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Must Read Books for Beginners on Machine Learning and Artificial Intelligence

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Machine Learning has granted incredible power to humans. The power to run tasks in automated manner, the power to make our lives comfrotable, the power to improve things continuously by studying decisions at large sacle . And the power to create species who think better than humans.

Don’t believe me? Read what Google’s CEO Mr. Sundar Pichai had to say last week:

‘Machine learning is a core, transformative way by which we’re rethinking everything we’re doing,’ Pichai said. ‘We’re thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. We’re in the early days, but you’ll see us in a systematic way think about how we can apply machine learning to all these areas.’

– Sundar Pichai, CEO, Google

Those who know of these advancements, are keen to master this concept, including me. When I started with this mission, 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.

Must read books on machine learning and artificial intelligence


Machine Learning

Programming Collective Intelligence


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.


Machine Learning for Hackers



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.




Machine Learning by Tom M Mitchell



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.

Free PDF Link: Download




The Elements of Statistical Learning



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.

Free PDF Link: Download



Learning from Data



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.

Free PDF Link: Download



Pattern Recognition and Machine Learning



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.

Free PDF Link: Download




Artificial Intelligence

Artificial Intelligence: A Modern Approach

11Who 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.

Free PDF Link: Download


Artificial Intelligence for Humans



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.



Paradigm of Artificial Intelligence Programming



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.



Artificial Intelligence: A New Synthesis



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.




The Emotion Machine: Commonsense Thinking, Artificial Intelligence and the Future of Human Mind



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




Artificial Intelligence (3rd Edition)



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.



End Notes

While compiling this list of books, I found the link of some of their legal pdf’s uploaded by their authors. I’ve 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.

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  • Arun Sundar S says:

    Really good. It would be great if the pdf links are shared..

  • DK Samuel says:

    Please paste the link of some of their legal pdf’s uploaded by their authors.

  • GRT says:

    Please mail me the links of the pdfs of these books and any other data science related books materials which you have.
    It would be great and a treasure house if these are uploaded in a drive and granted access.

  • Navneeth says:

    I second DK Samuel’s comment. A reputed site like AV should be sharing only those (free) PDFs which are made available by the authors or publishers (e.g. ESL by Hastie et al.).

  • Henderake says:

    Great article! By the way, there are actually 3 authors for Learning from Data, not only Prof. Yaser Abu Mostafa. You forgot to mention the other 2 authors.

    • Manish Saraswat says:

      I missed them! My Bad. Thank you so much for highlighting this error, I would have never known otherwise. Appreciate it.
      Added now.

  • Trinadh says:

    Quite helpful information on Machine Learning. Do share the links to PDFs.

  • ezequielm says:

    Hi. Thanks for this useful post, especially for beginers. It’ll be great if you can share those pdfs.

  • Ishan says:

    Quite fascinating list, please share the links for legal pdf’s uploaded by their authors.. And would it be possible to compile the list of books on Business/Data Analytics.

  • Deniz Appelbaum says:

    Thanks for this great list! Could you share the pdfs please?

  • Pankesh says:

    I think some of the more honourable mentions are Kevin Murphy’s Machine Learning, Pattern Classification by Duda, Hart, et, al and Hal Daume’s A course in Machine learning.

  • Manish Saraswat says:

    Hello all,

    I have added the pdf download links below their respective books in this article. Keep Learning Machine Learning and AI. 🙂

  • SZ says:

    I’d recommend “An Introduction to Statistical Learning with Applications in R” by James, Witten, Hastie, Tibshirani before “The Elements of Statistical Learning.” I wouldn’t consider the latter an intro book.

  • Deepak Gupta says:

    What do you think about Applied Predictive Modeling?

  • Robin White says:

    After you study machine learning by yourself by reading book, you might need to study some practical exercises to be better. I would recommend to take the machine leaning courses that I took, because it covered almost everything that I wanted. http://www.thedevmasters.com/machine-learning-in-python/

  • Ramkumar says:

    One great ML book that is not in your list: ML A probabilistic perspective by Kevin P Murphy

  • Biswajit Samal says:

    I am beginners with the basic knowledge on python and want to work more on ML.

    I do not know the right direction. Please share me ur thoughts as a beginners

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