The ML textbook that became a repo

What it does

This repository holds the complete set of Jupyter notebooks and exercise solutions for Aurélien Géron’s Hands-on Machine Learning (3rd edition). It covers the standard Python ML stack—Scikit-Learn, Keras, and TensorFlow 2—walking through fundamentals up to deep learning. The project is explicitly educational: every notebook maps to a chapter in the O’Reilly book.

The interesting bit

The author treats the repo as a living artifact, not an afterthought. There are three parallel editions (handson-ml, handson-ml2, handson-ml3), each maintained separately so readers don’t get stranded by API drift. That’s unusual discipline for a book companion.

Key highlights

• Runs in-browser via Colab, Kaggle, Binder, or Deepnote without local setup
• Docker image available for reproducible local runs
• Targets Python 3.10, with backward compatibility to 3.7
• Includes exercise solutions, not just demo code
• Community-maintained: contributors have reviewed every notebook and submitted fixes

Caveats

• The README is mostly installation quickstart; actual notebook content isn’t previewed
• Colab environment is temporary—work gets wiped if you don’t download it
• GPU support requires manual CUDA/cuDNN wrangling on local installs

Verdict

Worth bookmarking if you’re working through the book or need battle-tested, chapter-ordered ML examples. Skip it if you want a standalone course; this is strictly companion material.