: little pumpkins are way pricier than big ones, probably because there are so many more of them per bushel, given the unused space taken by one big hollow pie pumpkin.\n",
         "<br>\n"
-      ]
+      ],
+      "metadata": {
+        "id": "pS2GNPagbSdb"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "qql1SowfbdnP"
-      },
       "source": [
         "Now lastly, for the sheer sake of adventure ��♀�, let's also move the Month column to the first position i.e `before` column `Package`.\n",
         "\n",
         "`dplyr::relocate()` is used to change column positions."
-      ]
+      ],
+      "metadata": {
+        "id": "qql1SowfbdnP"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "JJ1x6kw8bixF"
-      },
+      "execution_count": null,
       "source": [
-        "# Create a new data frame new_pumpkins\n",
-        "new_pumpkins <- new_pumpkins %>% \n",
-        "  relocate(Month, .before = Package)\n",
-        "\n",
-        "new_pumpkins %>% \n",
+        "# Create a new data frame new_pumpkins\r\n",
+        "new_pumpkins <- new_pumpkins %>% \r\n",
+        "  relocate(Month, .before = Package)\r\n",
+        "\r\n",
+        "new_pumpkins %>% \r\n",
         "  slice_head(n = 7)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "JJ1x6kw8bixF"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "y8TJ0Za_bn5Y"
-      },
       "source": [
         "Good job!👌 You now have a clean, tidy dataset on which you can build your new regression model!\n",
         "<br>"
-      ]
+      ],
+      "metadata": {
+        "id": "y8TJ0Za_bn5Y"
+      }
     },
     {
       "cell_type": "markdown",
+      "source": [
+        "## 4. Data visualization with ggplot2\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/data-visualization.png\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+        "\r\n",
+        "\r\n",
+        "<!--![Infographic by Dasani Madipalli](../images/data-visualization.png){width=\"600\"}-->\r\n",
+        "\r\n",
+        "There is a *wise* saying that goes like this:\r\n",
+        "\r\n",
+        "> \"The simple graph has brought more information to the data analyst's mind than any other device.\" --- John Tukey\r\n",
+        "\r\n",
+        "Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.\r\n",
+        "\r\n",
+        "Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.\r\n",
+        "\r\n",
+        "R offers a number of several systems for making graphs, but [`ggplot2`](https://ggplot2.tidyverse.org/index.html) is one of the most elegant and most versatile. `ggplot2` allows you to compose graphs by **combining independent components**.\r\n",
+        "\r\n",
+        "Let's start with a simple scatter plot for the Price and Month columns.\r\n",
+        "\r\n",
+        "So in this case, we'll start with [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), supply a dataset and aesthetic mapping (with [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) then add a layers (like [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) for scatter plots.\r\n"
+      ],
       "metadata": {
         "id": "mYSH6-EtbvNa"
-      },
-      "source": [
-        "## 4. Data visualization with ggplot2\n",
-        "\n",
-        "![Infographic by Dasani Madipalli](../images/data-visualization.png){width=\"600\"}\n",
-        "\n",
-        "There is a *wise* saying that goes like this:\n",
-        "\n",
-        "> \"The simple graph has brought more information to the data analyst's mind than any other device.\" --- John Tukey\n",
-        "\n",
-        "Part of the data scientist's role is to demonstrate the quality and nature of the data they are working with. To do this, they often create interesting visualizations, or plots, graphs, and charts, showing different aspects of data. In this way, they are able to visually show relationships and gaps that are otherwise hard to uncover.\n",
-        "\n",
-        "Visualizations can also help determine the machine learning technique most appropriate for the data. A scatterplot that seems to follow a line, for example, indicates that the data is a good candidate for a linear regression exercise.\n",
-        "\n",
-        "R offers a number of several systems for making graphs, but [`ggplot2`](https://ggplot2.tidyverse.org/index.html) is one of the most elegant and most versatile. `ggplot2` allows you to compose graphs by **combining independent components**.\n",
-        "\n",
-        "Let's start with a simple scatter plot for the Price and Month columns.\n",
-        "\n",
-        "So in this case, we'll start with [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), supply a dataset and aesthetic mapping (with [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) then add a layers (like [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) for scatter plots.\n"
-      ]
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "g2YjnGeOcLo4"
-      },
+      "execution_count": null,
       "source": [
-        "# Set a theme for the plots\n",
-        "theme_set(theme_light())\n",
-        "\n",
-        "# Create a scatter plot\n",
-        "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+        "# Set a theme for the plots\r\n",
+        "theme_set(theme_light())\r\n",
+        "\r\n",
+        "# Create a scatter plot\r\n",
+        "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\r\n",
         "p + geom_point()"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "g2YjnGeOcLo4"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "Ml7SDCLQcPvE"
-      },
       "source": [
         "Is this a useful plot 🤷? Does anything about it surprise you?\n",
         "\n",
         "It's not particularly useful as all it does is display in your data as a spread of points in a given month.\n",
         "<br>"
-      ]
+      ],
+      "metadata": {
+        "id": "Ml7SDCLQcPvE"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "jMakvJZIcVkh"
-      },
       "source": [
         "### **How do we make it useful?**\n",
         "\n",
@@ -583,62 +600,65 @@
         "-   `dplyr::summarize()` creates a new data frame with one column for each grouping variable and one column for each of the summary statistics that you have specified.\n",
         "\n",
         "For example, we can use the `dplyr::group_by() %>% summarize()` to group the pumpkins into groups based on the **Month** columns and then find the **mean price** for each month."
-      ]
+      ],
+      "metadata": {
+        "id": "jMakvJZIcVkh"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "6kVSUa2Bcilf"
-      },
+      "execution_count": null,
       "source": [
-        "# Find the average price of pumpkins per month\n",
-        "new_pumpkins %>%\n",
-        "  group_by(Month) %>% \n",
+        "# Find the average price of pumpkins per month\r\n",
+        "new_pumpkins %>%\r\n",
+        "  group_by(Month) %>% \r\n",
         "  summarise(mean_price = mean(Price))"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "6kVSUa2Bcilf"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "Kds48GUBcj3W"
-      },
       "source": [
         "Succinct!✨\n",
         "\n",
         "Categorical features such as months are better represented using a bar plot 📊. The layers responsible for bar charts are `geom_bar()` and `geom_col()`. Consult `?geom_bar` to find out more.\n",
         "\n",
         "Let's whip up one!"
-      ]
+      ],
+      "metadata": {
+        "id": "Kds48GUBcj3W"
+      }
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "VNbU1S3BcrxO"
-      },
-      "source": [
-        "# Find the average price of pumpkins per month then plot a bar chart\n",
-        "new_pumpkins %>%\n",
-        "  group_by(Month) %>% \n",
-        "  summarise(mean_price = mean(Price)) %>% \n",
-        "  ggplot(aes(x = Month, y = mean_price)) +\n",
-        "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\n",
+      "execution_count": null,
+      "source": [
+        "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+        "new_pumpkins %>%\r\n",
+        "  group_by(Month) %>% \r\n",
+        "  summarise(mean_price = mean(Price)) %>% \r\n",
+        "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+        "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
         "  ylab(\"Pumpkin Price\")"
       ],
-      "execution_count": null,
-      "outputs": []
+      "outputs": [],
+      "metadata": {
+        "id": "VNbU1S3BcrxO"
+      }
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "zDm0VOzzcuzR"
-      },
       "source": [
         "🤩🤩This is a more useful data visualization! It seems to indicate that the highest price for pumpkins occurs in September and October. Does that meet your expectation? Why or why not?\n",
         "\n",
         "Congratulations on finishing the second lesson �! You prepared your data for model building, then uncovered more insights using visualizations!"
-      ]
+      ],
+      "metadata": {
+        "id": "zDm0VOzzcuzR"
+      }
     }
   ]
-}
+}
\ No newline at end of file

From b60b2e0faf4ede575b84cd9f7259cbe4587ef1aa Mon Sep 17 00:00:00 2001
From: R-icntay <63848664+R-icntay@users.noreply.github.com>
Date: Mon, 9 Aug 2021 16:55:20 +0300
Subject: [PATCH 356/368] Add R resources for lesson 08: logistic regression

---
 .../4-Logistic/images/r_learners_sm.jpeg      | Bin 0 -> 40334 bytes
 .../4-Logistic/solution/lesson_4-R.ipynb      | 751 ++++++++++++++++++
 2-Regression/4-Logistic/solution/lesson_4.Rmd | 430 ++++++++++
 3 files changed, 1181 insertions(+)
 create mode 100644 2-Regression/4-Logistic/images/r_learners_sm.jpeg
 create mode 100644 2-Regression/4-Logistic/solution/lesson_4-R.ipynb
 create mode 100644 2-Regression/4-Logistic/solution/lesson_4.Rmd

diff --git a/2-Regression/4-Logistic/images/r_learners_sm.jpeg b/2-Regression/4-Logistic/images/r_learners_sm.jpeg
new file mode 100644
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diff --git a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
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index 0000000000..5946857523
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+++ b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
@@ -0,0 +1,751 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 2,
+  "metadata": {
+    "colab": {
+      "name": "Untitled10.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "ir",
+      "display_name": "R"
+    },
+    "language_info": {
+      "name": "R"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Build a regression model: logistic regression\n",
+        "<br>\n"
+      ],
+      "metadata": {
+        "id": "fVfEucLYkV9T"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Build a logistic regression model - Lesson 4\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/logistic-linear.png\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+        "\r\n",
+        "<!--![Infographic by Dasani Madipalli](../images/logistic-linear.png){width=\"600\"}-->"
+      ],
+      "metadata": {
+        "id": "QizKKpzakfx2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### ** [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)**\n",
+        "\n",
+        "####  Introduction\n",
+        "\n",
+        "In this final lesson on Regression, one of the basic *classic* ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict `binary` `categories`. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not?\n",
+        "\n",
+        "In this lesson, you will learn:\n",
+        "\n",
+        "-   Techniques for logistic regression\n",
+        "\n",
+        "✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)\n",
+        "\n",
+        "#### **Prerequisite**\n",
+        "\n",
+        "Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one binary category that we can work with: `Color`.\n",
+        "\n",
+        "Let's build a logistic regression model to predict that, given some variables, *what color a given pumpkin is likely to be* (orange 🎃 or white 👻).\n",
+        "\n",
+        "> Why are we talking about binary classification in a lesson grouping about regression? Only for linguistic convenience, as logistic regression is [really a classification method](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), albeit a linear-based one. Learn about other ways to classify data in the next lesson group.\n",
+        "\n",
+        "For this lesson, we'll require the following packages:\n",
+        "\n",
+        "-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!\n",
+        "\n",
+        "-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.\n",
+        "\n",
+        "-   `janitor`: The [janitor package](https://github.com/sfirke/janitor) provides simple little tools for examining and cleaning dirty data.\n",
+        "\n",
+        "-   `ggbeeswarm`: The [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) provides methods to create beeswarm-style plots using ggplot2.\n",
+        "\n",
+        "You can have them installed as:\n",
+        "\n",
+        "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+        "\n",
+        "Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing."
+      ],
+      "metadata": {
+        "id": "KPmut75XkmXY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+        "\r\n",
+        "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "dnIGNNttkx_O"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ** Define the question**\r\n",
+        "\r\n",
+        "For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.\r\n",
+        "\r\n",
+        "> 🎃 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking!\r\n",
+        "\r\n",
+        "## **About logistic regression**\r\n",
+        "\r\n",
+        "Logistic regression differs from linear regression, which you learned about previously, in a few important ways.\r\n",
+        "\r\n",
+        "#### **Binary classification**\r\n",
+        "\r\n",
+        "Logistic regression does not offer the same features as linear regression. The former offers a prediction about a `binary category` (\"orange or not orange\") whereas the latter is capable of predicting `continual values`, for example given the origin of a pumpkin and the time of harvest, *how much its price will rise*.\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/pumpkin-classifier.png\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+        "\r\n",
+        "<!--![Infographic by Dasani Madipalli](images/pumpkin-classifier.png){width=\"600\"}-->"
+      ],
+      "metadata": {
+        "id": "ws-hP_SXk2O6"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Other classifications**\r\n",
+        "\r\n",
+        "There are other types of logistic regression, including multinomial and ordinal:\r\n",
+        "\r\n",
+        "-   **Multinomial**, which involves having more than one category - \"Orange, White, and Striped\".\r\n",
+        "\r\n",
+        "-   **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/multinomial-ordinal.png\"\r\n",
+        "   width=\"700\"/>\r\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+        "\r\n",
+        "<!--![Infographic by Dasani Madipalli](images/multinomial-ordinal.png){width=\"600\"}-->"
+      ],
+      "metadata": {
+        "id": "LkLN-ZgDlBEc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**It's still linear**\n",
+        "\n",
+        "Even though this type of Regression is all about 'category predictions', it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not.\n",
+        "\n",
+        "#### **Variables DO NOT have to correlate**\n",
+        "\n",
+        "Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations.\n",
+        "\n",
+        "#### **You need a lot of clean data**\n",
+        "\n",
+        "Logistic regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind.\n",
+        "\n",
+        "✅ Think about the types of data that would lend themselves well to logistic regression\n"
+      ],
+      "metadata": {
+        "id": "D8_JoVZtlHUt"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Tidy the data\n",
+        "\n",
+        "Now, the fun begins! Let's start by importing the data, cleaning the data a bit, dropping rows containing missing values and selecting only some of the columns:"
+      ],
+      "metadata": {
+        "id": "LPj8Ib1AlIua"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Load the core tidyverse packages\r\n",
+        "library(tidyverse)\r\n",
+        "\r\n",
+        "# Import the data and clean column names\r\n",
+        "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \r\n",
+        "  clean_names()\r\n",
+        "\r\n",
+        "# Select desired columns\r\n",
+        "pumpkins_select <- pumpkins %>% \r\n",
+        "  select(c(city_name, package, variety, origin, item_size, color)) \r\n",
+        "\r\n",
+        "# Drop rows containing missing values and encode color as factor (category)\r\n",
+        "pumpkins_select <- pumpkins_select %>% \r\n",
+        "  drop_na() %>% \r\n",
+        "  mutate(color = factor(color))\r\n",
+        "\r\n",
+        "# View the first few rows\r\n",
+        "pumpkins_select %>% \r\n",
+        "  slice_head(n = 5)\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "Q8oKJ8PAlLM0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Sometimes, we may want some little more information on our data. We can have a look at the `data`, `its structure` and the `data type` of its features by using the [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) function as below:"
+      ],
+      "metadata": {
+        "id": "tKY5eN8alPNn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "pumpkins_select %>% \r\n",
+        "  glimpse()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "wDpatL1WlShu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Wow! Seems that all our columns are all of type *character*, further alluding that they are all categorical.\n",
+        "\n",
+        "Let's confirm that we will actually be doing a binary classification problem:"
+      ],
+      "metadata": {
+        "id": "QbdC2b0JlU2G"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Subset distinct observations in outcome column\r\n",
+        "pumpkins_select %>% \r\n",
+        "  distinct(color)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "Gys-Q18rlZpE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🥳🥳 That went down well!\n",
+        "\n",
+        "## 2. Explore the data\n",
+        "\n",
+        "The goal of data exploration is to try to understand the `relationships` between its attributes; in particular, any apparent correlation between the *features* and the *label* your model will try to predict. One way of doing this is by using data visualization.\n",
+        "\n",
+        "Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3-R.ipynb).\n",
+        "\n",
+        "Tidymodels provides yet another neat package: [recipes](https://recipes.tidymodels.org/)- a package for preprocessing data. We'll define a `recipe` that specifies that all predictor columns should be encoded into a set of integers , `prep` it to estimates the required quantities and statistics needed by any operations and finally `bake` to apply the computations to new data.\n",
+        "\n",
+        "> Normally, recipes is usually used as a preprocessor for modelling where it defines what steps should be applied to a data set in order to get it ready for modelling. In that case it is **highly recommend** that you use a `workflow()` instead of manually estimating a recipe using prep and bake. We'll see all this in just a moment.\n",
+        ">\n",
+        "> However for now, we are using recipes + prep + bake to specify what steps should be applied to a data set in order to get it ready for data analysis and then extract the preprocessed data with the steps applied."
+      ],
+      "metadata": {
+        "id": "kn_20wSPldVH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Preprocess and extract data to allow some data analysis\r\n",
+        "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>% \r\n",
+        "  # Encode all columns to a set of integers\r\n",
+        "  step_integer(all_predictors(), zero_based = T) %>% \r\n",
+        "  prep() %>% \r\n",
+        "  bake(new_data = NULL)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Display the first few rows of preprocessed data\r\n",
+        "baked_pumpkins %>% \r\n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "syaCgFQ_lijg"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now let's compare the feature distributions for each label value using box plots. We'll begin by formatting the data to a *long* format to make it somewhat easier to make multiple `facets`."
+      ],
+      "metadata": {
+        "id": "RlkOZ_C5lldq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Pivot data to long format\r\n",
+        "baked_pumpkins_long <- baked_pumpkins %>% \r\n",
+        "  pivot_longer(!color, names_to = \"features\", values_to = \"values\")\r\n",
+        "\r\n",
+        "\r\n",
+        "# Print out restructured data\r\n",
+        "baked_pumpkins_long %>% \r\n",
+        "  slice_head(n = 10)\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "putq8DagltUQ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now, let's make some boxplots showing the distribution of the predictors with respect to the outcome color."
+      ],
+      "metadata": {
+        "id": "-RHm-12zlt-B"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "theme_set(theme_light())\r\n",
+        "#Make a box plot for each predictor feature\r\n",
+        "baked_pumpkins_long %>% \r\n",
+        "  mutate(color = factor(color)) %>% \r\n",
+        "  ggplot(mapping = aes(x = color, y = values, fill = features)) +\r\n",
+        "  geom_boxplot() + \r\n",
+        "  facet_wrap(~ features, scales = \"free\", ncol = 3) +\r\n",
+        "  scale_color_viridis_d(option = \"cividis\", end = .8) +\r\n",
+        "  theme(legend.position = \"none\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "3Py4i1p1l3hP"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Amazing🤩! For some of the features, there's a noticeable difference in the distribution for each color label. For instance, it seems the white pumpkins can be found in smaller packages and in some particular varieties of pumpkins. The *item_size* category also seems to make a difference in the color distribution. These features may help predict the color of a pumpkin.\n",
+        "\n",
+        "#### **Use a swarm plot**\n",
+        "\n",
+        "Color is a binary category (Orange or Not), it's called `categorical data`. There are other various ways of [visualizing categorical data](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar).\n",
+        "\n",
+        "Try a `swarm plot` to show the distribution of color with respect to the item_size.\n",
+        "\n",
+        "We'll use the [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) which provides methods to create beeswarm-style plots using ggplot2. Beeswarm plots are a way of plotting points that would ordinarily overlap so that they fall next to each other instead."
+      ],
+      "metadata": {
+        "id": "2LSj6_LCl68V"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create beeswarm plots of color and item_size\r\n",
+        "baked_pumpkins %>% \r\n",
+        "  mutate(color = factor(color)) %>% \r\n",
+        "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\r\n",
+        "  geom_quasirandom() +\r\n",
+        "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\r\n",
+        "  theme(legend.position = \"none\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "hGKeRgUemMTb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Violin plot**\n",
+        "\n",
+        "A 'violin' type plot is useful as you can easily visualize the way that data in the two categories is distributed. [`Violin plots`](https://en.wikipedia.org/wiki/Violin_plot) are similar to box plots, except that they also show the probability density of the data at different values. Violin plots don't work so well with smaller datasets as the distribution is displayed more 'smoothly'."
+      ],
+      "metadata": {
+        "id": "_9wdZJH5mOvN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create a violin plot of color and item_size\r\n",
+        "baked_pumpkins %>%\r\n",
+        "  mutate(color = factor(color)) %>% \r\n",
+        "  ggplot(mapping = aes(x = color, y = item_size, fill = color)) +\r\n",
+        "  geom_violin() +\r\n",
+        "  geom_boxplot(color = \"black\", fill = \"white\", width = 0.02) +\r\n",
+        "  scale_fill_brewer(palette = \"Dark2\", direction = -1) +\r\n",
+        "  theme(legend.position = \"none\")"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "LFFFymujmTAZ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color.\r\n",
+        "\r\n",
+        "## 3. Build your logistic regression model\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/logistic-linear.png\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Infographic by Dasani Madipalli</figcaption>\r\n",
+        "\r\n",
+        "> **🧮 Show Me The Math**\r\n",
+        ">\r\n",
+        "> Remember how `linear regression` often used `ordinary least squares` to arrive at a value? `Logistic regression` relies on the concept of 'maximum likelihood' using [`sigmoid functions`](https://wikipedia.org/wiki/Sigmoid_function). A Sigmoid Function on a plot looks like an `S shape`. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this:\r\n",
+        ">\r\n",
+        "> \r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/sigmoid.png\"\r\n",
+        "\r\n",
+        "\r\n",
+        "> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class 1 of the binary choice. If not, it will be classified as 0.\r\n",
+        "\r\n",
+        "Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value.\r\n",
+        "\r\n",
+        "It is best practice to hold out some of your data for **testing** in order to get a better estimate of how your models will perform on new data by comparing the predicted labels with the already known labels in the test set. [rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling:"
+      ],
+      "metadata": {
+        "id": "RA_bnMS9mVo8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Split data into 80% for training and 20% for testing\r\n",
+        "set.seed(2056)\r\n",
+        "pumpkins_split <- pumpkins_select %>% \r\n",
+        "  initial_split(prop = 0.8)\r\n",
+        "\r\n",
+        "# Extract the data in each split\r\n",
+        "pumpkins_train <- training(pumpkins_split)\r\n",
+        "pumpkins_test <- testing(pumpkins_split)\r\n",
+        "\r\n",
+        "# Print out the first 5 rows of the training set\r\n",
+        "pumpkins_train %>% \r\n",
+        "  slice_head(n = 5)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "PQdpEYYPmdGW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "🙌 We are now ready to train a model by fitting the training features to the training label (color).\n",
+        "\n",
+        "We'll begin by creating a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding categorical variables into a set of integers.\n",
+        "\n",
+        "There are quite a number of ways to specify a logistic regression model in Tidymodels. See `?logistic_reg()` For now, we'll specify a logistic regression model via the default `stats::glm()` engine."
+      ],
+      "metadata": {
+        "id": "MX9LipSimhn0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Create a recipe that specifies preprocessing steps for modelling\r\n",
+        "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \r\n",
+        "  step_integer(all_predictors(), zero_based = TRUE)\r\n",
+        "\r\n",
+        "\r\n",
+        "# Create a logistic model specification\r\n",
+        "log_reg <- logistic_reg() %>% \r\n",
+        "  set_engine(\"glm\") %>% \r\n",
+        "  set_mode(\"classification\")\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "0Eo5-SbSmm2-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities.\n",
+        "\n",
+        "In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components."
+      ],
+      "metadata": {
+        "id": "G599GKhXmqWf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Bundle modelling components in a workflow\r\n",
+        "log_reg_wf <- workflow() %>% \r\n",
+        "  add_recipe(pumpkins_recipe) %>% \r\n",
+        "  add_model(log_reg)\r\n",
+        "\r\n",
+        "# Print out the workflow\r\n",
+        "log_reg_wf\r\n"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "cRoU0tpbmu1T"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "After a workflow has been *specified*, a model can be `trained` using the [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) function. The workflow will estimate a recipe and preprocess the data before training, so we won't have to manually do that using prep and bake."
+      ],
+      "metadata": {
+        "id": "JnRXKmREnEpd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Train the model\r\n",
+        "wf_fit <- log_reg_wf %>% \r\n",
+        "  fit(data = pumpkins_train)\r\n",
+        "\r\n",
+        "# Print the trained workflow\r\n",
+        "wf_fit"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "ehFwfkjWnNCb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The model print out shows the coefficients learned during training.\n",
+        "\n",
+        "Now we've trained the model using the training data, we can make predictions on the test data using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Let's start by using the model to predict labels for our test set and the probabilities for each label. When the probability is more than 0.5, the predict class is `ORANGE` else `WHITE`."
+      ],
+      "metadata": {
+        "id": "w01dGNZjnOJQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make predictions for color and corresponding probabilities\r\n",
+        "results <- pumpkins_test %>% select(color) %>% \r\n",
+        "  bind_cols(wf_fit %>% \r\n",
+        "              predict(new_data = pumpkins_test)) %>%\r\n",
+        "  bind_cols(wf_fit %>%\r\n",
+        "              predict(new_data = pumpkins_test, type = \"prob\"))\r\n",
+        "\r\n",
+        "# Compare predictions\r\n",
+        "results %>% \r\n",
+        "  slice_head(n = 10)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "K8PNjPfTnak2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Very nice! This provides some more insights into how logistic regression works.\n",
+        "\n",
+        "Comparing each prediction with its corresponding \"ground truth\" actual value isn't a very efficient way to determine how well the model is predicting. Fortunately, Tidymodels has a few more tricks up its sleeve: [`yardstick`](https://yardstick.tidymodels.org/) - a package used to measure the effectiveness of models using performance metrics.\n",
+        "\n",
+        "One performance metric associated with classification problems is the [`confusion matrix`](https://wikipedia.org/wiki/Confusion_matrix). A confusion matrix describes how well a classification model performs. A confusion matrix tabulates how many examples in each class were correctly classified by a model. In our case, it will show you how many orange pumpkins were classified as orange and how many white pumpkins were classified as white; the confusion matrix also shows you how many were classified into the **wrong** categories.\n",
+        "\n",
+        "The [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) function from yardstick calculates this cross-tabulation of observed and predicted classes."
+      ],
+      "metadata": {
+        "id": "N3J-yW0wngKo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Confusion matrix for prediction results\r\n",
+        "conf_mat(data = results, truth = color, estimate = .pred_class)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "0RD77Dq1nl2j"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let's interpret the confusion matrix. Our model is asked to classify pumpkins between two binary categories, category `orange` and category `not-orange`\n",
+        "\n",
+        "-   If your model predicts a pumpkin as orange and it belongs to category 'orange' in reality we call it a `true positive`, shown by the top left number.\n",
+        "\n",
+        "-   If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a `false negative`, shown by the bottom left number.\n",
+        "\n",
+        "-   If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a `false positive`, shown by the top right number.\n",
+        "\n",
+        "-   If your model predicts a pumpkin as not orange and it belongs to category 'not-orange' in reality we call it a `true negative`, shown by the bottom right number.\n",
+        "\n",
+        "\n",
+        "| **Truth** |\n",
+        "|:-----:|\n",
+        "\n",
+        "\n",
+        "|               |        |       |\n",
+        "|---------------|--------|-------|\n",
+        "| **Predicted** | ORANGE | WHITE |\n",
+        "| ORANGE        | TP     | FP    |\n",
+        "| WHITE         | FN     | TN    |"
+      ],
+      "metadata": {
+        "id": "H61sFwdOnoiO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.\n",
+        "\n",
+        "The confusion matrix is helpful since it gives rise to other metrics that can help us better evaluate the performance of a classification model. Let's go through some of them:\n",
+        "\n",
+        "🎓 Precision: `TP/(TP + FP)` defined as the proportion of predicted positives that are actually positive. Also called [positive predictive value](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\")\n",
+        "\n",
+        "🎓 Recall: `TP/(TP + FN)` defined as the proportion of positive results out of the number of samples which were actually positive. Also known as `sensitivity`.\n",
+        "\n",
+        "🎓 Specificity: `TN/(TN + FP)` defined as the proportion of negative results out of the number of samples which were actually negative.\n",
+        "\n",
+        "🎓 Accuracy: `TP + TN/(TP + TN + FP + FN)` The percentage of labels predicted accurately for a sample.\n",
+        "\n",
+        "🎓 F Measure: A weighted average of the precision and recall, with best being 1 and worst being 0.\n",
+        "\n",
+        "Let's calculate these metrics!"
+      ],
+      "metadata": {
+        "id": "Yc6QUie2oQUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Combine metric functions and calculate them all at once\r\n",
+        "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\r\n",
+        "eval_metrics(data = results, truth = color, estimate = .pred_class)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "p6rXx_T3oVxX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### **Visualize the ROC curve of this model**\n",
+        "\n",
+        "For a start, this is not a bad model; its precision, recall, F measure and accuracy are in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables. It also seems that our model was not really able to identify the white pumpkins �. Could you guess why? One reason could be because of the high prevalence of ORANGE pumpkins in our training set making our model more inclined to predict the majority class.\n",
+        "\n",
+        "Let's do one more visualization to see the so-called [`ROC score`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):"
+      ],
+      "metadata": {
+        "id": "JcenzZo1oaKR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Make a roc_curve\r\n",
+        "results %>% \r\n",
+        "  roc_curve(color, .pred_ORANGE) %>% \r\n",
+        "  autoplot()"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "BcmkHHHwogRB"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. ROC curves typically feature `True Positive Rate`/Sensitivity on the Y axis, and `False Positive Rate`/1-Specificity on the X axis. Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly.\n",
+        "\n",
+        "Finally, let's use `yardstick::roc_auc()` to calculate the actual Area Under the Curve. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example."
+      ],
+      "metadata": {
+        "id": "P_an3vc1oqjI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "source": [
+        "# Calculate area under curve\r\n",
+        "results %>% \r\n",
+        "  roc_auc(color, .pred_ORANGE)"
+      ],
+      "outputs": [],
+      "metadata": {
+        "id": "SZyy5BT8ovew"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The result is around `0.67053`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is *pretty good*.\r\n",
+        "\r\n",
+        "In future lessons on classifications, you will learn how to improve your model's scores (such as dealing with imbalanced data in this case).\r\n",
+        "\r\n",
+        "But for now, congratulations 🎉🎉🎉! You've completed these regression lessons!\r\n",
+        "\r\n",
+        "You R awesome!\r\n",
+        "\r\n",
+        "<p >\r\n",
+        "   <img src=\"../images/r_learners_sm.jpeg\"\r\n",
+        "   width=\"600\"/>\r\n",
+        "   <figcaption>Artwork by @allison_horst</figcaption>\r\n",
+        "\r\n",
+        "<!--![Artwork by @allison_horst](images/r_learners_sm.jpeg)-->\r\n"
+      ],
+      "metadata": {
+        "id": "5jtVKLTVoy6u"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/2-Regression/4-Logistic/solution/lesson_4.Rmd b/2-Regression/4-Logistic/solution/lesson_4.Rmd
new file mode 100644
index 0000000000..8d99dc6207
--- /dev/null
+++ b/2-Regression/4-Logistic/solution/lesson_4.Rmd
@@ -0,0 +1,430 @@
+---
+title: 'Build a regression model: logistic regression'
+output:
+  html_document:
+    df_print: paged
+    theme: flatly
+    highlight: breezedark
+    toc: yes
+    toc_float: yes
+    code_download: yes
+---
+
+## Build a logistic regression model - Lesson 4
+
+![Infographic by Dasani Madipalli](../images/logistic-linear.png){width="600"}
+
+#### ** [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)**
+
+####  Introduction
+
+In this final lesson on Regression, one of the basic *classic* ML techniques, we will take a look at Logistic Regression. You would use this technique to discover patterns to predict `binary` `categories`. Is this candy chocolate or not? Is this disease contagious or not? Will this customer choose this product or not?
+
+In this lesson, you will learn:
+
+-   Techniques for logistic regression
+
+✅ Deepen your understanding of working with this type of regression in this [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-classification-models?WT.mc_id=academic-15963-cxa)
+
+#### **Prerequisite**
+
+Having worked with the pumpkin data, we are now familiar enough with it to realize that there's one binary category that we can work with: `Color`.
+
+Let's build a logistic regression model to predict that, given some variables, *what color a given pumpkin is likely to be* (orange 🎃 or white 👻).
+
+> Why are we talking about binary classification in a lesson grouping about regression? Only for linguistic convenience, as logistic regression is [really a classification method](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), albeit a linear-based one. Learn about other ways to classify data in the next lesson group.
+
+For this lesson, we'll require the following packages:
+
+-   `tidyverse`: The [tidyverse](https://www.tidyverse.org/) is a [collection of R packages](https://www.tidyverse.org/packages) designed to makes data science faster, easier and more fun!
+
+-   `tidymodels`: The [tidymodels](https://www.tidymodels.org/) framework is a [collection of packages](https://www.tidymodels.org/packages/) for modeling and machine learning.
+
+-   `janitor`: The [janitor package](https://github.com/sfirke/janitor) provides simple little tools for examining and cleaning dirty data.
+
+-   `ggbeeswarm`: The [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) provides methods to create beeswarm-style plots using ggplot2.
+
+You can have them installed as:
+
+`install.packages(c("tidyverse", "tidymodels", "janitor", "ggbeeswarm"))`
+
+Alternatiely, the script below checks whether you have the packages required to complete this module and installs them for you in case they are missing.
+
+```{r, message=F, warning=F}
+suppressWarnings(if (!require("pacman"))install.packages("pacman"))
+
+pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)
+```
+
+## ** Define the question**
+
+For our purposes, we will express this as a binary: 'Orange' or 'Not Orange'. There is also a 'striped' category in our dataset but there are few instances of it, so we will not use it. It disappears once we remove null values from the dataset, anyway.
+
+> 🎃 Fun fact, we sometimes call white pumpkins 'ghost' pumpkins. They aren't very easy to carve, so they aren't as popular as the orange ones but they are cool looking!
+
+## **About logistic regression**
+
+Logistic regression differs from linear regression, which you learned about previously, in a few important ways.
+
+#### **Binary classification**
+
+Logistic regression does not offer the same features as linear regression. The former offers a prediction about a `binary category` ("orange or not orange") whereas the latter is capable of predicting `continual values`, for example given the origin of a pumpkin and the time of harvest, *how much its price will rise*.
+
+![Infographic by Dasani Madipalli](../images/pumpkin-classifier.png){width="600"}
+
+#### **Other classifications**
+
+There are other types of logistic regression, including multinomial and ordinal:
+
+-   **Multinomial**, which involves having more than one category - "Orange, White, and Striped".
+
+-   **Ordinal**, which involves ordered categories, useful if we wanted to order our outcomes logically, like our pumpkins that are ordered by a finite number of sizes (mini,sm,med,lg,xl,xxl).
+
+![Infographic by Dasani Madipalli](../images/multinomial-ordinal.png){width="600"}
+
+\
+**It's still linear**
+
+Even though this type of Regression is all about 'category predictions', it still works best when there is a clear linear relationship between the dependent variable (color) and the other independent variables (the rest of the dataset, like city name and size). It's good to get an idea of whether there is any linearity dividing these variables or not.
+
+#### **Variables DO NOT have to correlate**
+
+Remember how linear regression worked better with more correlated variables? Logistic regression is the opposite - the variables don't have to align. That works for this data which has somewhat weak correlations.
+
+#### **You need a lot of clean data**
+
+Logistic regression will give more accurate results if you use more data; our small dataset is not optimal for this task, so keep that in mind.
+
+✅ Think about the types of data that would lend themselves well to logistic regression
+
+## 1. Tidy the data
+
+Now, the fun begins! Let's start by importing the data, cleaning the data a bit, dropping rows containing missing values and selecting only some of the columns:
+
+```{r, tidyr, message=F, warning=F}
+# Load the core tidyverse packages
+library(tidyverse)
+
+# Import the data and clean column names
+pumpkins <- read_csv(file = "https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv") %>% 
+  clean_names()
+
+# Select desired columns
+pumpkins_select <- pumpkins %>% 
+  select(c(city_name, package, variety, origin, item_size, color)) 
+
+# Drop rows containing missing values and encode color as factor (category)
+pumpkins_select <- pumpkins_select %>% 
+  drop_na() %>% 
+  mutate(color = factor(color))
+
+# View the first few rows
+pumpkins_select %>% 
+  slice_head(n = 5)
+
+```
+
+Sometimes, we may want some little more information on our data. We can have a look at the `data`, `its structure` and the `data type` of its features by using the [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) function as below:
+
+```{r glimpse}
+pumpkins_select %>% 
+  glimpse()
+```
+
+Wow! Seems that all our columns are all of type *character*, further alluding that they are all categorical.
+
+Let's confirm that we will actually be doing a binary classification problem:
+
+```{r distinct color}
+# Subset distinct observations in outcome column
+pumpkins_select %>% 
+  distinct(color)
+
+```
+
+🥳🥳 That went down well!
+
+## 2. Explore the data
+
+The goal of data exploration is to try to understand the `relationships` between its attributes; in particular, any apparent correlation between the *features* and the *label* your model will try to predict. One way of doing this is by using data visualization.
+
+Given our the data types of our columns, we can `encode` them and be on our way to making some visualizations. This simply involves `translating` a column with `categorical values` for example our columns of type *char*, into one or more `numeric columns` that take the place of the original. - Something we did in our [last lesson](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/3-Linear/solution/lesson_3-R.ipynb).
+
+Tidymodels provides yet another neat package: [recipes](https://recipes.tidymodels.org/)- a package for preprocessing data. We'll define a `recipe` that specifies that all predictor columns should be encoded into a set of integers , `prep` it to estimates the required quantities and statistics needed by any operations and finally `bake` to apply the computations to new data.
+
+> Normally, recipes is usually used as a preprocessor for modelling where it defines what steps should be applied to a data set in order to get it ready for modelling. In that case it is **highly recommend** that you use a `workflow()` instead of manually estimating a recipe using prep and bake. We'll see all this in just a moment.
+>
+> However for now, we are using recipes + prep + bake to specify what steps should be applied to a data set in order to get it ready for data analysis and then extract the preprocessed data with the steps applied.
+
+```{r recipe_prep_bake}
+# Preprocess and extract data to allow some data analysis
+baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>% 
+  # Encode all columns to a set of integers
+  step_integer(all_predictors(), zero_based = T) %>% 
+  prep() %>% 
+  bake(new_data = NULL)
+
+
+# Display the first few rows of preprocessed data
+baked_pumpkins %>% 
+  slice_head(n = 5)
+
+```
+
+Now let's compare the feature distributions for each label value using box plots. We'll begin by formatting the data to a *long* format to make it somewhat easier to make multiple `facets`.
+
+```{r pivot}
+# Pivot data to long format
+baked_pumpkins_long <- baked_pumpkins %>% 
+  pivot_longer(!color, names_to = "features", values_to = "values")
+
+
+# Print out restructured data
+baked_pumpkins_long %>% 
+  slice_head(n = 10)
+
+```
+
+
+Now, let's make some boxplots showing the distribution of the predictors with respect to the outcome color!
+
+```{r boxplots}
+theme_set(theme_light())
+#Make a box plot for each predictor feature
+baked_pumpkins_long %>% 
+  mutate(color = factor(color)) %>% 
+  ggplot(mapping = aes(x = color, y = values, fill = features)) +
+  geom_boxplot() + 
+  facet_wrap(~ features, scales = "free", ncol = 3) +
+  scale_color_viridis_d(option = "cividis", end = .8) +
+  theme(legend.position = "none")
+```
+
+Amazing🤩! For some of the features, there's a noticeable difference in the distribution for each color label. For instance, it seems the white pumpkins can be found in smaller packages and in some particular varieties of pumpkins. The *item_size* category also seems to make a difference in the color distribution. These features may help predict the color of a pumpkin.
+
+#### **Use a swarm plot**
+
+Color is a binary category (Orange or Not), it's called `categorical data`. There are other various ways of [visualizing categorical data](https://seaborn.pydata.org/tutorial/categorical.html?highlight=bar).
+
+Try a `swarm plot` to show the distribution of color with respect to the item_size.
+
+We'll use the [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) which provides methods to create beeswarm-style plots using ggplot2. Beeswarm plots are a way of plotting points that would ordinarily overlap so that they fall next to each other instead.
+
+```{r bee_swarm plot}
+# Create beeswarm plots of color and item_size
+baked_pumpkins %>% 
+  mutate(color = factor(color)) %>% 
+  ggplot(mapping = aes(x = color, y = item_size, color = color)) +
+  geom_quasirandom() +
+  scale_color_brewer(palette = "Dark2", direction = -1) +
+  theme(legend.position = "none")
+```
+
+#### **Violin plot**
+
+A 'violin' type plot is useful as you can easily visualize the way that data in the two categories is distributed. [`Violin plots`](https://en.wikipedia.org/wiki/Violin_plot) are similar to box plots, except that they also show the probability density of the data at different values. Violin plots don't work so well with smaller datasets as the distribution is displayed more 'smoothly'.
+
+```{r violin_plot}
+# Create a violin plot of color and item_size
+baked_pumpkins %>%
+  mutate(color = factor(color)) %>% 
+  ggplot(mapping = aes(x = color, y = item_size, fill = color)) +
+  geom_violin() +
+  geom_boxplot(color = "black", fill = "white", width = 0.02) +
+  scale_fill_brewer(palette = "Dark2", direction = -1) +
+  theme(legend.position = "none")
+
+```
+
+Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore logistic regression to determine a given pumpkin's likely color.
+
+## 3. Build your model
+
+> **🧮 Show Me The Math**
+>
+> Remember how `linear regression` often used `ordinary least squares` to arrive at a value? `Logistic regression` relies on the concept of 'maximum likelihood' using [`sigmoid functions`](https://wikipedia.org/wiki/Sigmoid_function). A Sigmoid Function on a plot looks like an `S shape`. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this:
+>
+> ![](../images/sigmoid.png)
+>
+> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class 1 of the binary choice. If not, it will be classified as 0.
+
+Let's begin by splitting the data into `training` and `test` sets. The training set is used to train a classifier so that it finds a statistical relationship between the features and the label value.
+
+It is best practice to hold out some of your data for **testing** in order to get a better estimate of how your models will perform on new data by comparing the predicted labels with the already known labels in the test set. [rsample](https://rsample.tidymodels.org/), a package in Tidymodels, provides infrastructure for efficient data splitting and resampling:
+
+```{r split_data}
+# Split data into 80% for training and 20% for testing
+set.seed(2056)
+pumpkins_split <- pumpkins_select %>% 
+  initial_split(prop = 0.8)
+
+# Extract the data in each split
+pumpkins_train <- training(pumpkins_split)
+pumpkins_test <- testing(pumpkins_split)
+
+# Print out the first 5 rows of the training set
+pumpkins_train %>% 
+  slice_head(n = 5)
+
+
+```
+
+🙌 We are now ready to train a model by fitting the training features to the training label (color).
+
+We'll begin by creating a recipe that specifies the preprocessing steps that should be carried out on our data to get it ready for modelling i.e: encoding categorical variables into a set of integers.
+
+There are quite a number of ways to specify a logistic regression model in Tidymodels. See `?logistic_reg()` For now, we'll specify a logistic regression model via the default `stats::glm()` engine.
+
+```{r log_reg}
+# Create a recipe that specifies preprocessing steps for modelling
+pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% 
+  step_integer(all_predictors(), zero_based = TRUE)
+
+
+# Create a logistic model specification
+log_reg <- logistic_reg() %>% 
+  set_engine("glm") %>% 
+  set_mode("classification")
+
+
+```
+
+Now that we have a recipe and a model specification, we need to find a way of bundling them together into an object that will first preprocess the data (prep+bake behind the scenes), fit the model on the preprocessed data and also allow for potential post-processing activities.
+
+In Tidymodels, this convenient object is called a [`workflow`](https://workflows.tidymodels.org/) and conveniently holds your modeling components.
+
+```{r workflow}
+# Bundle modelling components in a workflow
+log_reg_wf <- workflow() %>% 
+  add_recipe(pumpkins_recipe) %>% 
+  add_model(log_reg)
+
+# Print out the workflow
+log_reg_wf
+
+
+```
+
+After a workflow has been *specified*, a model can be `trained` using the [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) function. The workflow will estimate a recipe and preprocess the data before training, so we won't have to manually do that using prep and bake.
+
+```{r train}
+# Train the model
+wf_fit <- log_reg_wf %>% 
+  fit(data = pumpkins_train)
+
+# Print the trained workflow
+wf_fit
+
+```
+
+The model print out shows the coefficients learned during training.
+
+Now we've trained the model using the training data, we can make predictions on the test data using [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Let's start by using the model to predict labels for our test set and the probabilities for each label. When the probability is more than 0.5, the predict class is `ORANGE` else `WHITE`.
+
+```{r test_pred}
+# Make predictions for color and corresponding probabilities
+results <- pumpkins_test %>% select(color) %>% 
+  bind_cols(wf_fit %>% 
+              predict(new_data = pumpkins_test)) %>%
+  bind_cols(wf_fit %>%
+              predict(new_data = pumpkins_test, type = "prob"))
+
+# Compare predictions
+results %>% 
+  slice_head(n = 10)
+
+```
+
+Very nice! This provides some more insights into how logistic regression works.
+
+Comparing each prediction with its corresponding "ground truth" actual value isn't a very efficient way to determine how well the model is predicting. Fortunately, Tidymodels has a few more tricks up its sleeve: [`yardstick`](https://yardstick.tidymodels.org/) - a package used to measure the effectiveness of models using performance metrics.
+
+One performance metric associated with classification problems is the [`confusion matrix`](https://wikipedia.org/wiki/Confusion_matrix). A confusion matrix describes how well a classification model performs. A confusion matrix tabulates how many examples in each class were correctly classified by a model. In our case, it will show you how many orange pumpkins were classified as orange and how many white pumpkins were classified as white; the confusion matrix also shows you how many were classified into the **wrong** categories.
+
+The [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) function from yardstick calculates this cross-tabulation of observed and predicted classes.
+
+```{r conf_mat}
+# Confusion matrix for prediction results
+conf_mat(data = results, truth = color, estimate = .pred_class)
+
+
+```
+
+Let's interpret the confusion matrix. Our model is asked to classify pumpkins between two binary categories, category `orange` and category `not-orange`
+
+-   If your model predicts a pumpkin as orange and it belongs to category 'orange' in reality we call it a `true positive`, shown by the top left number.
+
+-   If your model predicts a pumpkin as not orange and it belongs to category 'orange' in reality we call it a `false negative`, shown by the bottom left number.
+
+-   If your model predicts a pumpkin as orange and it belongs to category 'not-orange' in reality we call it a `false positive`, shown by the top right number.
+
+-   If your model predicts a pumpkin as not orange and it belongs to category 'not-orange' in reality we call it a `true negative`, shown by the bottom right number.
+
+| Truth |
+|:-----:|
+
+
+|               |        |       |
+|---------------|--------|-------|
+| **Predicted** | ORANGE | WHITE |
+| ORANGE        | TP     | FP    |
+| WHITE         | FN     | TN    |
+
+As you might have guessed it's preferable to have a larger number of true positives and true negatives and a lower number of false positives and false negatives, which implies that the model performs better.
+
+The confusion matrix is helpful since it gives rise to other metrics that can help us better evaluate the performance of a classification model. Let's go through some of them:
+
+🎓 Precision: `TP/(TP + FP)` defined as the proportion of predicted positives that are actually positive. Also called [positive predictive value](https://en.wikipedia.org/wiki/Positive_predictive_value "Positive predictive value")
+
+🎓 Recall: `TP/(TP + FN)` defined as the proportion of positive results out of the number of samples which were actually positive. Also known as `sensitivity`.
+
+🎓 Specificity: `TN/(TN + FP)` defined as the proportion of negative results out of the number of samples which were actually negative.
+
+🎓 Accuracy: `TP + TN/(TP + TN + FP + FN)` The percentage of labels predicted accurately for a sample.
+
+🎓 F Measure: A weighted average of the precision and recall, with best being 1 and worst being 0.
+
+Let's calculate these metrics!
+
+```{r metric_set}
+# Combine metric functions and calculate them all at once
+eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)
+eval_metrics(data = results, truth = color, estimate = .pred_class)
+```
+
+#### **Visualize the ROC curve of this model**
+
+For a start, this is not a bad model; its precision, recall, F measure and accuracy are in the 80% range so ideally you could use it to predict the color of a pumpkin given a set of variables. It also seems that our model was not really able to identify the white pumpkins �. Could you guess why? One reason could be because of the high prevalence of ORANGE pumpkins in our training set making our model more inclined to predict the majority class.
+
+Let's do one more visualization to see the so-called [`ROC score`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):
+
+```{r roc_curve}
+# Make a roc_curve
+results %>% 
+  roc_curve(color, .pred_ORANGE) %>% 
+  autoplot()
+
+```
+
+ROC curves are often used to get a view of the output of a classifier in terms of its true vs. false positives. ROC curves typically feature `True Positive Rate`/Sensitivity on the Y axis, and `False Positive Rate`/1-Specificity on the X axis. Thus, the steepness of the curve and the space between the midpoint line and the curve matter: you want a curve that quickly heads up and over the line. In our case, there are false positives to start with, and then the line heads up and over properly.
+
+Finally, let's use `yardstick::roc_auc()` to calculate the actual Area Under the Curve. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example.
+
+```{r roc_aoc}
+# Calculate area under curve
+results %>% 
+  roc_auc(color, .pred_ORANGE)
+
+```
+
+The result is around `0.67053`. Given that the AUC ranges from 0 to 1, you want a big score, since a model that is 100% correct in its predictions will have an AUC of 1; in this case, the model is *pretty good*.
+
+In future lessons on classifications, you will learn how to improve your model's scores (such as dealing with imbalanced data in this case).
+
+But for now, congratulations 🎉🎉🎉! You've completed these regression lessons!
+
+You R awesome!
+
+![Artwork by \@allison_horst](../images/r_learners_sm.jpeg)
+
+

