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 - A Practitioner's Approach - Josh Patterson & Adam Gibson
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Neural Networks - A visual introduction for beginners - Michael Taylor
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Neural Networks and Deep Learning - Charu C.Aggarwal
Learn Keras for Deep Neural Networks - Jojo Moolayil
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Python Deep Learning Cookbook - Indra den Bakker
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Deep Learning with Python - Francois Cholletf
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Deep Learning with Theano - Christopher Bourez
Fundamentals of Deep Learning - Nikhil Bubuma
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Introduction to the Math of Neural Networks - Jeff Heaton
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning for Natural Language Processing - Jason Brownlee
Machine Learning with Python for everyone - Mark E.Fenner
Python Machine Learning Eqution Reference - Sebastian Raschka
An introduction to neural networks - Kevin Gurney & University of Sheffield
Deep Learning with PyTorch - Vishnu Subramanian
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel