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Best Machine Learning Books (Updated for 2020)

Machine Learning

The list of the best machine learning & deep learning books for 2020.

  • Alessio Gozzoli
Best Machine Learning Books (Updated for 2020)

New year, new books! As I did last year, I've come up with the best recently-published titles on deep learning and machine learning. I did my fair share of digging to pull together this list so you don't have to.

Here it is — the list of the best machine learning & deep learning books for 2020:

Coming Soon

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Concepts, Tools, and Techniques to Build Intelligent Systems (2nd edition)

Front Cover of "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Second edition"

Author: Aurélien Géron.

Categories: Machine & Deep Learning.

Why you should read it:

Aurélien shines as a great communicator of ideas and uses examples effectively. You'll get to apply what you are learning pretty quickly as you work through the book. To get a feel for Aurélien's passion and communication style, check out his YouTube channel.

If you are looking for something that mixes theory with practice, go ahead and just buy it! It’s also strongly recommended for those of you who want to just get started with a practical approach.

Where you can get it: You can get the second edition on Amazon or O'Reilly Shop.

Supplement: You can find the companion code on Github.

What's new in the Second Edition: Code updated for TensorFlow-2.0. In particular, it covers tf.keras,, distribution strategies, as well as tf.hub, tfds, tf-agents, and more! A couple of extra chapters on unsupervised learning tasks (clustering, anomaly detection, density estimation), NLP (RNN, attention, transformer) and training & deployment at scale. Previous content is also covered deeper.

Book abstract (2nd edition):

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.

The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow 2.0—to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2.0 to guide the reader through more advanced machine learning methods using 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.

NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2.o Introduced the high-level Keras API. New and expanded coverage including TensorFlow’s Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more.

The Hundred-Page Machine Learning Book

Author: Andriy Burkov.

Categories: Machine & Deep Learning.

Why you should read it:

The book was born from a challenge on LinkedIn,  (where Andriy is an influencer and has Top Voice distinction for his reach on that platform). His book doesn't need too much of an introduction; it’s the Amazon best seller in its category and probably the best condensed collection of knowledge on the topic.

Where you can get it: Buy on Amazon. This book is distributed on the “read first, buy later” principle, which means you can freely download the book, read it, and share it with your friends and colleagues, and if you liked the book or found it useful for your work or studies then buy it.

Supplement: You can find the companion wiki and the code examples on Github.

Here is what the experts think:

Building Machine Learning Powered Applications: Going from Idea to Product

Front cover of "Building Machine Learning Powered Applications: Going from Idea to Product"

Author: Emmanuel Ameisen.

Categories: Machine & Deep Learning.

Why you should read it:

It's 2020 and we all want to do one thing: bring ML models to production. How can you do this? Emmauel has structured the book in a way that follows the same lifecycle applied in industry: from heuristics (to establish a baseline) to model iterations.

This title is highly recommended if you're planning to invest more seriously & rigorously in the AI transformation.

Where you can get it: Buy on Amazon or O'Reilly Shop.

Supplement: dedicated book page and the code examples on Github.

Book abstract:

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.

Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.

This book will help you:

- Define your product goal and set up a machine learning problem
- Build your first end-to-end pipeline quickly and acquire an initial dataset
- Train and evaluate your ML models and address performance bottlenecks
- Deploy and monitor your models in a production environment

Grokking Deep Learning

Author: Andrew W. Trask.

Categories: Machine & Deep Learning.

Why you should read it:

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. I'm quite sure that you’ve enrolled in his Udacity course sponsored by Facebook, Secure & Private AI. (If you didn't, you should do it now—it's free! He also writes a great blog. No surprises on his book making our list. Yes, he's a FloydHub friend. No, we're not biased.

We really loved the first principles approach of learning from the ground up, teaching us the math behind neural networks using low-level building blocks with NumPy. This is probably the best approach to follow to learn how Deep Learning works behind the scenes.

Where you can get it: Buy on Amazon or Manning publications.

Supplement: You can find the companion code on Github.

Prerequisites: For readers with high school-level math and intermediate programming skills.

Book abstract:

Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks.

Deep Learning with Python

Front cover of "Deep Learning with Python"

Author: Francois Chollet.

Category: Deep Learning.

Why you should read it:

From the Keras inventor (and another FloydHub friend), this book will take you by the hand and lead you through mesmerizing mazes of Deep Learning — with Keras, of course. Similar to Grokking Deep Learning, this book strikes the right balance between theory and coding. A bonus is Francois's impressive ability to create great mental images.

Wondering if there will be a second edition? You can bet on it.

Where you can get it: Buy on Amazon, Manning publications or O'Reilly.

Supplement: You can find the companion code on Github.

Prerequisites: Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

Book abstract:

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

Deep Learning

Front cover of "Deep Learning"

Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville.

Category: Deep Learning.

Why you should read it:

This book is widely considered to be the Bible of Deep Learning. Written by three experts, including one of the godfathers of the field, this is the most comprehensive book you can find on the subject. The book is extremely technical & full of math, but the authors do a great job at explaining everything.

But it’s definitely not recommended reading if you’re just starting your Deep Learning journey or if you lack a solid algebraic foundation.

Where you can get it: Buy on Amazon or read here for free.

Supplement: You can also find the lectures with slides and exerciseson GitHub.

Book abstract:

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text 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, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

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. A website offers supplementary material for both readers and instructors.

Reinforcement Learning: An Introduction (2nd Edition)

Front Cover of "Reinforcement Learning: An Introduction(2nd edition)"

Authors: Richard S. Sutton, Andrew G. Barto.

Categories: Machine Learning, Reinforcement Learning, Deep Learning, Deep Reinforcement Learning, Artificial Intelligence.

Why you should read it:

If Deep Learning is considered the Bible of that subject, this masterpiece earns that title for Reinforcement Learning. If you want to get started in RL, this is the way. Just like you may have predicted (pat on the back for you), this is a pretty technical read. Our advice is to take a break after each chapter, load up on the coffee, and actually implement the algorithms (à la these famousrepos).

Where you can get it: Buy on Amazon. You can read the final draft of 2nd editionfor free.

Book abstract:

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Deep Reinforcement Learning Hands-On

Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition

Authors: Maxim Lapan.

Categories: Machine Learning, Reinforcement Learning, Deep Learning, Deep Reinforcement Learning, Artificial Intelligence

Why you should read it:

I came across Maxim's book from one of his blog posts. I literally fell in love with his writing style and the attention to detail (and I imagine you will, too). This book offers a practical approach to RL by balancing theory with coding practice. It’s a book to get your hands dirty with—but first, one that will give you a ton of knowledge about how to do it correctly and understand what is happening behind the scenes. In my opinion, it’s the best hands-on style book on RL.

Highly recommended for all the readers who want to get started on RL and are looking for the perfect tradeoff between theory & practice.

Where you can get it: Buy on Amazon or Packt.

Supplement: You can find the companion code on Github.

What's new: Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more.

Book abstract:

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

Front Cover of "TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers"

Authors: Pete Warden & Daniel Situnayake.

Categories: Machine Learning, Edge Deployment.

