Machine Learning has established incredible supremacy to humans. The power to run tasks in automated manner, the power to make our lives easy, the power to improve things endlessly by studying decisions at large sacle . And the power to create species who think better than humans.
Those who know of these developments are keen to master this idea, including me. When I started with this assignment, I found various form of digitized study material. They seemed promising, comprehensive, yet lacked a perspective. My curiosity didn't let me rest for long. I resorted to Books.
Don't you read books? Oh! I see, you don't get time. Right? Must be a busy man, perhaps!
When Elon Musk, the busiest man of planet right now, was asked about his secret of success, he replied, 'I used to read books. A LOT'. Later, Kimbal Musk, Elon's brother said, 'He would even complete two books in a day'.
The phenomenon of Machine Learning and Artificial Intelligence, is thoroughly covered in the books mentioned below.
In this article, I've listed the best books on Machine Learning and Artificial Intelligence. Books are in no order. The motive of this article is not to promote any particular book, but you make you aware of a world which exists beyond video tutorials, blogs and podcasts.
Programming Collective Intelligence, 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 tutorialsof 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.
While compiling this list of books, I found their authors upload the link of some of their legal pdf document. I have shared the links if you would want that (added above). In these books, the authors have not only explained the ML concepts precisely, but also mentioned their perspective and experiences using those concepts, which you would have missed otherwise!
Did I leave out a useful book on machine learning and artificial intelligence? Share you views in the comments section below