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:
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Deep Learning with Keras - Antonio Gulli & Sujit Pal
R Deep Learning Essentials - Dr. Joshua F.Wiley
Deep Learning with Hadoop - Dipayan Dev
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Medical Image Segmentation Using Artificial Neural Networks
Machine Learning with Python for everyone - Mark E.Fenner
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Python Data Structures and Algorithms - Benjamin Baka
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Machine Learning with spark and python - Michael Bowles
Artificial Intelligence by example - Denis Rothman
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Intelligent Projects Using Python - Santanu Pattanayak
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Deep Learning with Python - Francois Chollet
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Introduction to the Math of Neural Networks - Jeff Heaton
Deep Learning with Python - Francois Cholletf
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar