Merge pull request #1004 from Rohan-14-1/docs/fix-typos-grammar-links

leestott · web-flow · commit d0d0ea2b2d22 · 2026-07-14T00:14:38.000+01:00
[Docs] Fix typos, grammar errors, and broken links across lessons
diff --git a/1-Introduction/2-history-of-ML/README.md b/1-Introduction/2-history-of-ML/README.md
@@ -19,13 +19,13 @@ The history of artificial intelligence (AI) as a field is intertwined with the h
 ## 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.
+- 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.
+- 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.
 
@@ -54,7 +54,7 @@ The workshop is credited with having initiated and encouraged several discussion
 
 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.
+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.
 
 ---
 
diff --git a/1-Introduction/3-fairness/README.md b/1-Introduction/3-fairness/README.md
@@ -7,7 +7,7 @@
  
 ## 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, loan approvals or detecting fraud. So, it is important that these models work well to provide outcomes that are trustworthy. Just as any software application, AI systems are going to miss expectations or have an undesirable outcome. That is why it is essential to be about to understand and explain the behavior of an AI model. 
+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, loan approvals or detecting fraud. So, it is important that these models work well to provide outcomes that are trustworthy. Just as any software application, AI systems are going to miss expectations or have an undesirable outcome. That is why it is essential to be able to understand and explain the behavior of an AI model. 
 
 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 addition, what happens when the model has an adverse outcome and is harmful to people? Who is accountable for the AI systems behavior? These are some questions we will explore in this curriculum. 
 
@@ -40,7 +40,7 @@ AI systems should treat everyone fairly and avoid affecting similar groups of pe
 - **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.  For instance, 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)
 - **Denigration**. To unfairly criticize and label something or someone. For example, an image labeling technology infamously mislabeled images of dark-skinned people as gorillas.
 - **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.
-- **Stereotyping**. Associating a given group with pre-assigned attributes.  For example, a language translation system betweem English and Turkish may have inaccuraces due to words with stereotypical associations to gender.
+- **Stereotyping**. Associating a given group with pre-assigned attributes.  For example, a language translation system between English and Turkish may have inaccuracies due to words with stereotypical associations to gender.
 
 ![translation to Turkish](images/gender-bias-translate-en-tr.png)
 > translation to Turkish
@@ -54,19 +54,19 @@ When designing and testing AI systems, we need to ensure that AI is fair and not
 
 To build trust, AI systems need to be reliable, safe, and consistent under normal and unexpected conditions. It is important to know how AI systems will behavior in a variety of situations, especially when they are outliers. When building AI solutions, there needs to be a substantial amount of focus on how to handle a wide variety of circumstances that the AI solutions would encounter. For example, a self-driving car needs to put people's safety as a top priority. As a result, the AI powering the car need to consider all the possible scenarios that the car could come across such as night, thunderstorms or blizzards, kids running across the street, pets, road constructions etc. How well an AI system can handle a wild range of conditions reliably and safely reflects the level of anticipation the data scientist or AI developer considered during the design or testing of the system.  
 
-> [🎥 Click the here for a video: ](https://www.microsoft.com/videoplayer/embed/RE4vvIl)
+> [🎥 Click here for a video: Reliability and safety in AI](https://www.microsoft.com/videoplayer/embed/RE4vvIl)
 
 ### Inclusiveness
 
 AI systems should be designed to engage and empower everyone. When designing and implementing AI systems data scientists and AI developers identify and address potential barriers in the system that could unintentionally exclude people. For example, there are 1 billion people with disabilities around the world. With the advancement of AI, they can access a wide range of information and opportunities more easily in their daily lives. By addressing the barriers, it creates opportunities to innovate and develop AI products with better experiences that benefit everyone. 
 
-> [🎥 Click the here for a video: inclusiveness in AI](https://www.microsoft.com/videoplayer/embed/RE4vl9v)
+> [🎥 Click here for a video: Inclusiveness in AI](https://www.microsoft.com/videoplayer/embed/RE4vl9v)
 
 ### Security and privacy 
 
 AI systems should be safe and respect people’s privacy. People have less trust in systems that put their privacy, information, or lives at risk. When training machine learning models, we rely on data to produce the best results. In doing so, the origin of the data and integrity must be considered. For example, was the data user submitted or publicly available? Next, while working with the data, it is crucial to develop AI systems that can protect confidential information and resist attacks. As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. Privacy and data security issues require especially close attention for AI because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. 
 
-> [🎥 Click the here for a video: security in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF)
+> [🎥 Click here for a video: Security in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF)
 
 - As an industry we have made significant advancements in Privacy & security, fueled significantly by regulations like the GDPR (General Data Protection Regulation). 
 - Yet with AI systems we must acknowledge the tension between the need for more personal data to make systems more personal and effective – and privacy. 
@@ -78,7 +78,7 @@ AI systems should be safe and respect people’s privacy. People have less trust
 ### Transparency
 AI systems should be understandable. A crucial part of transparency is explaining the behavior of AI systems and their components. Improving the understanding of AI systems requires that stakeholders comprehend how and why they function so that they can identify potential performance issues, safety and privacy concerns, biases, exclusionary practices, or unintended outcomes. We also believe that those who use AI systems should be honest and forthcoming about when, why, and how they choose to deploy them. As well as the limitations of the systems they use. For example, if a bank uses an AI system to support its consumer lending decisions, it is important to examine the outcomes and understand which data influences the system’s recommendations. Governments are starting to regulate AI across industries, so data scientists and organizations must explain if an AI system meets regulatory requirements, especially when there is an undesirable outcome. 
 