From 3d3eddff772c24155adcfb5d3588a10e02427cb2 Mon Sep 17 00:00:00 2001
From: R-icntay <63848664+R-icntay@users.noreply.github.com>
Date: Mon, 9 Aug 2021 17:01:32 +0300
Subject: [PATCH 357/368] Update lesson_4-R.ipynb

---
 2-Regression/4-Logistic/solution/lesson_4-R.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
index 5946857523..c9e16ce9ec 100644
--- a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
+++ b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
@@ -432,7 +432,7 @@
         ">\r\n",
         "> \r\n",
         "<p >\r\n",
-        "   <img src=\"../images/sigmoid.png\"\r\n",
+        "   <img src=\"../images/sigmoid.png\">\r\n",
         "\r\n",
         "\r\n",
         "> where the sigmoid's midpoint finds itself at x's 0 point, L is the curve's maximum value, and k is the curve's steepness. If the outcome of the function is more than 0.5, the label in question will be given the class 1 of the binary choice. If not, it will be classified as 0.\r\n",

From 6b070ea3083e2f20aa89d69e37959a89c0acecc2 Mon Sep 17 00:00:00 2001
From: davidit33 <rd99t33@zoho.com>
Date: Mon, 9 Aug 2021 15:24:03 -0300
Subject: [PATCH 358/368] 2-Regression/translations/README.es.md

---
 2-Regression/translations/README.es.md | 33 ++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 2-Regression/translations/README.es.md

diff --git a/2-Regression/translations/README.es.md b/2-Regression/translations/README.es.md
new file mode 100644
index 0000000000..8d2f1ade23
--- /dev/null
+++ b/2-Regression/translations/README.es.md
@@ -0,0 +1,33 @@
+# Modelos de regresión para el machine learning
+## Tema regional: Modelos de regresión para los precios de las calabazas en América del Norte 🎃
+
+En América del Norte, las calabazas se tallan a menudo con caras aterradoras para Halloween. ¡Descubramos más sobre estas fascinantes verduras!
+
+![jack-o-lanterns](./images/jack-o-lanterns.jpg)
+> Foto de <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> en <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
+  
+## Lo que vas a aprender
+
+Las lecciones de esta sección cubren los tipos de regressión en el contexto de machine learning. Los modelos de regresión pueden ayudar a determinar la _relación_ entre variables. Este tipo de modelos puede predecir valores como la longitud, la temperatura o la edad, descubriendo así relaciones entre variables a medida que analiza puntos de datos.
+
+En esta serie de lecciones, descubrirá la diferencia entre la regresión lineal y la logística, y cuándo debe usar una u otra.
+
+En este grupo de lecciones, se preparará para comenzar las tares de machine learning, incluida la configuración de Visual Studio code para manejar los cuadernos, el entorno común para los científicos de datos. Descubrirá Scikit-learn, una librería para machine learning, y creará sus primeros modelos, centrándose en los modelos de Regresión en este capítulo.
+
+> Existen herramientas útiles low-code que pueden ayudarlo a aprender a trabjar con modelos de regresión. Pruebe [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+
+### Lecciones
+
+1. [Herramientas del oficio](1-Tools/README.md)
+2. [Gestión de datos](2-Data/README.md)
+3. [Regresión lineal y polinomial](3-Linear/README.md)
+4. [Regresión logística](4-Logistic/README.md)
+
+---
+### Créditos
+
+"ML con regresión" fue escrito con ♥� por [Jen Looper](https://twitter.com/jenlooper)
+
+♥� Los contribuyentes del cuestionario incluyen: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) y [Ornella Altunyan](https://twitter.com/ornelladotcom)
+
+El dateset de calabaza es sugerido por [este proyecto en Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) y sus datos provienen de [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distribuido por el Departamento de Agricultura de los Estados Unidos. Hemos agragado algunos puntos alrededor color basados en la variedad para normalizar la distribución. Estos datos son de dominio público.

From 7503c0edc36cd7f0a1c624a27c263f058867d729 Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Mon, 9 Aug 2021 16:57:51 -0300
Subject: [PATCH 359/368] Update 2-Regression/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 2-Regression/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/translations/README.es.md b/2-Regression/translations/README.es.md
index 8d2f1ade23..c0d7afc2c7 100644
--- a/2-Regression/translations/README.es.md
+++ b/2-Regression/translations/README.es.md
@@ -12,7 +12,7 @@ Las lecciones de esta sección cubren los tipos de regressión en el contexto de
 
 En esta serie de lecciones, descubrirá la diferencia entre la regresión lineal y la logística, y cuándo debe usar una u otra.
 
-En este grupo de lecciones, se preparará para comenzar las tares de machine learning, incluida la configuración de Visual Studio code para manejar los cuadernos, el entorno común para los científicos de datos. Descubrirá Scikit-learn, una librería para machine learning, y creará sus primeros modelos, centrándose en los modelos de Regresión en este capítulo.
+En este grupo de lecciones, se preparará para comenzar las tares de machine learning, incluida la configuración de Visual Studio Code para manejar los cuadernos, el entorno común para los científicos de datos. Descubrirá Scikit-learn, una librería para machine learning, y creará sus primeros modelos, centrándose en los modelos de Regresión en este capítulo.
 
 > Existen herramientas útiles low-code que pueden ayudarlo a aprender a trabjar con modelos de regresión. Pruebe [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
 

From b6c5f3806e6647e596fdbae9bb902144ee2cc8a8 Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Mon, 9 Aug 2021 16:58:27 -0300
Subject: [PATCH 360/368] Update 2-Regression/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 2-Regression/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/translations/README.es.md b/2-Regression/translations/README.es.md
index c0d7afc2c7..6fe82cf192 100644
--- a/2-Regression/translations/README.es.md
+++ b/2-Regression/translations/README.es.md
@@ -14,7 +14,7 @@ En esta serie de lecciones, descubrirá la diferencia entre la regresión lineal
 
 En este grupo de lecciones, se preparará para comenzar las tares de machine learning, incluida la configuración de Visual Studio Code para manejar los cuadernos, el entorno común para los científicos de datos. Descubrirá Scikit-learn, una librería para machine learning, y creará sus primeros modelos, centrándose en los modelos de Regresión en este capítulo.
 
-> Existen herramientas útiles low-code que pueden ayudarlo a aprender a trabjar con modelos de regresión. Pruebe [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
+> Existen herramientas útiles _low-code_ que pueden ayudarlo a aprender a trabjar con modelos de regresión. Pruebe [Azure ML para esta tarea](https://docs.microsoft.com/learn/modules/create-regression-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)
 
 ### Lecciones
 

From ce3ce967da55acb06386e1bd92bc253f144c8254 Mon Sep 17 00:00:00 2001
From: David Tamayo <63550116+davidit33@users.noreply.github.com>
Date: Mon, 9 Aug 2021 16:58:47 -0300
Subject: [PATCH 361/368] Update 2-Regression/translations/README.es.md

Co-authored-by: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
---
 2-Regression/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/translations/README.es.md b/2-Regression/translations/README.es.md
index 6fe82cf192..5e9bff0d48 100644
--- a/2-Regression/translations/README.es.md
+++ b/2-Regression/translations/README.es.md
@@ -30,4 +30,4 @@ En este grupo de lecciones, se preparará para comenzar las tares de machine lea
 
 ♥� Los contribuyentes del cuestionario incluyen: [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan) y [Ornella Altunyan](https://twitter.com/ornelladotcom)
 
-El dateset de calabaza es sugerido por [este proyecto en Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) y sus datos provienen de [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distribuido por el Departamento de Agricultura de los Estados Unidos. Hemos agragado algunos puntos alrededor color basados en la variedad para normalizar la distribución. Estos datos son de dominio público.
+El _dataset_ de calabaza es sugerido por [este proyecto en Kaggle](https://www.kaggle.com/usda/a-year-of-pumpkin-prices) y sus datos provienen de [Specialty Crops Terminal Markets Standard Reports](https://www.marketnews.usda.gov/mnp/fv-report-config-step1?type=termPrice) distribuido por el Departamento de Agricultura de los Estados Unidos. Hemos agragado algunos puntos alrededor color basados en la variedad para normalizar la distribución. Estos datos son de dominio público.

From e32254164bf5e46622d013a0d035d6ab6fecebc8 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Mon, 9 Aug 2021 20:09:14 -0400
Subject: [PATCH 362/368] editing path to image

---
 2-Regression/translations/README.es.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/2-Regression/translations/README.es.md b/2-Regression/translations/README.es.md
index 5e9bff0d48..280790fc7f 100644
--- a/2-Regression/translations/README.es.md
+++ b/2-Regression/translations/README.es.md
@@ -3,7 +3,7 @@
 
 En América del Norte, las calabazas se tallan a menudo con caras aterradoras para Halloween. ¡Descubramos más sobre estas fascinantes verduras!
 
-![jack-o-lanterns](./images/jack-o-lanterns.jpg)
+![jack-o-lanterns](../images/jack-o-lanterns.jpg)
 > Foto de <a href="https://unsplash.com/@teutschmann?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Beth Teutschmann</a> en <a href="https://unsplash.com/s/photos/jack-o-lanterns?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
 ## Lo que vas a aprender

From 7b4465c70dd2c1229d5f8fd919c10bba646d9924 Mon Sep 17 00:00:00 2001
From: Alfredo Deza <317847+alfredodeza@users.noreply.github.com>
Date: Tue, 10 Aug 2021 08:57:50 -0400
Subject: [PATCH 363/368] inform about required libraries and versions

---
 7-TimeSeries/2-ARIMA/solution/notebook.ipynb | 41 +++++++++++++-------
 1 file changed, 28 insertions(+), 13 deletions(-)