Why you should read it:

“Why the future of Machine Learning is tiny” was published by Pete about a year ago, but that article is still of great relevance and importance right now, as its central thesis remains true: "there’s a massive untapped market waiting to be unlocked with the right technology."

In this book, Pete and his co-author argue that the reason to deploy at the edge is pretty simple: given the Industry 4.0 trends about IoT, there are billions of micro-controllers out there with hardcoded tasks that consume thousands of sensor data per second, but most of this data is unfortunately wasted.

But what technology is able to process multiple data and at the same time make autonomous decisions? Bingo, Machine Learning models. Recent techniques are making deep learning models smaller & smaller without losing accuracy, and this book’s authors will show you how to do it properly. If you want to be at the forefront of the next ML disruption, you’ll want to prepare yourself with this book.

Where you can get it: Buy on Amazon or O'Reilly Shop.

Supplement: Dedicated page. Code on TensorFlow GitHub.

Book abstract:

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.

Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.

Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures

- Work with Arduino and ultra-low-power microcontrollers
- Learn the essentials of ML and how to train your own models
- Train models to understand audio, image, and accelerometer data
- Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML
- Debug applications and provide safeguards for privacy and security
- Optimize latency, energy usage, and model and binary size

Learning From Data

Front Cover of "Learning From Data"

Authors: Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin.

Category: Machine Learning.

Why you should read it:

If you’re looking to get started with the key concepts of Machine Learning, then you’ll love this book: easy to follow, simple, and clean. It’s probably the best resource after the Andrew Ng courses to get started!  This was my first book and course on Machine Learning :)

Where you can get it: Buy on Amazon.

Supplement: You can find the companion lectures and videos.

Book abstract:

This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. In addition, our readers are given free access to online e-Chapters that we update with the current trends in Machine Learning, such as deep learning and support vector machines. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. What we have emphasized are the necessary fundamentals that give any student of learning from data a solid foundation. The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

The Book of Why

Authors: Judea Pearl, Dana Mackenzie.

Categories: Data Science, AI and Machine Learning.

Why you should read it:

This is the most controversial book on our list. The author introduces the causality framework to overcome curve-fitting of ML/DL models and his views on the path to achieve Artificial General Intelligence. This is the right book if you are looking for something to make you think (a lot)!

Where you can get it: Buy on Amazon.

Book abstract:

"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

Rebooting AI

Building Artificial Intelligence We Can Trust

Front Cover of "Rebooting AI"

Author: Gary Marcus & Ernest Davis.

Categories: Machine Learning, Deep Learning, Artificial Intelligence.

Why you should read it:

What are we missing in order to achieve a robust AI model? Is Deep Learning enough, or just one part of the solution? According to the authors, Hybrid AI is important, but they argue that it is a necessary but not sufficient condition to achieve a robust AI model.

Similarly to The Book of Why, this book aims to open up a debate between symbolic & connectionist AI, using actionable research to explore what we could and should try next to reach AGI.

Marcus’s position is to have us take a seat while he talks to us and tries to convince us that we need hybrid AI. On the other side, we have Bengio who said, "I don't care what words you want to use, I'm just trying to build something that works." Rebooting AI certainly enriches the debate.

Where you can get it: Buy on Amazon.

Supplement: dedicated book page.

Book Abstract:

Two leaders in the field offer a compelling analysis of the current state of the art and reveal the steps we must take to achieve a truly robust artificial intelligence.

Despite the hype surrounding AI, creating an intelligence that rivals or exceeds human levels is far more complicated than we have been led to believe. Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer beating a human in Jeopardy does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements thus far have occurred in closed systems with fixed sets of rules, and these approaches are too narrow to achieve genuine intelligence.

The world we live in is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Marcus and Davis show us what we need to accomplish before we can get there and argue that if we are wise along the way, we need not worry about a future of machine overlords; we will be able to create an AI that we can trust in our homes, our cars, and our doctor's offices. Rebooting AI provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of how AI can make our lives better.

Machine Learning Yearning

Author: Andrew Ng.

Categories: Machine Learning, Deep Learning, Strategy & Planning.

Why you should read it:

This book comes from the years of practical experience that Andrew acquired while he led the Deep Learning teams at Baidu and Google Brain. This is one of few resources that show you how to set up your ML/DL projects to work for real. It’ll provide you a compass to help you to efficiently navigate in your experiments, which makes it a must read.

Where you can get it: You can get the latest draft for free.

Book abstract:

AI is transforming numerous industries. Machine Learning Yearning, a free book that Dr. Andrew Ng is currently writing, teaches you how to structure Machine Learning projects.

This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to:

- Prioritize the most promising directions for an AI project
- Diagnose errors in a machine learning system
- Build ML in complex settings, such as mismatched training/test sets
- Set up an ML project to compare to and/or surpass human-level performance
- Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.

An Introduction to Machine Learning Interpretability (2nd Edition)

Front cover of "An Introduction to Machine Learning Interpretability Second Edition"

Author:  Patrick Hall & Navdeep Gill.

Categories: Machine Learning, Interpretability.

Why you should read it:

It's 2020 and we are deploying more and more models in production, but a key question remains unanswered: do you trust and understand your predictive models? Sooner or later, someone will require your explanations about your models' behaviour. And no, a leap of faith won’t suffice.

This book is recommended reading for all practitioners wanting to adopt recent and disruptive breakthroughs in debugging, explainability, fairness, and interpretability techniques for machine learning.

Where you can get it: You can download for free here.

Book abstract:

Understanding and trusting models and their results is a hallmark of good science. Analysts, engineers, physicians, researchers, scientists, and humans in general have the need to understand and trust models and modeling results that affect our work and our lives.

Today, the trade-off between the accuracy and interpretability of predictive models has been broken (and maybe it never really existed). But, tools now exist to build accurate and sophisticated modeling systems based on heterogeneous data and machine learning algorithms and to enable human understanding and trust in these complex systems. In short, you can now have your accuracy and interpretability cake…and eat it too

Download this book to learn to make the most of recent and disruptive breakthroughs in debugging, explainability, fairness, and interpretability techniques for machine learning. In this report you’ll find

Definitions and examples
- Social and Commercial Motivations for Machine Learning
- A Machine Learning Interpretability Taxonomy for Applied Practitioner
- Common Interpretability Techniques
- Limitations and Precautions
- Testing Interpretability and Fairness
- Machine Learning Interpretability in Action

Interpretable Machine Learning

A Guide for Making Black Box Models Explainable

Front Cover of "Interpretable Machine Learning"

Author:  Christoph Molnar.

Categories: Machine Learning, Interpretability.

Why you should read it:

Interpretability is rapidly becoming a hot topic to solve in Deep Learning. Unboxing the black box is still an active research area for Deep Learning, but luckily for Machine Learning models, we actually have more tools available — this being one of the best ones.

Where you can get it: Buy on LeanPub or Lulu (paperback version). You can also read it for free, but if you like it, please support the author.

Book abstract:

Machine learning has great potential for improving products, processes and research. But computers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.

After exploring the concepts of interpretability, you will learn about simple, interpretable modelssuch as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.

All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.

Neural Networks and Deep Learning

Author: Michael Nielsen

Categories: Machine Learning, Deep Learning.

Why you should read it:

Neural Networks and Deep Learning is THE free online book. Period.

