$ show ageron/handson-ml
This repository is the companion code for the first edition of 'Hands-on Machine Learning with Scikit-Learn and TensorFlow' (2017). It contains a collection of Jupyter notebooks teaching fundamental and advanced machine learning concepts, covering classification, SVMs, decision trees, ensemble methods, dimensionality reduction, and deep learning with TensorFlow 1.x (including CNNs, RNNs, autoencoders, and reinforcement learning). The code is educational, with step-by-step implementations, inline explanations, and exercise solutions. It is not a production application but a self-contained learning environment, supplemented with datasets (California housing, OECD life satisfaction, MNIST) and a Docker setup for reproducible execution. Significant technical drift exists: TensorFlow 1.x APIs, deprecated scikit-learn imports, and Python 2/3 compatibility shims make the notebooks incompatible with modern environments without substantial rewriting. The README explicitly directs users to the second edition repository for up-to-date code.
The LLM identified 3 files worth highlighting for reuse:
A self-contained module that backports OneHotEncoder, OrdinalEncoder, and ColumnTransformer from modern scikit-learn (>=0.20) to older versions. It provides drop-in replacements for projects stuck on legacy sklearn, enabling robust categorical feature handling without upgrading the entire library. The code is isolated, well-structured, and easily extractable into any data pipeline.
A structured markdown checklist that outlines the end-to-end machine learning project lifecycle: framing the problem, data acquisition, exploration, preparation, modeling, tuning, deployment, and documentation. It is a reusable guide that can be adapted for any ML or data science project, promoting disciplined methodology and reproducibility.
A Dockerfile that builds a reproducible Jupyter environment with pinned dependencies (Python 3.7, TensorFlow 1.15, scikit-learn 0.24, etc.). Combined with the docker-compose.yml and Makefile, it provides a turnkey solution for running the legacy notebooks without host-system conflicts. The pattern (Docker + docker-compose + helper scripts) can be reused for any project requiring a controlled, shareable computational environment.