diff --git a/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
index 3e6307d21f..1a42ab1555 100644
--- a/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
+++ b/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -17,6 +17,18 @@
    ],
    "metadata": {}
   },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Install Dependencies\n",
+    "Get started by installing some of the required dependencies. These libraries with their corresponding versions are known to work for the solution:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
+   "metadata": {}
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -191,8 +203,8 @@
       "text/plain": [
        "<Figure size 1080x576 with 1 Axes>"
       ],
-      "image/png": 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83.742527 195.962437 \nL 83.837991 148.230038 \nL 83.869813 161.617056 \nL 84.092563 314.141275 \nL 84.156206 304.046147 \nL 84.378956 188.061902 \nL 84.506242 207.92297 \nL 84.601707 158.873814 \nL 84.633528 171.382995 \nL 84.856278 323.687755 \nL 84.919921 312.60506 \nL 85.11085 190.805144 \nL 85.142671 192.121899 \nL 85.238136 198.595949 \nL 85.269957 197.937571 \nL 85.365422 150.095442 \nL 85.397243 159.641922 \nL 85.619993 299.437501 \nL 85.683636 287.806158 \nL 85.810922 179.39326 \nL 85.874565 183.233797 \nL 86.033672 208.691077 \nL 86.065494 196.291626 \nL 86.129137 164.140838 \nL 86.160958 174.784614 \nL 86.41553 312.166141 \nL 86.479173 297.133178 \nL 86.63828 206.606214 \nL 86.670102 211.763507 \nL 86.765566 233.599709 \nL 86.797388 232.831601 \nL 86.892852 171.382995 \nL 86.956495 197.388923 \nL 87.179245 301.412635 \nL 87.242888 288.354806 \nL 87.401995 205.728377 \nL 87.465638 212.641345 \nL 87.529281 226.577011 \nL 87.561103 223.394851 \nL 87.624746 169.956509 \nL 87.688389 203.753243 \nL 87.94296 312.49533 \nL 87.974782 303.497498 \nL 88.102068 192.670548 \nL 88.165711 195.742978 \nL 88.324818 223.72404 \nL 88.356639 210.666211 \nL 88.420282 165.457594 \nL 88.452104 179.832178 \nL 88.674854 324.565592 \nL 88.738497 313.373167 \nL 88.929426 202.875406 \nL 88.961247 205.508917 \nL 89.056712 216.372153 \nL 89.088533 214.397019 \nL 89.183998 154.155439 \nL 89.24764 180.490556 \nL 89.438569 296.035882 \nL 89.470391 295.048315 \nL 89.534034 251.485642 \nL 89.597677 175.333262 \nL 89.661319 181.148934 \nL 89.820427 213.080263 \nL 89.852248 211.10513 \nL 89.947713 153.716521 \nL 90.011356 182.46569 \nL 90.202284 308.435333 \nL 90.234106 308.215873 \nL 90.297749 265.640768 \nL 90.361392 191.682981 \nL 90.425035 198.815408 \nL 90.615963 224.711607 \nL 90.711428 166.884079 \nL 90.775071 192.560818 \nL 90.966 312.60506 \nL 90.997821 311.398033 \nL 91.061464 268.384009 \nL 91.125107 195.962437 \nL 91.18875 202.875406 \nL 91.284214 219.005665 \nL 91.379679 231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 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322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 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348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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292.853722 \nL 363.675957 281.002919 \nL 363.866885 170.505158 \nL 363.96235 181.478123 \nL 364.025993 187.952173 \nL 364.121457 139.451665 \nL 364.153279 150.095442 \nL 364.40785 275.187248 \nL 364.471493 258.61807 \nL 364.630601 179.064071 \nL 364.662422 182.026771 \nL 364.757886 197.827841 \nL 364.789708 195.633248 \nL 364.853351 150.534361 \nL 364.916994 166.774349 \nL 365.171565 292.524533 \nL 365.235208 280.125082 \nL 365.426137 192.560818 \nL 365.48978 199.802975 \nL 365.521602 202.436487 \nL 365.553423 196.511086 \nL 365.617066 144.060311 \nL 365.680709 175.772181 \nL 365.903459 291.427236 \nL 365.967102 276.613733 \nL 366.158031 168.420294 \nL 366.189852 168.091105 \nL 366.285317 178.954341 \nL 366.317138 176.979207 \nL 366.412603 119.480868 \nL 366.476246 149.107875 \nL 366.667174 268.493739 \nL 366.698996 267.835361 \nL 366.762639 222.626743 \nL 366.826282 150.973279 \nL 366.889925 159.971111 \nL 367.01721 180.161367 \nL 367.049032 186.635417 \nL 367.080853 185.318661 \nL 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 399.920605 208.361888 \nL 399.952427 206.386754 \nL 400.047891 171.163536 \nL 400.079713 179.1738 \nL 400.270642 302.290472 \nL 400.302463 301.302905 \nL 400.366106 256.204017 \nL 400.429749 181.697582 \nL 400.493392 188.83001 \nL 400.588856 206.277025 \nL 400.684321 222.846203 \nL 400.747964 212.531615 \nL 400.811607 182.46569 \nL 400.843428 189.159199 \nL 401.066178 308.654792 \nL 401.129821 269.042387 \nL 401.225286 191.353792 \nL 401.257107 192.341359 \nL 401.448036 222.517014 \nL 401.479857 222.407284 \nL 401.575322 194.426222 \nL 401.607143 199.144597 \nL 401.829893 312.714789 \nL 401.861715 306.679658 \nL 402.052644 210.9954 \nL 402.148108 233.928898 \nL 402.211751 240.841866 \nL 402.243572 237.330517 \nL 402.339037 207.045132 \nL 402.370858 213.189993 \nL 402.593609 325.662888 \nL 402.62543 322.700188 \nL 402.720894 275.735896 \nL 402.816359 230.966197 \nL 402.84818 234.148357 \nL 402.975466 254.448343 \nL 403.007288 249.839697 \nL 403.102752 207.374321 \nL 403.134573 214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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228.991063 \nL 545.853845 225.479714 \nL 545.917488 211.434319 \nL 545.949309 227.015929 \nL 546.17206 353.973139 \nL 546.203881 351.668816 \nL 546.39481 248.193753 \nL 546.522096 261.471041 \nL 546.553917 261.141852 \nL 546.681203 229.100793 \nL 546.713025 245.88943 \nL 546.935775 361.325026 \nL 546.967596 358.910974 \nL 547.063061 308.215873 \nL 547.190347 255.545639 \nL 547.222168 257.740233 \nL 547.285811 265.201849 \nL 547.317632 262.458608 \nL 547.444918 213.189993 \nL 547.47674 231.844034 \nL 547.667669 352.985572 \nL 547.69949 352.327194 \nL 547.763133 311.398033 \nL 547.92224 218.676476 \nL 548.017705 215.933234 \nL 548.081348 223.72404 \nL 548.113169 220.761339 \nL 548.208633 186.086768 \nL 548.240455 207.593781 \nL 548.463205 347.169901 \nL 548.526848 306.679658 \nL 548.685955 214.616478 \nL 548.78142 209.129996 \nL 548.813241 210.9954 \nL 548.845063 211.982967 \nL 548.876884 209.678644 \nL 548.972349 183.782446 \nL 549.22692 344.865578 \nL 549.258742 335.867746 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 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588.52643 200.351623 \nL 588.558251 198.37649 \nL 588.621894 165.896512 \nL 588.653716 173.797047 \nL 588.940109 325.22397 \nL 589.003752 304.704524 \nL 589.162859 221.090528 \nL 589.194681 223.065662 \nL 589.258324 230.527278 \nL 589.290145 230.198089 \nL 589.353788 186.745146 \nL 589.385609 165.238134 \nL 589.449252 187.732713 \nL 589.703824 296.145611 \nL 589.767467 280.78346 \nL 589.958396 181.478123 \nL 590.022039 191.024603 \nL 590.05386 194.206763 \nL 590.085682 186.196498 \nL 590.149325 127.930052 \nL 590.212968 148.559227 \nL 590.435718 272.992654 \nL 590.499361 258.069421 \nL 590.626647 153.387332 \nL 590.69029 158.325166 \nL 590.817575 178.515422 \nL 590.849397 172.480291 \nL 590.91304 119.15168 \nL 590.976683 132.319238 \nL 591.199433 289.122914 \nL 591.263076 278.259678 \nL 591.454005 168.530024 \nL 591.581291 183.562986 \nL 591.613112 180.929475 \nL 591.676755 142.194907 \nL 591.740398 162.604622 \nL 591.99497 315.56776 \nL 592.058613 281.332108 \nL 592.21772 177.418126 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\nL 595.495331 155.252736 \nL 595.558974 179.28353 \nL 595.781724 316.555327 \nL 595.845367 303.05858 \nL 596.036296 183.672716 \nL 596.068117 183.892175 \nL 596.163582 190.256495 \nL 596.195403 185.099202 \nL 596.259046 138.903017 \nL 596.322689 158.105707 \nL 596.577261 316.774786 \nL 596.609082 306.350469 \nL 596.736368 196.840275 \nL 596.800011 199.693246 \nL 596.927297 210.556481 \nL 596.959118 205.947836 \nL 597.022761 145.815985 \nL 597.086404 157.557058 \nL 597.309154 306.899118 \nL 597.372797 294.060748 \nL 597.468262 183.014338 \nL 597.563726 194.426222 \nL 597.595548 194.097033 \nL 597.691012 204.411621 \nL 597.722833 201.668379 \nL 597.786476 142.633825 \nL 597.850119 153.716521 \nL 598.072869 307.008847 \nL 598.168334 263.446175 \nL 598.327441 177.198666 \nL 598.359263 177.527855 \nL 598.454727 184.550553 \nL 598.550191 139.890584 \nL 598.613834 158.215436 \nL 598.836585 319.847217 \nL 598.900228 310.081277 \nL 599.091156 187.842443 \nL 599.122978 189.268928 \nL 599.218442 199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 653.155826 278.369407 \nL 653.283112 194.316493 \nL 653.346755 198.815408 \nL 653.442219 210.337022 \nL 653.47404 208.032699 \nL 653.569505 162.824082 \nL 653.601326 175.99164 \nL 653.855898 294.938585 \nL 653.919541 283.416972 \nL 654.078648 200.241894 \nL 654.142291 206.277025 \nL 654.205934 217.908368 \nL 654.237756 213.299723 \nL 654.33322 143.401933 \nL 654.396863 174.894344 \nL 654.587792 277.162381 \nL 654.619613 275.077518 \nL 654.683256 237.988895 \nL 654.810542 157.447329 \nL 654.842363 157.995977 \nL 654.969649 182.794879 \nL 655.001471 181.368393 \nL 655.096935 123.101947 \nL 655.160578 148.998145 \nL 655.351507 262.348878 \nL 655.383328 260.154285 \nL 655.446971 218.895935 \nL 655.606079 143.840852 \nL 655.765186 156.350032 \nL 655.797007 149.875983 \nL 655.86065 118.603031 \nL 655.892472 134.623561 \nL 656.115222 283.746161 \nL 656.178865 270.688332 \nL 656.369794 172.480291 \nL 656.528901 175.113803 \nL 656.592544 145.157608 \nL 656.624366 136.598694 \nL 656.656187 150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 211.434319 \nL 680.554107 315.56776 \nL 680.61775 312.934248 \nL 680.713215 263.885093 \nL 680.808679 219.554313 \nL 680.840501 221.419717 \nL 680.935965 240.402947 \nL 680.999608 233.928898 \nL 681.095072 181.258664 \nL 681.126894 185.867309 \nL 681.286001 288.025617 \nL 681.317823 286.489402 \nL 681.381465 272.224547 \nL 681.47693 164.689486 \nL 681.572394 173.02894 \nL 681.731502 204.960269 \nL 681.795145 191.682981 \nL 681.858787 160.958678 \nL 681.890609 167.981376 \nL 682.081538 281.880756 \nL 682.113359 278.808326 \nL 682.177002 231.734305 \nL 682.240645 161.726785 \nL 682.304288 173.797047 \nL 682.495217 211.873237 \nL 682.527038 207.154862 \nL 682.622503 166.335431 \nL 682.654324 173.577588 \nL 682.845253 297.023449 \nL 682.877074 295.267774 \nL 682.940717 253.680235 \nL 683.099825 166.55489 \nL 683.195289 175.333262 \nL 683.258932 182.685149 \nL 683.290753 181.039204 \nL 683.386218 153.277602 \nL 683.418039 162.275434 \nL 683.608968 278.918056 \nL 683.64079 276.504003 \nL 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205.179728 \nL 690.005083 213.189993 \nL 690.132368 229.649441 \nL 690.259654 203.094865 \nL 690.291476 210.446752 \nL 690.514226 315.348301 \nL 690.546047 312.056411 \nL 690.736976 209.129996 \nL 690.832441 232.282953 \nL 690.896084 240.073758 \nL 690.927905 237.549976 \nL 691.023369 214.506749 \nL 691.055191 219.883502 \nL 691.277941 322.480728 \nL 691.309763 319.188839 \nL 691.405227 272.334277 \nL 691.500691 235.684572 \nL 691.532513 238.427813 \nL 691.659799 261.58077 \nL 691.69162 255.874828 \nL 691.818906 217.35972 \nL 692.009835 320.725054 \nL 692.041656 317.872083 \nL 692.105299 273.980221 \nL 692.168942 206.386754 \nL 692.232585 213.628912 \nL 692.264407 212.970534 \nL 692.423514 225.589444 \nL 692.455335 220.43215 \nL 692.5508 196.511086 \nL 692.582621 204.74081 \nL 692.77355 327.199104 \nL 692.805371 325.662888 \nL 692.869014 285.172646 \nL 693.028122 203.643513 \nL 693.187229 223.175392 \nL 693.219051 217.798638 \nL 693.314515 197.718112 \nL 693.346336 206.606214 \nL 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697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 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236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 \nL 802.01664 323.358566 \nL 802.048462 317.542894 \nL 802.334855 186.086768 \nL 802.589427 225.479714 \nL 802.780355 336.855313 \nL 802.843998 325.00451 \nL 803.034927 226.357551 \nL 803.162213 232.282953 \nL 803.289499 213.189993 \nL 803.32132 213.958101 \nL 803.54407 331.807749 \nL 803.607713 324.455862 \nL 803.703178 257.301314 \nL 803.894107 182.794879 \nL 804.053214 162.714352 \nL 804.085035 173.687318 \nL 804.307786 304.046147 \nL 804.339607 303.05858 \nL 804.435072 261.58077 \nL 804.626 146.474363 \nL 804.657822 146.584093 \nL 804.753286 125.625729 \nL 804.816929 127.710593 \nL 804.880572 177.088937 \nL 805.071501 302.180742 \nL 805.135144 265.531038 \nL 805.357894 106.42304 \nL 805.389715 99.071153 \nL 805.421537 104.228447 \nL 805.453358 112.019252 \nL 805.517001 103.02142 \nL 805.580644 102.801961 \nL 805.612466 114.872223 \nL 805.835216 284.733727 \nL 805.898859 254.777532 \nL 806.089788 149.437064 \nL 806.248895 139.890584 \nL 806.280717 140.878151 \nL 806.376181 158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 \nL 821.077698 342.561255 \nL 821.109519 342.122336 \nL 821.173162 304.814254 \nL 821.33227 222.846203 \nL 821.364091 220.980798 \nL 821.427734 221.858636 \nL 821.491377 226.79647 \nL 821.523198 224.162958 \nL 821.618663 192.999737 \nL 821.650484 212.751074 \nL 821.841413 346.511523 \nL 821.873234 344.975307 \nL 821.936877 303.607228 \nL 822.064163 223.943499 \nL 822.095985 225.699174 \nL 822.127806 217.140261 \nL 822.191449 220.761339 \nL 822.255092 228.552145 \nL 822.286914 226.138092 \nL 822.382378 196.181897 \nL 822.414199 214.06783 \nL 822.63695 344.09747 \nL 822.700593 305.911551 \nL 822.8597 220.43215 \nL 823.018807 227.784037 \nL 823.050629 226.68674 \nL 823.146093 193.877574 \nL 823.177915 212.202426 \nL 823.368843 336.745583 \nL 823.400665 336.635854 \nL 823.464308 299.437501 \nL 823.623415 215.494316 \nL 823.718879 212.531615 \nL 823.782522 218.127827 \nL 823.846165 215.604045 \nL 823.909808 202.765676 \nL 823.94163 217.140261 \nL 824.16438 342.451525 \nL 824.196201 339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 \nL 831.419674 224.711607 \nL 831.451496 223.50458 \nL 831.54696 189.817577 \nL 831.578781 212.531615 \nL 831.76971 339.159636 \nL 831.801532 337.623421 \nL 831.865175 299.327771 \nL 832.024282 203.753243 \nL 832.087925 208.581348 \nL 832.119746 208.142429 \nL 832.183389 217.46945 \nL 832.215211 214.17756 \nL 832.278854 188.83001 \nL 832.342497 209.020266 \nL 832.565247 336.965043 \nL 832.62889 296.035882 \nL 832.787997 204.63108 \nL 832.819819 204.082432 \nL 832.85164 205.399188 \nL 832.947104 216.701342 \nL 833.010747 209.349455 \nL 833.042569 190.695414 \nL 833.106212 210.88567 \nL 833.328962 337.074772 \nL 833.392605 298.230475 \nL 833.551712 221.968365 \nL 833.583534 221.639176 \nL 833.71082 232.392682 \nL 833.742641 227.345118 \nL 833.838105 194.865141 \nL 833.869927 211.214859 \nL 834.060856 337.513691 \nL 834.092677 337.074772 \nL 834.15632 300.973716 \nL 834.251784 218.786205 \nL 834.315427 219.883502 \nL 834.37907 224.053229 \nL 834.474535 234.806735 \nL 834.506356 234.148357 \nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 \nL 845.198368 217.24999 \nL 845.262011 196.730545 \nL 845.293833 206.057565 \nL 845.389297 271.23698 \nL 845.548405 343.109903 \nL 845.612048 326.869915 \nL 845.739333 250.388346 \nL 845.802976 251.485642 \nL 845.898441 269.920224 \nL 845.962084 260.483474 \nL 846.025727 225.260255 \nL 846.08937 249.620238 \nL 846.31212 348.376927 \nL 846.343941 347.169901 \nL 846.439406 300.754257 \nL 846.566691 244.462944 \nL 846.598513 245.011593 \nL 846.662156 257.411044 \nL 846.693977 254.667802 \nL 846.789442 200.461353 \nL 846.853085 229.320252 \nL 847.044013 342.561255 \nL 847.075835 340.91531 \nL 847.139478 298.998582 \nL 847.298585 211.324589 \nL 847.457693 233.489979 \nL 847.489514 225.260255 \nL 847.553157 183.562986 \nL 847.6168 210.9954 \nL 847.807729 337.294232 \nL 847.83955 337.074772 \nL 847.903193 295.816422 \nL 848.030479 208.361888 \nL 848.0623 211.544048 \nL 848.125943 217.24999 \nL 848.221408 225.918633 \nL 848.253229 222.626743 \nL 848.316872 181.258664 \nL 848.380515 210.337022 \nL 848.571444 338.172069 \nL 848.603265 337.294232 \nL 848.666908 297.791556 \nL 848.826016 207.045132 \nL 848.985123 219.225124 \nL 849.080587 184.770013 \nL 849.112409 194.645681 \nL 849.335159 337.513691 \nL 849.398802 325.443429 \nL 849.557909 210.337022 \nL 849.621552 219.993232 \nL 849.748838 230.527278 \nL 849.812481 207.92297 \nL 849.844302 188.83001 \nL 849.907945 206.825673 \nL 850.098874 331.36883 \nL 850.130696 330.600723 \nL 850.194339 289.561832 \nL 850.289803 209.788374 \nL 850.321624 212.092696 \nL 850.480732 237.549976 \nL 850.512553 236.891598 \nL 850.608018 217.908368 \nL 850.639839 228.003496 \nL 850.703482 253.460776 \nL 850.894411 336.855313 \nL 850.958054 321.712621 \nL 851.148982 218.127827 \nL 851.212625 224.382418 \nL 851.244447 226.357551 \nL 851.276268 224.931066 \nL 851.371733 199.144597 \nL 851.403554 213.738641 \nL 851.626304 333.673153 \nL 851.721769 315.128842 \nL 851.912698 206.606214 \nL 851.944519 211.873237 \nL 852.008162 224.821336 \nL 852.039984 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 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191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 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227.702836 315.238571 \nL 227.766479 291.427236 \nL 228.02105 135.391668 \nL 228.116515 126.503566 \nL 228.148336 128.039781 \nL 228.275622 168.200835 \nL 228.466551 311.727222 \nL 228.530194 285.831024 \nL 228.784765 105.764662 \nL 228.848408 100.278179 \nL 228.912051 102.143583 \nL 229.039337 142.085177 \nL 229.230266 295.377504 \nL 229.262087 291.536966 \nL 229.548481 84.696568 \nL 229.643945 97.864127 \nL 229.803052 143.182474 \nL 229.993981 278.369407 \nL 230.057624 251.156453 \nL 230.280374 103.789528 \nL 230.344017 117.396005 \nL 230.598589 209.239725 \nL 230.757696 299.108312 \nL 230.789518 298.559664 \nL 230.853161 280.564001 \nL 231.075911 157.666788 \nL 231.26684 168.310565 \nL 231.298661 167.761916 \nL 231.553233 301.632094 \nL 231.585054 296.4748 \nL 231.648697 262.239148 \nL 231.839626 164.908945 \nL 231.935091 146.254904 \nL 231.966912 138.573828 \nL 232.030555 148.120308 \nL 232.062376 146.035445 \nL 232.285127 300.863987 \nL 232.316948 295.048315 \nL 232.635163 98.961423 \nL 232.698806 100.168449 \nL 232.730627 101.704665 \nL 232.857913 151.302468 \nL 233.048842 295.487234 \nL 233.080663 291.207777 \nL 233.367056 101.046287 \nL 233.430699 107.191147 \nL 233.557985 130.014915 \nL 233.589807 127.710593 \nL 233.812557 294.280207 \nL 233.844378 289.561832 \nL 234.194415 111.690063 \nL 234.258057 119.480868 \nL 234.385343 155.801384 \nL 234.576272 292.414803 \nL 234.608094 288.245076 \nL 234.894487 125.625729 \nL 234.926308 127.491133 \nL 235.085416 148.559227 \nL 235.117237 147.68139 \nL 235.339987 312.166141 \nL 235.371809 306.569929 \nL 235.721845 120.687895 \nL 235.753666 121.346273 \nL 235.880952 144.608959 \nL 236.135524 304.046147 \nL 236.167345 296.4748 \nL 236.549203 193.877574 \nL 236.676489 220.322421 \nL 236.899239 337.513691 \nL 236.962882 327.528292 \nL 237.185632 225.589444 \nL 237.217454 226.79647 \nL 237.249275 224.272688 \nL 237.408383 203.314324 \nL 237.631133 332.466127 \nL 237.662954 326.211537 \nL 237.949347 160.958678 \nL 238.01299 163.592189 \nL 238.076633 163.921378 \nL 238.203919 202.985135 \nL 238.394848 321.273702 \nL 238.458491 289.122914 \nL 238.681241 151.083009 \nL 238.713063 147.791119 \nL 238.808527 148.778686 \nL 238.903991 167.871646 \nL 238.935813 165.786782 \nL 239.158563 329.283967 \nL 239.190385 321.93208 \nL 239.508599 152.948413 \nL 239.604064 156.459762 \nL 239.73135 197.059734 \nL 239.922278 324.236403 \nL 239.9541 319.188839 \nL 240.304136 128.36897 \nL 240.367779 132.428968 \nL 240.495065 176.430559 \nL 240.685993 307.996414 \nL 240.749636 280.89319 \nL 241.004208 127.052215 \nL 241.03603 127.710593 \nL 241.226958 154.594358 \nL 241.449709 319.408298 \nL 241.513352 309.532629 \nL 241.895209 177.088937 \nL 242.022495 208.361888 \nL 242.213424 332.136938 \nL 242.245245 331.039641 \nL 242.34071 290.988318 \nL 242.531638 190.256495 \nL 242.658924 164.799216 \nL 242.754389 172.260832 \nL 242.977139 315.896949 \nL 243.040782 285.062916 \nL 243.295354 120.029517 \nL 243.327175 116.298709 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345.414226 \nL 247.654894 330.600723 \nL 247.845823 238.208354 \nL 247.877645 242.048892 \nL 247.941288 246.438078 \nL 247.973109 242.59754 \nL 248.068573 234.916464 \nL 248.100395 239.41538 \nL 248.323145 354.192598 \nL 248.354967 351.120168 \nL 248.482252 275.296977 \nL 248.577717 229.539711 \nL 248.64136 231.075927 \nL 248.673181 230.966197 \nL 248.736824 220.651609 \nL 248.832289 201.010001 \nL 248.86411 206.496484 \nL 249.08686 340.586121 \nL 249.150503 302.94885 \nL 249.309611 191.573251 \nL 249.405075 187.513254 \nL 249.468718 192.01217 \nL 249.500539 189.378658 \nL 249.596004 178.844611 \nL 249.691468 261.6905 \nL 249.850575 324.785051 \nL 249.914218 283.526701 \nL 250.073326 161.507326 \nL 250.16879 158.764085 \nL 250.264255 163.043541 \nL 250.296076 161.946245 \nL 250.327897 161.507326 \nL 250.359719 153.387332 \nL 250.39154 165.567323 \nL 250.614291 321.602891 \nL 250.677934 284.953187 \nL 250.868862 168.420294 \nL 250.996148 160.958678 \nL 251.02797 161.946245 \nL 251.091613 166.44516 \nL 251.123434 154.923547 \nL 251.155256 164.031108 \nL 251.378006 320.834784 \nL 251.441649 286.379672 \nL 251.632578 152.509494 \nL 251.728042 140.878151 \nL 251.759863 142.304637 \nL 251.823506 155.472195 \nL 251.982614 235.135924 \nL 252.141721 317.433164 \nL 252.205364 301.412635 \nL 252.428114 181.039204 \nL 252.491757 185.318661 \nL 252.5554 187.184065 \nL 252.587221 185.977039 \nL 252.650864 180.819745 \nL 252.746329 254.887262 \nL 252.937258 337.403961 \nL 253.032722 300.863987 \nL 253.191829 239.41538 \nL 253.255472 243.585107 \nL 253.287294 241.939162 \nL 253.41458 213.628912 \nL 253.446401 225.260255 \nL 253.63733 351.229898 \nL 253.669151 351.120168 \nL 253.732794 312.60506 \nL 253.891902 212.202426 \nL 253.923723 209.568914 \nL 253.987366 211.763507 \nL 254.051009 221.419717 \nL 254.114652 216.152694 \nL 254.178295 203.533783 \nL 254.210116 217.35972 \nL 254.432866 352.656383 \nL 254.496509 311.178574 \nL 254.655617 224.711607 \nL 254.751081 216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 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196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 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348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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360.111952 184.660283 \nL 360.175595 198.37649 \nL 360.207417 194.97487 \nL 360.302881 126.942485 \nL 360.334703 142.633825 \nL 360.557453 272.992654 \nL 360.621096 260.044555 \nL 360.780203 149.546794 \nL 360.812025 150.314901 \nL 360.939311 163.921378 \nL 360.971132 160.848948 \nL 361.034775 119.261409 \nL 361.098418 149.107875 \nL 361.35299 288.683995 \nL 361.416633 247.206186 \nL 361.480275 172.91921 \nL 361.543918 174.016507 \nL 361.57574 174.235966 \nL 361.703026 187.184065 \nL 361.734847 185.757579 \nL 361.830312 137.695991 \nL 361.862133 152.180305 \nL 362.116705 306.899118 \nL 362.148526 299.437501 \nL 362.339455 182.136501 \nL 362.371277 183.014338 \nL 362.466741 195.413789 \nL 362.498562 194.865141 \nL 362.562205 153.497061 \nL 362.625848 173.138669 \nL 362.88042 323.907214 \nL 362.912241 314.799653 \nL 363.10317 206.715943 \nL 363.134992 209.568914 \nL 363.230456 219.993232 \nL 363.294099 194.97487 \nL 363.357742 150.534361 \nL 363.389563 159.861381 \nL 363.612314 292.853722 \nL 363.675957 281.002919 \nL 363.866885 170.505158 \nL 363.96235 181.478123 \nL 364.025993 187.952173 \nL 364.121457 139.451665 \nL 364.153279 150.095442 \nL 364.40785 275.187248 \nL 364.471493 258.61807 \nL 364.630601 179.064071 \nL 364.662422 182.026771 \nL 364.757886 197.827841 \nL 364.789708 195.633248 \nL 364.853351 150.534361 \nL 364.916994 166.774349 \nL 365.171565 292.524533 \nL 365.235208 280.125082 \nL 365.426137 192.560818 \nL 365.48978 199.802975 \nL 365.521602 202.436487 \nL 365.553423 196.511086 \nL 365.617066 144.060311 \nL 365.680709 175.772181 \nL 365.903459 291.427236 \nL 365.967102 276.613733 \nL 366.158031 168.420294 \nL 366.189852 168.091105 \nL 366.285317 178.954341 \nL 366.317138 176.979207 \nL 366.412603 119.480868 \nL 366.476246 149.107875 \nL 366.667174 268.493739 \nL 366.698996 267.835361 \nL 366.762639 222.626743 \nL 366.826282 150.973279 \nL 366.889925 159.971111 \nL 367.01721 180.161367 \nL 367.049032 186.635417 \nL 367.080853 185.318661 \nL 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 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159.203003 \nL 389.3877 188.72028 \nL 389.578629 316.884516 \nL 389.610451 316.555327 \nL 389.674094 271.12725 \nL 389.833201 188.61055 \nL 389.865022 189.488388 \nL 389.992308 201.55865 \nL 390.087773 157.337599 \nL 390.151416 185.099202 \nL 390.342344 310.959115 \nL 390.374166 310.520196 \nL 390.437809 269.152117 \nL 390.596916 186.745146 \nL 390.660559 196.181897 \nL 390.756024 215.494316 \nL 390.787845 210.666211 \nL 390.851488 177.747315 \nL 390.883309 188.939739 \nL 391.10606 317.981813 \nL 391.201524 297.462367 \nL 391.32881 229.320252 \nL 391.392453 237.111058 \nL 391.519739 263.007256 \nL 391.55156 256.094288 \nL 391.615203 206.606214 \nL 391.678846 227.015929 \nL 391.901596 326.211537 \nL 391.933418 325.22397 \nL 392.028882 286.92832 \nL 392.156168 237.440247 \nL 392.187989 242.158622 \nL 392.283454 261.141852 \nL 392.315275 256.423477 \nL 392.41074 198.486219 \nL 392.442561 210.446752 \nL 392.63349 331.47856 \nL 392.665311 330.161804 \nL 392.728954 288.793725 \nL 392.888062 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168.639753 \nL 528.288396 168.639753 \nL 528.352039 166.66462 \nL 528.383861 176.2111 \nL 528.606611 328.625589 \nL 528.670254 294.389937 \nL 528.829361 206.057565 \nL 528.861183 203.972702 \nL 528.924826 205.508917 \nL 528.988468 210.666211 \nL 529.083933 210.007833 \nL 529.115754 200.790542 \nL 529.147576 207.374321 \nL 529.370326 349.913142 \nL 529.433969 315.56776 \nL 529.593076 224.382418 \nL 529.688541 222.187825 \nL 529.784005 230.966197 \nL 529.847648 229.97863 \nL 529.879469 222.517014 \nL 529.911291 228.442415 \nL 530.134041 351.449357 \nL 530.165863 348.376927 \nL 530.261327 296.58453 \nL 530.388613 240.841866 \nL 530.420434 241.061325 \nL 530.515899 245.560241 \nL 530.643185 222.078095 \nL 530.675006 231.734305 \nL 530.897756 350.46179 \nL 530.929578 348.486656 \nL 531.025042 307.776955 \nL 531.18415 239.195921 \nL 531.279614 254.228884 \nL 531.311435 247.315915 \nL 531.4069 224.272688 \nL 531.438721 234.477546 \nL 531.62965 357.484488 \nL 531.661472 357.045569 \nL 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228.991063 \nL 545.853845 225.479714 \nL 545.917488 211.434319 \nL 545.949309 227.015929 \nL 546.17206 353.973139 \nL 546.203881 351.668816 \nL 546.39481 248.193753 \nL 546.522096 261.471041 \nL 546.553917 261.141852 \nL 546.681203 229.100793 \nL 546.713025 245.88943 \nL 546.935775 361.325026 \nL 546.967596 358.910974 \nL 547.063061 308.215873 \nL 547.190347 255.545639 \nL 547.222168 257.740233 \nL 547.285811 265.201849 \nL 547.317632 262.458608 \nL 547.444918 213.189993 \nL 547.47674 231.844034 \nL 547.667669 352.985572 \nL 547.69949 352.327194 \nL 547.763133 311.398033 \nL 547.92224 218.676476 \nL 548.017705 215.933234 \nL 548.081348 223.72404 \nL 548.113169 220.761339 \nL 548.208633 186.086768 \nL 548.240455 207.593781 \nL 548.463205 347.169901 \nL 548.526848 306.679658 \nL 548.685955 214.616478 \nL 548.78142 209.129996 \nL 548.813241 210.9954 \nL 548.845063 211.982967 \nL 548.876884 209.678644 \nL 548.972349 183.782446 \nL 549.22692 344.865578 \nL 549.258742 335.867746 \nL 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569.910873 275.626166 \nL 569.974516 190.037036 \nL 570.038159 197.827841 \nL 570.197266 223.61431 \nL 570.229087 227.125659 \nL 570.260909 219.444583 \nL 570.324552 164.689486 \nL 570.388195 190.037036 \nL 570.610945 322.04181 \nL 570.674588 280.234812 \nL 570.738231 193.109466 \nL 570.801874 195.633248 \nL 570.833695 195.523519 \nL 570.992803 226.68674 \nL 571.024624 222.407284 \nL 571.088267 172.480291 \nL 571.15191 197.498652 \nL 571.342839 325.333699 \nL 571.37466 325.00451 \nL 571.438303 282.539134 \nL 571.59741 195.962437 \nL 571.629232 195.852708 \nL 571.756518 203.094865 \nL 571.820161 186.086768 \nL 571.883804 197.388923 \nL 571.979268 275.845625 \nL 572.138375 340.476392 \nL 572.202018 305.143443 \nL 572.361126 204.192161 \nL 572.392947 204.850539 \nL 572.552054 230.966197 \nL 572.615697 199.254327 \nL 572.647519 214.06783 \nL 572.90209 352.436924 \nL 572.965733 333.782883 \nL 573.124841 247.535375 \nL 573.156662 250.498075 \nL 573.283948 267.835361 \nL 573.31577 258.947259 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207.92297 \nL 577.070702 216.262423 \nL 577.102524 212.202426 \nL 577.166167 172.91921 \nL 577.22981 191.134333 \nL 577.45256 329.283967 \nL 577.516203 316.116408 \nL 577.707132 205.728377 \nL 577.738953 206.386754 \nL 577.834418 213.080263 \nL 577.866239 210.007833 \nL 577.929882 162.385163 \nL 577.993525 183.892175 \nL 578.248097 323.687755 \nL 578.279918 317.981813 \nL 578.439025 231.844034 \nL 578.53449 240.073758 \nL 578.598133 251.156453 \nL 578.629954 246.986726 \nL 578.693597 194.097033 \nL 578.75724 217.688909 \nL 578.97999 340.147203 \nL 579.043633 334.331531 \nL 579.457312 181.917042 \nL 579.489134 193.987304 \nL 579.743706 334.441261 \nL 579.807348 322.590458 \nL 579.998277 204.301891 \nL 580.030099 206.386754 \nL 580.125563 219.334854 \nL 580.157385 215.384586 \nL 580.221027 157.00841 \nL 580.28467 180.819745 \nL 580.507421 328.40613 \nL 580.571064 310.520196 \nL 580.761992 181.917042 \nL 580.793814 182.794879 \nL 580.889278 191.463522 \nL 580.9211 188.500821 \nL 580.984743 132.648427 \nL 581.048386 149.327334 \nL 581.271136 295.926152 \nL 581.334779 280.454271 \nL 581.430243 179.28353 \nL 581.525708 185.867309 \nL 581.652993 198.925138 \nL 581.716636 162.165704 \nL 581.748458 134.074912 \nL 581.812101 147.68139 \nL 582.034851 297.462367 \nL 582.098494 285.282376 \nL 582.193958 187.732713 \nL 582.289423 200.571083 \nL 582.416709 219.773772 \nL 582.44853 215.604045 \nL 582.512173 160.519759 \nL 582.575816 173.797047 \nL 582.798566 314.251004 \nL 582.862209 301.083446 \nL 582.989495 197.279193 \nL 583.053138 204.74081 \nL 583.180424 227.125659 \nL 583.212245 222.736473 \nL 583.275888 175.99164 \nL 583.339531 196.401356 \nL 583.594103 327.199104 \nL 583.657746 307.557495 \nL 583.785032 227.784037 \nL 583.848675 236.781869 \nL 583.944139 260.483474 \nL 583.97596 255.106721 \nL 584.039603 194.97487 \nL 584.103246 218.127827 \nL 584.357818 335.319098 \nL 584.421461 321.383432 \nL 584.61239 225.040796 \nL 584.707854 229.539711 \nL 584.739676 222.846203 \nL 584.803318 176.320829 \nL 584.866961 205.838106 \nL 585.089712 342.232066 \nL 585.153355 329.503426 \nL 585.344283 212.202426 \nL 585.471569 232.063493 \nL 585.503391 230.746738 \nL 585.567034 173.797047 \nL 585.630677 192.999737 \nL 585.853427 331.259101 \nL 585.91707 316.226138 \nL 586.107999 200.571083 \nL 586.13982 200.022435 \nL 586.171641 202.985135 \nL 586.235284 202.546217 \nL 586.267106 198.15703 \nL 586.330749 137.476532 \nL 586.394392 154.813817 \nL 586.617142 296.365071 \nL 586.680785 283.636431 \nL 586.776249 181.807312 \nL 586.871714 191.463522 \nL 586.999 207.92297 \nL 587.030821 201.887839 \nL 587.094464 141.975448 \nL 587.158107 154.155439 \nL 587.380857 293.182911 \nL 587.4445 276.723463 \nL 587.539965 171.163536 \nL 587.603607 183.892175 \nL 587.762715 213.628912 \nL 587.794536 206.496484 \nL 587.858179 154.045709 \nL 587.921822 167.761916 \nL 588.144572 314.360734 \nL 588.208215 302.400202 \nL 588.399144 188.500821 \nL 588.430966 189.488388 \nL 588.52643 200.351623 \nL 588.558251 198.37649 \nL 588.621894 165.896512 \nL 588.653716 173.797047 \nL 588.940109 325.22397 \nL 589.003752 304.704524 \nL 589.162859 221.090528 \nL 589.194681 223.065662 \nL 589.258324 230.527278 \nL 589.290145 230.198089 \nL 589.353788 186.745146 \nL 589.385609 165.238134 \nL 589.449252 187.732713 \nL 589.703824 296.145611 \nL 589.767467 280.78346 \nL 589.958396 181.478123 \nL 590.022039 191.024603 \nL 590.05386 194.206763 \nL 590.085682 186.196498 \nL 590.149325 127.930052 \nL 590.212968 148.559227 \nL 590.435718 272.992654 \nL 590.499361 258.069421 \nL 590.626647 153.387332 \nL 590.69029 158.325166 \nL 590.817575 178.515422 \nL 590.849397 172.480291 \nL 590.91304 119.15168 \nL 590.976683 132.319238 \nL 591.199433 289.122914 \nL 591.263076 278.259678 \nL 591.454005 168.530024 \nL 591.581291 183.562986 \nL 591.613112 180.929475 \nL 591.676755 142.194907 \nL 591.740398 162.604622 \nL 591.99497 315.56776 \nL 592.058613 281.332108 \nL 592.21772 177.418126 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199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 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182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 653.155826 278.369407 \nL 653.283112 194.316493 \nL 653.346755 198.815408 \nL 653.442219 210.337022 \nL 653.47404 208.032699 \nL 653.569505 162.824082 \nL 653.601326 175.99164 \nL 653.855898 294.938585 \nL 653.919541 283.416972 \nL 654.078648 200.241894 \nL 654.142291 206.277025 \nL 654.205934 217.908368 \nL 654.237756 213.299723 \nL 654.33322 143.401933 \nL 654.396863 174.894344 \nL 654.587792 277.162381 \nL 654.619613 275.077518 \nL 654.683256 237.988895 \nL 654.810542 157.447329 \nL 654.842363 157.995977 \nL 654.969649 182.794879 \nL 655.001471 181.368393 \nL 655.096935 123.101947 \nL 655.160578 148.998145 \nL 655.351507 262.348878 \nL 655.383328 260.154285 \nL 655.446971 218.895935 \nL 655.606079 143.840852 \nL 655.765186 156.350032 \nL 655.797007 149.875983 \nL 655.86065 118.603031 \nL 655.892472 134.623561 \nL 656.115222 283.746161 \nL 656.178865 270.688332 \nL 656.369794 172.480291 \nL 656.528901 175.113803 \nL 656.592544 145.157608 \nL 656.624366 136.598694 \nL 656.656187 150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 211.434319 \nL 680.554107 315.56776 \nL 680.61775 312.934248 \nL 680.713215 263.885093 \nL 680.808679 219.554313 \nL 680.840501 221.419717 \nL 680.935965 240.402947 \nL 680.999608 233.928898 \nL 681.095072 181.258664 \nL 681.126894 185.867309 \nL 681.286001 288.025617 \nL 681.317823 286.489402 \nL 681.381465 272.224547 \nL 681.47693 164.689486 \nL 681.572394 173.02894 \nL 681.731502 204.960269 \nL 681.795145 191.682981 \nL 681.858787 160.958678 \nL 681.890609 167.981376 \nL 682.081538 281.880756 \nL 682.113359 278.808326 \nL 682.177002 231.734305 \nL 682.240645 161.726785 \nL 682.304288 173.797047 \nL 682.495217 211.873237 \nL 682.527038 207.154862 \nL 682.622503 166.335431 \nL 682.654324 173.577588 \nL 682.845253 297.023449 \nL 682.877074 295.267774 \nL 682.940717 253.680235 \nL 683.099825 166.55489 \nL 683.195289 175.333262 \nL 683.258932 182.685149 \nL 683.290753 181.039204 \nL 683.386218 153.277602 \nL 683.418039 162.275434 \nL 683.608968 278.918056 \nL 683.64079 276.504003 \nL 683.704432 234.806735 \nL 683.768075 167.761916 \nL 683.831718 175.333262 \nL 684.022647 212.312156 \nL 684.08629 201.668379 \nL 684.149933 174.784614 \nL 684.181754 180.490556 \nL 684.372683 305.582362 \nL 684.404505 305.362902 \nL 684.468148 268.16455 \nL 684.627255 183.014338 \nL 684.754541 204.301891 \nL 684.786362 209.568914 \nL 684.850005 206.496484 \nL 684.881827 206.167295 \nL 684.913648 200.571083 \nL 684.94547 207.154862 \nL 685.16822 314.251004 \nL 685.231863 307.447766 \nL 685.359149 226.467281 \nL 685.422792 232.173223 \nL 685.518256 253.460776 \nL 685.581899 246.876997 \nL 685.677363 216.701342 \nL 685.709185 223.61431 \nL 685.931935 323.468295 \nL 685.963756 321.273702 \nL 686.059221 269.042387 \nL 686.186507 199.034868 \nL 686.218328 200.132164 \nL 686.313793 211.763507 \nL 686.345614 206.496484 \nL 686.441078 186.086768 \nL 686.4729 194.426222 \nL 686.663829 312.056411 \nL 686.69565 309.31317 \nL 686.759293 263.665634 \nL 686.9184 172.91921 \nL 686.950222 172.480291 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205.179728 \nL 690.005083 213.189993 \nL 690.132368 229.649441 \nL 690.259654 203.094865 \nL 690.291476 210.446752 \nL 690.514226 315.348301 \nL 690.546047 312.056411 \nL 690.736976 209.129996 \nL 690.832441 232.282953 \nL 690.896084 240.073758 \nL 690.927905 237.549976 \nL 691.023369 214.506749 \nL 691.055191 219.883502 \nL 691.277941 322.480728 \nL 691.309763 319.188839 \nL 691.405227 272.334277 \nL 691.500691 235.684572 \nL 691.532513 238.427813 \nL 691.659799 261.58077 \nL 691.69162 255.874828 \nL 691.818906 217.35972 \nL 692.009835 320.725054 \nL 692.041656 317.872083 \nL 692.105299 273.980221 \nL 692.168942 206.386754 \nL 692.232585 213.628912 \nL 692.264407 212.970534 \nL 692.423514 225.589444 \nL 692.455335 220.43215 \nL 692.5508 196.511086 \nL 692.582621 204.74081 \nL 692.77355 327.199104 \nL 692.805371 325.662888 \nL 692.869014 285.172646 \nL 693.028122 203.643513 \nL 693.187229 223.175392 \nL 693.219051 217.798638 \nL 693.314515 197.718112 \nL 693.346336 206.606214 \nL 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697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 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236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 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188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 \nL 845.198368 217.24999 \nL 845.262011 196.730545 \nL 845.293833 206.057565 \nL 845.389297 271.23698 \nL 845.548405 343.109903 \nL 845.612048 326.869915 \nL 845.739333 250.388346 \nL 845.802976 251.485642 \nL 845.898441 269.920224 \nL 845.962084 260.483474 \nL 846.025727 225.260255 \nL 846.08937 249.620238 \nL 846.31212 348.376927 \nL 846.343941 347.169901 \nL 846.439406 300.754257 \nL 846.566691 244.462944 \nL 846.598513 245.011593 \nL 846.662156 257.411044 \nL 846.693977 254.667802 \nL 846.789442 200.461353 \nL 846.853085 229.320252 \nL 847.044013 342.561255 \nL 847.075835 340.91531 \nL 847.139478 298.998582 \nL 847.298585 211.324589 \nL 847.457693 233.489979 \nL 847.489514 225.260255 \nL 847.553157 183.562986 \nL 847.6168 210.9954 \nL 847.807729 337.294232 \nL 847.83955 337.074772 \nL 847.903193 295.816422 \nL 848.030479 208.361888 \nL 848.0623 211.544048 \nL 848.125943 217.24999 \nL 848.221408 225.918633 \nL 848.253229 222.626743 \nL 848.316872 181.258664 \nL 848.380515 210.337022 \nL 848.571444 338.172069 \nL 848.603265 337.294232 \nL 848.666908 297.791556 \nL 848.826016 207.045132 \nL 848.985123 219.225124 \nL 849.080587 184.770013 \nL 849.112409 194.645681 \nL 849.335159 337.513691 \nL 849.398802 325.443429 \nL 849.557909 210.337022 \nL 849.621552 219.993232 \nL 849.748838 230.527278 \nL 849.812481 207.92297 \nL 849.844302 188.83001 \nL 849.907945 206.825673 \nL 850.098874 331.36883 \nL 850.130696 330.600723 \nL 850.194339 289.561832 \nL 850.289803 209.788374 \nL 850.321624 212.092696 \nL 850.480732 237.549976 \nL 850.512553 236.891598 \nL 850.608018 217.908368 \nL 850.639839 228.003496 \nL 850.703482 253.460776 \nL 850.894411 336.855313 \nL 850.958054 321.712621 \nL 851.148982 218.127827 \nL 851.212625 224.382418 \nL 851.244447 226.357551 \nL 851.276268 224.931066 \nL 851.371733 199.144597 \nL 851.403554 213.738641 \nL 851.626304 333.673153 \nL 851.721769 315.128842 \nL 851.912698 206.606214 \nL 851.944519 211.873237 \nL 852.008162 224.821336 \nL 852.039984 222.626743 \nL 852.103626 191.682981 \nL 852.135448 200.461353 \nL 852.39002 332.356397 \nL 852.421841 330.052075 \nL 852.485484 283.85589 \nL 852.61277 217.908368 \nL 852.644591 218.127827 \nL 852.676413 219.444583 \nL 852.771877 228.881334 \nL 852.83552 202.217028 \nL 852.867342 172.480291 \nL 852.930985 188.939739 \nL 853.153735 324.016944 \nL 853.217378 310.191007 \nL 853.408307 208.032699 \nL 853.535592 220.761339 \nL 853.567414 217.140261 \nL 853.631057 177.308396 \nL 853.6947 192.451088 \nL 853.91745 332.356397 \nL 853.981093 320.725054 \nL 854.108379 216.481883 \nL 854.203843 220.980798 \nL 854.299308 232.831601 \nL 854.331129 231.624575 \nL 854.394772 181.258664 \nL 854.458415 197.169464 \nL 854.681165 338.940176 \nL 854.744808 325.882348 \nL 854.872094 210.9954 \nL 854.935737 211.214859 \nL 854.967558 210.775941 \nL 854.99938 215.274856 \nL 855.063023 214.06783 \nL 855.126666 188.281362 \nL 855.158487 172.041373 \nL 855.22213 193.109466 \nL 855.44488 330.052075 \nL 855.508523 320.834784 \nL 855.699452 202.875406 \nL 855.731273 204.850539 \nL 855.826738 214.506749 \nL 855.858559 211.653778 \nL 855.922202 171.931643 \nL 855.985845 192.341359 \nL 856.240417 316.555327 \nL 856.30406 296.365071 \nL 856.431346 226.028363 \nL 856.494989 233.489979 \nL 856.590453 250.498075 \nL 856.622275 243.914296 \nL 856.685917 189.268928 \nL 856.74956 213.738641 \nL 857.004132 334.002342 \nL 857.067775 323.468295 \nL 857.258704 241.500244 \nL 857.290525 247.645104 \nL 857.354168 254.996991 \nL 857.38599 247.535375 \nL 857.449633 187.952173 \nL 857.513276 210.117563 \nL 857.736026 333.782883 \nL 857.799669 320.615324 \nL 857.990598 211.434319 \nL 858.117883 227.015929 \nL 858.149705 226.028363 \nL 858.213348 175.772181 \nL 858.276991 193.877574 \nL 858.499741 328.625589 \nL 858.563384 316.116408 \nL 858.754313 219.554313 \nL 858.786134 224.492147 \nL 858.881599 231.734305 \nL 858.91342 228.661874 \nL 858.977063 177.308396 \nL 859.040706 198.486219 \nL 859.263456 338.610988 \nL 859.327099 326.979644 \nL 859.518028 214.945667 \nL 859.549849 215.055397 \nL 859.645314 221.748906 \nL 859.740778 178.186233 \nL 859.804421 197.608382 \nL 859.899885 276.284544 \nL 860.027171 324.126673 \nL 860.058993 320.725054 \nL 860.122636 276.284544 \nL 860.2181 209.020266 \nL 860.249922 210.007833 \nL 860.313564 214.28729 \nL 860.345386 219.554313 \nL 860.409029 218.566746 \nL 860.44085 213.080263 \nL 860.504493 163.592189 \nL 860.568136 178.734882 \nL 860.822708 314.031545 \nL 860.854529 302.509931 \nL 860.981815 194.535952 \nL 861.045458 198.815408 \nL 861.172744 208.910536 \nL 861.204565 205.618647 \nL 861.268208 161.507326 \nL 861.331851 181.478123 \nL 861.459137 270.249413 \nL 861.586423 307.008847 \nL 861.618245 300.973716 \nL 861.809173 214.616478 \nL 861.872816 225.479714 \nL 861.904638 231.624575 \nL 861.936459 231.075927 \nL 862.000102 191.90244 \nL 862.031924 171.053806 \nL 862.095567 190.037036 \nL 862.222852 272.114817 \nL 862.350138 302.071013 \nL 862.38196 298.340205 \nL 862.795639 165.567323 \nL 862.82746 176.869478 \nL 862.954746 271.34671 \nL 863.082032 319.737487 \nL 863.113853 318.201272 \nL 863.177496 272.005088 \nL 863.336604 175.99164 \nL 863.368425 175.113803 \nL 863.400247 176.869478 \nL 863.46389 180.380826 \nL 863.495711 176.979207 \nL 863.559354 151.960846 \nL 863.591175 160.739218 \nL 863.845747 316.335868 \nL 863.90939 299.87642 \nL 864.100319 195.0846 \nL 864.163962 200.900272 \nL 864.227605 199.912705 \nL 864.291248 161.287867 \nL 864.323069 135.281939 \nL 864.386712 151.192738 \nL 864.482176 229.210522 \nL 864.609462 280.234812 \nL 864.641284 279.027785 \nL 864.704927 235.794302 \nL 864.76857 166.44516 \nL 864.832213 176.650018 \nL 864.99132 193.767844 \nL 865.023141 186.525687 \nL 865.086784 137.037613 \nL 865.150427 147.242471 \nL 865.245892 229.8689 \nL 865.373177 289.671562 \nL 865.404999 288.025617 \nL 865.468642 244.133755 \nL 865.532285 173.138669 \nL 865.595928 179.832178 \nL 865.755035 192.890007 \nL 865.818678 161.178137 \nL 865.850499 143.511663 \nL 865.914142 156.350032 \nL 866.009607 231.185656 \nL 866.136893 284.843457 \nL 866.168714 283.307242 \nL 866.232357 238.318084 \nL 866.296 168.639753 \nL 866.359643 168.859213 \nL 866.423286 172.480291 \nL 866.51875 181.807312 \nL 866.550572 176.320829 \nL 866.614215 138.244639 \nL 866.677858 157.557058 \nL 866.773322 219.005665 \nL 866.932429 278.149948 \nL 866.996072 260.154285 \nL 867.123358 180.380826 \nL 867.187001 187.622984 \nL 867.282465 208.361888 \nL 867.314287 205.947836 \nL 867.37793 168.968942 \nL 867.409751 180.271097 \nL 867.696144 320.505595 \nL 867.759787 307.557495 \nL 867.918895 230.966197 \nL 867.982538 236.23322 \nL 868.046181 245.340782 \nL 868.078002 236.781869 \nL 868.141645 179.28353 \nL 868.205288 203.533783 \nL 868.428038 326.869915 \nL 868.491681 313.812086 \nL 868.68261 186.635417 \nL 868.714431 191.573251 \nL 868.809896 205.508917 \nL 868.841717 202.985135 \nL 868.90536 170.944076 \nL 868.969003 189.159199 \nL 869.191753 336.965043 \nL 869.255396 326.430996 \nL 869.446325 218.457016 \nL 869.478146 219.554313 \nL 869.573611 227.893767 \nL 869.605432 223.833769 \nL 869.669075 173.577588 \nL 869.732718 186.525687 \nL 869.955468 323.687755 \nL 870.019111 307.228306 \nL 870.273683 167.761916 \nL 870.305505 165.238134 \nL 870.337326 166.225701 \nL 870.369147 169.517591 \nL 870.400969 156.569492 \nL 870.464612 173.577588 \nL 870.751005 346.511523 \nL 870.782827 343.987741 \nL 870.941934 252.582939 \nL 871.005577 232.941331 \nL 871.037398 240.293218 \nL 871.132863 266.518605 \nL 871.164684 253.021857 \nL 871.196506 247.096456 \nL 871.260149 248.193753 \nL 871.323791 263.336445 \nL 871.482899 316.335868 \nL 871.51472 314.909382 \nL 871.578363 292.743992 \nL 871.769292 214.397019 \nL 871.864756 223.50458 \nL 871.896578 215.713775 \nL 871.960221 170.834347 \nL 872.023864 180.819745 \nL 872.15115 260.264015 \nL 872.278435 293.841289 \nL 872.342078 276.833192 \nL 872.501186 199.693246 \nL 872.533007 203.862972 \nL 872.628472 215.494316 \nL 872.660293 207.92297 \nL 872.723936 158.654355 \nL 872.787579 173.906777 \nL 872.883043 234.258087 \nL 873.042151 305.801821 \nL 873.073972 304.375335 \nL 873.169436 262.568337 \nL 873.296722 207.154862 \nL 873.328544 212.092696 \nL 873.392187 220.43215 \nL 873.424008 211.324589 \nL 873.487651 168.639753 \nL 873.551294 191.573251 \nL 873.805866 325.882348 \nL 873.837687 314.470464 \nL 874.028616 201.119731 \nL 874.060437 204.52135 \nL 874.155902 208.252159 \nL 874.187723 204.301891 \nL 874.251366 151.851116 \nL 874.315009 163.811649 \nL 874.442295 267.835361 \nL 874.537759 295.048315 \nL 874.569581 292.305074 \nL 874.633224 246.328349 \nL 874.728688 172.699751 \nL 874.76051 174.455425 \nL 874.855974 184.111635 \nL 874.919617 181.148934 \nL 874.98326 140.878151 \nL 875.015081 124.199244 \nL 875.078724 140.658692 \nL 875.333296 295.267774 \nL 875.396939 257.411044 \nL 875.556046 178.844611 \nL 875.587868 178.295963 \nL 875.683332 187.184065 \nL 875.715154 181.587853 \nL 875.778797 144.49923 \nL 875.84244 162.714352 \nL 876.06519 303.716958 \nL 876.128833 289.671562 \nL 876.319761 182.026771 \nL 876.415226 191.79271 \nL 876.447047 191.244062 \nL 876.542512 133.087346 \nL 876.637976 158.105707 \nL 876.828905 278.588867 \nL 876.860726 276.394274 \nL 876.924369 231.734305 \nL 876.988012 157.776518 \nL 877.051655 163.811649 \nL 877.210763 182.46569 \nL 877.242584 176.320829 \nL 877.306227 140.329503 \nL 877.36987 161.617056 \nL 877.465334 229.100793 \nL 877.624442 306.02128 \nL 877.656263 303.607228 \nL 877.783549 217.908368 \nL 877.879013 190.805144 \nL 877.910835 193.987304 \nL 877.974478 202.655946 \nL 878.006299 198.815408 \nL 878.069942 160.958678 \nL 878.101764 172.480291 \nL 878.356335 312.275871 \nL 878.4518 296.913719 \nL 878.610907 205.728377 \nL 878.67455 214.506749 \nL 878.738193 209.678644 \nL 878.801836 154.484628 \nL 878.833657 126.174377 \nL 878.8973 140.658692 \nL 879.12005 270.139683 \nL 879.183693 254.448343 \nL 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"
      },
      "metadata": {
       "needs_background": "light"
@@ -236,8 +248,8 @@
       "text/plain": [
        "<Figure size 1080x576 with 1 Axes>"
       ],
-      "image/png": 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403.048897 333.298061 \nL 403.621009 362.497703 \nL 404.193121 377.362976 \nL 404.765233 384.264709 \nL 405.337345 373.823625 \nL 405.909457 357.71958 \nL 406.48157 314.185568 \nL 407.053682 248.530616 \nL 407.625794 203.580864 \nL 408.197906 178.451476 \nL 409.914242 134.740496 \nL 410.486354 132.793854 \nL 411.058466 128.723601 \nL 411.630578 130.316308 \nL 412.202691 135.625334 \nL 412.774803 114.743166 \nL 413.346915 119.344322 \nL 413.919027 142.173133 \nL 415.635363 274.72181 \nL 416.779587 367.098859 \nL 417.351699 395.236696 \nL 417.923812 408.686228 \nL 418.495924 417.357637 \nL 419.068036 421.073955 \nL 419.640148 417.003701 \nL 420.21226 399.483916 \nL 421.356484 340.55373 \nL 422.500708 269.589752 \nL 423.07282 250.477259 \nL 423.644933 237.912564 \nL 424.217045 249.769389 \nL 424.789157 271.89033 \nL 425.361269 285.516829 \nL 425.933381 292.064628 \nL 426.505493 270.297622 \nL 427.077605 260.741375 \nL 427.649717 261.09531 \nL 428.221829 262.511051 \nL 428.793941 267.466141 \nL 429.366053 286.932569 \nL 430.510278 340.730697 \nL 431.08239 358.781385 \nL 431.654502 369.576404 \nL 432.226614 372.407885 \nL 432.798726 370.107307 \nL 433.370838 358.604418 \nL 433.94295 334.359867 \nL 434.515062 293.657335 \nL 436.231399 212.429241 \nL 436.803511 210.836533 \nL 437.375623 208.005052 \nL 437.947735 211.367436 \nL 438.519847 212.075306 \nL 439.091959 222.693357 \nL 439.664071 210.128663 \nL 440.236183 162.170463 \nL 440.808295 137.748944 \nL 441.380408 145.004613 \nL 441.95252 153.852989 \nL 442.524632 171.903677 \nL 443.096744 203.049962 \nL 444.240968 281.977479 \nL 444.81308 313.831633 \nL 445.385192 328.519938 \nL 445.957304 335.598639 \nL 446.529416 336.129542 \nL 447.101529 329.050841 \nL 447.673641 308.699575 \nL 448.817865 235.611987 \nL 449.389977 198.802741 \nL 449.962089 184.645339 \nL 450.534201 184.291404 \nL 451.106313 191.01617 \nL 451.678425 200.749384 \nL 452.250537 206.76628 \nL 452.82265 209.774728 \nL 453.394762 197.563968 \nL 453.966874 144.119775 \nL 454.538986 118.105549 \nL 455.111098 125.18425 \nL 456.255322 159.692918 \nL 456.827434 195.794293 \nL 457.971658 282.685349 \nL 458.543771 315.247374 \nL 459.115883 334.890769 \nL 459.687995 348.517269 \nL 460.260107 355.419002 \nL 460.832219 353.118424 \nL 461.404331 339.668892 \nL 461.976443 320.733367 \nL 462.548555 285.693797 \nL 463.120667 240.036175 \nL 463.692779 208.712923 \nL 464.264891 197.917903 \nL 464.837004 196.325196 \nL 465.409116 204.288734 \nL 465.981228 213.844981 \nL 466.55334 217.738266 \nL 467.125452 203.049962 \nL 467.697564 151.552411 \nL 468.269676 134.209594 \nL 468.841788 146.951256 \nL 469.4139 171.195807 \nL 469.986012 201.457254 \nL 471.130237 304.275387 \nL 471.702349 345.154886 \nL 472.274461 370.461242 \nL 472.846573 383.733806 \nL 473.418685 387.80406 \nL 473.990797 387.80406 \nL 474.562909 369.399437 \nL 475.135021 320.379432 \nL 475.707133 237.381662 \nL 476.279246 200.926351 \nL 476.851358 195.794293 \nL 477.42347 195.617326 \nL 477.995582 186.591982 \nL 479.139806 196.856098 \nL 479.711918 196.148228 \nL 480.28403 198.094871 \nL 480.856142 191.72404 \nL 481.428254 143.588873 \nL 482.000367 107.133562 \nL 482.572479 108.549303 \nL 483.144591 126.423023 \nL 483.716703 149.251834 \nL 485.433039 294.188238 \nL 486.005151 320.910334 \nL 486.577263 334.182899 \nL 487.149375 338.076185 \nL 487.721488 333.651996 \nL 488.2936 315.778276 \nL 488.865712 259.502603 \nL 489.437824 171.72671 \nL 490.009936 141.111327 \nL 490.582048 140.757392 \nL 491.726272 146.597321 \nL 492.298384 155.091762 \nL 492.870496 159.162015 \nL 493.442609 153.499054 \nL 494.014721 154.383892 \nL 494.586833 140.93436 \nL 495.158945 89.43681 \nL 495.731057 62.537746 \nL 496.303169 69.085544 \nL 496.875281 89.082875 \nL 497.447393 116.689809 \nL 498.019505 168.541294 \nL 498.591617 233.134441 \nL 499.163729 280.384771 \nL 499.735842 306.398997 \nL 500.307954 325.334523 \nL 500.880066 336.837412 \nL 501.452178 338.43012 \nL 502.02429 322.149107 \nL 502.596402 277.376323 \nL 503.168514 204.819637 \nL 503.740626 163.409236 \nL 504.312738 152.083314 \nL 504.88485 153.852989 \nL 505.456963 150.667574 \nL 506.029075 149.782736 \nL 506.601187 154.737827 \nL 507.173299 158.10021 \nL 507.745411 164.117106 \nL 508.317523 155.091762 \nL 508.889635 115.981939 \nL 509.461747 95.276738 \nL 510.033859 102.178472 \nL 511.178084 151.021509 \nL 512.89442 304.452355 \nL 513.466532 330.997484 \nL 514.038644 348.340301 \nL 514.610756 352.056619 \nL 515.182868 348.871204 \nL 515.75498 329.404776 \nL 516.327092 277.022388 \nL 516.899205 184.822307 \nL 517.471317 155.9766 \nL 518.615541 158.277178 \nL 519.187653 155.799632 \nL 519.759765 160.754723 \nL 520.331877 170.310969 \nL 520.903989 171.549742 \nL 521.476101 170.664904 \nL 522.048213 155.622665 \nL 522.620326 108.195367 \nL 523.192438 76.872115 \nL 523.76455 80.234498 \nL 524.336662 95.276738 \nL 524.908774 117.220711 \nL 526.62511 261.449246 \nL 527.197222 289.941017 \nL 527.769334 305.337192 \nL 528.341447 311.531056 \nL 528.913559 307.991705 \nL 529.485671 288.879212 \nL 530.057783 235.965922 \nL 530.629895 149.251834 \nL 531.202007 116.689809 \nL 531.774119 118.813419 \nL 532.918343 134.032626 \nL 534.062567 154.914794 \nL 534.63468 156.330535 \nL 535.206792 156.507502 \nL 535.778904 146.597321 \nL 536.351016 101.647569 \nL 536.923128 88.551972 \nL 537.49524 103.771179 \nL 538.639464 143.411905 \nL 539.211576 180.752053 \nL 540.355801 282.685349 \nL 540.927913 318.432789 \nL 541.500025 338.43012 \nL 542.072137 350.286944 \nL 542.644249 355.772937 \nL 543.216361 351.879652 \nL 543.788473 332.767159 \nL 544.932697 256.14022 \nL 545.504809 213.668013 \nL 546.076922 183.052631 \nL 546.649034 170.487937 \nL 547.221146 169.957034 \nL 547.793258 175.089093 \nL 548.36537 184.468371 \nL 548.937482 189.069527 \nL 549.509594 182.875664 \nL 550.081706 139.872555 \nL 550.653818 121.821867 \nL 551.22593 140.403457 \nL 552.370155 190.3083 \nL 553.514379 270.474589 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403.048897 333.298061 \nL 403.621009 362.497703 \nL 404.193121 377.362976 \nL 404.765233 384.264709 \nL 405.337345 373.823625 \nL 405.909457 357.71958 \nL 406.48157 314.185568 \nL 407.053682 248.530616 \nL 407.625794 203.580864 \nL 408.197906 178.451476 \nL 409.914242 134.740496 \nL 410.486354 132.793854 \nL 411.058466 128.723601 \nL 411.630578 130.316308 \nL 412.202691 135.625334 \nL 412.774803 114.743166 \nL 413.346915 119.344322 \nL 413.919027 142.173133 \nL 415.635363 274.72181 \nL 416.779587 367.098859 \nL 417.351699 395.236696 \nL 417.923812 408.686228 \nL 418.495924 417.357637 \nL 419.068036 421.073955 \nL 419.640148 417.003701 \nL 420.21226 399.483916 \nL 421.356484 340.55373 \nL 422.500708 269.589752 \nL 423.07282 250.477259 \nL 423.644933 237.912564 \nL 424.217045 249.769389 \nL 424.789157 271.89033 \nL 425.361269 285.516829 \nL 425.933381 292.064628 \nL 426.505493 270.297622 \nL 427.077605 260.741375 \nL 427.649717 261.09531 \nL 428.221829 262.511051 \nL 428.793941 267.466141 \nL 429.366053 286.932569 \nL 430.510278 340.730697 \nL 431.08239 358.781385 \nL 431.654502 369.576404 \nL 432.226614 372.407885 \nL 432.798726 370.107307 \nL 433.370838 358.604418 \nL 433.94295 334.359867 \nL 434.515062 293.657335 \nL 436.231399 212.429241 \nL 436.803511 210.836533 \nL 437.375623 208.005052 \nL 437.947735 211.367436 \nL 438.519847 212.075306 \nL 439.091959 222.693357 \nL 439.664071 210.128663 \nL 440.236183 162.170463 \nL 440.808295 137.748944 \nL 441.380408 145.004613 \nL 441.95252 153.852989 \nL 442.524632 171.903677 \nL 443.096744 203.049962 \nL 444.240968 281.977479 \nL 444.81308 313.831633 \nL 445.385192 328.519938 \nL 445.957304 335.598639 \nL 446.529416 336.129542 \nL 447.101529 329.050841 \nL 447.673641 308.699575 \nL 448.817865 235.611987 \nL 449.389977 198.802741 \nL 449.962089 184.645339 \nL 450.534201 184.291404 \nL 451.106313 191.01617 \nL 451.678425 200.749384 \nL 452.250537 206.76628 \nL 452.82265 209.774728 \nL 453.394762 197.563968 \nL 453.966874 144.119775 \nL 454.538986 118.105549 \nL 455.111098 125.18425 \nL 456.255322 159.692918 \nL 456.827434 195.794293 \nL 457.971658 282.685349 \nL 458.543771 315.247374 \nL 459.115883 334.890769 \nL 459.687995 348.517269 \nL 460.260107 355.419002 \nL 460.832219 353.118424 \nL 461.404331 339.668892 \nL 461.976443 320.733367 \nL 462.548555 285.693797 \nL 463.120667 240.036175 \nL 463.692779 208.712923 \nL 464.264891 197.917903 \nL 464.837004 196.325196 \nL 465.409116 204.288734 \nL 465.981228 213.844981 \nL 466.55334 217.738266 \nL 467.125452 203.049962 \nL 467.697564 151.552411 \nL 468.269676 134.209594 \nL 468.841788 146.951256 \nL 469.4139 171.195807 \nL 469.986012 201.457254 \nL 471.130237 304.275387 \nL 471.702349 345.154886 \nL 472.274461 370.461242 \nL 472.846573 383.733806 \nL 473.418685 387.80406 \nL 473.990797 387.80406 \nL 474.562909 369.399437 \nL 475.135021 320.379432 \nL 475.707133 237.381662 \nL 476.279246 200.926351 \nL 476.851358 195.794293 \nL 477.42347 195.617326 \nL 477.995582 186.591982 \nL 479.139806 196.856098 \nL 479.711918 196.148228 \nL 480.28403 198.094871 \nL 480.856142 191.72404 \nL 481.428254 143.588873 \nL 482.000367 107.133562 \nL 482.572479 108.549303 \nL 483.144591 126.423023 \nL 483.716703 149.251834 \nL 485.433039 294.188238 \nL 486.005151 320.910334 \nL 486.577263 334.182899 \nL 487.149375 338.076185 \nL 487.721488 333.651996 \nL 488.2936 315.778276 \nL 488.865712 259.502603 \nL 489.437824 171.72671 \nL 490.009936 141.111327 \nL 490.582048 140.757392 \nL 491.726272 146.597321 \nL 492.298384 155.091762 \nL 492.870496 159.162015 \nL 493.442609 153.499054 \nL 494.014721 154.383892 \nL 494.586833 140.93436 \nL 495.158945 89.43681 \nL 495.731057 62.537746 \nL 496.303169 69.085544 \nL 496.875281 89.082875 \nL 497.447393 116.689809 \nL 498.019505 168.541294 \nL 498.591617 233.134441 \nL 499.163729 280.384771 \nL 499.735842 306.398997 \nL 500.307954 325.334523 \nL 500.880066 336.837412 \nL 501.452178 338.43012 \nL 502.02429 322.149107 \nL 502.596402 277.376323 \nL 503.168514 204.819637 \nL 503.740626 163.409236 \nL 504.312738 152.083314 \nL 504.88485 153.852989 \nL 505.456963 150.667574 \nL 506.029075 149.782736 \nL 506.601187 154.737827 \nL 507.173299 158.10021 \nL 507.745411 164.117106 \nL 508.317523 155.091762 \nL 508.889635 115.981939 \nL 509.461747 95.276738 \nL 510.033859 102.178472 \nL 511.178084 151.021509 \nL 512.89442 304.452355 \nL 513.466532 330.997484 \nL 514.038644 348.340301 \nL 514.610756 352.056619 \nL 515.182868 348.871204 \nL 515.75498 329.404776 \nL 516.327092 277.022388 \nL 516.899205 184.822307 \nL 517.471317 155.9766 \nL 518.615541 158.277178 \nL 519.187653 155.799632 \nL 519.759765 160.754723 \nL 520.331877 170.310969 \nL 520.903989 171.549742 \nL 521.476101 170.664904 \nL 522.048213 155.622665 \nL 522.620326 108.195367 \nL 523.192438 76.872115 \nL 523.76455 80.234498 \nL 524.336662 95.276738 \nL 524.908774 117.220711 \nL 526.62511 261.449246 \nL 527.197222 289.941017 \nL 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"
      },
      "metadata": {
       "needs_background": "light"
@@ -389,8 +401,8 @@
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ],
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"
      },
      "metadata": {
       "needs_background": "light"
@@ -402,8 +414,8 @@
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
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"
      },
      "metadata": {
       "needs_background": "light"
@@ -1032,8 +1044,8 @@
       "text/plain": [
        "<Figure size 1080x576 with 1 Axes>"
       ],
-      "image/png": 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"
      },
      "metadata": {
       "needs_background": "light"
@@ -1055,8 +1067,8 @@
    "name": "python3"
   },
   "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
+   "name": "python3",
+   "display_name": "Python 3.8.10 64-bit ('tmp': conda)"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1068,7 +1080,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.8.10"
   },
   "nteract": {
    "version": "nteract-front-end@1.0.0"
@@ -1077,6 +1089,9 @@
    "interpreter": {
     "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
    }
+  },
+  "interpreter": {
+   "hash": "205095b8b86ea82a021a8fc2ffba2b3bc9f8d0be25999901963b225939f75778"
   }
  },
  "nbformat": 4,