Where you can get it: You can read it for free.

Supplement: You can find the companion code on Github.

Book abstract:

Neural networks are one of the most beautiful programming paradigms ever invented. In the conventional approach to programming, we tell the computer what to do, breaking big problems up into many small, precisely defined tasks that the computer can easily perform. By contrast, in a neural network we don't tell the computer how to solve our problem. Instead, it learns from observational data, figuring out its own solution to the problem at hand.

Automatically learning from data sounds promising. However, until 2006 we didn't know how to train neural networks to surpass more traditional approaches, except for a few specialized problems. What changed in 2006 was the discovery of techniques for learning in so-called deep neural networks. These techniques are now known as deep learning. They've been developed further, and today deep neural networks and deep learning achieve outstanding performance on many important problems in computer vision, speech recognition, and natural language processing. They're being deployed on a large scale by companies such as Google, Microsoft, and Facebook.

The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning. After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems. And you will have a foundation to use neural networks and deep learning to attack problems of your own devising

Generative Deep Learning

Teaching Machines to Paint, Write, Compose, and Play

Author: David Foster.

Categories: Machine Learning, Deep Learning, Generative Models.

Why you should read it:

Last year was the year of Generative models, so you’ve probably heard about Generative Adversarial Networks. Remember GPT-2, the AI that was too dangerous to release? Forgetting for a second about the harmful potential that these models have unlocked, Generative models are actually shining as a new tool to empower creatives and artists. From text generation to music composers, they extend the natural artist’s talent in a way that can help overcome any creative block.

Where you can get it: Buy on Amazon or O'Reilly Shop.

Supplement: You can find the companion code on Github.

Book abstract:

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.

- Discover how variational autoencoders can change facial expressions in photo
- Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation
- Create recurrent generative models for text generation and learn how to improve the models using attention
- Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting
- Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Coming Soon

Here’s a little preview of some of this year’s much-anticipated books that you should keep an eye on.

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD

Front cover of "Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD"

Authors: Jeremy Howard, Sylvain Gugger.

Category: Deep Learning.

Why you should read it:

Seriously, I shouldn’t need to convince you to keep an eye on a book that you have almost certainly already pre-ordered, right?

Where you can get it: Preorder on Amazon, or read for free as Jupyter notebooks on GitHub.

Release: July 14, 2020.

Book abstract:

Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away.

Using PyTorch and the fastai deep learning library, you’ll learn how to train a model to accomplish a wide range of tasks—including computer vision, natural language processing, tabular data, and generative networks. At the same time, you’ll dig progressively into deep learning theory so that by the end of the book you’ll have a complete understanding of the math behind the library’s functions.

The Machine Learning Engineering Book

Front cover of "The Machine Learning Engineering Book"

Author: Andriy Burkov.

Categories: Machine & Deep Learning.

Why you should read it:

Andriy is returning after the bestselling The Hundred Page of ML with a sequel, this time focusing on the engineering side of Machine Learning projects. Andriy will bring readers through the various steps of a machine learning pipeline to show the best practices & mental models you can apply to bring these systems from research to production.

Where you can get it: This book is distributed on the “read first, buy later” principle, which means you can freely download the book, read it, and share it with your friends and colleagues, and if you liked the book or found it useful for your work or studies then buy it.

Supplement: You can find the companion wiki.

Release: 2020.

Machine Learning Interviews Book

Author: Chip Huyen.

Categories: Machine & Deep Learning, Interview.

Why you should read it:

This book is currently in stealth mode as the startup that Chip has left NVIDIA to join. But you can get some insight into its content by looking at the terrific and extremely informative articles that Chip has published on her blog. We don't want to put too much pressure on Chip's shoulders, but the community’s general impression is that this will be the "cracking the data science code interview".

Release: 2020.

Human-in-the-Loop Machine Learning

Front Cover: "Human-in-the-loop ML"

Author: Robert Munro.

Categories: Machine & Deep Learning, Strategy & Planning.

Why you should read it:

Human interaction is central to most Machine Learning projects, from data collection and annotation to output consumption. In this book, Robert shares with readers his experience & algorithms to optimize the human-computer interaction of ML-driven systems. We particularly recommend this book for the readers who want to bridge the gap between how to structure & optimize Machine Learning projects and model development.

Where: Read on Manning publication.

Release: 7 out of 11 chapters available electronically as of March 2020. Remaining chapters and hard-copy coming in early 2020.

Book abstract:

Human-in-the-Loop Machine Learning is a guide to optimizing the human and machine parts of your machine learning systems, to ensure that your data and models are correct, relevant, and cost-effective. 20-year machine learning veteran Robert Munro lays out strategies to get machines and humans working together efficiently, including building reliable user interfaces for data annotation, Active Learning strategies to sample for human feedback, and Transfer Learning. By the time you’re done, you’ll be able to design machine learning systems that automatically select the right data for humans to review and ensure that those annotations are accurate and useful.

There you have it—our most-recommend books on machine learning and deep learning for 2020. Get to reading!


7 Machine Learning Books for Beginners You Can Buy on Amazon

Machine learning is one of the most important technologies today. It drives a lot of modern technologies and services, so it’s a worthwhile subject to study. If you’d like to learn more but don’t know where to start, there are plenty of machine learning books on Amazon.

If you’ve never studied the field before, you probably don’t know what’s a good resource and what isn’t. With that in mind, here are seven machine learning books on Amazon that are excellent for beginners.

1. Deep Learning

Elon Musk called this book “the only comprehensive book” about deep learning. “Comprehensive” is right because this text covers topics from the field’s mathematical background to different research perspectives. If you want to get the most information you can out of one book, this is the resource for you.

The scale of this book can seem a bit daunting at first, especially as a beginner. Still, it has such a wealth of information that it’s an excellent starting point for anyone interested in deep learning. You can buy a new copy on Amazon for $52 or rent it for $33.15.

2. Artificial Intelligence: A Modern Approach

The best-selling “Artificial Intelligence: A Modern Approach” is a popular choice for university AI programs. Whether or not you’re studying the subject in college, it’s a great beginner’s resource. It provides an in-depth look at several topics within the field of AI, specifically geared towards practical applications.

Coming in at over 1,000 pages, it’s not a quick read. Though it’s long, it lays out its information in a way that readers of various levels can understand. This comprehensive text is available for as low as $18 if you get the paperback edition.

3. Pattern Recognition and Machine Learning

Though it was initially published in 2006, “Pattern Recognition and Machine Learning” remains an excellent resource today. Like many of the other books on this list, this is a favorite of university machine learning classes. You don’t need an understanding of machine learning going into it, but you should probably have a grasp on algebra and calculus.

No matter your machine learning knowledge level, this book can help you test your skills. It includes questions at the end to reinforce the key concepts it discussed. You can grab it for around $65 on Amazon.

4. Machine Learning: A Probabilistic Perspective

Of course, there are more recent machine learning books on Amazon as well. Kevin P. Murphy’s “Machine Learning: A Probabilistic Perspective” is a prime example. As the title suggests, this book looks at machine learning through the lens of probability, but that’s not all it offers.

