Introduction to Machine Learning with Python

This book is more advanced than the first book I recommended. It presents ML theoretical and practical aspects step-by-step from the bottom up. Each chapter elaborates at length on a core building block in the ML life cycle. For example, feature engineering, supervised learning, and model evaluation have their own separate chapters, with intuitive discussions of how they work. Most of the concept is taught through the simple yet powerful Python Module Scikit-Learn so it won’t overburden you with heavy programming. This book will be perfect for practitioners with some understanding of statistics and linear algebra.