From a9ecd39401ed95f576785692982352d8f5e07d86 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Thu, 12 Aug 2021 09:38:35 -0400
Subject: [PATCH 364/368] small edit to show we have 25 lessons!

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 64ab0a82e2..99fe2da988 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@
 
 > � Travel around the world as we explore Machine Learning by means of world cultures �
 
-Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our forthcoming 'Data Science for Beginners' curriculum, as well!
+Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson (plus one!) curriculum all about **Machine Learning**. In this curriculum, you will learn about what is sometimes called **classic machine learning**, using primarily Scikit-learn as a library and avoiding deep learning, which is covered in our forthcoming 'AI for Beginners' curriculum. Pair these lessons with our forthcoming 'Data Science for Beginners' curriculum, as well!
 
 Travel with us around the world as we apply these classic techniques to data from many areas of the world. Each lesson includes pre- and post-lesson quizzes, written instructions to complete the lesson, a solution, an assignment and more. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.
 

From 2ee683431d15abf5813e2ec59bdabe1bdcc1cc6b Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Thu, 12 Aug 2021 22:08:31 -0400
Subject: [PATCH 365/368] ci: add Azure Static Web Apps workflow file
 on-behalf-of: @Azure opensource@microsoft.com

---
 ...-static-web-apps-white-water-09ec41f0f.yml | 45 +++++++++++++++++++
 1 file changed, 45 insertions(+)
 create mode 100644 .github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml

diff --git a/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml b/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml
new file mode 100644
index 0000000000..c448847034
--- /dev/null
+++ b/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml
@@ -0,0 +1,45 @@
+name: Azure Static Web Apps CI/CD
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    types: [opened, synchronize, reopened, closed]
+    branches:
+      - main
+
+jobs:
+  build_and_deploy_job:
+    if: github.event_name == 'push' || (github.event_name == 'pull_request' && github.event.action != 'closed')
+    runs-on: ubuntu-latest
+    name: Build and Deploy Job
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          submodules: true
+      - name: Build And Deploy
+        id: builddeploy
+        uses: Azure/static-web-apps-deploy@v1
+        with:
+          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_WHITE_WATER_09EC41F0F }}
+          repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments)
+          action: "upload"
+          ###### Repository/Build Configurations - These values can be configured to match your app requirements. ######
+          # For more information regarding Static Web App workflow configurations, please visit: https://aka.ms/swaworkflowconfig
+          app_location: "/quiz-app" # App source code path
+          api_location: "" # Api source code path - optional
+          output_location: "dist" # Built app content directory - optional
+          ###### End of Repository/Build Configurations ######
+
+  close_pull_request_job:
+    if: github.event_name == 'pull_request' && github.event.action == 'closed'
+    runs-on: ubuntu-latest
+    name: Close Pull Request Job
+    steps:
+      - name: Close Pull Request
+        id: closepullrequest
+        uses: Azure/static-web-apps-deploy@v1
+        with:
+          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_WHITE_WATER_09EC41F0F }}
+          action: "close"

From e833c5f9b9f282a5d94f7b528d27c9b3bcb175f9 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Thu, 12 Aug 2021 22:11:48 -0400
Subject: [PATCH 366/368] path to quizzes needs to change

---
 1-Introduction/1-intro-to-ML/README.md        |  4 +-
 .../1-intro-to-ML/translations/README.fr.md   |  4 +-
 .../1-intro-to-ML/translations/README.id.md   |  4 +-
 .../1-intro-to-ML/translations/README.it.md   |  4 +-
 .../1-intro-to-ML/translations/README.ja.md   |  4 +-
 .../1-intro-to-ML/translations/README.tr.md   |  4 +-
 .../translations/README.zh-cn.md              |  4 +-
 1-Introduction/2-history-of-ML/README.md      |  4 +-
 .../2-history-of-ML/translations/README.es.md |  4 +-
 .../2-history-of-ML/translations/README.fr.md |  4 +-
 .../2-history-of-ML/translations/README.id.md |  4 +-
 .../2-history-of-ML/translations/README.it.md |  4 +-
 .../2-history-of-ML/translations/README.ja.md |  4 +-
 .../2-history-of-ML/translations/README.tr.md |  4 +-
 .../translations/README.zh-cn.md              |  4 +-
 1-Introduction/3-fairness/README.md           |  4 +-
 .../3-fairness/translations/README.id.md      | 14 +++---
 .../3-fairness/translations/README.it.md      | 24 +++++-----
 .../3-fairness/translations/README.ja.md      | 24 +++++-----
 .../3-fairness/translations/README.zh-cn.md   |  4 +-
 1-Introduction/4-techniques-of-ML/README.md   |  4 +-
 .../translations/README.es.md                 |  4 +-
 .../translations/README.id.md                 |  4 +-
 .../translations/README.it.md                 |  4 +-
 .../translations/README.ja.md                 |  4 +-
 .../translations/README.zh-cn.md              |  4 +-
 2-Regression/1-Tools/README.md                |  4 +-
 .../1-Tools/translations/README.id.md         |  4 +-
 .../1-Tools/translations/README.it.md         |  4 +-
 .../1-Tools/translations/README.ja.md         |  4 +-
 .../1-Tools/translations/README.zh-cn.md      |  4 +-
 2-Regression/2-Data/README.md                 |  4 +-
 2-Regression/2-Data/translations/README.id.md |  4 +-
 2-Regression/2-Data/translations/README.it.md |  4 +-
 2-Regression/2-Data/translations/README.ja.md |  4 +-
 .../2-Data/translations/README.zh-cn.md       |  4 +-
 2-Regression/3-Linear/README.md               |  4 +-
 .../3-Linear/solution/lesson_3-R.ipynb        |  2 +-
 2-Regression/3-Linear/solution/lesson_3.Rmd   |  2 +-
 .../3-Linear/translations/README.id.md        |  4 +-
 .../3-Linear/translations/README.it.md        |  4 +-
 .../3-Linear/translations/README.ja.md        |  4 +-
 .../3-Linear/translations/README.zh-cn.md     |  4 +-
 2-Regression/4-Logistic/README.md             | 12 ++---
 .../4-Logistic/solution/lesson_4-R.ipynb      |  2 +-
 2-Regression/4-Logistic/solution/lesson_4.Rmd |  2 +-
 .../4-Logistic/translations/README.id.md      |  4 +-
 .../4-Logistic/translations/README.it.md      |  4 +-
 .../4-Logistic/translations/README.ja.md      | 12 ++---
 .../4-Logistic/translations/README.zh-cn.md   |  4 +-
 3-Web-App/1-Web-App/README.md                 |  4 +-
 3-Web-App/1-Web-App/translations/README.it.md |  4 +-
 3-Web-App/1-Web-App/translations/README.ja.md |  4 +-
 .../1-Web-App/translations/README.zh-cn.md    |  4 +-
 4-Classification/1-Introduction/README.md     |  4 +-
 .../1-Introduction/translations/README.it.md  |  4 +-
 .../1-Introduction/translations/README.tr.md  |  4 +-
 .../translations/README.zh-cn.md              |  4 +-
 4-Classification/2-Classifiers-1/README.md    | 40 ++++++++--------
 .../2-Classifiers-1/translations/README.it.md | 36 +++++++-------
 .../2-Classifiers-1/translations/README.tr.md | 38 +++++++--------
 .../translations/README.zh-cn.md              | 26 +++++-----
 4-Classification/3-Classifiers-2/README.md    |  4 +-
 .../3-Classifiers-2/translations/README.it.md |  4 +-
 .../3-Classifiers-2/translations/README.tr.md |  4 +-
 4-Classification/4-Applied/README.md          |  4 +-
 .../4-Applied/translations/README.it.md       |  4 +-
 .../4-Applied/translations/README.tr.md       |  4 +-
 5-Clustering/1-Visualize/README.md            |  4 +-
 .../1-Visualize/translations/README.it.md     | 48 +++++++++----------
 .../1-Visualize/translations/README.zh-cn.md  |  4 +-
 5-Clustering/2-K-Means/README.md              |  4 +-
 .../2-K-Means/translations/README.it.md       |  4 +-
 .../2-K-Means/translations/README.zh-cn.md    |  4 +-
 6-NLP/1-Introduction-to-NLP/README.md         |  4 +-
 .../translations/README.it.md                 |  4 +-
 .../translations/README.zh-cn.md              |  4 +-
 6-NLP/2-Tasks/README.md                       |  4 +-
 6-NLP/2-Tasks/translations/README.it.md       |  4 +-
 6-NLP/3-Translation-Sentiment/README.md       |  4 +-
 .../translations/README.it.md                 |  4 +-
 6-NLP/4-Hotel-Reviews-1/README.md             |  4 +-
 .../translations/README.it.md                 | 48 +++++++++----------
 6-NLP/5-Hotel-Reviews-2/README.md             |  4 +-
 .../translations/README.it.md                 | 20 ++++----
 7-TimeSeries/1-Introduction/README.md         |  4 +-
 7-TimeSeries/2-ARIMA/README.md                |  4 +-
 8-Reinforcement/1-QLearning/README.md         |  4 +-
 8-Reinforcement/2-Gym/README.md               |  4 +-
 9-Real-World/1-Applications/README.md         |  4 +-
 TRANSLATIONS.md                               |  2 +-
 91 files changed, 324 insertions(+), 324 deletions(-)

diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md
index db1c72b114..4e73a48249 100644
--- a/1-Introduction/1-intro-to-ML/README.md
+++ b/1-Introduction/1-intro-to-ML/README.md
@@ -4,7 +4,7 @@
 
 > 🎥 Click the image above for a video discussing the difference between machine learning, AI, and deep learning.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
 
 ### Introduction
 
@@ -96,7 +96,7 @@ In the near future, understanding the basics of machine learning is going to be
 
 Sketch, on paper or using an online app like [Excalidraw](https://excalidraw.com/), your understanding of the differences between AI, ML, deep learning, and data science. Add some ideas of problems that each of these techniques are good at solving.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
 
 ## Review & Self Study
 
diff --git a/1-Introduction/1-intro-to-ML/translations/README.fr.md b/1-Introduction/1-intro-to-ML/translations/README.fr.md
index d8915f581c..9079816069 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.fr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.fr.md
@@ -4,7 +4,7 @@
 
 > 🎥 Cliquer sur l'image ci-dessus afin de regarder une vidéo expliquant la différence entre machine learning, AI et deep learning.
 
