MACHINE LEARNING 1ED

This book on Machine Learning is designed as a textbook for undergraduate and post-graduate students of engineering. It provides a comprehensive coverage of fundamentals of machine learning. Spread over 16 chapters, the book starts with an overview of machine learning and discusses the need for understanding data and necessary mathematics. It goes on to explain the basics of learning theory, regression analysis, decision tree, and decision rule-based classification algorithms. The book provides an introduction to Bayesian learning and probabilistic graphical models. Important topics such as support vector machines, artificial neural networks, ensemble learning, clustering algorithms, reinforcement algorithms, and genetic algorithms are discussed in depth. It ends with the latest developments in deep learning. A perfect balance between theoretical and mathematical exposition is provided with several numerical examples, review questions, and Python programs.