-> [🎥 Click the here for a video: transparency in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF)
+> [🎥 Click here for a video: Transparency in AI](https://www.microsoft.com/videoplayer/embed/RE4voJF)
 
 - Because AI systems are so complex, it is hard to understand how they work and interpret the results. 
 - This lack of understanding affects the way these systems are managed, operationalized, and documented. 
@@ -96,7 +96,7 @@ Ultimately one of the biggest questions for our generation, as the first generat
 
 ## Impact assessment 
 
-Before training a machine learning model, it is important to conduct an impact assessmet to understand the purpose of the AI system; what the intended use is; where it will be deployed; and who will be interacting with the system.  These are helpful for reviewer(s) or testers evaluating the system to know what factors to take into consideration when identifying potential risks and expected consequences.
+Before training a machine learning model, it is important to conduct an impact assessment to understand the purpose of the AI system; what the intended use is; where it will be deployed; and who will be interacting with the system.  These are helpful for reviewer(s) or testers evaluating the system to know what factors to take into consideration when identifying potential risks and expected consequences.
 
 The following are areas of focus when conducting an impact assessment:
 
@@ -122,7 +122,7 @@ To prevent harms 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 throughout the machine learning lifecycle for detecting and correcting responible AI when it occurs 
+- develop better methods throughout the machine learning lifecycle for detecting and correcting irresponsible AI when it occurs 
 
 Think about real-life scenarios where a model's untrustworthiness is evident in model-building and usage. What else should we consider? 
 
diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md
@@ -86,7 +86,7 @@ According to their [website](https://scikit-learn.org/stable/getting_started.htm
 
 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.
+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 to create your first ML model with Scikit-learn with some basic data.
 
 ## Exercise - your first Scikit-learn notebook
 
diff --git a/5-Clustering/README.md b/5-Clustering/README.md
@@ -21,7 +21,7 @@ In this series of lessons, you will discover new ways to analyze data using clus
 
 ## Credits
 
-These lessons were written with 🎶 by [Jen Looper](https://www.twitter.com/jenlooper) with helpful reviews by [Rishit Dagli](https://rishit_dagli) and [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).
+These lessons were written with 🎶 by [Jen Looper](https://www.twitter.com/jenlooper) with helpful reviews by [Rishit Dagli](https://rishit_dagli/) and [Muhammad Sakib Khan Inan](https://twitter.com/Sakibinan).
 
 The [Nigerian Songs](https://www.kaggle.com/sootersaalu/nigerian-songs-spotify) dataset was sourced from Kaggle as scraped from Spotify.
 
diff --git a/7-TimeSeries/README.md b/7-TimeSeries/README.md
@@ -16,7 +16,7 @@ Photo by [Peddi Sai hrithik](https://unsplash.com/@shutter_log?utm_source=unspla
 
 1. [Introduction to time series forecasting](1-Introduction/README.md)
 2. [Building ARIMA time series models](2-ARIMA/README.md)
-3. [Building Support Vector Regressor for time series forcasting](3-SVR/README.md)
+3. [Building Support Vector Regressor for time series forecasting](3-SVR/README.md)
 
 ## Credits
 
diff --git a/README.md b/README.md
@@ -159,7 +159,7 @@ By ensuring that the content aligns with projects, the process is made more enga
 |      24       |             Introduction to reinforcement learning             | [Reinforcement learning](8-Reinforcement/README.md) | Introduction to reinforcement learning with Q-Learning                                                                          |                                             [Python](8-Reinforcement/1-QLearning/README.md)                                              |                        Dmitry                        |
 |      25       |                 Help Peter avoid the wolf! 🐺                  | [Reinforcement learning](8-Reinforcement/README.md) | Reinforcement learning Gym                                                                                                      |                                                [Python](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                         |
-|  Postscript   |            Model Debugging in ML using RAI dashboard          |      [ML in the Wild](9-Real-World/README.md)       | Model Debugging in Machine Learning using Responsible AI dashboard components                                                              |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                         Ruth Yakubu                       |
+|  Postscript   |            Model Debugging in ML using RAI dashboard            |      [ML in the Wild](9-Real-World/README.md)       | Model Debugging in Machine Learning using Responsible AI dashboard components                                                              |                                             [Lesson](9-Real-World/2-Debugging-ML-Models/README.md)                                              |                     Ruth Yakubu                      |
 
 > [find all additional resources for this course in our Microsoft Learn collection](https://learn.microsoft.com/en-us/collections/qrqzamz1nn2wx3?WT.mc_id=academic-77952-bethanycheum)
 
diff --git a/for-teachers.md b/for-teachers.md
@@ -12,7 +12,7 @@ These [full instructions](https://github.blog/2020-03-18-set-up-your-digital-cla
 
 If you would like to use this repo as it currently stands, without using GitHub Classroom, that can be done as well. You would need to communicate with your students which lesson to work through together.
 
-In an online format (Zoom, Teams, or other) you might form breakout rooms for the quizzes, and mentor students to help them get ready to learn. Then invite students to for the quizzes and submit their answers as 'issues' at a certain time. You might do the same with assignments, if you want students to work collaboratively out in the open.
+In an online format (Zoom, Teams, or other) you might form breakout rooms for the quizzes, and mentor students to help them get ready to learn. Then invite students to take the quizzes and submit their answers as 'issues' at a certain time. You might do the same with assignments, if you want students to work collaboratively out in the open.
 
 If you prefer a more private format, ask your students to fork the curriculum, lesson by lesson, to their own GitHub repos as private repos, and give you access. Then they can complete quizzes and assignments privately and submit them to you via issues on your classroom repo.
 