-## [Quiz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=fr)
+## [Quiz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=fr)
 
 ### Introduction
 
@@ -98,7 +98,7 @@ Dans un avenir proche, comprendre les bases du machine learning sera indispensab
 
 Esquisser, sur papier ou à l'aide d'une application en ligne comme [Excalidraw](https://excalidraw.com/), votre compréhension des différences entre l'IA, le ML, le deep learning et la data science. Ajouter quelques idées de problèmes que chacune de ces techniques est bonne à résoudre.
 
-## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=fr)
+## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=fr)
 
 ## Révision et auto-apprentissage
 
diff --git a/1-Introduction/1-intro-to-ML/translations/README.id.md b/1-Introduction/1-intro-to-ML/translations/README.id.md
index d0daadd89f..69a9157b59 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.id.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.id.md
@@ -4,7 +4,7 @@
 
 > 🎥 Klik gambar diatas untuk menonton video yang mendiskusikan perbedaan antara Machine Learning, AI, dan Deep Learning.
 
-## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
 
 ### Pengantar
 
@@ -96,7 +96,7 @@ Dalam waktu dekat, memahami dasar-dasar Machine Learning akan menjadi suatu keha
 
 Buat sketsa di atas kertas atau menggunakan aplikasi seperti [Excalidraw](https://excalidraw.com/), mengenai pemahaman kamu tentang perbedaan antara AI, ML, Deep Learning, dan Data Science. Tambahkan beberapa ide masalah yang cocok diselesaikan masing-masing teknik.
 
-## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
 
 ## Ulasan & Belajar Mandiri
 
diff --git a/1-Introduction/1-intro-to-ML/translations/README.it.md b/1-Introduction/1-intro-to-ML/translations/README.it.md
index 7d9fe43aed..715a9c90f5 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.it.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.it.md
@@ -4,7 +4,7 @@
 
 > 🎥 Fare clic sull'immagine sopra per un video che illustra la differenza tra machine learning, intelligenza artificiale (AI) e deep learning.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
 
 ### Introduzione
 
@@ -97,7 +97,7 @@ Nel prossimo futuro, comprendere le basi di machine learning sarà un must per l
 
 Disegnare, su carta o utilizzando un'app online come [Excalidraw](https://excalidraw.com/), la propria comprensione delle differenze tra AI, ML, deep learning e data science. Aggiungere alcune idee sui problemi che ciascuna di queste tecniche è in grado di risolvere.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/1-Introduction/1-intro-to-ML/translations/README.ja.md b/1-Introduction/1-intro-to-ML/translations/README.ja.md
index fd3c11d1e4..b88738d03e 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.ja.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.ja.md
@@ -4,7 +4,7 @@
 
 > 🎥 上�画�をクリック�る��機械学習�AI�深層学習�������説明��動画�表示�れ��。
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=ja)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=ja)
 
 ### イントロダクション
 
@@ -94,7 +94,7 @@
 ## 🚀 Challenge
 AI�ML�深層学習�データサイエンス��������解���る��を�紙や[Excalidraw](https://excalidraw.com/)���オンラインアプリを使��スケッ�������。����れ�れ�技術�得���る�題�アイデアを加��������。
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=ja)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=ja)
 
 ## 振り返り�自習
 
diff --git a/1-Introduction/1-intro-to-ML/translations/README.tr.md b/1-Introduction/1-intro-to-ML/translations/README.tr.md
index cfa7e1274a..669e649de9 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.tr.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.tr.md
@@ -4,7 +4,7 @@
 
 > 🎥  Makine öğrenimi, yapay zeka ve derin öğrenme arasındaki farkı tartışan bir video için yukarıdaki resme tıklayın.
 
-## [Ders öncesi sınav](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=tr)
+## [Ders öncesi sınav](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=tr)
 
 ### Introduction
 
@@ -103,7 +103,7 @@ Yakın gelecekte, yaygın olarak benimsenmesi nedeniyle makine öğreniminin tem
 
 Kağıt üzerinde veya [Excalidraw](https://excalidraw.com/) gibi çevrimiçi bir uygulama kullanarak AI, makine öğrenimi, derin öğrenme ve veri bilimi arasındaki farkları anladığınızdan emin olun. Bu tekniklerin her birinin çözmede iyi olduğu bazı problem fikirleri ekleyin.
 
-## [Ders sonrası test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2?loc=tr)
+## [Ders sonrası test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2?loc=tr)
 
 ## İnceleme ve Bireysel Çalışma
 
diff --git a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
index 45ec79be09..3e8ae621e9 100644
--- a/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
+++ b/1-Introduction/1-intro-to-ML/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
 
 > 🎥 点击上�的图片观看讨论机器学习�人工智能和深度学习之间区别的视频。
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/1/)
 
 ### 介�
 
@@ -96,7 +96,7 @@
 
 在纸上或使用[Excalidraw](https://excalidraw.com/)等在线应用程�绘制�图，了解你对AI�ML�深度学习和数�科学之间差异的�解。添加一些关于这些技术擅长解决的问题的想法。
 
-## [阅读�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/2/)
+## [阅读�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/2/)
 
 ## �习与自学
 
diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md
index 67c93dbf5f..a44703d680 100644
--- a/1-Introduction/2-history-of-ML/README.md
+++ b/1-Introduction/2-history-of-ML/README.md
@@ -3,7 +3,7 @@
 ![Summary of History of machine learning in a sketchnote](../../sketchnotes/ml-history.png)
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
 
 In this lesson, we will walk through the major milestones in the history of machine learning and artificial intelligence.
 
@@ -101,7 +101,7 @@ It remains to be seen what the future holds, but it is important to understand t
 
 Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
 
 ## Review & Self Study
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.es.md b/1-Introduction/2-history-of-ML/translations/README.es.md
index c5154176b7..28402267ae 100755
--- a/1-Introduction/2-history-of-ML/translations/README.es.md
+++ b/1-Introduction/2-history-of-ML/translations/README.es.md
@@ -3,7 +3,7 @@
 ![Resumen de la historoia del machine learning en un boceto](../../sketchnotes/ml-history.png)
 > Boceto por [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Cuestionario previo a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
 
 En esta lección, analizaremos los principales hitos en la historia del machine learning y la inteligencia artificial.
 
@@ -102,7 +102,7 @@ Queda por ver qué depara el futuro, pero es importante entender estos sistemas
 
 Sumérjase dentro de unos de estos momentos históricos y aprenda más sobre las personas detrás de ellos. Hay personajes fascinantes y nunca se creó ningún descubrimiento científico en un vacío cultural. ¿Qué descubres?
 
-## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
 
 ## Revisión y autoestudio
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.fr.md b/1-Introduction/2-history-of-ML/translations/README.fr.md
index 9c59eb6fbe..efe268777f 100644
--- a/1-Introduction/2-history-of-ML/translations/README.fr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.fr.md
@@ -3,7 +3,7 @@
 ![Résumé de l'histoire du machine learning dans un sketchnote](../../../sketchnotes/ml-history.png)
 > Sketchnote de [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Quizz préalable](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=fr)
+## [Quizz préalable](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=fr)
 
 Dans cette leçon, nous allons parcourir les principales étapes de l'histoire du machine learning et de l'intelligence artificielle.
 
@@ -102,7 +102,7 @@ Reste à savoir ce que l'avenir nous réserve, mais il est important de comprend
 
 Plongez dans l'un de ces moments historiques et apprenez-en plus sur les personnes derrière ceux-ci. Il y a des personnalités fascinantes, et aucune découverte scientifique n'a jamais été créée avec un vide culturel. Que découvrez-vous ?
 
-## [Quiz de validation des connaissances](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=fr)
+## [Quiz de validation des connaissances](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=fr)
 
 ## Révision et auto-apprentissage
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.id.md b/1-Introduction/2-history-of-ML/translations/README.id.md
index 5e6a6f0f1f..9e695a8a9a 100644
--- a/1-Introduction/2-history-of-ML/translations/README.id.md
+++ b/1-Introduction/2-history-of-ML/translations/README.id.md
@@ -3,7 +3,7 @@
 ![Ringkasan dari Sejarah Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-history.png)
 > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
 
 Dalam pelajaran ini, kita akan membahas tonggak utama dalam sejarah Machine Learning dan Artificial Intelligence. 
 
@@ -101,7 +101,7 @@ Kita masih belum tahu apa yang akan terjadi di masa depan, tetapi penting untuk
 
 Gali salah satu momen bersejarah ini dan pelajari lebih lanjut tentang orang-orang di baliknya. Ada karakter yang menarik, dan tidak ada penemuan ilmiah yang pernah dibuat dalam kekosongan budaya. Apa yang kamu temukan? 
 
-## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
 
 ## Ulasan & Belajar Mandiri
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.it.md b/1-Introduction/2-history-of-ML/translations/README.it.md
index c7e59fd77e..c7d5ce2345 100644
--- a/1-Introduction/2-history-of-ML/translations/README.it.md
+++ b/1-Introduction/2-history-of-ML/translations/README.it.md
@@ -3,7 +3,7 @@
 ![Riepilogo della storia di machine learning in uno sketchnote](../../../sketchnotes/ml-history.png)
 > Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
 
 In questa lezione, si camminerà attraverso le principali pietre miliari nella storia di machine learning e dell'intelligenza artificiale.
 
@@ -103,7 +103,7 @@ Resta da vedere cosa riserva il futuro, ma è importante capire questi sistemi i
 Approfondire uno di questi momenti storici e scoprire
  di più sulle persone che stanno dietro ad essi. Ci sono personaggi affascinanti e nessuna scoperta scientifica è mai stata creata in un vuoto culturale. Cosa si è scoperto?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.ja.md b/1-Introduction/2-history-of-ML/translations/README.ja.md
index 5c17650c78..6ba32096aa 100644
--- a/1-Introduction/2-history-of-ML/translations/README.ja.md
+++ b/1-Introduction/2-history-of-ML/translations/README.ja.md
@@ -3,7 +3,7 @@
 ![機械学習�歴�を����スケッ�](../../../sketchnotes/ml-history.png)
 > [Tomomi Imura](https://www.twitter.com/girlie_mac)�よるスケッ�
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=ja)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=ja)
 
 ��授業���機械学習�人工知能�歴����る主��出�事を紹介���。
 
@@ -99,7 +99,7 @@
 
 �れら�歴�的瞬間�1�を掘り下�����背後��る人々����学���ょ�。魅力的�人々������文化的�空白�状態�科学的発見���れ�����り��ん。��������見��る��ょ��？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=ja)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=ja)
 
 ## 振り返り�自習
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.tr.md b/1-Introduction/2-history-of-ML/translations/README.tr.md
index a67f45ec4a..af2346fb7e 100644
--- a/1-Introduction/2-history-of-ML/translations/README.tr.md
+++ b/1-Introduction/2-history-of-ML/translations/README.tr.md
@@ -3,7 +3,7 @@
 ![Bir taslak-notta makine öğrenimi geçmişinin özeti](../../../sketchnotes/ml-history.png)
 > [Tomomi Imura](https://www.twitter.com/girlie_mac) tarafından hazırlanan taslak-not
 
-## [Ders öncesi test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3?loc=tr)
+## [Ders öncesi test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3?loc=tr)
 
 Bu derste, makine öğrenimi ve yapay zeka tarihindeki önemli kilometre taşlarını inceleyeceğiz.
 
@@ -102,7 +102,7 @@ Geleceğin neler getireceğini birlikte göreceğiz, ancak bu bilgisayar sisteml
 
 Bu tarihi anlardan birine girin ve arkasındaki insanlar hakkında daha fazla bilgi edinin. Büyüleyici karakterler var ve kültürel bir boşlukta hiçbir bilimsel keşif yaratılmadı. Ne keşfedersiniz?
 
-## [Ders sonrası test](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4?loc=tr)
+## [Ders sonrası test](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4?loc=tr)
 
 ## İnceleme ve Bireysel Çalışma
 
diff --git a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
index 8ca7e690f7..70e85f8201 100644
--- a/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/2-history-of-ML/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
 ![机器学习历�概述](../../../sketchnotes/ml-history.png)
 > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/3/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/3/)
 
 在本课中，我们将走过机器学习和人工智能历�上的主�里程碑。 
 
@@ -101,7 +101,7 @@ Alan Turing，一个真正�出的人，[在2019年被公众投票选出](https
 
 深入了解这些历�时刻之一，并更多地了解它们背�的人。这里有许多引人入胜的人物，没有一项科学�现是在文化真空中创造出�的。你�现了什么？
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/4/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/4/)
 
 ## �习与自学
 
diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md
index 79bf481929..7e9c8f6d8f 100644
--- a/1-Introduction/3-fairness/README.md
+++ b/1-Introduction/3-fairness/README.md
@@ -3,7 +3,7 @@
 ![Summary of Fairness in Machine Learning in a sketchnote](../../sketchnotes/ml-fairness.png)
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
  
 ## Introduction
 
@@ -184,7 +184,7 @@ To prevent biases from being introduced in the first place, we should:
 
 Think about real-life scenarios where unfairness is evident in model-building and usage. What else should we consider? 
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
 ## Review & Self Study 
  
 In this lesson, you have learned some basics of the concepts of fairness and unfairness in machine learning.  
diff --git a/1-Introduction/3-fairness/translations/README.id.md b/1-Introduction/3-fairness/translations/README.id.md
index 6f09a14896..980cbd88d2 100644
--- a/1-Introduction/3-fairness/translations/README.id.md
+++ b/1-Introduction/3-fairness/translations/README.id.md
@@ -3,7 +3,7 @@
 ![Ringkasan dari Keadilan dalam Machine Learning dalam sebuah catatan sketsa](../../../sketchnotes/ml-fairness.png)
 > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
  
 ## Pengantar
 
@@ -90,11 +90,11 @@ Lima jenis bahaya utama ini tidak saling eksklusif, dan satu sistem dapat menunj
  
 ✅ **Diskusi**: Tinjau kembali beberapa contoh dan lihat apakah mereka menunjukkan bahaya yang berbeda.  
 
-|                         | Alokasi | Kualitas Layanan | Stereotip | Fitnah | Representasi yang kurang atau berlebihan |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| Sistem perekrutan otomatis |     x      |         x          |      x       |             |               x                |
-| Terjemahan mesin     |            |                    |              |             |                                |
-| Melabeli foto          |            |                    |              |             |                                |
+|                            | Alokasi | Kualitas Layanan | Stereotip | Fitnah | Representasi yang kurang atau berlebihan |
+| -------------------------- | :-----: | :--------------: | :-------: | :----: | :--------------------------------------: |
+| Sistem perekrutan otomatis |    x    |        x         |     x     |        |                    x                     |
+| Terjemahan mesin           |         |                  |           |        |                                          |
+| Melabeli foto              |         |                  |           |        |                                          |
 
 
 ## Mendeteksi Ketidakadilan 
@@ -185,7 +185,7 @@ Untuk mencegah kemunculan bias pada awalnya, kita harus:
 
 Pikirkan tentang skenario kehidupan nyata di mana ketidakadilan terbukti dalam pembuatan dan penggunaan model. Apa lagi yang harus kita pertimbangkan? 
 
-## [Quiz Pasca-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Quiz Pasca-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
 ## Ulasan & Belajar Mandiri 
  
 Dalam pelajaran ini, Kamu telah mempelajari beberapa dasar konsep keadilan dan ketidakadilan dalam pembelajaran mesin. 
diff --git a/1-Introduction/3-fairness/translations/README.it.md b/1-Introduction/3-fairness/translations/README.it.md
index 3c167f096f..e55fd6e7f9 100644
--- a/1-Introduction/3-fairness/translations/README.it.md
+++ b/1-Introduction/3-fairness/translations/README.it.md
@@ -3,7 +3,7 @@
 ![Riepilogo dell'equità in machine learning in uno sketchnote](../../../sketchnotes/ml-fairness.png)
 > Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
 
 ## Introduzione
 
@@ -89,11 +89,11 @@ Questi cinque principali tipi di danno non si escludono a vicenda e un singolo s
 
 ✅ **Discussione**: rivisitare alcuni degli esempi e vedere se mostrano danni diversi.
 
-|                         | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| Sistema di assunzione automatizzato |     x      |         x          |      x       |             |               x                |
-| Traduzione automatica     |            |                    |              |             |                                |
-| Eitchettatura foto          |            |                    |              |             |                                |
+|                                     | Allocatione | Qualita di servizio | Stereotipo | Denigrazione | Sovra o sotto rappresentazione |
+| ----------------------------------- | :---------: | :-----------------: | :--------: | :----------: | :----------------------------: |
+| Sistema di assunzione automatizzato |      x      |          x          |     x      |              |               x                |
+| Traduzione automatica               |             |                     |            |              |                                |
+| Eitchettatura foto                  |             |                     |            |              |                                |
 
 ## Rilevare l'ingiustizia
 
@@ -137,11 +137,11 @@ Si sono identificati i danni e un gruppo interessato, in questo caso, delineato
 
 ✅ In una futura lezione sul Clustering, si vedrà come costruire questa 'matrice di confusione' nel codice
 
-|            | percentuale di falsi positivi | Percentuale di falsi negativi |conteggio |
-| ---------- | ------------------- | ------------------- | ----- |
-| Donna | 0,37 | 0,27 | 54032 |
-| Uomo | 0,31 | 0.35 | 28620 |
-| Non binario | 0,33 | 0,31 | 1266 |
+|             | percentuale di falsi positivi | Percentuale di falsi negativi | conteggio |
+| ----------- | ----------------------------- | ----------------------------- | --------- |
+| Donna       | 0,37                          | 0,27                          | 54032     |
+| Uomo        | 0,31                          | 0.35                          | 28620     |
+| Non binario | 0,33                          | 0,31                          | 1266      |
 
 Questa tabella ci dice diverse cose. Innanzitutto, si nota che ci sono relativamente poche persone non binarie nei dati. I dati sono distorti, quindi si deve fare attenzione a come si interpretano  questi numeri.
 
@@ -183,7 +183,7 @@ Per evitare che vengano introdotti pregiudizi, in primo luogo, si dovrebbe:
 
 Si pensi a scenari di vita reale in cui l'ingiustizia è evidente nella creazione e nell'utilizzo del modello. Cos'altro si dovrebbe considerare?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/1-Introduction/3-fairness/translations/README.ja.md b/1-Introduction/3-fairness/translations/README.ja.md
index 9bb32639eb..ffa878c171 100644
--- a/1-Introduction/3-fairness/translations/README.ja.md
+++ b/1-Introduction/3-fairness/translations/README.ja.md
@@ -3,7 +3,7 @@
 ![機械学習���る公平性を����スケッ�](../../../sketchnotes/ml-fairness.png)
 > [Tomomi Imura](https://www.twitter.com/girlie_mac)�よるスケッ�
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5?loc=ja)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5?loc=ja)
  
 ## イントロダクション
 
@@ -88,11 +88,11 @@ AIや機械学習���る公平性��証���然���複雑�社
  
 ✅ **ディスカッション**: �����例を�検討��異�る害を示���る����を確�������。 
 
-|                         | アロケーション | サービス�質 | 固定観念 | 誹謗中傷 | �剰表�/��表� |
-| ----------------------- | :--------: | :----------------: | :----------: | :---------: | :----------------------------: |
-| 採用システム�自動化 |     x      |         x          |      x       |             |               x                |
-| 機械翻訳     |            |                    |              |             |                                |
-| 写真�ラベリング          |            |                    |              |             |                                |
+|                      | アロケーション | サービス�質 | 固定観念 | 誹謗中傷 | �剰表�/��表� |
+| -------------------- | :------------: | :----------: | :------: | :------: | :---------------: |
+| 採用システム�自動化 |       x        |      x       |    x     |          |         x         |
+| 機械翻訳             |                |              |          |          |                   |
+| 写真�ラベリング     |                |              |          |          |                   |
 
 
 ## �公平�検出
@@ -134,11 +134,11 @@ AIや機械学習���る公平性��証���然���複雑�社
 
 ✅ 今後�"クラスタリング"�レッスン�����"混�行列"をコード�構築�る方法を�紹介���。
 
-|            | �陽性率 | �陰性率 | サンプル数 |
-| ---------- | ------------------- | ------------------- | ----- |
-| 女性      | 0.37                | 0.27                | 54032 |
-| 男性        | 0.31                | 0.35                | 28620 |
-| ��ら�も属��� | 0.33                | 0.31                | 1266  |
+|                    | �陽性率 | �陰性率 | サンプル数 |
+| ------------------ | -------- | -------- | ---------- |
+| 女性               | 0.37     | 0.27     | 54032      |
+| 男性               | 0.31     | 0.35     | 28620      |
+| ��ら�も属��� | 0.33     | 0.31     | 1266       |
 
 ��表�ら�����������り��。���データ���れる男性�女性��ら�も��人�比較的少�������り��。従����データ�歪ん��り���数字を��解釈�る��注��必���。
 
@@ -178,7 +178,7 @@ AIや機械学習���る公平性��証���然���複雑�社
 
 モデル�構築や使用������公平�明ら���るよ���実�シナリオを考��������。他���よ����を考�る����ょ��？
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6?loc=ja)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6?loc=ja)
 ## Review & Self Study 
  
 ��レッスン���機械学習���る公平��公平�概念�基礎を学����。
diff --git a/1-Introduction/3-fairness/translations/README.zh-cn.md b/1-Introduction/3-fairness/translations/README.zh-cn.md
index 222045441a..952e819165 100644
--- a/1-Introduction/3-fairness/translations/README.zh-cn.md
+++ b/1-Introduction/3-fairness/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
 ![机器学习中的公平性概述](../../../sketchnotes/ml-fairness.png)
 > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/5/)
  
 ## 介�
 
@@ -186,7 +186,7 @@
 
 想想现实生活中的场景，在模型构建和使用中明显存在�公平。我们还应该考虑什么？
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/6/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/6/)
 ## �习与自学
  
 在本课中，你学习了机器学习中公平和�公平概念的一些基础知识。
diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md
index 70b9600021..1b87fd7ce2 100644
--- a/1-Introduction/4-techniques-of-ML/README.md
+++ b/1-Introduction/4-techniques-of-ML/README.md
@@ -5,7 +5,7 @@ The process of building, using, and maintaining machine learning models and the
 - Understand the processes underpinning machine learning at a high level.
 - Explore base concepts such as 'models', 'predictions', and 'training data'.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
 
 ## Introduction
 
@@ -103,7 +103,7 @@ In these lessons, you will discover how to use these steps to prepare, build, te
 
 Draw a flow chart reflecting the steps of a ML practitioner. Where do you see yourself right now in the process? Where do you predict you will find difficulty? What seems easy to you?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
 
 ## Review & Self Study
 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.es.md b/1-Introduction/4-techniques-of-ML/translations/README.es.md
index 114d6e8d77..0121527ef8 100755
--- a/1-Introduction/4-techniques-of-ML/translations/README.es.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.es.md
@@ -6,7 +6,7 @@ El proceso de creación, uso y mantenimiento de modelos de machine learning, y l
 - Explorar conceptos básicos como 'modelos', 'predicciones', y 'datos de entrenamiento'
 
  
-## [Cuestionario previo a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [Cuestionario previo a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
 ## Introducción
 
 A un alto nivel, el arte de crear procesos de machine learning (ML) se compone de una serie de pasos:
@@ -101,7 +101,7 @@ En estas lecciones, descubrirá cómo utilizar estos pasos para preparar, constr
 
 Dibuje un diagrama de flujos que refleje los pasos de practicante de ML. ¿Dónde te ves ahora mismo en el proceso? ¿Dónde predice que encontrará dificultades? ¿Qué te parece fácil? 
 
-## [Cuestionario posterior a la conferencia](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Cuestionario posterior a la conferencia](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
 
 ## Revisión & Autoestudio
 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.id.md b/1-Introduction/4-techniques-of-ML/translations/README.id.md
index 8e6fac43ca..699603702e 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.id.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.id.md
@@ -5,7 +5,7 @@ Proses membangun, menggunakan, dan memelihara model machine learning dan data ya
 - Memahami gambaran dari proses yang mendasari machine learning.
 - Menjelajahi konsep dasar seperti '*models*', '*predictions*', dan '*training data*'. 
   
-## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
 ## Pengantar
 
 Gambaran membuat proses machine learning (ML) terdiri dari sejumlah langkah: 
@@ -100,7 +100,7 @@ Dalam pelajaran ini, Kamu akan menemukan cara untuk menggunakan langkah-langkah
 
 Gambarlah sebuah flow chart yang mencerminkan langkah-langkah seorang praktisi ML. Di mana kamu melihat diri kamu saat ini dalam prosesnya? Di mana kamu memprediksi kamu akan menemukan kesulitan? Apa yang tampak mudah bagi kamu? 
 
-## [Quiz Pra-Pelajaran](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Quiz Pra-Pelajaran](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
 
 ## Ulasan & Belajar Mandiri
 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.it.md b/1-Introduction/4-techniques-of-ML/translations/README.it.md
index 4e36c9cc54..ecfb35e574 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.it.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.it.md
@@ -5,7 +5,7 @@ Il processo di creazione, utilizzo e mantenimento dei modelli di machine learnin
 - Comprendere i processi ad alto livello alla base di machine learning.
 - Esplorare concetti di base come "modelli", "previsioni" e "dati di addestramento".
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
 
 ## Introduzione
 
@@ -103,7 +103,7 @@ In queste lezioni si scoprirà come utilizzare questi passaggi per preparare, co
 
 Disegnare un diagramma di flusso che rifletta i passaggi di un professionista di ML. Dove ci si vede in questo momento nel processo? Dove si prevede che sorgeranno difficoltà? Cosa sembra facile?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.ja.md b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
index b964781de2..13dcdb3127 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.ja.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.ja.md
@@ -5,7 +5,7 @@
 - 機械学習を支�るプロセスを高�水準��解���。
 - 「モデル�「予測�「訓練データ����基本的�概念を調���。
 
-## [講義���テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7?loc=ja)
+## [講義���テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7?loc=ja)
 
 ## å°Žå…¥
 
@@ -103,7 +103,7 @@
 
 機械学習�学習者�ステップを�映��フロー�ャートを�������。今�自分���プロセス�����る������？���困難��る�予想����？�������簡�������何���？
 
-## [講義後��テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8?loc=ja)
+## [講義後��テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8?loc=ja)
 
 ## 振り返り�自主学習
 
diff --git a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
index c7b86c8e2a..2590b99a11 100644
--- a/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
+++ b/1-Introduction/4-techniques-of-ML/translations/README.zh-cn.md
@@ -6,7 +6,7 @@
 - 在高层次上�解支�机器学习的过程。 
 - 探索基本概念，例如“模型��“预测�和“训练数��。 
   
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/7/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/7/)
 ## 介�
 
 在较高的层次上，创建机器学习（ML）过程的工艺包括许多步骤：
@@ -101,7 +101,7 @@
 
 画一个�程图，�映ML的步骤。在这个过程中，你认为自己现在在哪里？你预测你在哪里会�到困难？什么对你�说很容易？ 
 
-## [阅读�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/8/)
+## [阅读�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/8/)
 
 ## �习与自学 
 
diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
index 69986fba29..275e79f567 100644
--- a/2-Regression/1-Tools/README.md
+++ b/2-Regression/1-Tools/README.md
@@ -4,7 +4,7 @@
 
 > Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
 
 > ### [This lesson is available in R!](./solution/lesson_1-R.ipynb)
 
@@ -199,7 +199,7 @@ Congratulations, you built your first linear regression model, created a predict
 ## 🚀Challenge
 
 Plot a different variable from this dataset. Hint: edit this line: `X = X[:, np.newaxis, 2]`. Given this dataset's target, what are you able to discover about the progression of diabetes as a disease?
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
 
 ## Review & Self Study
 
diff --git a/2-Regression/1-Tools/translations/README.id.md b/2-Regression/1-Tools/translations/README.id.md
index b7ab4bf8c1..87b2be53b4 100644
--- a/2-Regression/1-Tools/translations/README.id.md
+++ b/2-Regression/1-Tools/translations/README.id.md
@@ -4,7 +4,7 @@
 
 > Catatan sketsa oleh [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Kuis Pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [Kuis Pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
 ## Pembukaan
 
 Dalam keempat pelajaran ini, kamu akan belajar bagaimana membangun model regresi. Kita akan berdiskusi apa fungsi model tersebut dalam sejenak. Tetapi sebelum kamu melakukan apapun, pastikan bahwa kamu sudah mempunyai alat-alat yang diperlukan untuk memulai!
@@ -195,7 +195,7 @@ Selamat, kamu telah membangun model regresi linear pertamamu, membuat sebuah pre
 ## Tantangan
 
 Gambarkan sebuah variabel yang beda dari *dataset* ini. Petunjuk: edit baris ini: `X = X[:, np.newaxis, 2]`. Mengetahui target *dataset* ini, apa yang kamu bisa menemukan tentang kemajuan diabetes sebagai sebuah penyakit?
-## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
 
 ## Review & Pembelajaran Mandiri
 
diff --git a/2-Regression/1-Tools/translations/README.it.md b/2-Regression/1-Tools/translations/README.it.md
index 48c61d3404..a5e2e3842e 100644
--- a/2-Regression/1-Tools/translations/README.it.md
+++ b/2-Regression/1-Tools/translations/README.it.md
@@ -4,7 +4,7 @@
 
 > Sketchnote di [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [Qui Pre-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [Qui Pre-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
 
 ## Introduzione
 
@@ -197,7 +197,7 @@ Congratulazioni, si è costruito il primo modello di regressione lineare, creato
 
 Tracciare una variabile diversa da questo insieme di dati. Suggerimento: modificare questa riga: `X = X[:, np.newaxis, 2]`. Dato l'obiettivo di questo insieme di dati, cosa si potrebbe riuscire a scoprire circa la progressione del diabete come matattia?
 
-## [Qui post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [Qui post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
 
 ## Riepilogo e Auto Apprendimento
 
diff --git a/2-Regression/1-Tools/translations/README.ja.md b/2-Regression/1-Tools/translations/README.ja.md
index 0ecc0e8290..fcb5ce0f7a 100644
--- a/2-Regression/1-Tools/translations/README.ja.md
+++ b/2-Regression/1-Tools/translations/README.ja.md
@@ -4,7 +4,7 @@
 
 > [Tomomi Imura](https://www.twitter.com/girlie_mac) �よ��制作�れ�スケッ�ノート
 
-## [講義�クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9?loc=ja)
+## [講義�クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9?loc=ja)
 
 ## イントロダクション
 
@@ -205,7 +205,7 @@ s1 tc: T細胞（白血��一種）
 ## 🚀�ャレンジ
 
 ��データセット�ら別�変数を�択��プロット������。ヒント： `X = X[:, np.newaxis, 2]` �行を編集�る。今回�データセット�ターゲット��る�糖尿病���病気�進行�������よ��発見��る���ょ��？
-## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10?loc=ja)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10?loc=ja)
 
 ## レビュー & 自主学習
 
diff --git a/2-Regression/1-Tools/translations/README.zh-cn.md b/2-Regression/1-Tools/translations/README.zh-cn.md
index bb7f37c11f..019eb06f60 100644
--- a/2-Regression/1-Tools/translations/README.zh-cn.md
+++ b/2-Regression/1-Tools/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
 
 > 作者[Tomomi Imura](https://www.twitter.com/girlie_mac)
 
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/9/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/9/)
 ## 介�
 
 在这四节课中，你将了解如何构建回归模型。我们将很快讨论这些是什么。但在你�任何事情之�，请确�你有�适的工具�开始这个过程�
@@ -192,7 +192,7 @@ s1 tc：T细胞（一�白细胞）
 ## 🚀挑战
 
 从这个数�集中绘制一个��的��。�示：编辑这一行：`X = X[:, np.newaxis, 2]`。鉴于此数�集的目标，你能够�现糖尿病作为一�疾病的进展情况�？
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/10/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/10/)
 
 ## �习与自学 
 
diff --git a/2-Regression/2-Data/README.md b/2-Regression/2-Data/README.md
index 56493188bc..a647eb6b42 100644
--- a/2-Regression/2-Data/README.md
+++ b/2-Regression/2-Data/README.md
@@ -4,7 +4,7 @@
 
 Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
 
 > ### [This lesson is available in R!](./solution/lesson_2-R.ipynb)
 
@@ -192,7 +192,7 @@ To get charts to display useful data, you usually need to group the data somehow
 
 Explore the different types of visualization that Matplotlib offers. Which types are most appropriate for regression problems?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
 
 ## Review & Self Study
 
diff --git a/2-Regression/2-Data/translations/README.id.md b/2-Regression/2-Data/translations/README.id.md
index b9889ae8fb..9d8b5f18da 100644
--- a/2-Regression/2-Data/translations/README.id.md
+++ b/2-Regression/2-Data/translations/README.id.md
@@ -3,7 +3,7 @@
 ![Infografik visualisasi data](../images/data-visualization.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
 
 ## Pembukaan
 
@@ -191,7 +191,7 @@ Untuk menjadikan sebuah grafik menjadi berguna, biasanya datanya harus dikelompo
 
 Jelajahi jenis-jenis visualisasi yang beda dan yang disediakan Matplotlib. Jenis mana yang paling cocok untuk kasus regresi?
 
-## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
 
 ## Review & Pembelajaran Mandiri
 
diff --git a/2-Regression/2-Data/translations/README.it.md b/2-Regression/2-Data/translations/README.it.md
index 7b78ac52bb..ba57ee11b3 100644
--- a/2-Regression/2-Data/translations/README.it.md
+++ b/2-Regression/2-Data/translations/README.it.md
@@ -3,7 +3,7 @@
 > ![Infografica sulla visualizzazione dei dati](../images/data-visualization.png)
 > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
 
 ## Introduzione
 
@@ -190,7 +190,7 @@ Per fare in modo che i grafici mostrino dati utili, di solito è necessario ragg
 
 Esplorare i diversi tipi di visualizzazione offerti da Matplotlib. Quali tipi sono più appropriati per i problemi di regressione?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/2-Regression/2-Data/translations/README.ja.md b/2-Regression/2-Data/translations/README.ja.md
index 8a9f4d5522..ddd01a775e 100644
--- a/2-Regression/2-Data/translations/README.ja.md
+++ b/2-Regression/2-Data/translations/README.ja.md
@@ -4,7 +4,7 @@
 > 
 > [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
 
-## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11?loc=ja)
+## [講義��クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11?loc=ja)
 
 ## イントロダクション
 
@@ -195,7 +195,7 @@ Jupyter notebook����利用��るテータ�視化ライブラリ�
 
 Matplotlib��供�る様々�タイプ�ビジュアライゼーションを探�����ょ�。回帰��題����タイプ�最も����る��ょ��？
 
-## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12?loc=ja)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12?loc=ja)
 
 ## レビュー & 自主学習
 
diff --git a/2-Regression/2-Data/translations/README.zh-cn.md b/2-Regression/2-Data/translations/README.zh-cn.md
index bc273ab1e3..df542b15bf 100644
--- a/2-Regression/2-Data/translations/README.zh-cn.md
+++ b/2-Regression/2-Data/translations/README.zh-cn.md
@@ -3,7 +3,7 @@
 > ![数��视化信�图](../images/data-visualization.png)
 > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/11/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/11/)
 
 ## 介�
 
@@ -191,7 +191,7 @@
 
 探索Matplotlib�供的��类型的�视化。哪�类型最适�回归问题？
 
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/12/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/12/)
 
 ## �习与自学
 
diff --git a/2-Regression/3-Linear/README.md b/2-Regression/3-Linear/README.md
index 2a60658b2e..0a4e09b497 100644
--- a/2-Regression/3-Linear/README.md
+++ b/2-Regression/3-Linear/README.md
@@ -2,7 +2,7 @@
 
 ![Linear vs polynomial regression infographic](./images/linear-polynomial.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
 ### Introduction 
 
 So far you have explored what regression is with sample data gathered from the pumpkin pricing dataset that we will use throughout this lesson. You have also visualized it using Matplotlib. 
@@ -320,7 +320,7 @@ It does make sense, given the plot! And, if this is a better model than the prev
 
 Test several different variables in this notebook to see how correlation corresponds to model accuracy.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## Review & Self Study
 
diff --git a/2-Regression/3-Linear/solution/lesson_3-R.ipynb b/2-Regression/3-Linear/solution/lesson_3-R.ipynb
index 0cc040bbdf..09fadc9926 100644
--- a/2-Regression/3-Linear/solution/lesson_3-R.ipynb
+++ b/2-Regression/3-Linear/solution/lesson_3-R.ipynb
@@ -1058,7 +1058,7 @@
         "\n",
         "Test several different variables in this notebook to see how correlation corresponds to model accuracy.\n",
         "\n",
-        "## [**Post-lecture quiz**](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)\n",
+        "## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)\n",
         "\n",
         "## **Review & Self Study**\n",
         "\n",
diff --git a/2-Regression/3-Linear/solution/lesson_3.Rmd b/2-Regression/3-Linear/solution/lesson_3.Rmd
index a1b81780d5..91968a0656 100644
--- a/2-Regression/3-Linear/solution/lesson_3.Rmd
+++ b/2-Regression/3-Linear/solution/lesson_3.Rmd
@@ -662,7 +662,7 @@ The `polynomial model` prediction does make sense, given the scatter plots of `p
 
 Test several different variables in this notebook to see how correlation corresponds to model accuracy.
 
-## [**Post-lecture quiz**](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## **Review & Self Study**
 
diff --git a/2-Regression/3-Linear/translations/README.id.md b/2-Regression/3-Linear/translations/README.id.md
index f2ae6b7cfd..ce2ee4098a 100644
--- a/2-Regression/3-Linear/translations/README.id.md
+++ b/2-Regression/3-Linear/translations/README.id.md
@@ -2,7 +2,7 @@
 
 ![Infografik regresi linear vs polinomial](../images/linear-polynomial.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
 ### Pembukaan
 
 Selama ini kamu telah menjelajahi apa regresi itu dengan data contoh yang dikumpulkan dari *dataset* harga labu yang kita akan gunakan terus sepanjang pelajaran ini. Kamu juga telah memvisualisasikannya dengan Matplotlib.
@@ -324,7 +324,7 @@ Itu sangat masuk akal dengan bagan sebelumnya! Selain itu, jika ini model lebih
 
 Coba-cobalah variabel-variabel yang lain di *notebook* ini untuk melihat bagaimana korelasi berhubungan dengan akurasi model.
 
-## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## Review & Pembelajaran Mandiri
 
diff --git a/2-Regression/3-Linear/translations/README.it.md b/2-Regression/3-Linear/translations/README.it.md
index 1aafa6014a..81fd5a1167 100644
--- a/2-Regression/3-Linear/translations/README.it.md
+++ b/2-Regression/3-Linear/translations/README.it.md
@@ -3,7 +3,7 @@
 ![Infografica di regressione lineare e polinomiale](../images/linear-polynomial.png)
 > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
 
 ### Introduzione
 
@@ -328,7 +328,7 @@ Ben fatto! Sono stati  creati due modelli di regressione in una lezione. Nella s
 
 Testare diverse variabili in questo notebook per vedere come la correlazione corrisponde all'accuratezza del modello.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/2-Regression/3-Linear/translations/README.ja.md b/2-Regression/3-Linear/translations/README.ja.md
index cc3c3b7b07..2dbc0f3219 100644
--- a/2-Regression/3-Linear/translations/README.ja.md
+++ b/2-Regression/3-Linear/translations/README.ja.md
@@ -2,7 +2,7 @@
 
 ![線形回帰 vs 多項�回帰 �インフォグラフィック](../images/linear-polynomial.png)
 > [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
-## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [講義��クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
 ### イントロダクション 
 
 �れ�����レッスン�使用�るカボ�ャ�価格データセット�ら集��サンプルデータを使���回帰��何�を探������。���Matplotlibを使���視化を行����。
@@ -323,7 +323,7 @@ Scikit-learn���多項�回帰モデルを構築�る���便利�AP
 
 ��ノートブック������異�る変数をテスト��相関関係�モデル�精度���よ��影響�る�を確���������。
 
-## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## レビュー & 自主学習
 
diff --git a/2-Regression/3-Linear/translations/README.zh-cn.md b/2-Regression/3-Linear/translations/README.zh-cn.md
index 55c4d29395..12b57103e7 100644
--- a/2-Regression/3-Linear/translations/README.zh-cn.md
+++ b/2-Regression/3-Linear/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
 
 ![线性与多项�回归信�图](../images/linear-polynomial.png)
 > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/13/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/13/)
 ### 介� 
 
 到目�为止，你已�通过从我们将在本课程中使用的�瓜定价数�集收集的样本数�探索了什么是回归。你还使用Matplotlib对其进行了�视化。
@@ -321,7 +321,7 @@ Scikit-learn包�一个用于构建多项�回归模型的有用API - `make_pi
 
 在此notebook中测试几个��的��，以查看相关性与模型准确性的对应关系。
 
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/14/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/14/)
 
 ## �习与自学
 
diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index a2a938a718..afc9c629f1 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -2,7 +2,7 @@
 
 ![Logistic vs. linear regression infographic](./images/logistic-linear.png)
 > Infographic by [Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
 
 ## Introduction
 
@@ -222,10 +222,10 @@ While you can get a scoreboard report [terms](https://scikit-learn.org/stable/mo
 
 In Scikit-learn, confusion matrices Rows (axis 0) are actual labels and columns (axis 1) are predicted labels.
 
-|  |0|1|
-|:-:|:-:|:-:|
-|0|TN|FP|
-|1|FN|TP|
+|       |   0   |   1   |
+| :---: | :---: | :---: |
+|   0   |  TN   |  FP   |
+|   1   |  FN   |  TP   |
 
 What's going on here? Let's say our model is asked to classify pumpkins between two binary categories, category 'orange' and category 'not-orange'.
 
@@ -296,7 +296,7 @@ In future lessons on classifications, you will learn how to iterate to improve y
 
 There's a lot more to unpack regarding logistic regression! But the best way to learn is to experiment. Find a dataset that lends itself to this type of analysis and build a model with it. What do you learn? tip: try [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) for interesting datasets.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
 
 ## Review & Self Study
 
diff --git a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
index c9e16ce9ec..574cec0592 100644
--- a/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
+++ b/2-Regression/4-Logistic/solution/lesson_4-R.ipynb
@@ -45,7 +45,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "#### ** [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)**\n",
+        "#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**\n",
         "\n",
         "####  Introduction\n",
         "\n",
diff --git a/2-Regression/4-Logistic/solution/lesson_4.Rmd b/2-Regression/4-Logistic/solution/lesson_4.Rmd
index 8d99dc6207..e974594d1d 100644
--- a/2-Regression/4-Logistic/solution/lesson_4.Rmd
+++ b/2-Regression/4-Logistic/solution/lesson_4.Rmd
@@ -14,7 +14,7 @@ output:
 
 ![Infographic by Dasani Madipalli](../images/logistic-linear.png){width="600"}
 
-#### ** [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)**
+#### ** [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)**
 
 ####  Introduction
 
diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
index 213ef4aa3d..553205d712 100644
--- a/2-Regression/4-Logistic/translations/README.id.md
+++ b/2-Regression/4-Logistic/translations/README.id.md
@@ -3,7 +3,7 @@
 ![Infografik regresi logistik vs. linear](../images/logistic-linear.png)
 > Infografik oleh [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Kuis pra-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [Kuis pra-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
 
 ## Pembukaan
 
@@ -291,7 +291,7 @@ Nanti dalam pelajaran lebih lanjut tentang klasifikasi, kamu akan belajar bagaim
 
 Masih ada banyak tentang regresi logistik! Tetapi cara paling baik adalah untuk bereksperimen. Carilah sebuah *dataset* yang bisa diteliti seperti ini dan bangunlah sebuah model darinya. Apa yang kamu pelajari? Petunjuk: Coba [Kaggle](https://kaggle.com) untuk *dataset-dataset* menarik.
 
-## [Kuis pasca-ceramah](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [Kuis pasca-ceramah](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
 
 ## Review & Pembelajaran mandiri
 
diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md
index e9940203ce..deb51eaa1e 100644
--- a/2-Regression/4-Logistic/translations/README.it.md
+++ b/2-Regression/4-Logistic/translations/README.it.md
@@ -3,7 +3,7 @@
 ![Infografica di regressione lineare e logistica](../images/logistic-linear.png)
 > Infografica di [Dasani Madipalli](https://twitter.com/dasani_decoded)
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
 
 ## Introduzione
 
@@ -284,7 +284,7 @@ Nelle lezioni future sulle classificazioni si imparerà come eseguire l'iterazio
 
 C'è molto altro da svelare riguardo alla regressione logistica! Ma il modo migliore per imparare è sperimentare. Trovare un insieme di dati che si presti a questo tipo di analisi e costruire un modello con esso. Cosa si è appreso? suggerimento: provare [Kaggle](https://kaggle.com) per ottenere insiemi di dati interessanti.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/2-Regression/4-Logistic/translations/README.ja.md b/2-Regression/4-Logistic/translations/README.ja.md
index 3e384824b2..662a1eafba 100644
--- a/2-Regression/4-Logistic/translations/README.ja.md
+++ b/2-Regression/4-Logistic/translations/README.ja.md
@@ -2,7 +2,7 @@
 
 ![ロジスティク回帰 vs 線形回帰�インフォグラフィック](../images/logistic-linear.png)
 > [Dasani Madipalli](https://twitter.com/dasani_decoded) �よるインフォグラフィック
-## [講義��クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [講義��クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
 
 ## イントロダクション
 
@@ -225,10 +225,10 @@ Seabornプロット���変数を並��表示�る�������
 
 Scikit-learn���混�行列�行 (axis=0)�実際�ラベル�列 (axis=1)�予測ラベル��り��。
 
-|  |0|1|
-|:-:|:-:|:-:|
-|0|TN|FP|
-|1|FN|TP|
+|       |   0   |   1   |
+| :---: | :---: | :---: |
+|   0   |  TN   |  FP   |
+|   1   |  FN   |  TP   |
 
 ���何�起����る��？例���カボ�ャを「オレンジ色��「オレンジ色�������2��カテゴリー�分類�るよ��求�られ�����ょ�。
 
@@ -299,7 +299,7 @@ print(auc)
 
 ロジスティック回帰����������解�明����������ん�り��。����学����最良�方法��実験�る����。��種�分�����データセットを見�����れを使��モデルを構築�����ょ�。ヒント：�白�データセットを探����[Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) を試��������。
 
-## [講義後クイズ](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [講義後クイズ](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
 
 ## レビュー & 自主学習
 
diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
index 5de169fbec..dc1bb5e10b 100644
--- a/2-Regression/4-Logistic/translations/README.zh-cn.md
+++ b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -2,7 +2,7 @@
 
 ![逻辑与线性回归信�图](../images/logistic-linear.png)
 > 作者[Dasani Madipalli](https://twitter.com/dasani_decoded)
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/15/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/15/)
 
 ## 介�
 
@@ -282,7 +282,7 @@ print(auc)
 
 关于逻辑回归，还有很多东西需�解开�但最好的学习方法是实验。找到适�此类分�的数�集并用它构建模型。你学到了什么？�贴士：�试[Kaggle](https://kaggle.com)获�有趣的数�集。
 
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/16/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/16/)
 
 ## �习与自学
 
diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index fae04026d5..c983e679a9 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -11,7 +11,7 @@ We will continue our use of notebooks to clean data and train our model, but you
 
 To do this, you need to build a web app using Flask.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
 
 ## Building an app
 
@@ -334,7 +334,7 @@ In a professional setting, you can see how good communication is necessary betwe
 
 Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
 
 ## Review & Self Study
 
diff --git a/3-Web-App/1-Web-App/translations/README.it.md b/3-Web-App/1-Web-App/translations/README.it.md
index 82454852a2..f31eb2d4b5 100644
--- a/3-Web-App/1-Web-App/translations/README.it.md
+++ b/3-Web-App/1-Web-App/translations/README.it.md
@@ -11,7 +11,7 @@ Si continuerà a utilizzare il notebook per pulire i dati e addestrare il modell
 
 Per fare ciò, è necessario creare un'app Web utilizzando Flask.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
 
 ## Costruire un'app
 
@@ -334,7 +334,7 @@ In un ambiente professionale, si può vedere quanto sia necessaria una buona com
 
 Invece di lavorare su un notebook e importare il modello nell'app Flask, si può addestrare il modello direttamente nell'app Flask! Provare a convertire il codice Python nel notebook, magari dopo che i dati sono stati puliti, per addestrare il modello dall'interno dell'app su un percorso chiamato `/train`. Quali sono i pro e i contro nel seguire questo metodo?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/3-Web-App/1-Web-App/translations/README.ja.md b/3-Web-App/1-Web-App/translations/README.ja.md
index edfaa34260..b23050dd62 100644
--- a/3-Web-App/1-Web-App/translations/README.ja.md
+++ b/3-Web-App/1-Web-App/translations/README.ja.md
@@ -11,7 +11,7 @@
 
 �������Flaskを使��Webアプリを構築�る必���り��。
 
-## [講義���テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17?loc=ja)
+## [講義���テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17?loc=ja)
 
 ## アプリ�構築
 
@@ -334,7 +334,7 @@ print(model.predict([[50,44,-12]]))
 
 ノートブック�作業��モデルをFlaskアプリ�イン�ート�る代�り��Flaskアプリ�中�モデルをトレーニング�る�������。��ら�データをクリーニング��後��り����ノートブック内�Pythonコードを変����アプリ内� `train` ���パス�モデルを学習��������。��方法を採用�る���長所�短所�何��ょ��？
 
-## [講義後��テスト](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18?loc=ja)
+## [講義後��テスト](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18?loc=ja)
 
 ## 振り返り�自主学習
 
diff --git a/3-Web-App/1-Web-App/translations/README.zh-cn.md b/3-Web-App/1-Web-App/translations/README.zh-cn.md
index 9110f2b864..e53b11bcd5 100644
--- a/3-Web-App/1-Web-App/translations/README.zh-cn.md
+++ b/3-Web-App/1-Web-App/translations/README.zh-cn.md
@@ -11,7 +11,7 @@
 
 为此，你需�使用Flask构建一个web应用程�。
 
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/17/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/17/)
 
 ## 构建应用程�
 
@@ -334,7 +334,7 @@ print(model.predict([[50,44,-12]]))
 
 你�以在Flask应用程�中训练模型，而�是在notebook上工作并将模型导入Flask应用程���试在notebook中转�Python代�，�能是在清除数�之�，从应用程�中的一个�为`train`的路径训练模型。采用这�方法的利弊是什么？
 
-## [课�测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/18/)
+## [课�测](https://white-water-09ec41f0f.azurestaticapps.net/quiz/18/)
 
 ## �习与自学 
 
diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index 4a3373767b..b6dde02f7a 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -19,7 +19,7 @@ Remember:
 
 Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this cuisine data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
 
 ### Introduction
 
@@ -286,7 +286,7 @@ Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev
 
 This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
 
 ## Review & Self Study
 
diff --git a/4-Classification/1-Introduction/translations/README.it.md b/4-Classification/1-Introduction/translations/README.it.md
index fabfec5e25..76c8b0a279 100644
--- a/4-Classification/1-Introduction/translations/README.it.md
+++ b/4-Classification/1-Introduction/translations/README.it.md
@@ -19,7 +19,7 @@ Ricordare:
 
 La classificazione utilizza vari algoritmi per determinare altri modi per definire l'etichetta o la classe di un punto dati. Si lavorerà con questi dati di cucina per vedere se, osservando un gruppo di ingredienti, è possibile determinarne la cucina di origine.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
 
 ### Introduzione
 
@@ -286,7 +286,7 @@ Ora che i dati sono puliti, si usa [SMOTE](https://imbalanced-learn.org/dev/refe
 
 Questo programma di studi contiene diversi insiemi di dati interessanti. Esaminare le cartelle `data` e vedere se contiene insiemi di dati che sarebbero appropriati per la classificazione binaria o multiclasse. Quali domande si farebbero a questo insieme di dati?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/4-Classification/1-Introduction/translations/README.tr.md b/4-Classification/1-Introduction/translations/README.tr.md
index a2b1d92f32..e1b32ec90b 100644
--- a/4-Classification/1-Introduction/translations/README.tr.md
+++ b/4-Classification/1-Introduction/translations/README.tr.md
@@ -19,7 +19,7 @@ Hatırlayın:
 
 Sınıflandırma, bir veri noktasının etiketini veya sınıfını belirlemek için farklı yollar belirlemek üzere çeşitli algoritmalar kullanır. Bir grup malzemeyi gözlemleyerek kökeninin hangi mutfak olduğunu belirleyip belirleyemeyeceğimizi görmek için bu mutfak verisiyle çalışalım.
 
-## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/?loc=tr)
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/?loc=tr)
 
 ### GiriÅŸ
 
@@ -287,7 +287,7 @@ Veriyi temizlediniz, ÅŸimdi [SMOTE](https://imbalanced-learn.org/dev/references/
 
 Bu öğretim programı farklı ilgi çekici veri setleri içermekte. `data` klasörlerini inceleyin ve ikili veya çok sınıflı sınıflandırma için uygun olabilecek veri setleri bulunduran var mı, bakın. Bu veri seti için hangi soruları sorabilirdiniz?
 
-## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/?loc=tr)
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/?loc=tr)
 
 ## Gözden Geçirme & Kendi Kendine Çalışma
 
diff --git a/4-Classification/1-Introduction/translations/README.zh-cn.md b/4-Classification/1-Introduction/translations/README.zh-cn.md
index df585e3f5b..978f4a0674 100644
--- a/4-Classification/1-Introduction/translations/README.zh-cn.md
+++ b/4-Classification/1-Introduction/translations/README.zh-cn.md
@@ -19,7 +19,7 @@
 
 分类方法采用多�算法�确定其他�以用�确定一个数�点的标签或类别的方法。让我们�研究一下这个数�集，看看我们能�通过观察�肴的原料�确定它的�头。
 
-## [课程�的�问题](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/19/)
+## [课程�的�问题](https://white-water-09ec41f0f.azurestaticapps.net/quiz/19/)
 
 分类是机器学习研究者和数�科学家使用的一�基本方法。从基本的二元分类（这是�是一份垃圾邮件？）到��的图片分类和使用计算机视觉的分割技术，它都是将数�分类并�出相关问题的有效工具。
 
@@ -280,7 +280,7 @@ Scikit-learn项目�供多�对数�进行分类的算法，你需�根�
 
 本项目的全部课程�有很多有趣的数�集。 探索一下 `data`文件夹，看看这里�有没有适�二元分类�多元分类算法的数�集，�想一下你对这些数�集有没有什么想问的问题。
 
-## [课�练习](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/20/)
+## [课�练习](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)
 
 ## 回顾 & 自学
 
diff --git a/4-Classification/2-Classifiers-1/README.md b/4-Classification/2-Classifiers-1/README.md
index c6b35219f9..68877e6ace 100644
--- a/4-Classification/2-Classifiers-1/README.md
+++ b/4-Classification/2-Classifiers-1/README.md
@@ -4,7 +4,7 @@ In this lesson, you will use the dataset you saved from the last lesson full of
 
 You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
 # Preparation
 
 Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a _cleaned_cuisines.csv_ file exists in the root `/data` folder for these four lessons.
@@ -67,13 +67,13 @@ Assuming you completed [Lesson 1](../1-Introduction/README.md), make sure that a
 
     Your features look like this:
 
-| | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
-| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | -----: |
-|      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   | 0 |
-|      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   | 0 |
-|      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   | 0 |
-|      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   | 0 |
-|      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   | 0 |
+|      | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+| ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
+|    0 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    1 |      1 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    2 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    3 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|    4 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      1 |        0 | 0 |
 
 Now you are ready to train your model!
 
@@ -199,8 +199,8 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
 
     The result is printed - Indian cuisine is its best guess, with good probability:
 
-    |          |        0 | 
-    | -------: | -------: | 
+    |          |        0 |
+    | -------: | -------: |
     |   indian | 0.715851 |
     |  chinese | 0.229475 |
     | japanese | 0.029763 |
@@ -217,21 +217,21 @@ Since you are using the multiclass case, you need to choose what _scheme_ to use
     ```
 
     |              | precision | recall | f1-score | support |
-    | ------------ | ------ | -------- | ------- | ---- |
-    | chinese      | 0.73   | 0.71     | 0.72    | 229  |
-    | indian       | 0.91   | 0.93     | 0.92    | 254  |
-    | japanese     | 0.70   | 0.75     | 0.72    | 220  |
-    | korean       | 0.86   | 0.76     | 0.81    | 242  |
-    | thai         | 0.79   | 0.85     | 0.82    | 254  |
-    | accuracy     | 0.80   | 1199     |         |      |
-    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |
-    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |
+    | ------------ | --------- | ------ | -------- | ------- |
+    | chinese      | 0.73      | 0.71   | 0.72     | 229     |
+    | indian       | 0.91      | 0.93   | 0.92     | 254     |
+    | japanese     | 0.70      | 0.75   | 0.72     | 220     |
+    | korean       | 0.86      | 0.76   | 0.81     | 242     |
+    | thai         | 0.79      | 0.85   | 0.82     | 254     |
+    | accuracy     | 0.80      | 1199   |          |         |
+    | macro avg    | 0.80      | 0.80   | 0.80     | 1199    |
+    | weighted avg | 0.80      | 0.80   | 0.80     | 1199    |
 
 ## 🚀Challenge
 
 In this lesson, you used your cleaned data to build a machine learning model that can predict a national cuisine based on a series of ingredients. Take some time to read through the many options Scikit-learn provides to classify data. Dig deeper into the concept of 'solver' to understand what goes on behind the scenes.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
 
 ## Review & Self Study
 
diff --git a/4-Classification/2-Classifiers-1/translations/README.it.md b/4-Classification/2-Classifiers-1/translations/README.it.md
index 674a59c269..5611870b4e 100644
--- a/4-Classification/2-Classifiers-1/translations/README.it.md
+++ b/4-Classification/2-Classifiers-1/translations/README.it.md
@@ -4,7 +4,7 @@ In questa lezione, si utilizzerà l'insieme di dati salvati dall'ultima lezione,
 
 Si utilizzerà questo insieme di dati con una varietà di classificatori per _prevedere una determinata cucina nazionale in base a un gruppo di ingredienti_. Mentre si fa questo, si imparerà di più su alcuni dei modi in cui gli algoritmi possono essere sfruttati per le attività di classificazione.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
 # Preparazione
 
 Supponendo che la [Lezione 1](../1-Introduction/README.md) sia stata completata, assicurarsi che _esista_ un file clean_cuisines.csv nella cartella in radice `/data` per queste quattro lezioni.
@@ -200,13 +200,13 @@ Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_
 
    Il risultato è stampato: la cucina indiana è la sua ipotesi migliore, con buone probabilità:
 
-   |          | 0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-   | indiano | 0,715851 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | cinese | 0.229475 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   |            |        0 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | ---------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+   |    indiano | 0,715851 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   |     cinese | 0.229475 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
    | Giapponese | 0,029763 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | Coreano | 0.017277 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | thai | 0.007634 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   |    Coreano | 0.017277 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   |       thai | 0.007634 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
 
    ✅ Si è in grado di spiegare perché il modello è abbastanza sicuro che questa sia una cucina indiana?
 
@@ -217,22 +217,22 @@ Poiché si sta utilizzando il caso multiclasse, si deve scegliere quale _schema_
    print(classification_report(y_test,y_pred))
    ```
 
-   | precisione | recall | punteggio f1 | supporto |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-   | cinese | 0,73 | 0,71 | 0,72 | 229 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | indiano | 0,91 | 0,93 | 0,92 | 254 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | Giapponese | 0.70 | 0,75 | 0,72 | 220 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | Coreano | 0,86 | 0,76 | 0,81 | 242 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | thai | 0,79 | 0,85 | 0.82 | 254 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | accuratezza | 0,80 | 1199 |         |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | macro media | 0,80 | 0,80 | 0,80 | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
-   | Media ponderata | 0,80 | 0,80 | 0,80 | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | precisione      | recall | punteggio f1 | supporto |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | --------------- | ------ | ------------ | -------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+   | cinese          | 0,73   | 0,71         | 0,72     | 229  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | indiano         | 0,91   | 0,93         | 0,92     | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Giapponese      | 0.70   | 0,75         | 0,72     | 220  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Coreano         | 0,86   | 0,76         | 0,81     | 242  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | thai            | 0,79   | 0,85         | 0.82     | 254  |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | accuratezza     | 0,80   | 1199         |          |      |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | macro media     | 0,80   | 0,80         | 0,80     | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
+   | Media ponderata | 0,80   | 0,80         | 0,80     | 1199 |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |     |
 
 ## 🚀 Sfida
 
 In questa lezione, sono stati utilizzati dati puliti per creare un modello di apprendimento automatico in grado di prevedere una cucina nazionale basata su una serie di ingredienti. Si prenda del tempo per leggere le numerose opzioni fornite da Scikit-learn per classificare i dati. Approfondire il concetto di "risolutore" per capire cosa succede dietro le quinte.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
 ## Revisione e Auto Apprendimento
 
 Approfondire un po' la matematica alla base della regressione logistica in [questa lezione](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)
diff --git a/4-Classification/2-Classifiers-1/translations/README.tr.md b/4-Classification/2-Classifiers-1/translations/README.tr.md
index f02bd759a9..30f36b133c 100644
--- a/4-Classification/2-Classifiers-1/translations/README.tr.md
+++ b/4-Classification/2-Classifiers-1/translations/README.tr.md
@@ -4,7 +4,7 @@ Bu derste, mutfaklarla ilgili dengeli ve temiz veriyle dolu, geçen dersten kayd
 
 Bu veri setini çeşitli sınıflandırıcılarla _bir grup malzemeyi baz alarak verilen bir ulusal mutfağı öngörmek_ için kullanacaksınız. Bunu yaparken, sınıflandırma görevleri için algoritmaların leveraj edilebileceği yollardan bazıları hakkında daha fazla bilgi edineceksiniz.
 
-## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/?loc=tr)
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/?loc=tr)
 # Hazırlık
 
 [Birinci dersi](../../1-Introduction/README.md) tamamladığınızı varsayıyoruz, dolayısıyla bu dört ders için _cleaned_cuisines.csv_ dosyasının kök `/data` klasöründe var olduğundan emin olun.
@@ -68,12 +68,12 @@ Bu veri setini çeşitli sınıflandırıcılarla _bir grup malzemeyi baz alarak
     Öznitelikleriniz şöyle görünüyor:
 
 | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
-| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | 
-|      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-|      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-|      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-|      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-|      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: |
+|      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0 |
+|      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0 |
 
 Şimdi modelinizi eğitmek için hazırsınız!
 
@@ -199,8 +199,8 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
 
     Sonuç bastırılır - Hint mutfağı iyi olasılıkla en iyi öngörü:
 
-    |          |        0 | 
-    | -------: | -------: | 
+    |          |        0 |
+    | -------: | -------: |
     |   indian | 0.715851 |
     |  chinese | 0.229475 |
     | japanese | 0.029763 |
@@ -217,21 +217,21 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
     ```
 
     |              | precision | recall | f1-score | support |
-    | ------------ | ------ | -------- | ------- | ---- |
-    | chinese      | 0.73   | 0.71     | 0.72    | 229  |
-    | indian       | 0.91   | 0.93     | 0.92    | 254  |
-    | japanese     | 0.70   | 0.75     | 0.72    | 220  |
-    | korean       | 0.86   | 0.76     | 0.81    | 242  |
-    | thai         | 0.79   | 0.85     | 0.82    | 254  |
-    | accuracy     | 0.80   | 1199     |         |      |
-    | macro avg    | 0.80   | 0.80     | 0.80    | 1199 |
-    | weighted avg | 0.80   | 0.80     | 0.80    | 1199 |
+    | ------------ | --------- | ------ | -------- | ------- |
+    | chinese      | 0.73      | 0.71   | 0.72     | 229     |
+    | indian       | 0.91      | 0.93   | 0.92     | 254     |
+    | japanese     | 0.70      | 0.75   | 0.72     | 220     |
+    | korean       | 0.86      | 0.76   | 0.81     | 242     |
+    | thai         | 0.79      | 0.85   | 0.82     | 254     |
+    | accuracy     | 0.80      | 1199   |          |         |
+    | macro avg    | 0.80      | 0.80   | 0.80     | 1199    |
+    | weighted avg | 0.80      | 0.80   | 0.80     | 1199    |
 
 ## :rocket: Meydan Okuma
 
 Bu derste, bir grup malzemeyi baz alarak bir ulusal mutfağı öngörebilen bir makine öğrenimi modeli oluşturmak için temiz verinizi kullandınız. Scikit-learn'ün veri sınıflandırmak için sağladığı birçok yöntemi okumak için biraz vakit ayırın. Arka tarafta neler olduğunu anlamak için 'çözücü' kavramını derinlemesine inceleyin.
 
-## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/?loc=tr)
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/?loc=tr)
 
 ## Gözden geçirme & kendi kendine çalışma
 
diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
index 83aa4fc4c2..4795333387 100644
--- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
+++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
 
 你将使用此数�集和��分类器，_根�一组�料预测这是哪一国家的美食_。在此过程中，你将学到更多用��衡分类任务算法的方法  
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/21/)
 # 准备工作
 
 �如你已�完�了[课程1](../../1-Introduction/translations/README.zh-cn.md), 确�在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件�进行接下�的四节课程。
@@ -68,13 +68,13 @@
 
     你的特�集看上去将会是这样:
 
-    | | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |     
-    | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
-    |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      1 |        1 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      2 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      3 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        0 | 0   |
-    |      4 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |    0 |     ... |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 |        1 | 0   |
+    |      | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke |  ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood |  yam | yeast | yogurt | zucchini |
+    | ---: | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------- |
+    |    0 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 | 0        |
+    |    1 |      1 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 | 0        |
+    |    2 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 | 0        |
+    |    3 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      0 | 0        |
+    |    4 |      0 |        0 |     0 |          0 |     0 |            0 |       0 |        0 |         0 |         0 |  ... |       0 |           0 |          0 |                       0 |    0 |    0 |    0 |     0 |      1 | 0        |
 
 现在，你已�准备好�以开始训练你的模型了�
 
@@ -201,11 +201,11 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
     �行�的输出如下———�以�现这是一��度�的�能性最大，是最��的猜测:
 
     |          |        0 |
-    | -------: | -------: | 
+    | -------: | -------: |
     |   indian | 0.715851 |
     |  chinese | 0.229475 |
     | japanese | 0.029763 |
-    |   korean | 0.017277 | 
+    |   korean | 0.017277 |
     |     thai | 0.007634 |
 
     ✅ 你能解释下为什么模型会如此确定这是一��度�么？
@@ -218,8 +218,8 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
     ```
 
     | precision    | recall | f1-score | support |      |
-    | ------------ | ------ | -------- | ------- | ---- | 
-    | chinese      | 0.73   | 0.71     | 0.72    | 229  | 
+    | ------------ | ------ | -------- | ------- | ---- |
+    | chinese      | 0.73   | 0.71     | 0.72    | 229  |
     | indian       | 0.91   | 0.93     | 0.92    | 254  |
     | japanese     | 0.70   | 0.75     | 0.72    | 220  |
     | korean       | 0.86   | 0.76     | 0.81    | 242  |
@@ -232,7 +232,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
 
 在本课程中，你使用了清洗�的数�建立了一个机器学习的模型，这个模型能够根�输入的一系列的�料�预测���自于哪个国家。请�花点时间阅读一下Scikit-learn所�供的关于�以用�分类数�的其他方法的资料。此外，你也�以深入研究一下“solver�的概念并�试一下�解其背�的原�。
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/22/)
 ## 回顾与自学
 
 [这个课程](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)将对逻辑回归背�的数学原�进行更加深入的讲解
diff --git a/4-Classification/3-Classifiers-2/README.md b/4-Classification/3-Classifiers-2/README.md
index 9720c76379..291b3a7515 100644
--- a/4-Classification/3-Classifiers-2/README.md
+++ b/4-Classification/3-Classifiers-2/README.md
@@ -2,7 +2,7 @@
 
 In this second classification lesson, you will explore more ways to classify numeric data. You will also learn about the ramifications for choosing one classifier over the other.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
 
 ### Prerequisite
 
@@ -224,7 +224,7 @@ This method of Machine Learning "combines the predictions of several base estima
 
 Each of these techniques has a large number of parameters that you can tweak. Research each one's default parameters and think about what tweaking these parameters would mean for the model's quality.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
 
 ## Review & Self Study
 
diff --git a/4-Classification/3-Classifiers-2/translations/README.it.md b/4-Classification/3-Classifiers-2/translations/README.it.md
index 4a3a431fe3..3d011db879 100644
--- a/4-Classification/3-Classifiers-2/translations/README.it.md
+++ b/4-Classification/3-Classifiers-2/translations/README.it.md
@@ -2,7 +2,7 @@
 
 In questa seconda lezione sulla classificazione, si esploreranno più modi per classificare i dati numerici. Si Impareranno anche le ramificazioni per la scelta di un classificatore rispetto all'altro.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/)
 
 ### Prerequisito
 
@@ -224,7 +224,7 @@ Questo metodo di Machine Learning "combina le previsioni di diversi stimatori di
 
 Ognuna di queste tecniche ha un gran numero di parametri che si possono modificare. Ricercare i parametri predefiniti di ciascuno e pensare a cosa significherebbe modificare questi parametri per la qualità del modello.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/4-Classification/3-Classifiers-2/translations/README.tr.md b/4-Classification/3-Classifiers-2/translations/README.tr.md
index 24ea3dfd84..56c1e11e9f 100644
--- a/4-Classification/3-Classifiers-2/translations/README.tr.md
+++ b/4-Classification/3-Classifiers-2/translations/README.tr.md
@@ -2,7 +2,7 @@
 
 Bu ikinci sınıflandırma dersinde, sayısal veriyi sınıflandırmak için daha fazla yöntem öğreneceksiniz. Ayrıca, bir sınıflandırıcıyı diğerlerine tercih etmenin sonuçlarını da öğreneceksiniz.
 
-## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/23/?loc=tr)
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/23/?loc=tr)
 
 ### Ön koşul
 
@@ -224,7 +224,7 @@ Makine Öğreniminin bu yöntemi, modelin kalitesini artırmak için, "birçok t
 
 Bu yöntemlerden her biri değiştirebileceğiniz birsürü parametre içeriyor. Her birinin varsayılan parametrelerini araştırın ve bu parametreleri değiştirmenin modelin kalitesi için ne anlama gelebileceği hakkında düşünün.
 
-## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/24/?loc=tr)
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/24/?loc=tr)
 
 ## Gözden Geçirme & Kendi Kendine Çalışma
 
diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index 66597fbf6d..cad728f34c 100644
--- a/4-Classification/4-Applied/README.md
+++ b/4-Classification/4-Applied/README.md
@@ -8,7 +8,7 @@ One of the most useful practical uses of machine learning is building recommenda
 
 > 🎥 Click the image above for a video: Andrew Ng introduces recommendation system design
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
 
 In this lesson you will learn:
 
@@ -321,7 +321,7 @@ Congratulations, you have created a 'recommendation' web app  with a few fields.
 
 Your web app is very minimal, so continue to build it out using ingredients and their indexes from the [ingredient_indexes](../data/ingredient_indexes.csv) data. What flavor combinations work to create a given national dish?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
 
 ## Review & Self Study
 
diff --git a/4-Classification/4-Applied/translations/README.it.md b/4-Classification/4-Applied/translations/README.it.md
index 4512aabacb..72dc6c3800 100644
--- a/4-Classification/4-Applied/translations/README.it.md
+++ b/4-Classification/4-Applied/translations/README.it.md
@@ -8,7 +8,7 @@ Uno degli usi pratici più utili dell'apprendimento automatico è la creazione d
 
 > 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng introduce la progettazione di un sistema di raccomandazione
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/)
 
 In questa lezione, si imparerà:
 
@@ -321,7 +321,7 @@ Congratulazioni, si è creato un'app web di "raccomandazione" con pochi campi. S
 
 L'app web è molto minimale, quindi continuare a costruirla usando gli ingredienti e i loro indici dai dati [ingredient_indexes](../../data/ingredient_indexes.csv) . Quali combinazioni di sapori funzionano per creare un determinato piatto nazionale?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/4-Classification/4-Applied/translations/README.tr.md b/4-Classification/4-Applied/translations/README.tr.md
index 651fc85b7a..d3418e9ab8 100644
--- a/4-Classification/4-Applied/translations/README.tr.md
+++ b/4-Classification/4-Applied/translations/README.tr.md
@@ -8,7 +8,7 @@ Makine öğreniminin en faydalı pratik kullanımlarından biri, önerici/tavsiy
 
 > :movie_camera: Video için yukarıdaki fotoğrafa tıklayın: Andrew Ng introduces recommendation system design (Andrew Ng önerici sistem tasarımını tanıtıyor)
 
-## [Ders öncesi kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/25/?loc=tr)
+## [Ders öncesi kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/25/?loc=tr)
 
 Bu derste şunları öğreneceksiniz:
 
@@ -321,7 +321,7 @@ Tebrikler, birkaç değişkenle bir 'önerici' web uygulaması oluşturdunuz! Bu
 
 Web uygulamanız çok minimal, bu yüzden [ingredient_indexes](../../data/ingredient_indexes.csv) verisinden malzemeleri ve indexlerini kullanarak web uygulamanızı oluşturmaya devam edin. Verilen bir ulusal yemeği yapmak için hangi tat birleşimleri işe yarıyor?
 
-## [Ders sonrası kısa sınavı](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/26/?loc=tr)
+## [Ders sonrası kısa sınavı](https://white-water-09ec41f0f.azurestaticapps.net/quiz/26/?loc=tr)
 
 ## Gözden Geçirme & Kendi Kendine Çalışma
 
diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md
index 8453c45129..c5ef453f52 100644
--- a/5-Clustering/1-Visualize/README.md
+++ b/5-Clustering/1-Visualize/README.md
@@ -5,7 +5,7 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup
 [![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
 
 > 🎥 Click the image above for a video. While you're studying machine learning with clustering, enjoy some Nigerian Dance Hall tracks - this is a highly rated song from 2014 by PSquare.
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
 ### Introduction
 
 [Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
@@ -317,7 +317,7 @@ In general, for clustering, you can use scatterplots to show clusters of data, s
 
 In preparation for the next lesson, make a chart about the various clustering algorithms you might discover and use in a production environment. What kinds of problems is the clustering trying to address?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
 
 ## Review & Self Study
 
diff --git a/5-Clustering/1-Visualize/translations/README.it.md b/5-Clustering/1-Visualize/translations/README.it.md
index 104507c914..0d8510b009 100644
--- a/5-Clustering/1-Visualize/translations/README.it.md
+++ b/5-Clustering/1-Visualize/translations/README.it.md
@@ -5,7 +5,7 @@ Il clustering è un tipo di [apprendimento non supervisionato](https://wikipedia
 [![No One Like You di PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You di PSquare")
 
 > 🎥 Fare clic sull'immagine sopra per un video. Mentre si studia machine learning con il clustering, si potranno gradire brani della Nigerian Dance Hall: questa è una canzone molto apprezzata del 2014 di PSquare.
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
 
 ### Introduzione
 
@@ -33,18 +33,18 @@ In questo [modulo di apprendimento](https://docs.microsoft.com/learn/modules/tra
 
 [Scikit-learn offre una vasta gamma](https://scikit-learn.org/stable/modules/clustering.html) di metodi per eseguire il clustering. Il tipo scelto dipenderà dal caso d'uso. Secondo la documentazione, ogni metodo ha diversi vantaggi. Ecco una tabella semplificata dei metodi supportati da Scikit-learn e dei loro casi d'uso appropriati:
 
-| Nome del metodo | Caso d'uso |
-| :--------------------------- | :--------------------------------------------------------------------- |
-| K-MEANS | uso generale, induttivo |
-| Affinity propagation (Propagazione dell'affinità) | molti, cluster irregolari, induttivo |
-| Mean-shift (Spostamento medio) | molti, cluster irregolari, induttivo |
-| Spectral clustering (Raggruppamento spettrale) | pochi, anche grappoli, trasduttivi |
-| Ward hierarchical clustering (Cluster gerarchico) | molti, cluster vincolati, trasduttivi |
-| Agglomerative clustering (Raggruppamento agglomerativo) | molte, vincolate, distanze non euclidee, trasduttive |
-| DBSCAN | geometria non piatta, cluster irregolari, trasduttivo |
-| OPTICS | geometria non piatta, cluster irregolari con densità variabile, trasduttivo |
-| Gaussian mixtures (miscele gaussiane) | geometria piana, induttiva |
-| BIRCH | insiemi di dati di grandi dimensioni con valori anomali, induttivo |
+| Nome del metodo                                         | Caso d'uso                                                                  |
+| :------------------------------------------------------ | :-------------------------------------------------------------------------- |
+| K-MEANS                                                 | uso generale, induttivo                                                     |
+| Affinity propagation (Propagazione dell'affinità)       | molti, cluster irregolari, induttivo                                        |
+| Mean-shift (Spostamento medio)                          | molti, cluster irregolari, induttivo                                        |
+| Spectral clustering (Raggruppamento spettrale)          | pochi, anche grappoli, trasduttivi                                          |
+| Ward hierarchical clustering (Cluster gerarchico)       | molti, cluster vincolati, trasduttivi                                       |
+| Agglomerative clustering (Raggruppamento agglomerativo) | molte, vincolate, distanze non euclidee, trasduttive                        |
+| DBSCAN                                                  | geometria non piatta, cluster irregolari, trasduttivo                       |
+| OPTICS                                                  | geometria non piatta, cluster irregolari con densità variabile, trasduttivo |
+| Gaussian mixtures (miscele gaussiane)                   | geometria piana, induttiva                                                  |
+| BIRCH                                                   | insiemi di dati di grandi dimensioni con valori anomali, induttivo          |
 
 > 🎓 Il modo in cui si creno i cluster ha molto a che fare con il modo in cui si raccolgono punti dati in gruppi. Si esamina un po' di vocabolario:
 >
@@ -197,16 +197,16 @@ Il clustering come tecnica è notevolmente aiutato da una corretta visualizzazio
    df.describe()
    ```
 
-   |       | release_date | lenght | popularity | danceability | acousticness | Energia | strumentale | vitalità | livello di percezione sonora | parlato | tempo | #ora_firma |
-   | ----- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ---------------- | -------- | --------- | ----------- | ---------- | -------------- |
-   | estero) | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 | 530 |
-   | mezzo | 2015.390566 | 222298.1698 | 17.507547 | 0.741619 | 0.265412 | 0.760623 | 0,016305 | 0,147308 | -4.953011 | 0,130748 | 116.487864 | 3.986792 |
-   | std | 3.131688 | 39696.82226 | 18.992212 | 0,117522 | 0.208342 | 0.148533 | 0.090321 | 0,123588 | 2.464186 | 0,092939 | 23.518601 | 0.333701 |
-   | min | 1998 | 89488 | 0 | 0,255 | 0,000665 | 0,111 | 0 | 0,0283 | -19,362 | 0,0278 | 61.695 | 3 |
-   | 25% | 2014 | 199305 | 0 | 0,681 | 0,089525 | 0,669 | 0 | 0,07565 | -6.29875 | 0,0591 | 102.96125 | 4 |
-   | 50% | 2016 | 218509 | 13 | 0,761 | 0.2205 | 0.7845 | 0.000004 | 0,1035 | -4.5585 | 0,09795 | 112.7145 | 4 |
-   | 75% | 2017 | 242098.5 | 31 | 0,8295 | 0.403 | 0.87575 | 0.000234 | 0,164 | -3.331 | 0,177 | 125.03925 | 4 |
-   | max | 2020 | 511738 | 73 | 0.966 | 0,954 | 0,995 | 0,91 | 0,811 | 0,582 | 0.514 | 206.007 | 5 |
+   |         | release_date | lenght      | popularity | danceability | acousticness | Energia  | strumentale | vitalità | livello di percezione sonora | parlato  | tempo      | #ora_firma |
+   | ------- | ------------ | ----------- | ---------- | ------------ | ------------ | -------- | ----------- | -------- | ---------------------------- | -------- | ---------- | ---------- |
+   | estero) | 530          | 530         | 530        | 530          | 530          | 530      | 530         | 530      | 530                          | 530      | 530        | 530        |
+   | mezzo   | 2015.390566  | 222298.1698 | 17.507547  | 0.741619     | 0.265412     | 0.760623 | 0,016305    | 0,147308 | -4.953011                    | 0,130748 | 116.487864 | 3.986792   |
+   | std     | 3.131688     | 39696.82226 | 18.992212  | 0,117522     | 0.208342     | 0.148533 | 0.090321    | 0,123588 | 2.464186                     | 0,092939 | 23.518601  | 0.333701   |
+   | min     | 1998         | 89488       | 0          | 0,255        | 0,000665     | 0,111    | 0           | 0,0283   | -19,362                      | 0,0278   | 61.695     | 3          |
+   | 25%     | 2014         | 199305      | 0          | 0,681        | 0,089525     | 0,669    | 0           | 0,07565  | -6.29875                     | 0,0591   | 102.96125  | 4          |
+   | 50%     | 2016         | 218509      | 13         | 0,761        | 0.2205       | 0.7845   | 0.000004    | 0,1035   | -4.5585                      | 0,09795  | 112.7145   | 4          |
+   | 75%     | 2017         | 242098.5    | 31         | 0,8295       | 0.403        | 0.87575  | 0.000234    | 0,164    | -3.331                       | 0,177    | 125.03925  | 4          |
+   | max     | 2020         | 511738      | 73         | 0.966        | 0,954        | 0,995    | 0,91        | 0,811    | 0,582                        | 0.514    | 206.007    | 5          |
 
 > 🤔 Se si sta lavorando con il clustering, un metodo non supervisionato che non richiede dati etichettati, perché si stanno mostrando questi dati con etichette? Nella fase di esplorazione dei dati, sono utili, ma non sono necessari per il funzionamento degli algoritmi di clustering. Si potrebbero anche rimuovere le intestazioni delle colonne e fare riferimento ai dati per numero di colonna.
 
@@ -319,7 +319,7 @@ In generale, per il clustering è possibile utilizzare i grafici a dispersione p
 
 In preparazione per la lezione successiva, creare un grafico sui vari algoritmi di clustering che si potrebbero scoprire e utilizzare in un ambiente di produzione. Che tipo di problemi sta cercando di affrontare il clustering?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/5-Clustering/1-Visualize/translations/README.zh-cn.md b/5-Clustering/1-Visualize/translations/README.zh-cn.md
index 5f41ffa7e6..e3161473b2 100644
--- a/5-Clustering/1-Visualize/translations/README.zh-cn.md
+++ b/5-Clustering/1-Visualize/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
 [![No One Like You by PSquare](https://img.youtube.com/vi/ty2advRiWJM/0.jpg)](https://youtu.be/ty2advRiWJM "No One Like You by PSquare")
 
 > 🎥 点击上�的图片观看视频。当您通过�类学习机器学习时，请欣�一些尼日利亚舞厅曲目 - 这是2014 年PSquare上高度评价的歌曲。
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/27/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/27/)
 ### 介�
 
 [�类](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124)对于数�探索�常有用。让我们看看它是�有助于�现尼日利亚观众消费音�的趋势和模�。
@@ -323,7 +323,7 @@
 
 �类试图解决什么样的问题？
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/28/)
 
 ## �习与自学
 
diff --git a/5-Clustering/2-K-Means/README.md b/5-Clustering/2-K-Means/README.md
index d85654ae35..3f4c5fca6b 100644
--- a/5-Clustering/2-K-Means/README.md
+++ b/5-Clustering/2-K-Means/README.md
@@ -4,7 +4,7 @@
 
 > 🎥 Click the image above for a video: Andrew Ng explains clustering
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
 
 In this lesson, you will learn how to create clusters using Scikit-learn and the Nigerian music dataset you imported earlier. We will cover the basics of K-Means for Clustering. Keep in mind that, as you learned in the earlier lesson, there are many ways to work with clusters and the method you use depends on your data. We will try K-Means as it's the most common clustering technique. Let's get started!
 
@@ -238,7 +238,7 @@ Spend some time with this notebook, tweaking parameters. Can you improve the acc
 
 Hint: Try to scale your data. There's commented code in the notebook that adds standard scaling to make the data columns resemble each other more closely in terms of range. You'll find that while the silhouette score goes down, the 'kink' in the elbow graph smooths out. This is because leaving the data unscaled allows data with less variance to carry more weight. Read a bit more on this problem [here](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
 
 ## Review & Self Study
 
diff --git a/5-Clustering/2-K-Means/translations/README.it.md b/5-Clustering/2-K-Means/translations/README.it.md
index ce1dc9d271..9c94d19f57 100644
--- a/5-Clustering/2-K-Means/translations/README.it.md
+++ b/5-Clustering/2-K-Means/translations/README.it.md
@@ -4,7 +4,7 @@
 
 > 🎥 Fare clic sull'immagine sopra per un video: Andrew Ng spiega il clustering
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
 
 In questa lezione si imparerà come creare cluster utilizzando Scikit-learn e l'insieme di dati di musica nigeriana importato in precedenza. Si tratteranno le basi di K-Means per Clustering. Si tenga presente che, come appreso nella lezione precedente, ci sono molti modi per lavorare con i cluster e il metodo usato dipende dai propri dati. Si proverà K-Means poiché è la tecnica di clustering più comune. Si inizia!
 
@@ -238,7 +238,7 @@ Trascorrere un po' di tempo con questo notebook, modificando i parametri. E poss
 
 Suggerimento: provare a ridimensionare i dati. C'è un codice commentato nel notebook che aggiunge il ridimensionamento standard per rendere le colonne di dati più simili tra loro in termini di intervallo. Si scoprirà che mentre il punteggio della silhouette diminuisce, il "kink" nel grafico del gomito si attenua. Questo perché lasciare i dati non scalati consente ai dati con meno varianza di avere più peso. Leggere un po' di più su questo problema [qui](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/5-Clustering/2-K-Means/translations/README.zh-cn.md b/5-Clustering/2-K-Means/translations/README.zh-cn.md
index 32a3301555..a5348eacb3 100644
--- a/5-Clustering/2-K-Means/translations/README.zh-cn.md
+++ b/5-Clustering/2-K-Means/translations/README.zh-cn.md
@@ -4,7 +4,7 @@
 
 > 🎥 �击上图观看视频：Andrew Ng 解释�类
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/29/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/29/)
 
 在本课中，您将学习如何使用 Scikit-learn 和您之�导入的尼日利亚音�数�集创建�类。我们将介� K-Means �类 的基础知识。请记�，正如您在上一课中学到的，使用�类的方法有很多�，您使用的方法�决于您的数�。我们将�试 K-Means，因为它是最常�的�类技术。让我们开始��
 
@@ -239,7 +239,7 @@ K-Means �类过程[分三步执行](https://scikit-learn.org/stable/modules/cl
 
 �示：�试缩放您的数�。笔记本中的注释代�添加了标准缩放，使数�列在范围方�更加相似。您会�现，当轮廓分数下�时，肘部图中的“扭结��得平滑。这是因为�缩放数��以让方差较�的数�承载更多的��。在[这里](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)阅读更多关于这个问题的[信�](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)。
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/30/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/30/)
 
 ## �习与自学
 
diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index 5123585624..ef7444cb58 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -2,7 +2,7 @@
 
 This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
 
 ## Introduction
 
@@ -149,7 +149,7 @@ Choose one of the "stop and consider" elements above and either try to implement
 
 In the next lesson, you'll learn about a number of other approaches to parsing natural language and machine learning.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
 
 ## Review & Self Study
 
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.it.md b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
index e85bd13611..67c0406e36 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.it.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.it.md
@@ -2,7 +2,7 @@
 
 Questa lezione copre una breve storia e concetti importanti dell' *elaborazione del linguaggio naturale*, un sottocampo della *linguistica computazionale*.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
 
 ## Introduzione
 
@@ -149,7 +149,7 @@ Scegliere uno degli elementi "fermarsi e riflettere" qui sopra e provare a imple
 
 Nella prossima lezione si impareranno una serie di altri approcci all'analisi del linguaggio naturale e dell'machine learning.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
index e9df88a322..3826ccfd12 100644
--- a/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
+++ b/6-NLP/1-Introduction-to-NLP/translations/README.zh-cn.md
@@ -1,7 +1,7 @@
 # 自然语言处�介�
 这节课讲解了 *自然语言处�* 的简�历�和��概念，*自然语言处�*是计算语言学的一个�领域。
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/31/)
 
 ## 介�
 众所周知，自然语言处� (Natural Language Processing, NLP) 是机器学习在生产软件中应用最广泛的领域之一。
@@ -146,7 +146,7 @@
 
 在下一课中，您将了解解�自然语言和机器学习的许多其他方法。
 
-## [课�测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
+## [课�测验](https://white-water-09ec41f0f.azurestaticapps.net/quiz/32/)
 
 ## �习与自学
 
diff --git a/6-NLP/2-Tasks/README.md b/6-NLP/2-Tasks/README.md
index d816b7fe3b..829df67baa 100644
--- a/6-NLP/2-Tasks/README.md
+++ b/6-NLP/2-Tasks/README.md
@@ -2,7 +2,7 @@
 
 For most *natural language processing* tasks, the text to be processed, must be broken down, examined, and the results stored or cross referenced with rules and data sets. These tasks, allows the programmer to derive the _meaning_ or _intent_ or only the _frequency_ of terms and words in a text.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/)
 
 Let's discover common techniques used in processing text. Combined with machine learning, these techniques help you to analyse large amounts of text efficiently. Before applying ML to these tasks, however, let's understand the problems encountered by an NLP specialist.
 
@@ -203,7 +203,7 @@ Implement the bot in the prior knowledge check and test it on a friend. Can it t
 
 Take a task in the prior knowledge check and try to implement it. Test the bot on a friend. Can it trick them? Can you make your bot more 'believable?'
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/)
 
 ## Review & Self Study
 
diff --git a/6-NLP/2-Tasks/translations/README.it.md b/6-NLP/2-Tasks/translations/README.it.md
index afe455e737..d553eea3bf 100644
--- a/6-NLP/2-Tasks/translations/README.it.md
+++ b/6-NLP/2-Tasks/translations/README.it.md
@@ -2,7 +2,7 @@
 
 Per la maggior parte delle attività di *elaborazione del linguaggio naturale* , il testo da elaborare deve essere suddiviso, esaminato e i risultati archiviati o incrociati con regole e insiemi di dati. Queste attività consentono al programmatore di derivare il _significato_ o l'_intento_ o solo la _frequenza_ di termini e parole in un testo.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/33/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/33/)
 
 Si esaminano le comuni tecniche utilizzate nell'elaborazione del testo. Combinate con machine learning, queste tecniche aiutano ad analizzare grandi quantità di testo in modo efficiente. Prima di applicare machine learning a queste attività, tuttavia, occorre cercare di comprendere i problemi incontrati da uno specialista in NLP.
 
@@ -203,7 +203,7 @@ Implementare il bot nel controllo delle conoscenze precedenti e testarlo su un a
 
 Prendere un'attività dalla verifica delle conoscenze qui sopra e provare a implementarla. Provare il bot su un amico. Può ingannarlo? Si può rendere il bot più 'credibile?'
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/34/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/34/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/6-NLP/3-Translation-Sentiment/README.md b/6-NLP/3-Translation-Sentiment/README.md
index 0c6b568bf5..dd6bd1bffc 100644
--- a/6-NLP/3-Translation-Sentiment/README.md
+++ b/6-NLP/3-Translation-Sentiment/README.md
@@ -2,7 +2,7 @@
 
 In the previous lessons you learned how to build a basic bot using `TextBlob`, a library that embeds ML behind-the-scenes to perform basic NLP tasks such as noun phrase extraction. Another important challenge in computational linguistics is accurate _translation_ of a sentence from one spoken or written language to another.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/35/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/)
 
 Translation is a very hard problem compounded by the fact that there are thousands of languages and each can have very different grammar rules. One approach is to convert the formal grammar rules for one language, such as English, into a non-language dependent structure, and then translate it by converting back to another language. This approach means that you would take the following steps:
 
@@ -176,7 +176,7 @@ Here is a sample [solution](solution/notebook.ipynb).
 
 Can you make Marvin even better by extracting other features from the user input?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/36/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/)
 
 ## Review & Self Study
 
diff --git a/6-NLP/3-Translation-Sentiment/translations/README.it.md b/6-NLP/3-Translation-Sentiment/translations/README.it.md
index 32e52922f8..d1116a9e08 100644
--- a/6-NLP/3-Translation-Sentiment/translations/README.it.md
+++ b/6-NLP/3-Translation-Sentiment/translations/README.it.md
@@ -2,7 +2,7 @@
 
 Nelle lezioni precedenti si è imparato come creare un bot di base utilizzando `TextBlob`, una libreria che incorpora machine learning dietro le quinte per eseguire attività di base di NPL come l'estrazione di frasi nominali. Un'altra sfida importante nella linguistica computazionale è _la traduzione_ accurata di una frase da una lingua parlata o scritta a un'altra.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/35/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/35/)
 
 La traduzione è un problema molto difficile, aggravato dal fatto che ci sono migliaia di lingue e ognuna può avere regole grammaticali molto diverse. Un approccio consiste nel convertire le regole grammaticali formali per una lingua, come l'inglese, in una struttura non dipendente dalla lingua e quindi tradurla convertendola in un'altra lingua. Questo approccio significa che si dovrebbero eseguire i seguenti passaggi:
 
@@ -176,7 +176,7 @@ Ecco una [soluzione](../solution/notebook.ipynb) di esempio.
 
 Si può rendere Marvin ancora migliore estraendo altre funzionalità dall'input dell'utente?
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/36/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/36/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/6-NLP/4-Hotel-Reviews-1/README.md b/6-NLP/4-Hotel-Reviews-1/README.md
index a4b5a556f7..dd6a573a9e 100644
--- a/6-NLP/4-Hotel-Reviews-1/README.md
+++ b/6-NLP/4-Hotel-Reviews-1/README.md
@@ -6,7 +6,7 @@ In this section you will use the techniques in the previous lessons to do some e
 - how to calculate some new data based on the existing columns
 - how to save the resulting dataset for use in the final challenge
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/)
 
 ### Introduction
 
@@ -393,7 +393,7 @@ Now that you have explored the dataset, in the next lesson you will filter the d
 
 This lesson demonstrates, as we saw in previous lessons, how critically important it is to understand your data and its foibles before performing operations on it. Text-based data, in particular, bears careful scrutiny. Dig through various text-heavy datasets and see if you can discover areas that could introduce bias or skewed sentiment into a model. 
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/)
 
 ## Review & Self Study
 
diff --git a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
index a541d0d6f4..cbbca6109c 100644
--- a/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
+++ b/6-NLP/4-Hotel-Reviews-1/translations/README.it.md
@@ -6,7 +6,7 @@ In questa sezione si utilizzeranno le tecniche delle lezioni precedenti per eseg
 - come calcolare alcuni nuovi dati in base alle colonne esistenti
 - come salvare l'insieme di dati risultante per l'uso nella sfida finale
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/37/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/37/)
 
 ### Introduzione
 
@@ -287,15 +287,15 @@ Trattare le seguenti domande come attività di codifica e provare a rispondere s
    display(hotel_freq_df)
    ```
    
-   | Hotel_Name (Nome Hotel) | Total_Number_of_Reviews (Numero totale di recensioni) | Total_Reviews_Found |
-   | :----------------------------------------: | :---------------------: | :-----------------: |
-   | Britannia International Hotel Canary Wharf | 9086 | 4789 |
-   | Park Plaza Westminster Bridge Londra | 12158 | 4169 |
-   | Copthorne Tara Hotel London Kensington | 7105 | 3578 |
-   | ... | ... | ... |
-   | Mercure Paris Porte d'Orléans | 110 | 10 |
-   | Hotel Wagner | 135 | 10 |
-   | Hotel Gallitzinberg | 173 | 8 |
+   |          Hotel_Name (Nome Hotel)           | Total_Number_of_Reviews (Numero totale di recensioni) | Total_Reviews_Found |
+   | :----------------------------------------: | :---------------------------------------------------: | :-----------------: |
+   | Britannia International Hotel Canary Wharf |                         9086                          |        4789         |
+   |    Park Plaza Westminster Bridge Londra    |                         12158                         |        4169         |
+   |   Copthorne Tara Hotel London Kensington   |                         7105                          |        3578         |
+   |                    ...                     |                          ...                          |         ...         |
+   |       Mercure Paris Porte d'Orléans        |                          110                          |         10          |
+   |                Hotel Wagner                |                          135                          |         10          |
+   |            Hotel Gallitzinberg             |                          173                          |          8          |
    
 
    Si potrebbe notare che il *conteggio nell'insieme di dati* non corrisponde al valore in `Total_Number_of_Reviews`. Non è chiaro se questo valore nell'insieme di dati rappresentasse il numero totale di recensioni che l'hotel aveva, ma non tutte sono state recuperate o qualche altro calcolo. `Total_Number_of_Reviews` non viene utilizzato nel modello a causa di questa non chiarezza.
@@ -324,19 +324,19 @@ Trattare le seguenti domande come attività di codifica e provare a rispondere s
 
    Ci si potrebbe anche chiedere del valore `Average_Score` e perché a volte è diverso dal punteggio medio calcolato. Poiché non è possibile sapere perché alcuni valori corrispondano, ma altri hanno una differenza, in questo caso è più sicuro utilizzare i punteggi delle recensioni a disposizione per calcolare autonomamente la media. Detto questo, le differenze sono solitamente molto piccole, ecco gli hotel con la maggiore deviazione dalla media dell'insieme di dati e dalla media calcolata:
 
-   | Average_Score_Difference | Average_Score | Calc_Average_Score | Hotel_Name (Nome Hotel) |
-   | :----------------------: | :-----------: | :----------------: | ------------------------------------------: |
-   | -0,8 | 7,7 | 8,5 | Best Western Hotel Astoria |
-   | -0,7 | 8,8 | 9,5 | Hotel Stendhal Place Vend me Parigi MGallery |
-   | -0,7 | 7,5 | 8.2 | Mercure Paris Porte d'Orléans |
-   | -0,7 | 7,9 | 8,6 | Renaissance Paris Vendome Hotel |
-   | -0,5 | 7,0 | 7,5 | Hotel Royal Elys es |
-   | ... | ... | ... | ... |
-   | 0.7 | 7,5 | 6.8 | Mercure Paris Op ra Faubourg Montmartre |
-   | 0,8 | 7,1 | 6.3 | Holiday Inn Paris Montparnasse Pasteur |
-   | 0,9 | 6.8 | 5,9 | Villa Eugenia |
-   | 0,9 | 8,6 | 7,7 | MARCHESE Faubourg St Honor Relais Ch teaux |
-   | 1,3 | 7,2 | 5,9 | Kube Hotel Ice Bar |
+   | Average_Score_Difference | Average_Score | Calc_Average_Score |                      Hotel_Name (Nome Hotel) |
+   | :----------------------: | :-----------: | :----------------: | -------------------------------------------: |
+   |           -0,8           |      7,7      |        8,5         |                   Best Western Hotel Astoria |
+   |           -0,7           |      8,8      |        9,5         | Hotel Stendhal Place Vend me Parigi MGallery |
+   |           -0,7           |      7,5      |        8.2         |                Mercure Paris Porte d'Orléans |
+   |           -0,7           |      7,9      |        8,6         |              Renaissance Paris Vendome Hotel |
+   |           -0,5           |      7,0      |        7,5         |                          Hotel Royal Elys es |
+   |           ...            |      ...      |        ...         |                                          ... |
+   |           0.7            |      7,5      |        6.8         |      Mercure Paris Op ra Faubourg Montmartre |
+   |           0,8            |      7,1      |        6.3         |       Holiday Inn Paris Montparnasse Pasteur |
+   |           0,9            |      6.8      |        5,9         |                                Villa Eugenia |
+   |           0,9            |      8,6      |        7,7         |   MARCHESE Faubourg St Honor Relais Ch teaux |
+   |           1,3            |      7,2      |        5,9         |                           Kube Hotel Ice Bar |
 
    Con un solo hotel con una differenza di punteggio maggiore di 1, significa che probabilmente si può ignorare la differenza e utilizzare il punteggio medio calcolato.
 
@@ -401,7 +401,7 @@ Ora che si è esplorato l'insieme di dati, nella prossima lezione si filtreranno
 
 Questa lezione dimostra, come visto nelle lezioni precedenti, quanto sia di fondamentale importanza comprendere i dati e le loro debolezze prima di eseguire operazioni su di essi. I dati basati su testo, in particolare, sono oggetto di un attento esame. Esaminare vari insiemi di dati contenenti principalmente testo e vedere se si riesce a scoprire aree che potrebbero introdurre pregiudizi o sentiment distorti in un modello.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/38/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/38/)
 
 ## Revisione e Auto Apprendimento
 
diff --git a/6-NLP/5-Hotel-Reviews-2/README.md b/6-NLP/5-Hotel-Reviews-2/README.md
index 12b9a15af0..c764b8e1ce 100644
--- a/6-NLP/5-Hotel-Reviews-2/README.md
+++ b/6-NLP/5-Hotel-Reviews-2/README.md
@@ -1,7 +1,7 @@
 # Sentiment analysis with hotel reviews
 
 Now that you have a explored the dataset in detail, it's time to filter the columns and then use NLP techniques on the dataset to gain new insights about the hotels.
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/)
 
 ### Filtering & Sentiment Analysis Operations
 
@@ -360,7 +360,7 @@ To review, the steps are:
 
 When you started, you had a dataset with columns and data but not all of it could be verified or used. You've explored the data, filtered out what you don't need, converted tags into something useful, calculated your own averages, added some sentiment columns and hopefully, learned some interesting things about processing natural text.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/)
 
 ## Challenge
 
diff --git a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
index f6d22b563a..46e92c7b64 100644
--- a/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
+++ b/6-NLP/5-Hotel-Reviews-2/translations/README.it.md
@@ -2,7 +2,7 @@
 
 Ora che si è  esplorato in dettaglio l'insieme di dati, è il momento di filtrare le colonne e quindi utilizzare le tecniche NLP sull'insieme di dati per ottenere nuove informazioni sugli hotel.
 
