Machine Learning Refined: A certainly refined and refreshing introduction into Machine Learning

Modern machine-learning textbooks are often divided into two categories: Theory and application. 

Often books either skimp on the details and go straight to educating the reader on how to code or spend their entire time dictating the interesting (but often verbose) mathematical background and derivation of the statistical learning techniques which underpin the algorithms. 

This books strikes happy medium between the two with excellent code examples of how to implement the as well as beautiful explained derivations and motivations of the mathematics underpinning the techniques used through the text. Furthermore, the explanations used throughout the text are very well explained, so well in fact, that I'd argue that this is one the best introductions to machine learning that is avaliable on the market currently. 

The text first begins with a simple, yet poignant overview of machine learning techniques as a whole, without the uses of mathematics or code examples. 

The section then unfolds into a complete and thorough review of mathematical optimisation ( a key component of machine learning) before looking at linear (such as regression and PCA analysis) and non-linear statistical learning techniques. 

This text is most suited for anyone (where beginner or professional) looking to gain a strong foundation in the conceptual as well as practical aspects of machine learning.

The greatest strength of this volume is its use of visual aides to explain, reinforce and illustrate ideas to the reader. This makes the content far more digestible and memorable in comparison to other texts of a similar nature. 

Machine Learning Refined
Foundations, Algorithms, and Applications

2nd Edition 

by Jeremy Watt, Reza Borhani and Aggelos K. Katsaggelos

ISBN: 9781108480727


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