As one of the most comprehensive machine learning texts around, this book does justice to the field’s incredible richness, but without losing sight of the unifying principles.
Peter Flach’s clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. He covers a wide range of logical, geometric and statistical models, and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features.
Machine Learning will set a new standard as an introductory textbook:
 The Prologue and Chapter 1 are freely available on-line, providing an accessible first step into machine learning.
 The use of established terminology is balanced with the introduction of new and useful concepts.
 Well-chosen examples and illustrations form an integral part of the text.
 Boxessummariserelevantbackgroundmaterialandprovidepointersforrevision.
 Each chapter concludes with a summary and suggestions for further reading.
 A list of ‘Important points to remember’ is included at the back of the book
together with an extensive index to help readers navigate through the material.