“Machine learning is a core, transformative way by which we’re rethinking everything we’re doing.”
~ Sundar Pichai, CEO, Google
From connected devices to autonomous vehicles, machine learning is certainly disrupting every sphere of life. So, it only makes sense to learn all you can about it to stay relevant. Here’s a list of the 10 best books on this subject.
If this technology sounds Greek to you, this is the book to start with. It explains concepts in simple English, such that you don’t even need experience with coding to understand. It contains visual examples that make the book both engaging and easy to follow. The new edition has added several more topics not covered in the first edition.
Machine Learning: For Absolute Beginners. The Ultimate Beginners Guide for Algorithms, Neural Networks, Random Forests and Decision Trees
By Ryan Roberts
This book is what you need to stay ahead of the changing ways in which we are interacting with machines and the potential for the future. Siri and Alexa are only the starting point. Intelligent machines can make processes and life much easier. This book tells you why machine learning is important, its algorithms, most popular applications and more.
This book offers an insight into the fundamental concepts that underlie machine learning and how mathematical derivations transform these concepts into applicable algorithms. It discusses a wide variety of topics, from crucial algorithmic paradigms to concepts of stability and convexity, neural networks and more.
Paradigms of Artificial Intelligence Programming
By Peter Norvig
This book has been acclaimed as one of the best written on programming. With an easy-to-understand writing style and clear examples, you don’t need to be a master coder to understand machine learning. The only problem is that it is over 900 pages long!
Machine Learning: The New AI
By Ethem Alpaydin
One of latest books on machine learning, this book gives an understanding of the algorithms for data sets, helping coders learn to write codes from such data sets. The author has wide experience of teaching the subject and gives an insight into the evolution of machine learning and its applications.
This book is what you need to learn all the mathematics required to work with machine learning and deep learning algorithms. Elon Must, cofounder and CEO of Tesla Motors and SpaceX described this book, saying, “Written by thre experts in the field, Deep Learning is the only comprehensive book on the subject.”
Pattern Recognition and Machine Learning
By Christopher M. Bishop
This one is for people with experience, such as developers and data scientists and gives great insight into pattern recognition. It is possibly one of the best resources for machine learning concepts. The best part is the simple style of writing and the real-life examples that offer a lot of practical knowledge.
By Kevin P. Murphy
The easy and informal writing style of this book and the numerous illustrations and worked examples offer a simple way to understand machine learning and the various heuristic methods that can be used.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
If you want to know how machine learning find practical applications across diverse fields from finance to medicine and even marketing, this is the book you need to read. Although it takes a statistical approach, the focus is on explaining the concepts. The new edition includes several topics not covered in the first edition.
Machine Learning: The Art and Science of Algorithms that Make Sense of Data
By Peter Flach
This book is a great choice for intermediate to advanced level developers. The numerous real-life examples make it a must read. This is the resource you need to learn all about how statistical models can be generated, analyzed and predicted using machine learning.
So, there you have it. The best books on Machine learning. Which one is your favorite? Let me know in comments.
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