The book starts with an introduction to machine learning, making it more accessible to beginners. Its tone is also less formal, making it easier to grasp some of the more in-depth, practical concepts it addresses. Hard copies go for around $85 and above, but you can rent it or get an electronic copy for less.

5. Python Machine Learning By Example

Another newer book, “Python Machine Learning By Example,” touts itself as the easiest way to get into machine learning. Like the last entry, this one begins with an introduction to machine learning. It’s more specific than some others, though, focusing on coding algorithms with Python.

The book has an introduction to Python, too, so you don’t need much coding experience to understand it. It’s one of the most practical texts out there, with plenty of real-world examples and applications. It’s more affordable than others, too, going for $49.99.

6. Machine Learning for Absolute Beginners

As the title says, “Machine Learning for Absolute Beginners” is for people with no machine learning background. This text isn’t as in-depth as some of the others here, but it’s an outstanding introduction to the field. It starts with the most basic of basics, going over where to get free data sets and tools you’ll need.

This book features plenty of illustrations and diagrams to help you grasp its concepts. It will also walk you through building your first machine learning model with Python. Best of all, you can get it for as low as $17.50.

7. The Elements of Statistical Learning

The second edition of “The Elements of Statistical Learning” is one of the best-selling machine learning books on Amazon. It’s easy to see why, too, as it covers a broad range of topics, from neural networks to testing methods.

This textbook is a little more advanced than some others, but it encourages readers to look into things themselves. That tone of exploration makes it a fantastic learning tool. You can grab this one for around $52 or rent it for less.

Learn More About Machine Learning Today

Machine learning can be an intimidating subject, but there are plenty of resources to help you. These seven books are by no means the only ones, but they’re among the best for beginners. Hop on Amazon and grab one or two of these books to start learning about machine learning today.

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Having recently reviewed the Machine Learning online course Machine Learning A-Z: Hands-On Python & R In Data Science, I decided to shift my focus to a more conventional method of learning i.e. books. In this article I have enlisted the most popular Machine Learning books and classified them using various criteria.

Where to Get Them

Once we have decided to go for a Machine Learning book, there are various sources where we can get them. Depending upon the personal preference, readers can either opt for a physical copy of a book or an e-book which can be read using various electronic devices. A step further from e-books, a lot of books these days are available in the audio format for us to simply listen to. The books can either be borrowed for a certain duration from libraries, acquaintances, etc., or purchased from local stores or online.

Some of the more popular online stores where books can be purchased are:

Amazon: As we all are aware, Amazon offers a huge book collection for us to purchase including hardbacks, paperbacks, new as well as used books. The prices and shipping durations offered are at par with other booksellers. Amazon also promotes Kindle editions for most of the books at lower prices. Recently, the publishing giant O'Reilly Media has announced that they will be using Amazon's e-commerce framework for selling books online, and won't be using their own website for that purpose.

Google Books: Catching up after Amazon is search giant Google offering its own portal for ordering books online. While their shipping mechanism isn't as streamlined as Amazon's, it's worth visiting Google Books portal before placing an order, as better deals are offered at times.

eBay: While the collection of books at eBay is not as large as Amazon, the bidding process for used books may end up getting you a book at an attractive price.

Packt Publishing: They offer a lot of recently released ebooks at a price of 10 USD. Most of their books are available on Amazon as well.

Manning Publications: An interesting feature offered by Manning is "liveBooks", where the online reader offers exciting features such as searching through the books that we don't own.

For this article, I explored plethora of books available at these sources and came up with the list of following ML books which I believe are must read. Depending upon the reader's requirement, specific books can be chosen.

Note: The prices listed with each book are as of the time of this writing and are subject to change.

Best Paid Book

Python Machine Learning By Example: The easiest way to get into machine learning

Author: Yuxi (Hayden) Liu
Price: $49.99
Amazon rating: 5/5
Goodreads rating: 4/5

What makes it the best: As the name suggests, the book takes a practical approach while explaining the Machine Learning concepts to readers. The book also helps the reader with Python concepts, enabling them to implement their knowledge using the rich set of libraries offered by Python frameworks. It covers many ML concepts, such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation.

Author Yuxi engages readers into various exercises and helps them at every step to implement some of the important ML models.

Overall, the book offers a broader coverage as well as in-depth understanding of Machine Learning as a field. The excellent reader reviews and user ratings proves this fact. And best of all, it's reasonably priced compared to other practical ML books.

Best Free Book

Understanding Machine Learning: From Theory to Algorithms

Authors: Shai Shalev-Shwartz and Shai Ben-David
Goodreads rating: 4.1/5

What makes it the best: The beauty of this book is in its unique approach in building the fundamentals and switching seamlessly into the applications of theoretical concepts. It explains in detail how to transform the mathematical equations into effective ML algorithms, such as stochastic gradient descent, neural networks, and structured output learning.

While the physical copy of the book is only available for purchase, Cambridge University Press allows the PDF version to be downloaded for free for personal use.

Best Book for Beginners

Machine Learning for Absolute Beginners

Author: Oliver Theobald
Price: $13.50
Amazon rating: 4.5/5
Goodreads rating: 4/5

What makes it the best: As the target audience for the book is absolute beginners, it considers that the readers have no prior technical knowledge and does its best to explain the terminologies in simple language. The usage of lots of diagrams helps readers better grasp the concepts.

It covers a fair amount of ML concepts with some additional related streams, such as Big Data and Data Analytics. While it covers the essential ML concepts such as regression, SVM algorithms, and Decision Trees, as well advanced concepts such as Deep Learning and neural networks; it also has appendices which focus upon further recommendations and ML careers for interested folks.

Best Book for Advanced Readers

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Author: Aurélien Géron
Price: $38.20
Amazon rating: 4.4/5
Goodreads ratings: 4.5/5

What makes it the best: Targeted towards advanced readers, the book has minimal theory and focuses mainly on the coding aspects of ML models using the solid Python frameworks viz, Scikit-Learn, and TensorFlow. Scikit-Learn, is an easily available and proven framework which enables users to implement ML algorithms efficiently.

Author Aurélien, being a former Googler and ML expert, has a good grip on both the frameworks and it shows in the book. Especially while covering TensorFlow, a complex library mainly used to achieve mathematical computations on a huge scale, his attention to detail proves that he is a Guru on the topic. This is a must-have book for advanced professionals trying to solve complex ML problems and achieve scalable goals in the field!

Other Paid Books

Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow

Author: Sebastian Raschka and Vahid Mirjalili
Price: $35.99
Amazon rating: 4.3/5
Goodreads: 4.3/5

The book begins with the fundamentals of ML and then switches to the implementation of the same using Python. What I like about this book is that the authors, Sebastian Raschka and Vahid Mirjalili, have made the book comprehensive, covering the breadths of ML, Deep Learning, TensorFlow. Since the medium of implementation is Python, readers will get acquainted with Python as well. To cover these many topics in a single book is quite a feat and I would certainly say that the authors have done their best to do so.

Another important aspect of the book is the best practices being followed in the industry to accomplish a task. So, not only do the readers learn the concepts, but they'll also be prepared to apply ML concepts and practices to their own respective fields.

