Since I spend so much time on my computer I actually turn to books pretty frequently to learn additional concepts in this field. Sometimes a good old-fashioned textbook really can be best. This varies from person to person but most of the time I don’t usually get a whole lot from online lectures about machine learning. What’s difficult is to match where the video starts and where my current knowledge is. usually, I find myself already knowing something or having absolutely no clue what the lecturer is talking about. So striking that balance is a little bit difficult with video lectures the exact same is true with books. However, to me, it’s easier to kind of scan through and finds the right starting place rather than videos trying to scrub through and find the right starting point. Anyways books are one of my favorite media to learn machine learning.
So we’re going to cover my top three picks for machine learning books in 2022. These are definitely very different books they have their own unique target audience. So I have a particular order in which I’m gonna present them and the order really is where I would start if I were just getting involved with the field today. Each of these books is actually pretty challenging in its own way but it starts with more application-focused and goes more toward theory.
1. Approaching (Almost) Any Machine Learning Problem
Our first book is Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur. This book is really great for anyone just starting out in the field. Especially for the people who have actually been in the field for a long time. Probably one of the main reasons that you’re going to be getting this book is because Abhishek is an absolute legend within the Kaggle community. He was the first triple grand master in Kaggle. So this means he not only knows how to train models extremely well but he’s a great communicator too. It’s extremely difficult to place that well in kegel competitions. So have no doubt, that he is an expert at applying machine learning to real-world business problems that are put forth by some of the biggest companies around.
This book focuses a lot on application and is going to include a lot of code for you to try. I think this is definitely my recommendation if you’re just starting out. If you don’t want to start learning with a dense theoretical textbook that’s just not going to get you excited or fired up about the field but you want to spark enough curiosity and questions that leave you challenged but not discouraged then this is exactly what I think you’re going to get out of Abhishek’s book. It’s going to get you excited and leave you wanting more which may lead you into buying some of the other books I’m going to cover next.
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2. Hundred Page Machine Learning Book
The next book that I’d recommend which I would say digs a little more into the theory of machine learning is The Hundred-Page Machine Learning Book. my copy is a little beat up and that’s because I use it very frequently as a reference guide this book is great mostly because it’s very approachable and short and concise. Since you actually believe that you can read this book it’s not a 500-page textbook it’s 100 pages you can actually read this thing cover to cover and it’s coming from Andriy Burkov who’s a great teacher in the field of machine learning.
This is going to be a great reference book for years to come. If you want a quick concise definition of a certain model or a certain operation that you want to perform in the field it’s best. Especially great for people like technical project managers who have that technical acumen and can understand it pretty well but don’t want to spend hours on end diving into the weeds. I would think of this book as sort of the best technical bang for your buck. Even from here, you can download its free PDF.
Sure you can go into more depth but you’ll also be sinking a lot more time into it and definitely reading a lot more pages of material. The technical depth is not exhaustive since that’s really not the point of the book. If you want something like that you’re going to want to take a look at the next book that I have for you.
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3. Mathematics for Machine Learning
And that book is Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong – Mathematics For Machine Learning. This is the book to go to if you want to delve into the subject and really understand it from its core. Buy this book or download its free PDF. This is one of the best put-together books on the details of machine learning models that I’ve seen. Don’t get me wrong like I said be prepared to put some blood sweat and tears into this but you’re going to be rewarded if you can do that. I highly recommend this book to anyone wanting to pursue a career in this field. Since it’s really imperative that you understand the math at a deep level to apply these models. So those are my top three book picks that I have one more for you.
Links For Book
