I decided to share my updated shortlist of top machine learning books in 2021. Below are some of the most popular Google search phrases from my readers:
- How do you get started in AI?
- What are the best machine learning books for beginners?
- How to become a data scientist?
- Best AI books
I’m a big believer of democratizing AI and getting rid of gatekeeping. Whether you are a software engineer, a data scientist or a product manager, there is a machine learning book out there that will bring you to your next level.
So here we go:
The Hundred-Page Machine Learning Book
Audience: Project managers
This best-seller from Andriy Burkov is one of the top machine learning books. It contains a little over 100 pages, and yet manages to cover the most important aspects of modern machine learning. Don’t expect code samples or deep mathematical discussions. Instead, you get a theoretically sound but understandable overview and explanation of the core concepts of AI. This is a great book for project managers that want to understand what machine learning is all about!
Deep learning with Python
Audience: Software engineers
This is probably my favorite deep learning book all times. To be fair, I only read the first edition, but the second edition is expected this year! The author, François Chollet, is the creator of the famous Keras deep learning library, and is a senior deep learning researcher at Google. This book is aimed at data and software engineers that want to get into the field of machine learning. In a step-by-step approach, you’ll learn to build really cool models, with practical examples and real-world tips and tricks. This might well be one of the top machine learning books all times.
Pattern Recognition and Machine Learning
Audience: PhD students & researchers
This book is where it all started for me. The author of this book, Christopher Michael Bishop, leads the renowned machine learning and perception group at Microsoft Research, and is considered one of the big guys in the machine learning industry. Personally, I believe this to be one of the best books to get started with machine learning if you have a strong mathematical background. If you want to dig deep into the mathematics of machine learning to get a thorough theoretical understanding, this is the one of the top machine learning books for you.
I think Pattern Classification is great but was still difficult to understand for me. I first read Learning from data which I think is much more accessible for beginners. I’m going to try Duda’s book next
Thanks for your feedback, David. ‘Learning from data’ is not in my top-4 shortlist but it would definitely be in my top-5. Great addition!
+1 for “learning from data”. especially there’re some mooc using this book as main reference. very easy to begin with.
I used Machine Learning from Mitchell during my master course on machine learning. It is a great book and I still use lots of what I learned from it in my current job. But the book is quite expensive, wish there was an e-book version.
Great to hear that, Xin.do! I agree that Mitchell’s book is more accessible than the work of Bishop or Duda, but I would still recommend these to deepen your knowledge and understanding of what is introduced in Mitchell’s book.
Great list, what are the best books on python for machine learning?
Although there are quite some books about using python for machine learning, I believe you are better off with the infinite number of online python tutorials that are out there. Start with scikit-learn (http://scikit-learn.org/stable/tutorial/) and then add Pandas (http://pandas.pydata.org/) to your list.
To really understand the fundamental concepts of the most important machine learning algorithms however, I would still recommend reading a book that focusses solely on machine learning.
The programming language you choose to implement your ideas is merely a semantic detail, and learning such language is easy compared with learning about data science and pattern recognition.
Sebastian Raschka’s book, (published after the above post) is rapidly becoming very highly regarding in this respect. There is also Joel Grus “Data Science from Scratch2 (also Python). Both are based on the concept of using Python to aid in understanding (as opposed to just being an engineering type guide to “getting the job done in Python” ). There are more detailed explanations of relative merits of each on Amazon.
Very elegantly written, and easy to follow articles. Drove home the concepts of Eigenvalues and PCA really well. Thanks Vincent.
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/
Thanks for the recommendation! I just wanted to point out that the second and third “Buy Now” Amazon buttons do not redirect to the correct books page…
Thanks Josep! Fixed.