Machine Learning: A Probabilistic Perspective

Author: Kevin P. Murphy
Price: $99.20
Amazon rating: 4/5
Goodreads rating: 4.4/5

Check out our hands-on, practical guide to learning Git, with best-practices, industry-accepted standards, and included cheat sheet. Stop Googling Git commands and actually learn it!

As the name suggests, the book offers a probabilistic approach towards Machine Learning. It covers topics such as probability, optimization, linear algebra, and focuses on recent developments in the field, including viz. conditional random fields, L1 regularization, and deep learning. The language used in this book is straight-forward and it includes pseudo code for implementation of the ML algorithms. Most of the models are implemented in Probabilistic Modelling Toolkit, a MATLAB software package that is freely available.

As a prerequisite, readers are expected to have prior knowledge of college-level mathematical concepts to better understand the contents.

Machine Learning

Author: Tom M. Mitchell
Price: $41.37
Amazon rating: 4/5
Goodreads rating: 4/5

Author Tom Mitchell covers the basics of ML in a detailed fashion in this book, coupled with summaries of ML algorithms. A feature of this book which stands out is the case studies, which sheds light on the concepts being talked about in the book. The book is great for introductory and higher-level graduate students with the academics including ML topics.

Other Free Books

Machine Learning (An Algorithmic Perspective)

Author: Stephen Marsland
Goodreads rating: 3.6/5

Targeted towards college students willing to learn ML, this book includes a lot of NumPy implementations for ML models, which are available on the book's website. The sample datasets are also provided for readers to play with. The book and associated code is freely available for download with proper attribution.

Neural Networks and Deep Learning

Author: Michael Nielsen
Goodreads rating: 4.5/5

Aimed towards the advanced ML professionals, this book covers the next steps after Machine Learning, namely Neural networks and Deep Learning. Neural networks are basically programming paradigms which enable machines to make decisions based upon data observations. Deep learning covers the techniques for designing neural networks.

The author explains the concepts in detail with their applications, such as image recognition, voice recognition, and natural language processing. The book is maintained on a web portal as its continually being updated.

Machine Learning & Big Data

Author: Kareem Alkaseer

Following a rather unconventional approach, Kareem maintains this book online by himself, enabling him to keep it updated. He tries to balance between theory and implementation for readers to implement machine learning models by themselves without relying too much on libraries. Its an interesting and honest thought, which helps readers avoid the complex libraries and have a look under the hood to understand concepts better.

Another fact which stands out about the book is his usage of different programming languages to solve a problem at hand. While most of the ML books focus on Python, this book has implementations in C++, Java, and Scala as well. Very few people are aware of the C and C++ libraries such as dlib and Thrill for machine learning, neural networks, and deep learning, which you'll get exposure to in this book.

Bayesian Reasoning and Machine Learning

Author: David Barber
Goodreads rating: 4/5

A classic by author David Barber, this book covers a bit of background before the rise of Machine Learning. It takes a Bayesian statistics approach to machine learning, which is one of the older and better-known concepts in the field.

Other books for Beginners

Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller, Sarah Guido
Price: $24.18
Amazon rating: 4.1/5
Goodreads rating: 4.3/5

Due to its rich set of libraries, Python is a great choice for implementation of ML models. Keeping this in mind, the book helps readers grasp ML concepts using Python itself. The Scikit-Learn library makes it easy to design efficient models quickly, taking a practical approach for Machine Learning. The readers already familiar with NumPy and matplotlib libraries will get much more out of the book.

Also, the book begins by introducing required mathematical concepts such as probability, optimization, and linear algebra which paves the way for ML concepts to come.

Introduction To Machine Learning

Author: Ethem Alpaydın
Price: $15
Amazon rating: 3.3/5
Goodreads rating: 3.7/5

While the ratings don't look as good as other beginner level books, I found they don't do justice to the contents. The book is comprehensive, covering an array of topics not covered in other introductory books. It covers a vast variety of concepts such as statistics, pattern recognition, neural networks, artificial intelligence, signal processing and data mining, to present a unified treatment of machine learning problems. A rather interesting approach to introduce readers to the ML world!

Other Books for Advanced Readers

Pattern Recognition and Machine Learning

Author: Christopher M. Bishop
Price: $90.20
Amazon rating: 4.1/5
Goodreads rating: 4.2/5

Arguably the most popular choice for advanced professionals, the book talks about application of ML for pattern recognition. While it doesn't expect a prior ML knowledge, it's assumed that readers are aware of multivariate calculus and basic linear algebra. Also, familiarity with probability theory would prove useful but not essential as it's covered in the introductory sections of the book.

Machine Learning Yearning

Author: Andrew Ng
Goodreads rating: 4.5/5

Written by the ML guru Andrew Ng, the book is designed for professionals to learn advanced ML concepts quickly. Thoroughly technical, this book helps professionals understand the concepts and make better decisions while building ML projects.


While there are plenty of Machine Learning books out there, I have tried to categorize and summarize the top few books depending upon their availability and target audience. Hope the article helps learners choose the right book for their needs.

Other Recommendations

If books are your thing, make sure to take a look at these:

If you're preparing for a big interview, I'd also suggest that you read up on some tips that will help you improve your chances of landing the job:

Or if you're interested in reading articles on some of the most in-demand and popular programming languages in the world today, check out our Node, Python, or Java articles.


7 Great Books About Machine Learning (ML) For Beginners

Machine learning and artificial intelligence are growing fields and growing topics of study. While the advanced implementations of machine learning we hear about in the news might sound scary and inaccessible, the core concepts are actually pretty easy to grasp. In this article, we’ll review some of the most popular resources for machine learning beginners (or anyone just curious to learn). Some of these books will require familiarity with some coding languages and math, but we’ll be sure to mention it when that’s the case.

1. “Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition)” by Oliver Theobald

Author: Oliver Theobald Website:Amazon The title is kind of explanatory, right? If you want the complete introduction to machine learning for beginners, this might be a good place to start. When Theobald says “absolute beginners,” he absolutely means it. No mathematical background is needed, nor coding experience — this is the most basic introduction to the topic for anyone interested in machine learning. “Plain” language is highly valued here to prevent beginners from being overwhelmed by technical jargon. Clear, accessible explanations and visual examples accompany the various algorithms to make sure things are easy to follow. Some simple programming is also introduced to put machine learning in context.

2. “Machine Learning For Dummies” by John Paul Mueller and Luca Massaron

Authors: John Paul Mueller and Luca Massaron Website:Amazon While we’re going with “absolute beginners,” the popular “Dummies” series is another useful starting point. This book aims to get readers familiar with the basic concepts and theories of machine learning and how it applies to the real world. It presents the programming languages and tools integral to machine learning and illustrates how to turn seemingly-esoteric machine learning into something practical. The book introduces a little coding in Python and R used to teach machines to find patterns and analyze results. From those small tasks and patterns, we can extrapolate how machine learning is useful in daily lives through web searches, internet ads, email filters, fraud detection, and so on. With this book, you can take a small step into the realm of machine learning.

3. “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

Authors: John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy Website:Amazon This book covers all the fundamentals of machine learning, diving into the theory of the subject and using practical applications, working examples, and case studies to drive the knowledge home. “Fundamentals” is best read by people with some analytics knowledge. It presents the different learning approaches with machine learning and accompanies each learning concept with algorithms and models, along with working examples to show the concepts in practice.

4. “Programming Collective Intelligence” by Toby Segaran

Author: Toby Segaran Website:O'Reilly | Amazon This is more of a practical field guide for implementing machine learning rather than an introduction to machine learning. In this book, you’ll learn about how to create algorithms in machine learning to gather data useful to specific projects. It teaches readers how to create programs to access data from websites, collect data from applications, and figure out what that data means once you’ve collected it. “Programming Collective Intelligence” also showcases filtering techniques, methods to detect groups or patterns, search engine algorithms, ways to make predictions, and more. Each chapter includes exercises to display the lessons in application.

5. “Machine Learning for Hackers” by Drew Conway and John Myles White

Authors: Drew Conway and John Myles White Website:O’Reilly | Amazon Here, the word ‘hackers’ is used in the more technical sense: programmers who hack together code for specific goals and practical projects. For those who aren’t well versed in the mathematics, but are experienced with programming and coding languages, “Machine Learning for Hackers” comes in. Machine learning is usually based on a lot of math, due to the algorithms needed for it to parse data, but a lot of experienced coders don’t always develop those math skills. The book uses hands-on case studies to present the material in real-world practical applications rather than going heavy on mathematical theory. It presents typical problems in machine learning and how to solve them with the R programming language. From comparing U.S. Senators based on their voting records to building a recommendation system for who to follow on Twitter, to detecting spam emails based on the email text, machine learning applications are endless.

6. “Machine Learning in Action” by Peter Harrington

Author: Peter Harrington Website:Amazon “Machine Learning in Action” is a guide to walk newcomers through the techniques needed for machine learning as well as the concepts behind the practices. It acts as a tutorial to teach developers how to code their own programs to acquire data for analysis. In this book you’ll learn the techniques used in practice with a strong focus on the algorithms themselves. The programming language snippets feature code and algorithm examples to get you started and see how it advances machine learning. Familiarity with Python programming language is helpful since it is used in most of the examples.

7. “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank, and Mark A. Hall

Authors: Ian H. Witten, Eibe Frank, and Mark A. Hall Website:Amazon In “Data Mining,” the authors focus on the technical work in machine learning and how to gather the data you need from specific mining techniques. They go into the technical details for machine learning, teaching the methods to obtain data, as well as how to use different inputs and outputs to evaluate results. Because machine learning is ever-changing, the book also discusses modernization and new software that shape the field. Traditional techniques are also presented alongside new research and tools. Of particular note is the authors’ own software, Weka, developed for applied machine learning. Disclaimer: Tableau does not officially endorse nor profit from any products, or opinions therein, listed in this article and as such this page does not engage with any affiliate link programs. This article is intended purely for educational purposes and the above information about products and publications is made available so that readers can make informed decisions for themselves.


Machine books amazon learning

Amazon scientists author popular deep-learning book

Dive into Deep Learning gets an update

The book now includes PyTorch and TensorFlow. We asked the authors why they decided to update their deep-learning book.

Machine learning – a field of computer science that gives a computer the ability to learn – is changing the world. It’s being used to improve weather forecasting, deliver better healthcare, create self-driving cars, and much more. Amazon is a pioneer in the field, and uses machine learning to make product recommendations, detect fraud, forecast demand, power Alexa, run the Amazon Go Store, and more. And, of course, with Amazon SageMaker the company provides developers and data scientists with the ability to build, train, and deploy machine learning (ML) models quickly and at scale.

Demand is exploding for scientists, data scientists and developers proficient in machine learning, with demand far outstripping supply.

To help close that gap, over the past two years a team of Amazon scientists has compiled a book that is gaining wide popularity with universities that teach machine learning, as well as developers who want to up their machine learning game. The book is called Dive into Deep Learning, and it’s an open source, interactive book that teaches the ideas, the mathematical theory, and the code that powers deep learning, all through a unified medium.

Its authors are Aston Zhang, an AWS senior applied scientist; Zachary Lipton, an AWS scientist and assistant professor of Operations Research and Machine Learning at Carnegie Mellon University; Mu Li, AWS principal scientist; and Alex Smola, AWS vice president and distinguished scientist.

Dive into Deep Learning is a book I wish existed when I got started with machine learning,” says Smola. “It’s easy to become engrossed in the general theory of machine learning without the ability to build things. Dive into Deep Learning makes it easy for everyone to experiment and learn. Moreover, this publishing approach forces us, the book’s authors, to focus on effects that are significant in practice. After all, anything that is taught needs to be demonstrated with code and data.”

The book got its start in 2017, when the authors set about teaching the wider ML community how the then-new Gluon interface, an open source deep-learning interface that allowed developers to more easily and quickly build machine learning models.

At the time, there were a number of classic textbooks that taught the mathematics of machine learning and scattered open source implementations of popular deep learning models, but existing resources didn’t combine the qualities of a good textbook with the best parts of a hands-on tutorial. That’s especially problematic, for deep learning, which is largely an empirical discipline. In other words, really understanding how it works requires running experiments. So during an internship at Amazon, Lipton created an open-source project, a casual set of tutorials called Deep Learning: the Straight Dope (now deprecated).

While the project was initially created as source material for a set of hands-on tutorials, it rapidly gained wider traction and began to take the form of a book as an open-source community of contributors joined to refine and expand the offering. As Lipton embarked on a faculty position at CMU, Zhang and Li expanded the coverage of some of its foundational topics , and added many more topics to keep pace with the latest innovations in machine learning. They then created a series of video lectures on deep learning in Chinese, which proved popular with students in China.

“We got a lot of feedback from students who said our lectures were helping them ‘get their hands dirty’,” says Zhang, the book’s lead author. “They asked us to turn our lecture notes into something more like a textbook.”

The goal was to make machine learning more accessible to everyone, says Li. “We wanted to teach concepts ‘just in time,’ giving people concepts at the time they need them to accomplish a particular task,” he says. “We wanted people to have the satisfaction of creating their first model before worrying about more esoteric concepts.”

From the start, one key aspiration of the authors was to make the book enjoyable to read – not an endless trudge. Its writing is conversational and approachable, even for relative novices.

It’s easy to become engrossed in the general theory of machine learning without the ability to build things. Dive into Deep Learning makes it easy for everyone to experiment and learn.

Alex Smola, AWS vice president and distinguished scientist

Still, creating a book that combined accessibility, breadth, and hands-on learning wasn’t easy. To provide convenient access, Dive into Deep Learning is published on GitHub, which also allows GitHub users to suggest changes and new content. The book was created with Jupyter Notebooks, which allows interactive computing with many programming languages.

“One cool thing about Jupyter Notebooks,” says Lipton, “is not only can you write regular text (with Markdown) and code (here, Python), but you can also include clean mathematical typesetting – using the LaTeX plug-in, which allows you to write mathematical expressions cleanly.”

The book also employs the NumPy interface – a Python-based programming library familiar to most students.

Dive into Deep Learning was originally published in Chinese. Subsequently, the authors translated it into English, while also adding many new topics by incorporating feedback from users.

Perhaps the most interesting aspect of the book is its emphasis on learning by doing. Says Lipton: “I always think of computer science and engineering as autodidactic disciplines, and certainly one of the ideas behind the book is to let people try things out quickly. The book lends itself to self-study – you’re not likely to get stuck, even if you are going it alone.”