-## [Quiz Pre-Lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/39/)
+## [Quiz Pre-Lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/39/)
 
 ### Operazioni di Filtraggio e Analisi del Sentiment
 
@@ -59,14 +59,14 @@ Occorre pulire un po' di più i dati. Si aggiungono colonne che saranno utili in
       display(df.groupby("Hotel_Address").agg({"Hotel_Name": "nunique"}))
       ```
 
-      | Hotel_Address | Hotel_Name (Nome Hotel) |
-      | :--------------------- | :--------: |
-      | Amsterdam, Paesi Bassi | 105 |
-      | Barcellona, Spagna | 211 |
-      | Londra, Regno Unito | 400 |
-      | Milano, Italia | 162 |
-      | Parigi, Francia | 458 |
-      | Vienna, Austria | 158 |
+      | Hotel_Address          | Hotel_Name (Nome Hotel) |
+      | :--------------------- | :---------------------: |
+      | Amsterdam, Paesi Bassi |           105           |
+      | Barcellona, Spagna     |           211           |
+      | Londra, Regno Unito    |           400           |
+      | Milano, Italia         |           162           |
+      | Parigi, Francia        |           458           |
+      | Vienna, Austria        |           158           |
 
 2. Elaborazione colonne di meta-recensione dell'hotel
 
@@ -361,7 +361,7 @@ Per riepilogare, i passaggi sono:
 
 Quando si è iniziato, si disponeva di un insieme di dati con colonne e dati, ma non tutto poteva essere verificato o utilizzato. Si sono esplorati i dati, filtrato ciò che non serve, convertito i tag in qualcosa di utile, calcolato le proprie medie, aggiunto alcune colonne di sentiment e, si spera, imparato alcune cose interessanti sull'elaborazione del testo naturale.
 
-## [Quiz post-lezione](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/40/)
+## [Quiz post-lezione](https://white-water-09ec41f0f.azurestaticapps.net/quiz/40/)
 
 ## Sfida
 
diff --git a/7-TimeSeries/1-Introduction/README.md b/7-TimeSeries/1-Introduction/README.md
index ae2a69e1ca..1d75763f0b 100644
--- a/7-TimeSeries/1-Introduction/README.md
+++ b/7-TimeSeries/1-Introduction/README.md
@@ -10,7 +10,7 @@ In this lesson and the following one, you will learn a bit about time series for
 
 > 🎥 Click the image above for a video about time series forecasting
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/41/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/41/)
 
 It's a useful and interesting field with real value to business, given its direct application to problems of pricing, inventory, and supply chain issues. While deep learning techniques have started to be used to gain more insights to better predict future performance, time series forecasting remains a field greatly informed by classic ML techniques.
 
@@ -174,7 +174,7 @@ In the next lesson, you will create an ARIMA model to create some forecasts.
 
 Make a list of all the industries and areas of inquiry you can think of that would benefit from time series forecasting. Can you think of an application of these techniques in the arts? In Econometrics? Ecology? Retail? Industry? Finance? Where else?
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/42/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/42/)
 
 ## Review & Self Study
 
diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md
index 3134d9392f..19da562216 100644
--- a/7-TimeSeries/2-ARIMA/README.md
+++ b/7-TimeSeries/2-ARIMA/README.md
@@ -6,7 +6,7 @@ In the previous lesson, you learned a bit about time series forecasting and load
 
 > 🎥 Click the image above for a video: A brief introduction to ARIMA models. The example is done in R, but the concepts are universal.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/43/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/43/)
 
 ## Introduction
 
@@ -383,7 +383,7 @@ Check the accuracy of your model by testing its mean absolute percentage error (
 
 Dig into the ways to test the accuracy of a Time Series Model. We touch on MAPE in this lesson, but are there other methods you could use? Research them and annotate them. A helpful document can be found [here](https://otexts.com/fpp2/accuracy.html)
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/44/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/44/)
 
 ## Review & Self Study
 
diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md
index 6301c46e12..fbba835230 100644
--- a/8-Reinforcement/1-QLearning/README.md
+++ b/8-Reinforcement/1-QLearning/README.md
@@ -11,7 +11,7 @@ By using reinforcement learning and a simulator (the game), you can learn how to
 
 > 🎥 Click the image above to hear Dmitry discuss Reinforcement Learning
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/45/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/45/)
 
 ## Prerequisites and Setup
 
@@ -314,6 +314,6 @@ The learnings can be summarized as:
 
 Overall, it is important to remember that the success and quality of the learning process significantly depends on parameters, such as learning rate, learning rate decay, and discount factor. Those are often called **hyperparameters**, to distinguish them from **parameters**, which we optimize during training (for example, Q-Table coefficients). The process of finding the best hyperparameter values is called **hyperparameter optimization**, and it deserves a separate topic.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/46/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/46/)
 
 ## Assignment [A More Realistic World](assignment.md)
diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md
index b1a2e2a22e..933967fe7f 100644
--- a/8-Reinforcement/2-Gym/README.md
+++ b/8-Reinforcement/2-Gym/README.md
@@ -2,7 +2,7 @@
 
 The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems also share this scenario - including playing Chess or Go. They are similar, because we also have a board with given rules and a **discrete state**.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/47/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/47/)
 
 ## Introduction
 
@@ -329,7 +329,7 @@ You should see something like this:
 
 > **Task 4**: Here we were not selecting the best action on each step, but rather sampling with corresponding probability distribution. Would it make more sense to always select the best action, with the highest Q-Table value? This can be done by using `np.argmax` function to find out the action number corresponding to highers Q-Table value. Implement this strategy and see if it improves the balancing.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/48/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/48/)
 
 ## Assignment: [Train a Mountain Car](assignment.md)
 
diff --git a/9-Real-World/1-Applications/README.md b/9-Real-World/1-Applications/README.md
index 154c57d6df..b06b8b7167 100644
--- a/9-Real-World/1-Applications/README.md
+++ b/9-Real-World/1-Applications/README.md
@@ -8,7 +8,7 @@ In this curriculum, you have learned many ways to prepare data for training and
 
 While a lot of interest in industry has been garnered by AI, which usually leverages deep learning, there are still valuable applications for classical machine learning models. You might even use some of these applications today! In this lesson, you'll explore how eight different industries and subject-matter domains use these types of models to make their applications more performant, reliable, intelligent, and valuable to users.
 
-## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/49/)
+## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/49/)
 
 ## 💰 Finance
 
@@ -152,7 +152,7 @@ https://ai.inqline.com/machine-learning-for-marketing-customer-segmentation/
 
 Identify another sector that benefits from some of the techniques you learned in this curriculum, and discover how it uses ML.
 
-## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/50/)
+## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/50/)
 
 ## Review & Self Study
 
diff --git a/TRANSLATIONS.md b/TRANSLATIONS.md
index 22b0d37ad4..01f2f13956 100644
--- a/TRANSLATIONS.md
+++ b/TRANSLATIONS.md
@@ -26,7 +26,7 @@ Similar to Readme's, please translate the assignments as well.
 
 3. Edit the quiz-app's [translations index.js file](https://github.com/microsoft/ML-For-Beginners/blob/main/quiz-app/src/assets/translations/index.js) to add your language.
 
-4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1 becomes https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1?loc=id
+4. Finally, edit ALL the quiz links in your translated README.md files to point directly to your translated quiz: https://white-water-09ec41f0f.azurestaticapps.net/quiz/1 becomes https://white-water-09ec41f0f.azurestaticapps.net/quiz/1?loc=id
 
 **THANK YOU**
 

From 381b609f188e9684a3cba01faf3e3cf420e85e7e Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Thu, 12 Aug 2021 22:14:28 -0400
Subject: [PATCH 367/368] removing old workflow

---
 ...re-static-web-apps-jolly-sea-0a877260f.yml | 41 -------------------
 ...-static-web-apps-white-water-09ec41f0f.yml |  4 --
 2 files changed, 45 deletions(-)
 delete mode 100644 .github/workflows/azure-static-web-apps-jolly-sea-0a877260f.yml

diff --git a/.github/workflows/azure-static-web-apps-jolly-sea-0a877260f.yml b/.github/workflows/azure-static-web-apps-jolly-sea-0a877260f.yml
deleted file mode 100644
index 6e77ae1ecc..0000000000
--- a/.github/workflows/azure-static-web-apps-jolly-sea-0a877260f.yml
+++ /dev/null
@@ -1,41 +0,0 @@
-name: Azure Static Web Apps CI/CD
-
-on:
-  push:
-    branches:
-      - main
-
-jobs:
-  build_and_deploy_job:
-    if: github.event_name == 'push' || (github.event_name == 'pull_request' && github.event.action != 'closed')
-    runs-on: ubuntu-latest
-    name: Build and Deploy Job
-    steps:
-      - uses: actions/checkout@v2
-        with:
-          submodules: true
-      - name: Build And Deploy
-        id: builddeploy
-        uses: Azure/static-web-apps-deploy@v0.0.1-preview
-        with:
-          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_JOLLY_SEA_0A877260F }}
-          repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments)
-          action: "upload"
-          ###### Repository/Build Configurations - These values can be configured to match you app requirements. ######
-          # For more information regarding Static Web App workflow configurations, please visit: https://aka.ms/swaworkflowconfig
-          app_location: "/quiz-app" # App source code path
-          api_location: "api" # Api source code path - optional
-          output_location: "dist" # Built app content directory - optional
-          ###### End of Repository/Build Configurations ######
-
-  close_pull_request_job:
-    if: github.event_name == 'pull_request' && github.event.action == 'closed'
-    runs-on: ubuntu-latest
-    name: Close Pull Request Job
-    steps:
-      - name: Close Pull Request
-        id: closepullrequest
-        uses: Azure/static-web-apps-deploy@v0.0.1-preview
-        with:
-          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_JOLLY_SEA_0A877260F }}
-          action: "close"
diff --git a/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml b/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml
index c448847034..62bfbc96de 100644
--- a/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml
+++ b/.github/workflows/azure-static-web-apps-white-water-09ec41f0f.yml
@@ -4,10 +4,6 @@ on:
   push:
     branches:
       - main
-  pull_request:
-    types: [opened, synchronize, reopened, closed]
-    branches:
-      - main
 
 jobs:
   build_and_deploy_job:

From ae0524af803ef0a1d35be911bc76026923faed16 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Thu, 12 Aug 2021 22:21:06 -0400
Subject: [PATCH 368/368] path to quiz fixex

---
 README.md                    | 56 ++++++++++++++++-----------------
 translations/README.ja.md    |  2 +-
 translations/README.pt.md    |  2 +-
 translations/README.tr.md    |  2 +-
 translations/README.zh-cn.md | 60 ++++++++++++++++++------------------
 5 files changed, 61 insertions(+), 61 deletions(-)

diff --git a/README.md b/README.md
index 99fe2da988..4b55b063db 100644
--- a/README.md
+++ b/README.md
@@ -73,35 +73,35 @@ By ensuring that the content aligns with projects, the process is made more enga
 - assignment
 - post-lecture quiz
 
-> **A note about quizzes**: All quizzes are contained [in this app](https://jolly-sea-0a877260f.azurestaticapps.net), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
-
-| Lesson Number |                             Topic                              |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                     Linked Lesson                     |     Author     |
-| :-----------: | :------------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |                Introduction to machine learning                |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
-|      02       |                The History of machine learning                 |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
-|      03       |                 Fairness and machine learning                  |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
-|      04       |                Techniques for machine learning                 |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
-|      05       |                   Introduction to regression                   |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
-|      06       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
-|      07       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |                North American pumpkin prices 🎃                |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                          A Web App 🔌                          |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |                 Introduction to classification                 |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](4-Classification/1-Introduction/README.md)  | Jen and Cassie |
-|      11       |             Delicious Asian and Indian cuisines �             |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
-|      12       |             Delicious Asian and Indian cuisines �             |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
-|      13       |             Delicious Asian and Indian cuisines �             |    [Classification](4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                   Introduction to clustering                   |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |              Exploring Nigerian Musical Tastes 🎧              |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |        Introduction to natural language processing ☕�         |   [Natural language processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                      Common NLP Tasks ☕�                      |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |             Translation and sentiment analysis ♥�              |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                  Romantic hotels of Europe ♥�                  |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 1                                                                                         |      [lesson](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                  Romantic hotels of Europe ♥�                  |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |            Introduction to time series forecasting             |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
+> **A note about quizzes**: All quizzes are contained [in this app](https://white-water-09ec41f0f.azurestaticapps.net/), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `quiz-app` folder.
+
+| Lesson Number |                           Topic                            |                   Lesson Grouping                   | Learning Objectives                                                                                                             |                     Linked Lesson                     |     Author     |
+| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
+|      01       |              Introduction to machine learning              |      [Introduction](1-Introduction/README.md)       | Learn the basic concepts behind machine learning                                                                                |   [lesson](1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
+|      02       |              The History of machine learning               |      [Introduction](1-Introduction/README.md)       | Learn the history underlying this field                                                                                         |  [lesson](1-Introduction/2-history-of-ML/README.md)   |  Jen and Amy   |
+|      03       |               Fairness and machine learning                |      [Introduction](1-Introduction/README.md)       | What are the important philosophical issues around fairness that students should consider when building and applying ML models? |     [lesson](1-Introduction/3-fairness/README.md)     |     Tomomi     |
+|      04       |              Techniques for machine learning               |      [Introduction](1-Introduction/README.md)       | What techniques do ML researchers use to build ML models?                                                                       | [lesson](1-Introduction/4-techniques-of-ML/README.md) | Chris and Jen  |
+|      05       |                 Introduction to regression                 |        [Regression](2-Regression/README.md)         | Get started with Python and Scikit-learn for regression models                                                                  |       [lesson](2-Regression/1-Tools/README.md)        |      Jen       |
+|      06       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Visualize and clean data in preparation for ML                                                                                  |        [lesson](2-Regression/2-Data/README.md)        |      Jen       |
+|      07       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build linear and polynomial regression models                                                                                   |       [lesson](2-Regression/3-Linear/README.md)       |      Jen       |
+|      08       |              North American pumpkin prices 🎃               |        [Regression](2-Regression/README.md)         | Build a logistic regression model                                                                                               |      [lesson](2-Regression/4-Logistic/README.md)      |      Jen       |
+|      09       |                        A Web App 🔌                         |           [Web App](3-Web-App/README.md)            | Build a web app to use your trained model                                                                                       |        [lesson](3-Web-App/1-Web-App/README.md)        |      Jen       |
+|      10       |               Introduction to classification               |    [Classification](4-Classification/README.md)     | Clean, prep, and visualize your data; introduction to classification                                                            |  [lesson](4-Classification/1-Introduction/README.md)  | Jen and Cassie |
+|      11       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | Introduction to classifiers                                                                                                     | [lesson](4-Classification/2-Classifiers-1/README.md)  | Jen and Cassie |
+|      12       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | More classifiers                                                                                                                | [lesson](4-Classification/3-Classifiers-2/README.md)  | Jen and Cassie |
+|      13       |           Delicious Asian and Indian cuisines �            |    [Classification](4-Classification/README.md)     | Build a recommender web app using your model                                                                                    |    [lesson](4-Classification/4-Applied/README.md)     |      Jen       |
+|      14       |                 Introduction to clustering                 |        [Clustering](5-Clustering/README.md)         | Clean, prep, and visualize your data; Introduction to clustering                                                                |     [lesson](5-Clustering/1-Visualize/README.md)      |      Jen       |
+|      15       |            Exploring Nigerian Musical Tastes 🎧             |        [Clustering](5-Clustering/README.md)         | Explore the K-Means clustering method                                                                                           |      [lesson](5-Clustering/2-K-Means/README.md)       |      Jen       |
+|      16       |       Introduction to natural language processing ☕�        |   [Natural language processing](6-NLP/README.md)    | Learn the basics about NLP by building a simple bot                                                                             |    [lesson](6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
+|      17       |                     Common NLP Tasks ☕�                     |   [Natural language processing](6-NLP/README.md)    | Deepen your NLP knowledge by understanding common tasks required when dealing with language structures                          |           [lesson](6-NLP/2-Tasks/README.md)           |    Stephen     |
+|      18       |            Translation and sentiment analysis ♥�            |   [Natural language processing](6-NLP/README.md)    | Translation and sentiment analysis with Jane Austen                                                                             |   [lesson](6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
+|      19       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 1                                                                                         |      [lesson](6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
+|      20       |                Romantic hotels of Europe ♥�                 |   [Natural language processing](6-NLP/README.md)    | Sentiment analysis with hotel reviews 2                                                                                         |      [lesson](6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
+|      21       |          Introduction to time series forecasting           |        [Time series](7-TimeSeries/README.md)        | Introduction to time series forecasting                                                                                         |    [lesson](7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
 |      22       | ⚡� World Power Usage ⚡� - time series forecasting with ARIMA |        [Time series](7-TimeSeries/README.md)        | Time series forecasting with ARIMA                                                                                              |       [lesson](7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |             Introduction to reinforcement learning             | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                 Help Peter avoid the wolf! �                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  Postscript   |            Real-World ML scenarios and applications            |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](9-Real-World/1-Applications/README.md)    |      Team      |
+|      23       |           Introduction to reinforcement learning           | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |    [lesson](8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
+|      24       |                Help Peter avoid the wolf! �                | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |       [lesson](8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
+|  Postscript   |          Real-World ML scenarios and applications          |      [ML in the Wild](9-Real-World/README.md)       | Interesting and revealing real-world applications of classical ML                                                               |    [lesson](9-Real-World/1-Applications/README.md)    |      Team      |
 
 ## Offline access
 
diff --git a/translations/README.ja.md b/translations/README.ja.md
index 39ab3fae82..80ae38e9e1 100644
--- a/translations/README.ja.md
+++ b/translations/README.ja.md
@@ -71,7 +71,7 @@
 - 課題
 - 講義後��テスト
 
-> **�テスト�関�る注�**: �����テスト� [��アプリ](https://jolly-sea-0a877260f.azurestaticapps.net) ���れ��り��3��ら�る50個��テスト��り��。�れら�レッスン内�らリンク�れ������アプリをローカル�実行�る��も����。`quiz-app` フォルダ内�指示�従������。
+> **�テスト�関�る注�**: �����テスト� [��アプリ](https://white-water-09ec41f0f.azurestaticapps.net/) ���れ��り��3��ら�る50個��テスト��り��。�れら�レッスン内�らリンク�れ������アプリをローカル�実行�る��も����。`quiz-app` フォルダ内�指示�従������。
 
 | レッスン番� |                   トピック                   |                    レッスングループ                    | 学習�目的                                                                                 |                            関連�るレッスン                             |      著者      |
 | :----------: | :------------------------------------------: | :----------------------------------------------------: | ------------------------------------------------------------------------------------------ | :---------------------------------------------------------------------: | :------------: |
diff --git a/translations/README.pt.md b/translations/README.pt.md
index 6985cbf430..fc6e4e8d0b 100644
--- a/translations/README.pt.md
+++ b/translations/README.pt.md
@@ -71,7 +71,7 @@ Ao garantir que o conteúdo esteja alinhado com os projetos, o processo torna-se
 - tarefa
 - teste pós-aula
 
-> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://jolly-sea-0a877260f.azurestaticapps.net), para um total de 50 testes de 3 perguntas cada. Eles estão vinculados às aulas, mas a aplicação do teste pode ser executada localmente; segue as intruções na pasta `quiz-app`.
+> **Uma nota sobre testes**: Podes encontrar todos os testes [nesta app](https://white-water-09ec41f0f.azurestaticapps.net/), para um total de 50 testes de 3 perguntas cada. Eles estão vinculados às aulas, mas a aplicação do teste pode ser executada localmente; segue as intruções na pasta `quiz-app`.
 
 | Número de aula |                                Tópico                                 |                 Agrupamento de Aulas                  | Objetivos de aprendizagem                                                                                                            |                   Aula vinculada                    |    Autor     |
 | :------------: | :-------------------------------------------------------------------: | :---------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------------ | :-------------------------------------------------: | :----------: |
diff --git a/translations/README.tr.md b/translations/README.tr.md
index f4dab353a7..6ac6fa6e7a 100644
--- a/translations/README.tr.md
+++ b/translations/README.tr.md
@@ -68,7 +68,7 @@ Bu eğitim programını oluştururken iki pedagojik ilke seçtik: uygulamalı **
 - ödev
 - ders sonrası kısa sınavı
 
-> **Kısa sınavlar hakkında bir not**: Her biri üç sorudan oluşan ve toplamda 50 tane olan tüm kısa sınavlar [bu uygulamada](https://jolly-sea-0a877260f.azurestaticapps.net) bulunmaktadır. Derslerin içinden de bağlantı yoluyla ulaşılabilirler ancak kısa sınav uygulaması yerelde çalıştırılabilir; `quiz-app` klasöründeki yönergeleri takip edin.
+> **Kısa sınavlar hakkında bir not**: Her biri üç sorudan oluşan ve toplamda 50 tane olan tüm kısa sınavlar [bu uygulamada](https://white-water-09ec41f0f.azurestaticapps.net/) bulunmaktadır. Derslerin içinden de bağlantı yoluyla ulaşılabilirler ancak kısa sınav uygulaması yerelde çalıştırılabilir; `quiz-app` klasöründeki yönergeleri takip edin.
 
 
 | Ders Numarası |                           Konu                             |                     Ders Gruplandırması                     |  Öğrenme Hedefleri                                                                                                              |                         Ders                          |     Yazar      |
diff --git a/translations/README.zh-cn.md b/translations/README.zh-cn.md
index dfe0760cc3..e1735cc1e9 100644
--- a/translations/README.zh-cn.md
+++ b/translations/README.zh-cn.md
@@ -69,36 +69,36 @@
 - 作业
 - 课�测验
 
-> **关于测验**：所有的测验都在[这个应用里](https://jolly-sea-0a877260f.azurestaticapps.net)，总共 50 个测验，�个测验三个问题。它们的链接在�节课中，而且这个测验应用�以在本地�行。请�考 `quiz-app` 文件夹中的指�。
-
-
-| 课程编� |                           主体                            |                       课程组                       | 学习目标                                                                                                             |                     课程链接                     |     作者     |
-| :-----------: | :--------------------------------------------------------: | :-------------------------------------------------: | ------------------------------------------------------------------------------------------------------------------------------- | :---------------------------------------------------: | :------------: |
-|      01       |              机器学习简介              |      [简介](../1-Introduction/README.md)       | 了解机器学习背�的基本概念                                                                                |   [课程](../1-Introduction/1-intro-to-ML/README.md)    |    Muhammad    |
-|      02       |              机器学习的历�               |      [简介](../1-Introduction/README.md)       | 了解该领域的历�                                                                                         |  [课程](../1-Introduction/2-history-of-ML/README.md)   |  Jen 和 Amy   |
-|      03       |               机器学习与公平                |      [简介](../1-Introduction/README.md)       | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的��哲学问题？ |     [课程](../1-Introduction/3-fairness/README.md)     |     Tomomi     |
-|      04       |              机器学习的技术工具               |      [简介](../1-Introduction/README.md)       | 机器学习研究者使用哪些技术�构建机器学习模型？                                                                       | [课程](../1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen  |
-|      05       |                 回归简介                 |        [回归](../2-Regression/README.md)         | 开始使用 Python 和 Scikit-learn 构建回归模型                                                                  |       [课程](../2-Regression/1-Tools/README.md)        |      Jen       |
-|      06       |              北美�瓜价格 🎃               |        [回归](../2-Regression/README.md)         | �视化�进行数�清�，为机器学习�准备                                                                                  |        [课程](../2-Regression/2-Data/README.md)        |      Jen       |
-|      07       |              北美�瓜价格 🎃               |        [回归](../2-Regression/README.md)         | 建立线性和多项�回归模型                                                                                   |       [课程](../2-Regression/3-Linear/README.md)       |      Jen       |
-|      08       |              北美�瓜价格 🎃               |        [回归](../2-Regression/README.md)         | 构建逻辑回归模型                                                                                               |      [课程](../2-Regression/4-Logistic/README.md)      |      Jen       |
-|      09       |                        一个网页应用 🔌                         |           [网页应用](../3-Web-App/README.md)            | 构建一个 Web 应用程�以使用�过训练的模型                                                                                       |        [课程](../3-Web-App/1-Web-App/README.md)        |      Jen       |
-|      10       |               分类简介               |    [分类](../4-Classification/README.md)     | 清��准备和�视化数�； 分类简介                                                            |  [课程](../4-Classification/1-Introduction/README.md)  | Jen 和 Cassie |
-|      11       |          美味的亚洲和�度美食 �            |    [分类](../4-Classification/README.md)     | 分类器简介                                                                                                     | [课程](../4-Classification/2-Classifiers-1/README.md)  | Jen 和 Cassie |
-|      12       |          美味的亚洲和�度美食 �            |    [分类](../4-Classification/README.md)     | 关于分类器的更多内容                                                                                                                | [课程](../4-Classification/3-Classifiers-2/README.md)  | Jen 和 Cassie |
-|      13       |          美味的亚洲和�度美食 �            |    [分类](../4-Classification/README.md)     | 使用您的模型构建一个�以「推��的 Web 应用                                                                                    |    [课程](../4-Classification/4-Applied/README.md)     |      Jen       |
-|      14       |                 �类简介                 |        [�类](../5-Clustering/README.md)         | 清��准备和�视化数�； �类简介                                                                |     [课程](../5-Clustering/1-Visualize/README.md)      |      Jen       |
-|      15       |            探索尼日利亚人的音��味 🎧             |        [�类](../5-Clustering/README.md)         | 探索 K-Means �类方法                                                                                           |      [课程](../5-Clustering/2-K-Means/README.md)       |      Jen       |
-|      16       |       自然语言处� (NLP) 简介  ☕�       |   [自然语言处�](../6-NLP/README.md)    | 通过构建一个简�的 bot (机器人) �了解 NLP 的基础知识                                                                             |    [课程](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen     |
-|      17       |                     常�的 NLP 任务 ☕�                     |   [自然语言处�](../6-NLP/README.md)    | 通过�解处�语言结构时所需的常�任务�加深对于自然语言处� (NLP) 的�解                          |           [课程](../6-NLP/2-Tasks/README.md)           |    Stephen     |
-|      18       |            翻译和情感分� ♥�            |   [自然语言处�](../6-NLP/README.md)    | 对简·奥斯汀的文本进行翻译和情感分�                                                                             |   [课程](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen     |
-|      19       |                欧洲的浪漫酒店 ♥�                 |   [自然语言处�](../6-NLP/README.md)    | 对于酒店评价进行情感分�（上）                                                                                        |      [课程](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen     |
-|      20       |                欧洲的浪漫酒店 ♥�                 |   [自然语言处�](../6-NLP/README.md)    | 对于酒店评价进行情感分�（下）                                                                                         |      [课程](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen     |
-|      21       |          时间�列预测简介           |        [时间�列](../7-TimeSeries/README.md)        | 时间�列预测简介 forecasting                                                                                         |    [课程](../7-TimeSeries/1-Introduction/README.md)    |   Francesca    |
-|      22       | ⚡� 世界用电� ⚡� - 使用 ARIMA 进行时间�列预测 |        [时间�列](../7-TimeSeries/README.md)        | 使用 ARIMA 进行时间�列预测                                                                                              |       [课程](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca    |
-|      23       |           强化学习简介           | [强化学习](../8-Reinforcement/README.md) | Q-Learning 强化学习简介                                                                          |    [课程](../8-Reinforcement/1-QLearning/README.md)    |     Dmitry     |
-|      24       |                帮助 Peter �开狼��                | [强化学习](../8-Reinforcement/README.md) | 强化学习练习                                                                                                      |       [课程](../8-Reinforcement/2-Gym/README.md)       |     Dmitry     |
-|  �记   |          现实世界中的机器学习场景和应用          |      [自然场景下的机器学习](../9-Real-World/README.md)       | 探索有趣的�典机器学习方法，了解现实世界中机器学习的应用                                                               |    [课程](../9-Real-World/1-Applications/README.md)    |      团队      |
+> **关于测验**：所有的测验都在[这个应用里](https://white-water-09ec41f0f.azurestaticapps.net/)，总共 50 个测验，�个测验三个问题。它们的链接在�节课中，而且这个测验应用�以在本地�行。请�考 `quiz-app` 文件夹中的指�。
+
+
+| 课程编� |                     主体                     |                      课程组                       | 学习目标                                                                |                        课程链接                        |     作者      |
+| :------: | :------------------------------------------: | :-----------------------------------------------: | ----------------------------------------------------------------------- | :----------------------------------------------------: | :-----------: |
+|    01    |                 机器学习简介                 |        [简介](../1-Introduction/README.md)        | 了解机器学习背�的基本概念                                              |   [课程](../1-Introduction/1-intro-to-ML/README.md)    |   Muhammad    |
+|    02    |                机器学习的历�                |        [简介](../1-Introduction/README.md)        | 了解该领域的历�                                                        |  [课程](../1-Introduction/2-history-of-ML/README.md)   |  Jen 和 Amy   |
+|    03    |                机器学习与公平                |        [简介](../1-Introduction/README.md)        | 在构建和应用机器学习模型时，我们应该考虑哪些有关公平的��哲学问题？    |     [课程](../1-Introduction/3-fairness/README.md)     |    Tomomi     |
+|    04    |              机器学习的技术工具              |        [简介](../1-Introduction/README.md)        | 机器学习研究者使用哪些技术�构建机器学习模型？                          | [课程](../1-Introduction/4-techniques-of-ML/README.md) | Chris 和 Jen  |
+|    05    |                   回归简介                   |         [回归](../2-Regression/README.md)         | 开始使用 Python 和 Scikit-learn 构建回归模型                            |       [课程](../2-Regression/1-Tools/README.md)        |      Jen      |
+|    06    |                北美�瓜价格 🎃                |         [回归](../2-Regression/README.md)         | �视化�进行数�清�，为机器学习�准备                                  |        [课程](../2-Regression/2-Data/README.md)        |      Jen      |
+|    07    |                北美�瓜价格 🎃                |         [回归](../2-Regression/README.md)         | 建立线性和多项�回归模型                                                |       [课程](../2-Regression/3-Linear/README.md)       |      Jen      |
+|    08    |                北美�瓜价格 🎃                |         [回归](../2-Regression/README.md)         | 构建逻辑回归模型                                                        |      [课程](../2-Regression/4-Logistic/README.md)      |      Jen      |
+|    09    |                一个网页应用 🔌                |        [网页应用](../3-Web-App/README.md)         | 构建一个 Web 应用程�以使用�过训练的模型                               |        [课程](../3-Web-App/1-Web-App/README.md)        |      Jen      |
+|    10    |                   分类简介                   |       [分类](../4-Classification/README.md)       | 清��准备和�视化数�； 分类简介                                       |  [课程](../4-Classification/1-Introduction/README.md)  | Jen 和 Cassie |
+|    11    |            美味的亚洲和�度美食 �            |       [分类](../4-Classification/README.md)       | 分类器简介                                                              | [课程](../4-Classification/2-Classifiers-1/README.md)  | Jen 和 Cassie |
+|    12    |            美味的亚洲和�度美食 �            |       [分类](../4-Classification/README.md)       | 关于分类器的更多内容                                                    | [课程](../4-Classification/3-Classifiers-2/README.md)  | Jen 和 Cassie |
+|    13    |            美味的亚洲和�度美食 �            |       [分类](../4-Classification/README.md)       | 使用您的模型构建一个�以「推��的 Web 应用                             |    [课程](../4-Classification/4-Applied/README.md)     |      Jen      |
+|    14    |                   �类简介                   |         [�类](../5-Clustering/README.md)         | 清��准备和�视化数�； �类简介                                       |     [课程](../5-Clustering/1-Visualize/README.md)      |      Jen      |
+|    15    |          探索尼日利亚人的音��味 🎧          |         [�类](../5-Clustering/README.md)         | 探索 K-Means �类方法                                                   |      [课程](../5-Clustering/2-K-Means/README.md)       |      Jen      |
+|    16    |          自然语言处� (NLP) 简介  ☕�          |        [自然语言处�](../6-NLP/README.md)         | 通过构建一个简�的 bot (机器人) �了解 NLP 的基础知识                   |    [课程](../6-NLP/1-Introduction-to-NLP/README.md)    |    Stephen    |
+|    17    |              常�的 NLP 任务 ☕�               |        [自然语言处�](../6-NLP/README.md)         | 通过�解处�语言结构时所需的常�任务�加深对于自然语言处� (NLP) 的�解 |           [课程](../6-NLP/2-Tasks/README.md)           |    Stephen    |
+|    18    |               翻译和情感分� ♥�               |        [自然语言处�](../6-NLP/README.md)         | 对简·奥斯汀的文本进行翻译和情感分�                                     |   [课程](../6-NLP/3-Translation-Sentiment/README.md)   |    Stephen    |
+|    19    |               欧洲的浪漫酒店 ♥�               |        [自然语言处�](../6-NLP/README.md)         | 对于酒店评价进行情感分�（上）                                          |      [课程](../6-NLP/4-Hotel-Reviews-1/README.md)      |    Stephen    |
+|    20    |               欧洲的浪漫酒店 ♥�               |        [自然语言处�](../6-NLP/README.md)         | 对于酒店评价进行情感分�（下）                                          |      [课程](../6-NLP/5-Hotel-Reviews-2/README.md)      |    Stephen    |
+|    21    |               时间�列预测简介               |       [时间�列](../7-TimeSeries/README.md)       | 时间�列预测简介 forecasting                                            |    [课程](../7-TimeSeries/1-Introduction/README.md)    |   Francesca   |
+|    22    | ⚡� 世界用电� ⚡� - 使用 ARIMA 进行时间�列预测 |       [时间�列](../7-TimeSeries/README.md)       | 使用 ARIMA 进行时间�列预测                                             |       [课程](../7-TimeSeries/2-ARIMA/README.md)        |   Francesca   |
+|    23    |                 强化学习简介                 |     [强化学习](../8-Reinforcement/README.md)      | Q-Learning 强化学习简介                                                 |    [课程](../8-Reinforcement/1-QLearning/README.md)    |    Dmitry     |
+|    24    |             帮助 Peter �开狼��             |     [强化学习](../8-Reinforcement/README.md)      | 强化学习练习                                                            |       [课程](../8-Reinforcement/2-Gym/README.md)       |    Dmitry     |
+|   �记   |        现实世界中的机器学习场景和应用        | [自然场景下的机器学习](../9-Real-World/README.md) | 探索有趣的�典机器学习方法，了解现实世界中机器学习的应用                |    [课程](../9-Real-World/1-Applications/README.md)    |     团队      |
 ## 离线访问
 
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