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
Machine Learning with Python for everyone - Mark E.Fenner
Deep Learning for Natural Language Processing - Jason Brownlee
Data Science and Big Data Analytics - EMC Education Services
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Deep Learning with Python - Francois Cholletf
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Introduction to Scientific Programming with Python - Joakim Sundnes
Python Deep Learning Cookbook - Indra den Bakker
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Python Machine Learning Eqution Reference - Sebastian Raschka
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Deep Learning with Applications Using Python - Navin Kumar Manaswi
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
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Pattern recognition and machine learning - Christopher M.Bishop
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Python Machine Learning - Sebastian Raschka
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Python Data Structures and Algorithms - Benjamin Baka
Deep Learning with PyTorch - Vishnu Subramanian
Medical Image Segmentation Using Artificial Neural Networks
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
R Deep Learning Essentials - Dr. Joshua F.Wiley
Introduction to the Math of Neural Networks - Jeff Heaton
Grokking Deep Learning - MEAP v10 - Andrew W.Trask