In a typical chapter, Computer Vision, for example, the authors begin with a discussion of topics such as altering images to enhance a computer’s ability to identify something (in the book’s example, a cat) even if the image is changed through cropping, color, or brightness. At the end, readers are asked to use a data set to help a computer identify 120 different dog breeds. They are walked through how to download the appropriate data set, organize it, and train the model to identify the breeds.

For the most part, the book’s chapters were written by different members of the team, depending on their own interests and expertise. All the authors then reviewed and edited each chapter.

Thus far the book has proven extremely popular and helped cement Amazon’s status as a center for machine learning excellence. Some 70 universities use the book in machine learning classes, a number that’s growing.

“This is a timely, fascinating book, providing not only a comprehensive overview of deep learning principles but also detailed algorithms with hands-on programming code, and moreover, a state-of-the-art introduction to deep learning in computer vision and natural language processing,” said Jiawei Han, Michael Aiken Chair Professor, University of Illinois at Urbana-Champaign, “Dive into this book if you want to dive into deep learning.”

Adds Jensen Huang, founder and CEO of NVIDIA, “Dive into Deep Learning is an excellent text on deep learning and deserves attention from anyone who wants to learn why deep learning has ignited the AI revolution: the most powerful technology force of our time.”

Right now, the authors’ focus is to keep updating and improving the book based on input from its many users. “It’s a two-way collaboration,” says Zhang. “We help its readers with machine-learning know-how, and they provide feedback to us to improve its quality and stay relevant.”

Video: Dive into Deep Learning lecture series

While working on the book, Aston Zhang and Mu Li edited some of its foundational topics, added additional topics, and created a series of video lectures on deep learning in Chinese, which proved popular with students in China. There are 20 videos in total, which you can watch from the playlist below.

My Top 3 Machine Learning Books!!

Machine Learning Books You Must Read in 2021

In this article, we will explain briefly about some of the best books that can help you understand the concepts of Machine Learning, and guide you in your journey in becoming an expert in this engaging domain. Moreover, these books are a great source of inspiration, filled with ideas and innovations, granted that you are familiar with the fundamentals of programming languages. Read on to know more —

1. Machine Learning for Absolute Beginners: A Plain English Introduction

Author: Oliver Theobald

Publisher — Scatterplot Press

Difficulty Level: Beginner

Get Book here — Amazon

Machine Learning for Absolute Beginners
Machine Learning for Absolute Beginners

As the title explains, if you’re an absolute beginner to Machine Learning, this book should be your entry point. Requiring little to no codingor mathematical background, all the concepts in the book have been explained very clearly.

Examples are followed by visuals to present the topics in a friendlier manner, for understanding the vitals of ML.

Oliver Theobald has simplified several complex topics related to ML, such as its basics, and other techniques such as Data Scrubbing, Regression Analysis, Clustering, Bias, Artificial Neural Networks, and more in his book. The book also provides additional resources to further learning.

“Analysis As the ‘Hello World’ of the machine”
Oliver Theobald

2. Deep Learning

Author: Ian Goodfellow, Yoshua Bengio and Aaron Courville

Publisher — MIT Press

Difficulty Level: Beginner

Get Book here — Amazon

Book — Deep Learning
Book — Deep Learning

Regarded as a very beginner-friendly book, it introduces you to a wide range of topics on Deep Learning while also covering related aspects of Machine Learning.

The fundamental concepts of DL are thoroughly explained in this book from scratch, for a stronger foothold in the domain. The book explains relevant concepts of Linear Algebra, Probability and Information Theory, Numerical Computation, industry-standard techniques such as Optimization Algorithms, Convolutional Networks, Computer Vision, and research topics such as Monte Carlo methods, Partition Function. Sufficient supplementary material is bundled for a deeper understanding.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject”⁠ — Elon Musk, cofounder and CEO of Tesla and SpaceX

3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (First Edition)

Author: Aurelien Geron

Publisher — O’Reilly Media

Difficulty Level: Beginner

Get Book here — Amazon

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Easily one of the best-selling books out there for anyone planning to start with Machine Learning or an enthusiast in the domain. Requiring prior knowledge of the Python programming language, it explains some of the most-used ML libraries Scikit-Learn, Keras, and TensorFlow 2, for building intelligent systems.

Intuitively explained concepts and easy to implement examples allow for smoother practical implementation and understanding. Topics included are Support Vector Machines, Random Forests, Neural Nets, Deep Reinforcement Learning, Eager Execution, Time-Series Handling, and more. The book contains updated code examples for several libraries, and APIs involved.

Supplement: You can also find the lectures with slides and exerciseson GitHub.

“In Machine Learning this is called overfitting: it means that the model performs well on the training data, but it does not generalize well.”
Aurélien Géron

Is this the best book on Machine learning?

Check out the 2nd edition of the book —

4. Machine Learning (in Python and R) For Dummies

Author: John Paul Mueller and Luca Massaron

Publisher — For Dummies

Difficulty Level: Beginner

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Machine Learning (in Python and R) For Dummies
Machine Learning (in Python and R) For Dummies

All books from the famous “Dummies” series have been extremely newbie-friendly. This book, just like others in the series, has its concepts laid out in a manner that readers find easy to follow.

The book includes introductory concepts and theories in ML along with the tools and programming languages involved. The topics covered in the book start with installing R on Windows, Linux and macOS, followed by Matrix Creation, working with Vectors, and Data Frames, working with RStudio or Anaconda to code in either R or Python. It is a handy guide for fundamental concepts of data mining and analysis.

“As a kind of learning, it resembles the methods humans use to figure out that certain objects or events are from the same class, such as by observing the degree of similarity between objects.”
John Paul Mueller

5. Machine Learning in Action

Author: Peter Harrington

Publisher — Manning Publications

Difficulty Level: Beginner

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Machine Learning in Action
Machine Learning in Action

A valuable book aimed at giving developers a hands-on experience of techniques required for Machine Learning. It’s an equally essential book for familiarizing oneself with ML related Python code snippets, although requiring prior experience with Python.

The book contains code for various algorithms for Statistical Data Processing, Data Analysis, and Data Visualization along with tasks such as Classification, Forecasting, Recommendations, Simplification, and more. With minimal theory, the book cuts straight to the practical implementation of these algorithms.

6. Pattern Recognition and Machine Learning

Author: Christopher M. Bishop

Publisher — Springer

Difficulty Level: Intermediate

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Github repo: —

Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning

Directed towards individuals who have a fundamental idea of Pattern Recognition and Machine Learning, this book assumes readers have some degree of prior knowledge in multivariate calculus and algebra.

The concepts in this book aim to explain the recent developments in the underlying algorithms and techniques in the domain of ML. Covering widely used topics such as Bayesian Methods, Regression, Classification, Neural Networks, Graphical Models, Sampling Methods, and more, this book is highly suitable for understanding ML, Statistics, Computer Vision, and Mining. The book comes fully stacked with a broad range of exercises and additional material.

