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This repository accompanies the well-known textbook “Python Machine Learning, 2nd Edition” by Sebastian Raschka and Vahid Mirjalili, serving as a complete codebase of examples, notebooks, scripts and supporting materials for the book. It covers a wide range of topics including supervised learning, unsupervised learning, dimensionality reduction, model evaluation, deep learning with TensorFlow, and embedding models into web apps. Each chapter has Jupyter notebooks and Python scripts that replicate the examples in the book, allowing readers to run, inspect, and tweak code directly as they follow material. The structure also includes errata documentation and assets (images) that appear in the printed edition, providing a rich supplement to learning. The repository is suitable both for classroom use and for self-study, as well as being a go-to reference for data scientists revisiting techniques.
Features
- Full code repository of Jupyter notebooks and Python scripts aligned chapter-by-chapter
- Covers broad machine learning algorithm categories and real-world applications
- Examples include scikit-learn, TensorFlow, deep learning, model pipelines and evaluation
- Accompanying assets (images, datasets, errata) for full learning experience
- Suitable for classrooms, tutorials, or self-paced study by practitioners
- MIT-licensed and actively maintained so you can adapt or extend the examples
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