machine-learning-book

Why we included this project

This repo holds the notebooks that go with Sebastian Raschka's machine learning book, and they are a practical way to follow along when you want to actually run the code. The chapters start with scikit-learn workflows like preprocessing, model evaluation, and ensemble methods, then move into PyTorch for deep networks, CNNs, RNNs, and transformers. Each folder matches the book's structure, so you can reproduce an experiment quickly and then adapt it to your own data. There are also setup notes and a community guide for running the examples on Google Colab, which removes a lot of friction if you don't want to install everything locally.