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.
Related posts:
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Deep Learning with Theano - Christopher Bourez
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Deep Learning and Neural Networks - Jeff Heaton
Artificial Intelligence by example - Denis Rothman
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Java Deep Learning Essentials - Yusuke Sugomori
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Amazon Machine Learning Developer Guild Version Latest
Data Science and Big Data Analytics - EMC Education Services
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Python Machine Learning - Sebastian Raschka
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Fundamentals of Deep Learning - Nikhil Bubuma
Machine Learning with spark and python - Michael Bowles
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Python Machine Learning Eqution Reference - Sebastian Raschka
Pattern recognition and machine learning - Christopher M.Bishop
An introduction to neural networks - Kevin Gurney & University of Sheffield
Deep Learning with Applications Using Python - Navin Kumar Manaswi