microsoft/ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 146 contributors on GitHub.
Peaked at #94 in the weekly ranking the week of Dec 7, its best in the past year.
Use Cases & Benefits
- Provides a 12-week, project-based curriculum teaching classic machine learning concepts using Python and R with Scikit-learn and Jupyter Notebooks.
- Offers a hands-on, multi-language learning experience with frequent quizzes and real-world projects to enhance retention and practical skills.
- Use for beginners seeking a structured, comprehensive introduction to machine learning fundamentals and algorithms.
- Use for educators looking to adopt a ready-made, flexible curriculum with lesson plans, quizzes, and assignments for teaching ML.
- Use for self-learners wanting to build practical ML projects and understand applications across diverse global datasets.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
MIT
Permissive: commercial use, modification and redistribution are allowed with attribution.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
We have a Discord learn with AI series ongoing, learn more and join us at Learn with AI Series from 18 - 30 September, 2025. You will get tips and tricks of using GitHub Copilot for Data Science.
🌍 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. Pair these lessons with our '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'.
✍️ Hearty thanks to our authors Jen Looper, Stephen Howell, Francesca Lazzeri, Tomomi Imura, Cassie Breviu, Dmitry Soshnikov, Chris Noring, Anirban Mukherjee, Ornella Altunyan, Ruth Yakubu and Amy Boyd
🎨 Thanks as well to our illustrators Tomomi Imura, Dasani Madipalli, and Jen Looper
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
22.8K+1.0K
Open issues
25+18
Compared with peers in Education
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| rohitg00/ai-engineering-from-scratch | 78.3K | +1.6K | 75 | 1 day ago |
| mnfst/awesome-free-llm-apis | 12.8K | +754 | 75 | — |
| luongnv89/claude-howto | 46.9K | +440 | 75 | 161 days ago |
| github/awesome-copilot | 41.0K | +251 | 75 | — |
| ripienaar/free-for-dev | 141.8K | — | 45 | — |
| microsoft/ML-For-Beginners | 92.0K | — | 45 | — |
| Category median | 46.9K | +597 | 75 | — |
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