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:
Machine Learning with Python for everyone - Mark E.Fenner
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning for Natural Language Processing - Palash Goyal & Sumit Pandey & Karan Jain
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Introduction to the Math of Neural Networks - Jeff Heaton
Python Machine Learning - Sebastian Raschka
Machine Learning with spark and python - Michael Bowles
Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Python 3 for Absolute Beginners - Tim Hall & J.P Stacey
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Amazon Machine Learning Developer Guild Version Latest
Fundamentals of Deep Learning - Nikhil Bubuma
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Coding Theory - Algorithms, Architectures and Application
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Python Machine Learning Eqution Reference - Sebastian Raschka
Neural Networks and Deep Learning - Charu C.Aggarwal
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Machine Learning - A Probabilistic Perspective - Kevin P.Murphy
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Introduction to Deep Learning - Eugene Charniak
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Data Science and Big Data Analytics - EMC Education Services