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:
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
R Deep Learning Essentials - Dr. Joshua F.Wiley
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Deep Learning and Neural Networks - Jeff Heaton
Pattern recognition and machine learning - Christopher M.Bishop
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Deep Learning for Natural Language Processing - Jason Brownlee
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Python Machine Learning Cookbook - Practical solutions from preprocessing to Deep Learning - Chris A...
Machine Learning with spark and python - Michael Bowles
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Introduction to Deep Learning - Eugene Charniak
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Data Science and Big Data Analytics - EMC Education Services
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
An introduction to neural networks - Kevin Gurney & University of Sheffield
Amazon Machine Learning Developer Guild Version Latest
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Introduction to the Math of Neural Networks - Jeff Heaton
Neural Networks - A visual introduction for beginners - Michael Taylor