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:
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Data Science and Big Data Analytics - EMC Education Services
Deep Learning with Theano - Christopher Bourez
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Java Deep Learning Essentials - Yusuke Sugomori
An introduction to neural networks - Kevin Gurney & University of Sheffield
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning for Natural Language Processing - Jason Brownlee
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
The hundred-page Machine Learning Book - Andriy Burkov
Introduction to Scientific Programming with Python - Joakim Sundnes
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Medical Image Segmentation Using Artificial Neural Networks
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Python Machine Learning - Sebastian Raschka
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Neural Networks - A visual introduction for beginners - Michael Taylor
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Artificial Intelligence by example - Denis Rothman