Hope you all are doing good. We are again here in front you all with another successive post on Machine Learning. We almost have covered the theoretical portion of the course and will be doing the hands-on practical soon. We all know that there are plenty of resources on the internet that we can use to study and learn almost anything. But again availability of the contents in such a humongous amount haunts the learners that where to start their journey and very often a learner ends up confused and irritated. Great scholars suggest reading books, ain’t they? So why don’t we take the easier path? While the internet is full of plenty of choices that seem very confusing for a novice, we would suggest to start the journey with conventional steps, of course books.
Again you guys do not really worry or need to wander here and there in search of books neither you have to ask someone else’s suggestion for what book to have. Here we have complied a list of some useful books that will give a kick-start to your effort towards data sciences and analytics also on the other hand are interesting to read. Moreover keeping in mind our readers convenience, we’ve also provided the links from where you can order books of your choice without even stepping out of the comfort of your home. So without talking much let’s get started and step toward the list we have compiled for you.
Best Books for Machine Learning (ML)
As the name itself suggests, this book aims at explaining the algorithms of machine learning mathematically with a tint of statistics. The three authors are Trevor Hastie, Robert Tibshirani and Jerome Friedman has emphasized on explaining the logic behind the machine learning algorithms with the help of mathematical derivations.
Note: If you have a good grasp of linear algebra, we would suggest to go with this book.
Instead you can buy this book written by Yuxi (Hayden) Liu. With this book you will be able to learn the fundamentals of machine learning and would be able to build your own intelligent applications.
Note: Please note there is no pre-requisite to start with this book. Even a person with zero knowledge about machine learning can easily get a grasp over the course.
This very books provide a simplified understanding of the complex areas of machine learning. Instead of lengthy explanations, small and to-the point explanation is being provided by Yaser Abu Mostafa, Malik Magdon Ismail and Hsuan-Tien Lin. We would suggest this book as a good means to learn and apply the principles of machine learning for the beginners.
Moreover in addition to the book reading you can also refer to online tutorials by Yaser Abu Mostafa.
This book popularly known as PCI in the world of machine learning is said to have all that requires to start learning machine learning. It is believed that this book was written long before the evolution of machine learning as we see it today, but to our surprise, the topics and chapters discussed entirely relate to the version of machine learning we have today.
We strongly recommend this book to every aspiring data scientist, ml enthusiast and even folks who are into machine learning since quite a few time. We bet you won’t regret giving this book a try.
After reading the book mentioned just above, we would recommend you to give this too a try. Tom has tried to make his readers understand the concept of machine learning with the help of pseudocodes and case studies. You will also find some interesting basic examples to understand the algorithms with ease.
Best Books for Artificial Intelligence (AI)
This book is considered as the holy book for understanding the immense field of AI. Peter Norvig and Stuart Russell worked together to make this art happen. This book is suited to the people new to AI. Not only this provides an overview about AI but also covers some advanced topics like search algorithms, working with logic, machine learning, language processing, etc.
This book too is written by Peter Norvig. This book primarily aims at teaching its readers the common lisp techniques to build robust AI systems. Instead of just teaching theory, in this book Norvig has put more emphasis on the practical part to let his readers develop programs and systems at their own. If a personnel want to make his/her career in the AI domain, this book is worth giving a shot.
Jeff Heaton, the author of this book aims to teach his readers the basic AI algorithms like clustering, error calculation, linear regression, etc. This book is well equipped with good examples and relevant test cases. Moreover this book demands good grasp on mathematics in order to understand the equations described.
This book is an introductory step towards AI and written by Deepak Khemani. This book is written in such a manner that a person from non-programming background can also understand the concepts easily. Although the advanced topics are not explained into depth, but the overall structure of the book is acceptable. The books explains the classical methods and the updated concepts as well.
Any doubts or suggestions are welcomed in the comment section below. Also let us know if there is any other best books for machine learning and artificial intelligence you have read and is worth mentioning in the list.