*Have you ever wondered how social media platforms manage to pin down a long-lost friend or colleague under the category “people you may know”? Or how a someone you took a picture with and uploaded is automatically recognised as your friend? The magic behind all of these cool features and many more is none other than Machine Learning. If you’re looking for ways to get started in Machine Learning, this list of 13 Machine Learning books will definitely cover what you’re looking for.*

According to Gartner, a research and advisory company, there will be 2.3 million jobs in the

field of Artificial Intelligence and Machine Learning by 2022. If these resonate as compelling reasons to get introduced to Machine Learning and mastering it but you don’t know where to start, we’ve got you covered.

## 1. The hundred-page Machine Learning Book By Andriy Burkov

Well, as the title states, the concepts of Machine Learning explained in a hundred pages. Now, that sounds a bit sceptical, doesn’t it? But the professionals at leading companies like Google, Microsoft, Tesla don’t think that way after reading it. This book covers an introduction to a remarkable number of topics from Neural networks, gradient descent to autoencoders, and transfer learning. The only catch is that it’s not meant for absolute beginners but with a little bit of background on Machine Learning and a thorough

reading of this book, you’ll be able to ace any interview on the topic.

## 2. Programming Collective Intelligence: Building Smart Web 2.0 Applications By Toby Segaran

This is a great introductory book for anyone who’s looking to perceive the working of websites using Web 2.0, a tool that allows users to interact and collaborate with each other. The concepts of search rankings, product recommendations, and online matchmaking become a lot clearer with an in-depth knowledge of all the topics covered in it.

The base language used to instruct the reader is Python, one of the most powerful yet simple programming languages out there. Not only does it deal with the theoretical understanding of the concepts but also with a set of extensive exercises, you’ll be able to use the algorithms like a pro in your websites.

## 3. Machine Learning for Hackers: Case Studies and Algorithms to Get you Started By Drew Conway and John Myles White

The word hackers here isn’t intended to remind you of a nefarious teenager hacking into illegal systems. Hacker here is someone who likes to solve problems and experiment with new technologies. An introduction to the R language, that the book heavily uses to instruct the readers is also given in the first chapter. But the real essence of the book lies in something else. We tend to relate and understand better with real-world examples and this book deals with numerous concepts of ML along with a supporting case study to help you understand it in a much cohesive way.

## 4. Machine Learning By Tom M. Mitchell

If you are a complete beginner and looking to start a career in machine learning, then you must not miss out on this book as it provides a single-source introduction to that field. It starts with the very basics and slowly builds its way to more complex topics while also being packed with tons of examples and case studies. It covers all the basics of machine learning: perceptron, support vector machines, neural networks, decision trees, Bayesian learning, etc. Even after you advance into Machine learning, this will be a great book to come back to as a reference point.

## 5. The Elements of Statistical Learning: Data Mining, Inference and Prediction By Trevor Hastie, Robert Tibshirani and Jerome Friedman

Having a background in data mining will help a lot in grasping the concepts of machine learning as they deal with predictions and classifications as well. From the title of the book, you’ll be able to make out that this book heavily deals with the statistical approach of predictive systems while emphasising on the concepts rather than mathematics.

It covers important ideas in a variety of fields such as medicine, biology, finance and marketing in a common conceptual framework. Many examples are given, with liberal use of colour graphics which most readers seem to enjoy. If you’re a statistician or someone interested in statistics, this book will be a gem for you.

## 6. Learning from Data: A Short Course By Yaser Abu Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin

We all have learned a skill or two from online crash courses which usually reduces the learning time of a concept to 1/3rd of the actual time. Learning from data: A Short Course is similar to that as it eliminates the traditional roundabout explanations on topics which most of the time seems irrelevant to what we’re looking for.

It has a balanced treatment of both theoretical and practical aspects of Machine Learning with more than 50 colour illustrations and 100 problems and exercises and it will be the fit for anyone who has a good understanding of mathematics. To back this book up, the author has a series of online tutorials that can be used to further references.

## 7. Pattern Recognition and Machine Learning By Christopher M. Bishop

This book is aimed mainly at advanced undergraduate students and PhD students as it dives deep and acts as a comprehensive guide to pattern recognition and its concepts. One of its strong points is that no previous knowledge of pattern recognition or machine learning concepts is assumed.

Similar to the book “The Elements of Statistical Learning”, this follows a statistical perspective

with a requirement of adequate grip on linear algebra and multivariate calculus. With a good understanding of probability, you will be able to accelerate the learning process.

## 8. Natural Language Processing with Python By Steven Bird, Ewan Klein and Edward Lope

Python has a powerful library called Natural Language Toolkit (NLTK) and this book is mostly

dependent on this library throughout the start till the end. Natural language processing, being one of the pillars of Artificial Intelligence, allows the model to parse sentences written in natural language and extract structured information.

The book is intensely practical, containing hundreds of fully worked examples and graded

exercises along with sufficient python codes to describe the working. Even a newcomer to programming would be able to get by this book as long as they aren’t afraid to tackle new

concepts and develop new computing skills.

## 9. Bayesian Reasoning and Machine Learning By David Barber

This hands-on text opens these opportunities to computer science students with modest

mathematical backgrounds who have limited knowledge in linear algebra and calculus.

Along with the conception of Machine learning, students will be able to develop analytical and problem-solving skills that are required to build a real-life model. Access to MATLAB toolkits and other resources are provided to the instructors and the students.

## 10. Understanding Machine Learning By Shai Shalev-Shwartz and Shai Ben-David

Cited as one of the best books to learn the fundamentals of Machine Learning, it brings a

complete wide array of central topics along with mathematical derivations that transform these principles into practical algorithms. The main goal of the book is to provide rigorous, yet easy to follow, introduction to the main concepts underlying machine learning to advanced theories such as Rademacher Complexities. Anyone with a little bit of knowledge in computer science would be able to ground themselves in Machine learning.

## 11. Machine Learning for Absolute Beginners: A Plain English Introduction By Oliver Theobald

As the title of the book suggests, this book is for anyone and everyone who have not learned to code before and have no prior knowledge in computer science as well as statistics.

It starts with explaining what “Machine” and “Learning” are from the technology’s name itself

and progresses step by step with many supporting visual examples to make sure the reader

perceives it thoroughly. With a straightforward and simple approach, this is a must-read for any beginner looking to glance into the world of Machine Learning.

## 12. Machine Learning for Dummies By John Paul Mueller and Luca Massaron

Loaded with real-world examples, the book is a great choice for a beginner to get introduced to the basic concepts of Machine Learning. You get explanations of many of the algorithms used in the book so that you can understand how the algorithms work. It also gives an introduction to R language and Python and how to set up the Integrated Development Environments RStudio and Anaconda that both the languages require to execute. Machine Learning for Dummies enables you to fully understand what you’re doing, but without requiring you to have a PhD in math.

## 13. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies By John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy

Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. Covering both theoretical and practical concepts, this book provides a detailed look into the most important machine learning approaches using predictive data analytics. Each of these approaches is explained with the underlying concept, followed by mathematical models and algorithms illustrated by detailed examples. It is best suited for undergraduates from

Computer Science, Engineering, Statistics or Mathematics.

Machine learning is no longer meant only for specialists in the field of scientific research alone. It has found its way in many real-life technologies we use and it is imperative for one to be well-versed, if not at least familiar, with the techniques pertaining to it. This compiled list of books might help oneself to move a step forward in their lucrative career by mastering the essential Machine Learning concepts.

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