LEARNING · GITHUB, AURÉLIEN GÉRON
Free notebooks; book optional and paid
Free companion notebooks for practical machine learning, with runnable examples across classical ML and neural networks. Pair chapters with your daily Java/AI study rather than reading it cover to cover.
The repository provides notebooks accompanying the third edition of Hands-On Machine Learning. It is a practical sequence using Python tools such as Scikit-Learn, Keras and TensorFlow.
The notebooks are free to access, while the book itself is optional and may cost money. Use the repository to reproduce selected experiments rather than buying the book unless you know you need the explanation and can afford it.
Suggested approach: choose one topic relevant to your current study, run the notebook, change one parameter or dataset feature, and record what changed in a short README. Keep a reproducible environment and note package versions.
This is not Java-focused. The value is learning ML workflow and model evaluation; translate the production integration back into Java using your existing backend projects.