7. An Introduction to Statistical Learning (with applications in R)

Author: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Publisher — Springer

Difficulty Level: Intermediate

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An Introduction to Statistical Learning (with applications in R)
An Introduction to Statistical Learning (with applications in R)

This book, although requiring some prior knowledge of linear regression, is an excellent tool for understanding the concepts of Statistical Learning. By providing a balanced insight into how to make use of large and complex datasets, it aims to educate a wide range of statisticians and non-statisticians alike and enable them to understand the data in their hands.

It covers several vital concepts of Statistical Learning, such as Linear Regression, Classification, Tree-Based Models, Support Vector Machines, Resampling Methods, and more. Various examples and tutorials make the learning process more enjoyable, and it includes several R labs, demonstrating the implementation of these statistical methods.

8. Applied Predictive Modeling

Author: Max Kuhn, and Kjell Johnson

Publisher — Springer

Difficulty Level: Intermediate

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Applied Predictive Modeling
Applied Predictive Modeling

Regarded as an exceptional reference book for many of the Predictive Modelling concepts, this book requires a sound understanding of statistics, R programming language, and Machine Learning concepts. The author has focussed on explaining data collection, manipulation, and transformation process as it is often overlooked in ML books.

The applied nature of this book makes it an excellent choice for analyzing real problems faced by industries. Readers can dive into data preprocessing, splitting, and model tuning, followed by regression, classification, handling class imbalance, selecting predictors.

9. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started

Authors: Drew Conway & John Myles

Publisher — O’Reilly Media

Difficulty Level: Intermediate

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Machine Learning for Hackers
Machine Learning for Hackers

As the title says, this book is not for hackers but for people who are interested in the hands-on case studies. Requiring a strong programming background, this book aims to train you with the algorithms driving Machine Learning. Various chapters focus on each of the problems in ML, such as Classification, Optimization, Prediction, and Recommendation.

The book also trains you in R, and how to analyze datasets and gets you started on writing simple ML algorithms. One significant way it differs from other books is its low dependency on maths to teach ML.

10. Programming Collective Intelligence: Building Smart Web 2.0 Applications

Author: Toby Segaran

Publisher — O’Reilly Media

Difficulty Level: Intermediate

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Programming Collective Intelligence
Programming Collective Intelligence

Considered by many as the best guide for Machine Learning, this book prefers to teach you the implementation of ML, assuming you know Python. It includes steps for creating algorithms and programs for accessing datasets off websites, collecting data on your own, and analyzing and making use of data.

Taking you into ML and statistics, the book includes examples for crawlers, indexers, optimization, PageRank algorithms, filtering techniques, decision trees. Aimed at walking you through the entire process of creating algorithms at your pace, this book does its job excellently.

11. The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Author: Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Difficulty Level: Expert

Publisher — Springer

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The Elements of Statistical Learning
The Elements of Statistical Learning

This book focusses on concepts rather than the mathematics behind the concepts. It holds a vast collection of ideas about the implementation of Statistical Learning in several sectors. Filled with relatable examples and visualizations, it should be an essential piece in any statistician or data mining enthusiast’s library.

The book covers supervised and unsupervised learning, including topics such as Support Vector Machines, Classification Trees, Neural Networks, Boosting, Ensemble Methods, Graphical Models, Spectral Clustering, Least Angle Regression, and Path Algorithms, to name a few.

12. Python Machine Learning

Author: Sebastian Raschka, and Vahid Mirjalili

Publisher — Packt

Difficulty Level: Expert

Get Book here — Amazon

Python Machine Learning
Python Machine Learning

Assuming you already have a strong understanding of many of the core notions of Python and Machine Learning, this book cuts straight to the practical implementation of the concepts. The concepts in the book include up-to-date explanations of NumPy, Scikit-learn, TensorFlow2, and SciPy. The book prepares you to undertake real-world challenges by teaching you from the real-world challenges faced in the industry. It includes various topics such as Dimensionality Reduction, Ensemble Learning, Regression, and Clustering Analysis, Neural Networks, and more.

“Eventually, the performance of a classifier, computational power as well as predictive power, depends heavily on the underlying data that are available for learning. The five main steps that are involved in training a machine learning algorithm can be summarized as follows: Selection of features. Choosing a performance metric. Choosing a classifier and optimization algorithm. Evaluating the performance of the model. Tuning the algorithm.”
Sebastian Raschka, Python Machine Learning


You will also like:

By Matthew Mayo, KDnuggets.

The recent explosion of interest in data science, data mining, and related disciplines has been mirrored by an explosion in book titles on these same topics. One of the best ways to decide which books could be useful for your career is to look at which books others are reading. This post details the 10 most popular titles in Amazon's Artificial Intelligence & Machine Learning Books category as of Nov 24, 2016, skipping over repeated titles as well as titles which have been obviously miscategorized and are of no use to our readers.

Note: KDnuggets gets absolutely no royalties from Amazon - this list is presented only to help our readers evaluate interesting books.

Top Amazon AI & ML Books 2016

1. Deep Learning (Adaptive Computation and Machine Learning series)
Ian Goodfellow, Yoshua Bengio, Aaron Courville
4.8 out of 5 stars (4 reviews)
Hardcover, $67.55

This sums it up nicely:

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

2. Amazon Echo: The Ultimate Guide to Learn Amazon Echo In No Time
Andrew Butler
4.2 out of 5 stars (74 reviews)
Paperback, $9.95 (Kindle, $2.88)

Learn all about building custom and smart home skills to make your Echo even more personal! Smooth, secure, fast, and foolproof, Alexa Skills Kit helps you keep Echo learning. This guide is also suited for the intermediate, tech-savvy users who want a quick, sure-fire way of getting to know their Echo device, and how best to acquaint themselves with the Echo’s functionality and customizable potential.

3. Gödel, Escher, Bach: An Eternal Golden Braid
Douglas R. Hofstadter
4.5 out of 5 stars (472 reviews)
Paperback, $13.98

If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gödel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.

4. Make Your Own Neural Network
Tariq Rashid
4.2 out of 5 stars (65 reviews)
Kindle, $3.86

Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.

This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included.

5. Python Machine Learning
Sebastian Raschka
4.3 out of 5 stars (80 reviews)
Paperback, $40.49

  • Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization
  • Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
  • Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets

6. Superintelligence: Paths, Dangers, Strategies
Nick Bostrom
3.9 out of 5 stars (284 reviews)
Paperback, $13.72 (Kindle, $8.13)

Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence.

This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain. Yet the writing is so lucid that it somehow makes it all seem easy.

7. Markov Models: Master Data Science and Unsupervised Machine Learning in Python
4.0 out of 5 stars (1 reviews)
Kindle, $4.91

We’re going to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick. We’re going to talk about how Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO. We’ll build language models that can be used to identify a writer and even generate text - imagine a machine doing your writing for you.

8. Machine Learning: The New AI: The MIT Press Essential Knowledge Series
Ethem Alpaydi
3.5 out of 5 stars (2 reviews)
Audio, $14.95 (Paperback, $10.63)

Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.

9. An Introduction to Statistical Learning: with Applications in R
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
4.8 out of 5 stars (127 reviews)
Hardcover, $72.62

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

10. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
4.7 out of 5 stars (15 reviews)
Hardcover, $74.00

This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.

After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning.



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