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:
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
The hundred-page Machine Learning Book - Andriy Burkov
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Machine Learning with Python for everyone - Mark E.Fenner
Intelligent Projects Using Python - Santanu Pattanayak
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Medical Image Segmentation Using Artificial Neural Networks
Python Machine Learning - Sebastian Raschka
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Coding Theory - Algorithms, Architectures and Application
Deep Learning with Python - Francois Chollet
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Machine Learning with spark and python - Michael Bowles
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
R Deep Learning Essentials - Dr. Joshua F.Wiley
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli
Python Data Structures and Algorithms - Benjamin Baka
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Deep Learning in Python - LazyProgrammer
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Amazon Machine Learning Developer Guild Version Latest
Python Deep Learning Cookbook - Indra den Bakker
Pattern recognition and machine learning - Christopher M.Bishop
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron