So you have decided to learn machine learning? Good choice! Machine learning is currently one of the most in-demand skills in the technology sector. The supply and demand rule is in favor of new learners - there is more demand for machine learning experts than the current supply.
So how do you go from a machine learning enthusiast to expert level?
It's very easy to get started with learning ML for beginners. There are many courses on the internet - paid and free, self-paced and focused, concise and exhaustive - there's no shortage of learning resources.
However, machine learning takes dedication on your part. You cannot simply learn ML from one tutorial or blog post. You need to dedicate a lot of time to the basics of ML concepts.
There's a lot of mathematics in ML, although you don't need to know all the math to get started with ML. It's good to have some math background though - as you can understand the underlying concepts of ML much easier. If you are only concerned with learning how to use ML for applications - you may not need to know the math stuff. If, however, you are planning to go into ML research, you'll have to learn statistics, a bit of calculus, linear algebra, optimization etc.
Don't get scared though, there are many beginner friendly tutorials, books and courses out there to ease you into machine learning. Here we'll talk about the most recommended machine learning books for beginners. Some of the books will be more geared towards theory, while some use practical examples to help understand machine learning concepts for beginners.
Looking for cheap books?
You may find some of the books to be very expensive, don't worry though - we have provided links to Amazon, which now provides you the option to buy used books as well. Simply click on the links to the books provided in the list below, and under "More Buying Choices" you have the option to select from "New", "Used" and "Rental", with different price choices from different sellers. A cool and nifty trick, isn't it?
Even though it is not focused entirely in machine learning, it is a must-have book. Any AI or ML enthusiast's book-shelf is incomplete without this book. It's authors are Peter Norvig and Stuart Russell, who are among the top researchers in the field. Peter Norvig, as you may know him to be the Director of Research at Google, has even maintained a repository on Github for solutions to the problems and exercises in the book, coded in Java, javascript, python, C#, Lisp and Scala. The book is a thorough guide for the field of artificial intelligence, and covers machine learning in depth as well. It is not only very beginner-friendly, it is regularly updated and covers almost every topic in AI that all beginners should know. What makes this book standout is the online code repository for its solutions - no other book provides this feature, which is very handy for beginners trying out solutions to problems on their own. If at any point you get stuck, you can refer to the online codes and get a better understanding of how to translate pseudocodes of algorithms into working codes in some of the most popular languages.
This book gives a nice overview of machine learning, and introduces the foundational concepts of ML. It is one of the classics, and you'll find it recommended almost everywhere. While not the most up-to-date book, it provides a deep understanding of common algorithms used in ML. It includes topics such as bayesian learning, RL, neural nets, analytical learning, decision trees etc. This book is slightly math-focused and is most suitable for those with a background in mathematics and computational science at a bachelor's level.
This book aims to be a complete guide for machine learning enthusiasts. The book teaches all the commonly used algorithms and concepts such as supervised learning, unsupervised learning etc. in detail. It also includes all the different ML techniques - regression and classification problems, decision trees, random forests, neural networks - basically everything you need to know to get started. Get this book if you are more interested in the theory behind ML techniques and would like to understand how to develop your own algorithms.
Looking for a practical book that teaches how to apply machine learning concepts? Look no further, as this is the best book you could own that teaches you ML with actual examples so you can get a more hands-on training with ML. The book teaches you how to build machine learning projects and is a very good resource for ML enthusiasts and beginners who are just starting out and would love to have at least a project under their belt. It uses examples, and codes to help the reader understand how we can apply the different techniques to common applications. The content teaches you how to use (train) data for an ML project, and includes all important topics such as classification, decision trees, forests, neural nets, deep learning networks, etc. It uses scikit-learn and tensorflow, so you'll get an introduction to the most popular ML library and framework as well.
There are many more books that we can recommend, but you'll probably be overwhelmed by now - so many choices, what to buy? We suggest taking your time to read through the Table of Contents of each book and deciding which ones have topics that you are more interested in. If you go the Amazon links provided in this post, you'll see a "Look Inside" text with arrow on the image of the book. When you click on it, you can read through the book's introduction, preface, and table of contents. This will help you to decide which books are most suited to your learning style.
Below are some more books we found interesting, do give them a look to see if you like them!
Practical Books for Machine Learning for Beginners
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25/12/2017 03:33
Australian Eastern Daylight Time (New South Wales)
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top 5 best Machine learning books for beginners - there are many beginner friendly tutorials, books and courses out there to ease you into machine learning. Here we'll talk about the most recommended machine learning books for beginners. Some of the books will be more geared towards theory, while some use practical examples to help understand machine learning concepts for beginners.
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