Machine Learning is taking the entire world by storm. From simple Spam Detection to Self Driving cars, Machine learning is everywhere. The possibility is that you are using it in one way or the other and you don’t even know about it. Most of the companies had already turned into Machine learning and the rests are thinking to do so. A prediction says that by the end of 2020, AI will create 2.3 million jobs while eliminating 1.8 million. Machine Learning is what drives AI. That means this is the best time than ever to get into this fascinating field.

If you are here then you must be passionate to master this concept and you think **books** would be the best choice to do so. In this article, I have curated a list of **the best Machine learning books** out there to learn Machine Learning in 2020. No matter if you are a beginner in machine learning or already an Intermediate/Expert in this field, there is an awesome book waiting for you! Without further ado, let's get started.

### 1). Machine Learning For Absolute Beginners: A Plain English Introduction (2nd Edition)

- Author - Oliver Theobald

If you are an absolute beginner, then before you embark on your great journey into Machine Learning, it is always good to gain an overall understanding of the field itself. That's where this book comes in handy. It is a practical and high-level introduction to Machine Learning for absolute beginners. This book organizes the theoretical and practical aspects of various Machine Learning techniques in a very simple way which is great for a beginner. While this is not sufficient for anybody expecting to master Machine Learning, it's a great start to look further! It gives you an overall idea of what, why, and how of Machine learning. Buy Machine Learning for Absolute Beginners Here.

### 2). The Hundred-Page Machine Learning Book

- Author - Andriy Burkov

The depth of the topics covered in this 100-page book is just absolutely amazing! Burkov doesn't hesitate to go into the math equations. You can use this book to gain a broad view of the Machine Learning field. You can even finish this book in a single day. The Hundred Page Machine Learning Book is a reference, something you can keep coming back to. Buy The Hundred-Page Machine Learning Book Here.

### 3). Introduction to Machine Learning with Python

- Authors - Sarah Guido, Andreas Mueller

Now let's get our hands dirty! Introduction to Machine Learning with Python is one such book that teaches you the practical ways to build your own machine learning solutions!

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and Matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques
- Suggestions for improving your machine learning and data science skills

And this book is mainly targeted for beginners in Machine Learning! Buy Introduction to Machine Learning with Python Here.

### 4). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 (3rd Edition)

- Author - Sebastian Raschka

This book will teach you the fundamentals of machine learning and how to utilize them in real-world applications using Python. Step-by-step, you will expand your skillset with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.

You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world! That sounds awesome, right? If you are a web developer, then look no further than this book, because you will develop a Machine Learning Web App using the popular Flask framework! Buy Python Machine Learning Here.

5). Hands-On Machine Learning with Scikit–Learn and TensorFlow (2nd Edition)

- Author - Aurelien Geron

By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive 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. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

In this book, you will:

- Explore the machine learning landscape, particularly neural nets
- Use Scikit-Learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets

This is one of the best Machine Learning books out there. It dives deep into the practical implementation of Scikit Learn and Tensorflow. Also, dives deep enough into the math side of Machine Learning. It will worth every penny you spend on this book!. This is a must-have book for every Machine Learning enthusiastic out there! So grab your copy now! Buy Hands-On Machine Learning Book Here.

### 6). Pattern Recognition and Machine Learning

- Author - Christopher M. Bishop

This book is aimed at advanced undergraduates or first-year Ph.D. students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The book is mainly about the Bayesian approach. If you don't have a solid background in Algebra, then this book can be painful. You can Buy Pattern Recognition and Machine Learning Here.

### 7). Deep Learning

- Authors - Ian Goodfellow, Yoshua Bengio, and Aaron Courville

This book introduces a broad range of topics in deep learning. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives as well.

**Deep Learning** can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. Buy Deep Learning Here.

### 8). Grokking Deep Learning

- Author - Andrew W. Trask

If you don't know the author, Andrew Trask is the force behind OpenMined, an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence. He also writes a blog.

Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will understand how to build these algorithms completely from scratch. You will understand how Deep Learning can learn at levels greater than humans. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a **Python** **hacker** who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game! Sounds Incredible right?! Buy Grokking Deep Learning Here.

### 9). Deep Learning with Python

- Author - Francois Chollet

Another great introductory book, that gives a pretty clear practical explanation of DeepLearning and its limitations. This book also touches more advanced topics like building Variational Autoencoders and Generative Adversarial Networks from scratch. And one more important thing to notice here is that this book was written by the Keras inventor himself and he was a Google AI researcher too!

In short, this book builds your understanding through intuitive explanations and practical examples. Buy Deep Learning with Python Here.

## Wrapping Up

I hope you will find this book suggestions helpful in your great Machine Learning Journey and beyond.

If you think I have forgotten any great book then please comment them below. Thank you ;)

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