Part I, Introduction -- An Introduction to Machine Learning and Artificial Intelligence (AI) -- Part II, An In-Depth Overview of Machine Learning -- Machine Learning Algorithms -- Performance Evaluation of Machine Learning Models -- Machine Learning Data -- Part III, Automatic Speech Recognition -- Automatic Speech Recognition -- Part IV, Biometrics Recognition -- Face Recognition -- Speaker Recognition -- Part V, Machine Learning by Example -- Machine Learning by Example -- Part VI, The AI-Toolkit: Machine Learning Made Simple -- The AI-Toolkit: Machine Learning Made Simple -- App. A, From Regular Expressions to HMM -- References -- Index.

This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming. After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments. The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.