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 for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Python Deep Learning Cookbook - Indra den Bakker
Introduction to Deep Learning - Eugene Charniak
Data Science and Big Data Analytics - EMC Education Services
Pattern recognition and machine learning - Christopher M.Bishop
Coding Theory - Algorithms, Architectures and Application
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Medical Image Segmentation Using Artificial Neural Networks
Deep Learning with Hadoop - Dipayan Dev
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Machine Learning with Python for everyone - Mark E.Fenner
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning for Natural Language Processing - Jason Brownlee
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Neural Networks and Deep Learning - Charu C.Aggarwal
R Deep Learning Essentials - Dr. Joshua F.Wiley
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Python Machine Learning Eqution Reference - Sebastian Raschka