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:
Artificial Intelligence by example - Denis Rothman
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Deep Learning with Python - Francois Chollet
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Neural Networks and Deep Learning - Charu C.Aggarwal
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Introduction to the Math of Neural Networks - Jeff Heaton
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Neural Networks - A visual introduction for beginners - Michael Taylor
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Amazon Machine Learning Developer Guild Version Latest
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Machine Learning with spark and python - Michael Bowles
Fundamentals of Deep Learning - Nikhil Bubuma
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
R Deep Learning Essentials - Dr. Joshua F.Wiley
Deep Learning with Applications Using Python - Navin Kumar Manaswi