From 1c5f6db8a90b7389788dc9160d7388ff57078ece Mon Sep 17 00:00:00 2001 From: Shree Yadav Date: Sat, 12 Sep 2026 20:53:28 +0530 Subject: [PATCH] [Docs] Add GitHub Codespaces quickstart and curriculum tracks roadmap to README --- README.md | 81 ++++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 78 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 6ba4afedc..4e19943f2 100644 --- a/README.md +++ b/README.md @@ -46,6 +46,8 @@ We have a Discord learn with AI series ongoing, learn more and join us at [Learn # Machine Learning for Beginners - A Curriculum +[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/ML-For-Beginners) + > 🌍 Travel around the world as we explore Machine Learning by means of world cultures 🌍 Cloud Advocates at Microsoft are pleased to offer a 12-week, 26-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 [AI for Beginners' curriculum](https://aka.ms/ai4beginners). Pair these lessons with our ['Data Science for Beginners' curriculum](https://aka.ms/ds4beginners), as well! @@ -62,9 +64,68 @@ Travel with us around the world as we apply these classic techniques to data fro # Getting Started -Follow these steps: -1. **Fork the Repository**: Click on the "Fork" button at the top-right corner of this page. -2. **Clone the Repository**: `git clone https://github.com/microsoft/ML-For-Beginners.git` +## πŸ“š Quick Documentation Links + +- πŸ”§ **[Troubleshooting Guide](TROUBLESHOOTING.md)** - Solutions to common installation, environment, and notebook issues +- πŸ‘©β€πŸ« **[For Teachers](for-teachers.md)** - Suggestions and guidelines for classroom instruction +- 🀝 **[Contributing Guide](CONTRIBUTING.md)** - How to contribute to this curriculum +- πŸ§ͺ **[Quiz Application](./quiz-app/)** - 52 interactive pre- and post-lecture assessments +- πŸ“„ **[Curriculum PDF](https://microsoft.github.io/ML-For-Beginners/pdf/readme.pdf)** - Offline curriculum with clickable links + +--- + +## πŸš€ Choose Your Learning Setup + +### Option 1: Cloud Setup with GitHub Codespaces (Recommended β€” Zero Installation) + +Start learning immediately in your browser with Python, Jupyter, Scikit-learn, and all dependencies pre-configured: +1. Click the **[Open in GitHub Codespaces](https://codespaces.new/microsoft/ML-For-Beginners)** badge at the top (or click the green **Code** button and select **Create codespace on main**). +2. The environment automatically builds using the repository's `.devcontainer` configuration. +3. Open any lesson notebook (e.g., `2-Regression/1-Tools/notebook.ipynb`) and start executing code right away! + +--- + +### Option 2: Local Development Setup + +1. **Fork the Repository**: Click on the **Fork** button at the top-right corner of this page to create your own copy. +2. **Clone using Sparse-Checkout** (Recommended): + Because this repository includes 50+ language translations, cloning everything is very large. Use sparse-checkout to download only the core curriculum: + + **Bash / macOS / Linux:** + ```bash + git clone --filter=blob:none --sparse https://github.com//ML-For-Beginners.git + cd ML-For-Beginners + git sparse-checkout set --no-cone '/*' '!translations' '!translated_images' + ``` + + **Windows (Command Prompt / PowerShell):** + ```cmd + git clone --filter=blob:none --sparse https://github.com//ML-For-Beginners.git + cd ML-For-Beginners + git sparse-checkout set --no-cone "/*" "!translations" "!translated_images" + ``` + +3. **Set Up a Virtual Environment**: + ```bash + # Create virtual environment + python -m venv .venv + + # Activate environment + # On Windows: + .venv\Scripts\activate + # On macOS/Linux: + source .venv/bin/activate + ``` + +4. **Install Core Dependencies**: + ```bash + pip install jupyter scikit-learn pandas numpy matplotlib seaborn + ``` + +5. **Start Jupyter Notebook**: + ```bash + jupyter notebook + ``` > [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) @@ -131,6 +192,20 @@ By ensuring that the content aligns with projects, the process is made more enga > **A note about quizzes**: All quizzes are contained in [Quiz App folder](./quiz-app/), for 52 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 to locally host or deploy to Azure. +### πŸ—ΊοΈ Curriculum Tracks at a Glance + +| Track | Focus Area | Lessons | Key Concepts & Projects | +| :--- | :--- | :---: | :--- | +| **1. Introduction** | ML Foundations & Ethics | Lessons 01–04 | History, core concepts, algorithmic fairness & bias | +| **2. Regression** | Continuous Predictions | Lessons 05–08 | Scikit-learn tools, linear & polynomial regression, pumpkin prices | +| **3. Web App** | Model Deployment | Lesson 09 | Building and deploying a Flask web application with a trained model | +| **4. Classification** | Categorical Predictions | Lessons 10–13 | Classification algorithms, Asian & Indian cuisine recommender | +| **5. Clustering** | Unsupervised Grouping | Lessons 14–15 | Data clustering, K-Means, exploring Nigerian musical tastes | +| **6. NLP** | Natural Language Processing | Lessons 16–20 | Tokenization, sentiment analysis, romantic hotel reviews | +| **7. Time Series** | Temporal Forecasting | Lessons 21–23 | Time series analysis, ARIMA, Support Vector Regressors | +| **8. Reinforcement** | Agent-Based Learning | Lessons 24–25 | Q-Learning, OpenAI Gym, Peter and the Wolf game | +| **9. Real-World ML** | Practical Applications | Postscript | Real-world case studies, Responsible AI (RAI) dashboard debugging | + | 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 |