Includes bibliographical references and index.

Notation and definitions -- Fundamental algorithms -- Anatomy of a learning algorithm -- Basic practice -- Neural networks and deep learning -- Problems and solutions -- Advanced practice -- Unsupervised learning -- Other forms of learning.

Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much aboutmachine learningapriori. The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.