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:
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Neural Networks and Deep Learning - Charu C.Aggarwal
Learn Keras for Deep Neural Networks - Jojo Moolayil
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Python Machine Learning - Sebastian Raschka
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Medical Image Segmentation Using Artificial Neural Networks
Deep Learning with Applications Using Python - Navin Kumar Manaswi
The hundred-page Machine Learning Book - Andriy Burkov
Artificial Intelligence by example - Denis Rothman
Deep Learning for Natural Language Processing - Jason Brownlee
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Coding Theory - Algorithms, Architectures and Application
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Deep Learning with Hadoop - Dipayan Dev
Amazon Machine Learning Developer Guild Version Latest
R Deep Learning Essentials - Dr. Joshua F.Wiley
An introduction to neural networks - Kevin Gurney & University of Sheffield
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Deep Learning in Python - LazyProgrammer
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Python Machine Learning Eqution Reference - Sebastian Raschka
Python Deep Learning Cookbook - Indra den Bakker
Data Science and Big Data Analytics - EMC Education Services
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh