diff --git a/README.md b/README.md index d0d65e362..10d9cbb7b 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,26 @@ Welcome! This repository contains all of the code, notebooks, images and other m * **12 September 2024** - Working on updating the materials for 2025, see progress in [#105](https://github.com/mrdbourke/zero-to-mastery-ml/discussions/105) * **12 October 2023** - Created an online book version of the course materials, see: https://dev.mrdbourke.com/zero-to-mastery-ml/ +## Contents + +The following contents are listed in suggested chronological order. + +But feel free to mix in match in anyway you feel fit. + +> **Note:** All of the datasets we use in the course are available in the [`data/`](https://github.com/mrdbourke/zero-to-mastery-ml/tree/master/data) folder. + +| **Section** | **Resource** | **Description** | +|-----|-----|-----| +| 00 | [A 6 step framework for approaching machine learning projects](https://dev.mrdbourke.com/zero-to-mastery-ml/a-6-step-framework-for-approaching-machine-learning-projects/) | A guideline for different kinds of machine learning projects and how to break them down into smaller steps. | +| 01 | [Introduction to NumPy](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-numpy/) | NumPy stands for Numerical Python. It's one of the most used Python libraries for numerical processing (which is what much of data science and machine learning is). | +| 02 | [Introduction to pandas](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-pandas/) | pandas is a Python library for manipulating and analysing data. You can imagine pandas as a programmatic form of an Excel spreadsheet. | +| 03 | [Introduction to Matplotlib](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-matplotlib/) | Matplotlib helps to visualize data. You can create plots and graphs programmatically based on various data sources. | +| 04 | [Introduction to Scikit-Learn](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-scikit-learn/) | Scikit-Learn or sklearn is full of data processing techniques as well as pre-built machine learning algorithms for many different tasks. | +| 05 | [Milestone Project 1: End-to-end Heart Disease Classification](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-heart-disease-classification/) | Here we'll put together everything we've gone through in the previous sections to create a machine learning model that is capable of classifying if someone has heart disease or not based on their health characteristics. We'll start with a raw dataset and work through performing an exploratory data analysis (EDA) on it before trying out several different machine learning models to see which performs best. | +| 06 | [Milestone Project 2: End-to-end Bulldozer Price Prediction](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-3-structured-data-projects/end-to-end-bluebook-bulldozer-price-regression.ipynb) | In this project we'll work with an open-source dataset of bulldozer sales information. We'll use this data to build a machine learning model capable of predicting the sales price of a bulldozer based on several input parameters such as size and brand. Since this dataset isn't perfect, we'll work through several data preprocessing steps before building a model. And since we'll be working towards predicting a number (price of bulldozers), this project is known as regression project. | +| 07 | [Milestone Project 3: Introduction to TensorFlow/Keras and Deep Learning](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-dog-vision-v2/) | TensorFlow/Keras are deep learning frameworks written in Python. Originally created by Google and are now open-source. These frameworks allow you to build and train neural networks, one of the most powerful kinds of machine learning models. In this section we'll learn about deep learning and TensorFlow/Keras by building Dog Vision πŸΆπŸ‘οΈ, a neural network to identify dog breeds in images. | +| 08 | [Communicating your work](https://dev.mrdbourke.com/zero-to-mastery-ml/communicating-your-work/) | One of the most important parts of machine learning and any software project is communicating what you've found/done. This module takes the learnings from the previous sections and gives tips and tricks on how you can communicate your work to others. | + ## What this course focuses on 1. Create a framework for working through problems ([6 step machine learning modelling framework](https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-1-getting-ready-for-machine-learning/a-6-step-framework-for-approaching-machine-learning-projects.md)) diff --git a/docs/index.md b/docs/index.md index 389bf3ccd..e91baa128 100644 --- a/docs/index.md +++ b/docs/index.md @@ -11,6 +11,10 @@ This webpage contains all of the materials for the Zero to Mastery Data Science ## Contents +The following contents are listed in suggested chronological order. + +But feel free to mix in match in anyway you feel fit. + | **Resource** | **Description** | |--------------|-----------------| | [A 6 step framework for approaching machine learning projects](https://dev.mrdbourke.com/zero-to-mastery-ml/a-6-step-framework-for-approaching-machine-learning-projects/) | A guideline for different kinds of machine learning projects and how to break them down into smaller steps. | @@ -18,8 +22,9 @@ This webpage contains all of the materials for the Zero to Mastery Data Science | [Introduction to pandas](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-pandas/) | pandas is a Python library for manipulating and analysing data. You can imagine pandas as a programmatic form of an Excel spreadsheet. | | [Introduction to Matplotlib](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-matplotlib/) | Matplotlib helps to visualize data. You can create plots and graphs programmatically based on various data sources. | | [Introduction to Scikit-Learn](https://dev.mrdbourke.com/zero-to-mastery-ml/introduction-to-scikit-learn/) | Scikit-Learn or sklearn is full of data processing techniques as well as pre-built machine learning algorithms for many different tasks. | +| [Milestone Project 1: End-to-end Heart Disease Classification](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-heart-disease-classification/) | Here we'll put together everything we've gone through in the previous sections to create a machine learning model that is capable of classifying if someone has heart disease or not based on their health characteristics. We'll start with a raw dataset and work through performing an exploratory data analysis (EDA) on it before trying out several different machine learning models to see which performs best. | | [Introduction to TensorFlow/Keras and Deep Learning](https://dev.mrdbourke.com/zero-to-mastery-ml/end-to-end-dog-vision-v2/) | TensorFlow/Keras are deep learning frameworks written in Python. Originally created by Google and are now open-source. These frameworks allow you to build and train neural networks, one of the most powerful kinds of machine learning models. In this section we'll learn about deep learning and TensorFlow/Keras by building Dog Vision πŸΆπŸ‘οΈ, a neural network to identify dog breeds in images. | -| [Communicating your work](https://dev.mrdbourke.com/zero-to-mastery-ml/communicating-your-work/) | One of the most important parts of machine learning and any software project is communicating what you've found/done. This module takes the learnings from the previous sections and gives tips and tricks ons how you can communicate your work to others. | +| [Communicating your work](https://dev.mrdbourke.com/zero-to-mastery-ml/communicating-your-work/) | One of the most important parts of machine learning and any software project is communicating what you've found/done. This module takes the learnings from the previous sections and gives tips and tricks on how you can communicate your work to others. | If you have any questions, leave an issue/discussion on the [course GitHub](https://github.com/mrdbourke/zero-to-mastery-ml/).