This book is intended to be a practical guide for students, engineers, and researchers in electromagnetics with minimal background in ML. It can also serve as a basic reference book for ML algorithms, advanced topics of electromagnetics, and applications of ML in antenna array processing. The book is divided into two parts. The first part contains five chapters, which cover the theoretical principles of the most common ML architectures and algorithms used today in machines, kernel methods for antenna array, neural networks, and deep learning for computational electromagnetics. The second part consists of four chapters that provide detailed applications of the algorithms of electromagnetic problems, such as in antenna array beamforming, angle of arrival detection, computational electromagnetics, antenna optimization, reconfigurable antennas, cognitive radio, and other aspects of electromagnetic design. This book is the front runner of ML as it applies to electromagnetics and antenna array processing. It requires extensive mathematics and statistics skills and knowledge by the reader to read, understand, and use this book. The original graphics and charts were in color, with explanations of the charts based on the color in the charts, but the color has been removed in the published copy, which may cause the reader some confusion.