This repository provides the official code examples from a 770-page published book on machine learning. It covers foundational ML concepts including classification algorithms, regression, model evaluation, hyperparameter optimization, ensemble methods, and sentiment analysis. The notebooks demonstrate implementations using both scikit-learn for classical ML and PyTorch for deep learning neural networks. The content is structured as chapter-by-chapter walkthroughs designed to accompany the textbook.