Part 1 - Machine Learning

This part introduces the foundations of machine learning with Python. We cover the core concepts and algorithms that every practitioner should understand: supervised and unsupervised learning, classification, regression, and clustering.

We start with a survey of machine learning approaches and work through practical examples using scikit-learn. We then cover regression and clustering in depth, followed by a chapter on exploratory data analysis and feature engineering — the often-overlooked skills that determine whether a model succeeds or fails in practice.