Have you always wanted to explore Machine but never knew where to start? Or have you been wanting to invest more on your knowledge on Machine Learning but never knew what book would do the trick? Well, we’ve got you covered! In this article, I will give you the Best Machine Learning Books that can unleash the Machine Learning genius in you.
1. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
If you really want to explore Machine Learning, this book is the way to go. Author Aurélien Géron has definitely made a complex topic simpler in his 16-chapter Machine Learning book. Developer or non-developer, this book would definitely be a perfect fit for you as an introduction to machine learning. It would help you gain an instinctive understanding of the concepts and tools for building intelligent systems.
You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. This book doesn’t only educate you, it also challenges your recently acquired knowledge with exercises at the end of every chapter.
However, if you’re an expert looking for deep reading, this book’s depth may not be enough for you as it is more focused on getting up and running rather than optimisation.
Pattern Recognition and Machine Learning is the book for you if you want an introduction to Machine Learning through the Bayesian perspective.
Before you start reading this book make sure that you have little background in linear algebra, probability, calculus, and preferably some statistics as this book leans on mathematical equations especially linear algebra and matrix manipulations. It gives great emphasis on the mathematics behind supervised and unsupervised learning.
The book may need more real-world test cases but still, it remains to be of the best when it comes to books for introductory Machine Learning.
A ‘short course’ as specified in the title but this book will definitely lay down the foundations of VC dimension, regularization, overfitting, bias and variance in great details for you. It may not be as long as the other books in this review but there is great depth in the presentation of topics. The authors have produced a remarkably well-written and carefully presented book, with some great color illustrations as well.
Remember the book Pattern Recognition and Machine Learning mentioned earlier? Well, this book is a good complementary to that. Compared to Bishop’s book, it is more complete on paper, but as you browse through each chapter, you may notice the writing quality starting to vary.
This book is preferably for graduate-level readership and assumes a mathematical background that includes calculus, statistics and linear algebra. A ‘mixed bag’ as some reviews call it because this book has a bit of everything in it but it doesn’t have enough room to cover everything in depth.
So if you’re a student looking to learn on Machine Learning, this book may not be a good start. But if you are an instructor looking for additional resources to supplement a specific course or a researcher digging for references, then this book may be for you.
5. Machine Learning with R – Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems
Interested in learning about Machine Learning methods applied in R? Here’s the book for you. This book provides a quick overview of some of the most popular machine learning algorithms and their implementation in R. Author Brett Lantz has written his book very easy to follow for new users both of R and of Machine Learning.
This book has been commended for its readability, straightforwardness and practical examples that demonstrate the key concepts in real-world terms, and up-to-date information about the use of advanced R packages for parallel processing and very large datasets.
In this book, the authors based on statistical learning theory and gave good theoretical backgrounds before jumping into the algorithms.
This book doesn’t only provide the basic proofs, but also extends the theories to well-known practical algorithms, supporting the success of these algorithms and showing how theories can be used to design or analyze practical algorithms.
There is also a brief summary at the beginning of each chapter gives a clear sense of what will be accomplished in it, and
Authors have given attention to notation to make sure that mathematics supports understanding rather than getting in the way but there have been comments on the mathematical formalism of this book that still hinders a reader from understanding its message.
Need a more theory based book? Author Tom Mitchell has got it for you. This book covers the foundational material in machine learning such as perceptrons, support vector machines, neural networks, decision trees, Bayesian learning, etc. It also focuses on theoretical grounding in how many popular machine learning models are constructed and provides some knowledge on some linear algebra or set theory.
But for a 21 year old book, it is not advisable to use it as a practical guide for many tools we use today did not exist when this book was published.
This book can be recommended you like to conceptually understand the different topics and models of Machine Learning as it exists today. It provides a non-technical review of Machine Learning unlike the other books which focused more on algorithms, linear algebra etc. It would be great for people who would like to understand how Machine Learning might influence design, strategy, and culture.
A basic background in programming may help you get through this book. However, this book may not be advisable for those who already have wide experience on Machine Learning for it is more focused on history and practical applications.
If you’re an absolute beginner, this book will definitely help you out. It explains machine learning principles and tools and provides an overview of the major techniques. So before reading specialized books, consider this book as a good starting book for the topic.
As stated in its title, this book is definitely focused on the basics and may not meet your expectations if you are looking for a high-level overview regarding Machine Learning.
If you find Machine Learning hard to understand, then this book may just be for you. A background in math & logic is enough for you to get through this book; no hardcore programming experience required. Here you’ll learn the deep history behind machine learning how it’s different from AI. This book uses Python code examples but there is actually no need to know Python first for you to understand this book.
With a field as wide as Machine Learning, it is important to arm yourself with books that will not only answer your curiosities but also harness your skills. We highly recommend Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems if you’re really looking for something to learn from and at the same time help you apply every bit of information you have read.