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3 authors for Learning from Data, not only Prof. Yaser Abu Mostafa.

Business/Data Analytics

Kevin Murphy’s Machine Learning, Pattern Classification by Duda, Hart, et, al and Hal Daume’s A course in Machine learning.

“An Introduction to Statistical Learning with Applications in R” by James, Witten, Hastie, Tibshirani

“The Elements of Statistical Learning.”

Applied Predictive Modeling?

PCI as it is popularly known, is one of the best books to start learning machine learning. If there is one book to choose on machine learning – it is this one. I haven’t met a data scientist yet who has read this book and does not recommend to keep it on your bookshelf. A lot of them have re-read this book multiple times.

The book was written long before data science and machine learning acquired the cult status they have today – but the topics and chapters are entirely relevant even today! Some of the topics covered in the book are collaborative filtering techniques, search engine features, Bayesian filtering and Support vector machines. If you don’t have a copy of this book – order it as soon as you finish reading this article! The book uses Python to deliver machine learning in a fascinating manner.

This book is written by Drew Conway and John Myles White. It is majorly based on data analysis in R. This books is best suited for beginners having basic knowledge on R. It further covers the use of advanced R in data wrangling. It has interesting case studies which will help you to understand the importance of using machine learning algorithms.

After you’ve read the above books, you are good to dive into machine learning. This is a great introductory book on machine learning. It provides a nice overview of ml theorems with pseudocode summaries of their algorithms. Apart from case studies, Tom has used basic examples to help you understand these algorithms easily.

This book is written by Trevor Hastie, Robert Tibshirani, Jerome Friedman. This book aptly explains the machine learning algorithms mathematically from a statistical perspective. It provides a powerful world created by statistics and machine learning. This books lays emphasis on mathematical derivations to define the underlying logic behind an algorithm. This book demands a rudimentary understanding of linear algebra.

This book is written by Yaser Abu Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin. This book provides a perfect introduction to machine learning. This book prepares you to understand complex areas of machine learning. Yaser has provided ‘to the point’ explanations instead of lengthy and go-around explanations. If you choose this book, I’d suggest you to refer to online tutorials of Yaser Abu Mostafa as well. They’re awesome.

This book is written by Christopher M Bishop. This book serves as a excellent reference for students keen to understand the use of statistical techniques in machine learning and pattern recognition. This books assumes the knowledge of linear algebra and multivariate calculus. It provides a comprehensive introduction to statistical pattern recognition techniques using practice exercises.

Who else might be the best coach to learn AI than Peter Norvig? You have to take a course from Norvig to understand his style of teaching. But once you do, you will long for it!

This book is written by Stuart Russell and Peter Norvig. It is best suited for people new to A.I. More than just providing an overview of artificial intelligence, this book thoroughly covers subjects from search algorithms, reducing problems to search problems, working with logic, planning, and more advanced topics in AI such as reasoning with partial observability, machine learning and language processing. Make it the first book on A.I in your book shelf.

This book is written by Jeff Heaton. It teaches basic artificial intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. It explains these algorithms using interesting examples and cases. Needless to say, this book requires good commands over mathematics. Otherwise, you’ll have tough time deciphering the equations.

Another one by Peter Norvig! This book teaches advanced common lisp techniques to build major A.I systems. It delves deep into the practical aspects of A.I and teaches its readers the method to build and debug robust practical programs. It also demonstrates superior programming style and essential AI concepts. I’d recommend reading this book, if you are serious about a career in A.I specially.

This book is written by Nils J Nilsson. After reading the above 3 books, you’d like something which could challenge your mind. Here’s what you are looking for. This books covers topics such as Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks and explains them with great ease. I wouldn’t recommend this book for a beginner. However, it’s a must read for advanced level user.

This book is written by Marvin Minsky. In this book, Marvin offers a fascinating model of how our mind works. He tries to infer the future of human mind by examining different forms of mind activity. You’ll find path breaking research findings where Marvin has challenge the status quo. This book is great to develop perspective and become aware of present to future transition of A.I

This book is written by Patrick Henry Winston. This is an introductory book on artificial intelligence. Non-programmers can easily understand the explanations and concepts. More advanced AI topics are covered but haven’t been explained in depth. However some chapters, do cover a great deal of information. It teaches to build intelligent systems using various real life examples. All in all, this book imparts a new shape to complicated intelligence with simple explanation.