This is the companion code repository for the third edition of the book Python Machine Learning. It delivers the entire learning path as a structured collection of Jupyter notebooks that progress from classical machine learning algorithms to advanced deep learning models, with every concept demonstrated through executable code and narrative text.
What distinguishes this resource is its pedagogical design. Each notebook cell encapsulates a single conceptual step, letting readers run, inspect, and modify discrete units of learning. The code provides interchangeable implementations of deep learning models using TensorFlow, PyTorch, and scikit-learn, enabling direct comparison of frameworks. All library versions are pinned to guarantee deterministic execution that matches the printed edition.
Beyond the core tutorial structure, the notebooks cover the full spectrum of machine learning education — implementing classical algorithms for classification, regression, clustering, and dimensionality reduction; training neural networks for image classification and language modeling; and building advanced architectures such as generative adversarial networks and reinforcement learning agents. The material also includes systematic workflows for hyperparameter tuning and cross-validation to refine model performance.
Requirements files and environment specifications are included, ensuring the code runs reproducibly on any compatible setup.