Deep Learning dummies second edition – John Paul Mueller & Luca Massaronf

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.
• Boxes summarise relevant background material and provide pointers for revision.
• 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.