machine-learning-book

Includes code for all 19 chapters, from basic classification to advanced topics like transformers and GANs, providing a structured learning path from fundamentals to cutting-edge techniques.

Examples use both PyTorch for deep learning and scikit-learn for traditional ML, enabling learners to compare and apply two popular frameworks in real-world scenarios.

Organized Jupyter notebooks per chapter with clear implementations allow for immediate experimentation and hands-on practice without extensive setup.

Code aligns with the book by recognized authors, ensuring accuracy, and includes detailed environment setup instructions and a Google Colab guide for easy reproducibility.