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:
Introduction to the Math of Neural Networks - Jeff Heaton
Deep Learning with Theano - Christopher Bourez
Machine Learning with Python for everyone - Mark E.Fenner
Introduction to Deep Learning - Eugene Charniak
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Python Data Structures and Algorithms - Benjamin Baka
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Deep Learning with Python - Francois Chollet
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Neural Networks - A visual introduction for beginners - Michael Taylor
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Deep Learning with Hadoop - Dipayan Dev
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Pattern recognition and machine learning - Christopher M.Bishop
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Deep Learning in Python - LazyProgrammer
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Python Machine Learning - Sebastian Raschka
Deep Learning with PyTorch - Vishnu Subramanian
Amazon Machine Learning Developer Guild Version Latest