Hands-On Machine Learning with Scikit-Learn and PyTorch (github.com)

The repository accompanies the first edition of O’Reilly’s Hands-On Machine Learning with Scikit-Learn and PyTorch, providing the example code and exercise solutions to teach core machine‑learning concepts in Python. It’s a practical resource for learners and practitioners who want runnable notebooks that bridge classical scikit‑learn pipelines and PyTorch modeling. If you prefer the TensorFlow/Keras flavor of the book, the author points users to the ageron/handson-ml3 notebooks. Practical details matter: notebooks run on Colab (ephemeral — download any data you need) or GitHub’s notebook viewer (slower, may misrender math or fail on large notebooks). The repo includes installation guidance (recommendation: Python 3.12; 3.10/3.11 should work; some packages aren’t ready for 3.13). Common issues and fixes are documented — e.g., HTTP errors when calling load_housing_data usually mean mismatched code or network issues, and macOS SSL errors can be fixed by installing certs via the provided terminal commands. Instructions for updating the project and conda packages live in INSTALL.md. Overall, this is a highly practical, reproducible toolkit for learning and prototyping ML workflows with modern Python tooling.