The most recommended machine learning books

Who picked these books? Meet our 39 experts.

39 authors created a book list connected to machine learning, and here are their favorite machine learning books.
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Deep Learning for Coders with Fastai and Pytorch

By Jeremy Howard, Sylvain Gugger,

Book cover of Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD

Jakub Langr Author Of GANs in Action: Deep Learning with Generative Adversarial Networks

From the list on applied deep learning.

Who am I?

I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.

Jakub's book list on applied deep learning

Why did Jakub love this book?

Jeremy Howard is the lead author and has always been a world-class educator. This book is based on his fast.ai course, which has managed to splice all rigor, simplicity, and cutting edge techniques into one course. It also uses its custom fast.ai framework built on PyTorch, which is the dominant language for researchers. This book is very practically oriented and gets you off the ground very quickly with your own projects!

By Jeremy Howard, Sylvain Gugger,

Why should I read it?

2 authors picked Deep Learning for Coders with Fastai and Pytorch as one of their favorite books, and they share why you should read it.

What is this book about?

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.

Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to…


The Alignment Problem

By Brian Christian,

Book cover of The Alignment Problem: Machine Learning and Human Values

Benjamin Todd Author Of 80,000 Hours: Find a Fulfilling Career That Does Good

From the list on how to have a positive social impact with careers.

Who are we?

We’re a nonprofit that aims to help people have a positive social impact with their careers. Since you have, on average, 80,000 hours in your career, what you decide to do with that time might be your biggest opportunity to make a difference. Over the past ten years, we’ve conducted careful research into high-impact careers, and have helped thousands of people plan a career that has a high positive impact. 

Benjamin's book list on how to have a positive social impact with careers

Why did Benjamin love this book?

One example of an especially pressing threat facing humanity is the rapid development of artificial intelligence. If we want this new technology to go well, it needs to be ‘aligned’ – that is, it should have or act on the same values as us. 

In this book, Brian sets out why aligning artificial intelligence is an extremely tricky issue and one which deserves more attention from talented and dedicated people.

By Brian Christian,

Why should I read it?

1 author picked The Alignment Problem as one of their favorite books, and they share why you should read it.

What is this book about?

Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us-and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem.

Systems cull resumes until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole-and appear to assess Black…


Atlas of AI

By Kate Crawford,

Book cover of Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

Matt Zandstra Author Of PHP 8 Objects, Patterns, and Practice: Mastering OO Enhancements, Design Patterns, and Essential Development Tools

From the list on non-fiction that turn their topics upside down.

Who am I?

Software developers love to question the assumptions that underpin their practice. Some of the most exciting phases of my career have come about as a result of such questions. Often they are revolutionary in the literal sense that they ask you to turn your thinking upside down – to design systems from the bottom up rather than the top down, for example, or to write your tests before your components. I may not adopt every practice, but each challenge enriches the conceptual world in which I work. Over the years, I have come to look for similar shifts and inversions across other subject areas. Here are some recommendations from my reading.

Matt's book list on non-fiction that turn their topics upside down

Why did Matt love this book?

As a coder and a lifelong SF reader, I am fascinated by AI. I've even written a LLM chat client named Shelley. Fascination, though, is not the same as uncritical fanboyism.

It is tempting to treat AI as a natural or magical apparition. Crawford's book turns this illusion on its head and explores AI literally from the ground up, beginning with its vast hunger for natural resources. She describes a similar hunger for training data as well as the implicit (or disguised) biases underlying the systems of classification that drive an AI's "understanding" of the world. 

This is not a book about the future of AI so much as a particular map of the state of the project – a look into the wizard's booth. It offers an essential first step in considering what is to come and how we might negotiate it.

By Kate Crawford,

Why should I read it?

1 author picked Atlas of AI as one of their favorite books, and they share why you should read it.

What is this book about?

The hidden costs of artificial intelligence-from natural resources and labor to privacy, equality, and freedom

"This study argues that [artificial intelligence] is neither artificial nor particularly intelligent. . . . A fascinating history of the data on which machine-learning systems are trained."-New Yorker

"A valuable corrective to much of the hype surrounding AI and a useful instruction manual for the future."-John Thornhill, Financial Times

"It's a masterpiece, and I haven't been able to stop thinking about it."-Karen Hao, senior editor, MIT Tech Review

What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our…


Hello World

By Hannah Fry,

Book cover of Hello World: Being Human in the Age of Algorithms

Tim Harford Author Of The Data Detective: Ten Easy Rules to Make Sense of Statistics

From the list on think clearly about data.

Who am I?

Tim Harford is the author of nine books, including The Undercover Economist and The Data Detective, and the host of the Cautionary Tales podcast. He presents the BBC Radio programs More or Less, Fifty Things That Made The Modern Economy, and How To Vaccinate The World. Tim is a senior columnist for the Financial Times, a member of Nuffield College, Oxford, and the only journalist to have been made an honorary fellow of the Royal Statistical Society.

Tim's book list on think clearly about data

Why did Tim love this book?

This is a clever and highly readable guide to the brave new world of algorithms: what they are, how they work, and their strengths and weaknesses. It’s packed with stories and vivid examples, but Dr Fry is a serious mathematician and when it comes to the crunch she is well able to show it with clear and rigorous analysis.

By Hannah Fry,

Why should I read it?

1 author picked Hello World as one of their favorite books, and they share why you should read it.

What is this book about?

When it comes to artificial intelligence, we either hear of a paradise on earth or of our imminent extinction. It's time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we'll be discussing these issues long after the last page is turned.


Practical Natural Language Processing

By Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta

Book cover of Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

Valliappa Lakshmanan Author Of Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and Mlops

From the list on to become a machine learning engineer.

Who am I?

I have been building real-time, production machine learning models for over 20 years. My book, and my book recommendations, are informed by that experience. I have a lot of empathy for people who are new to machine learning because I’ve taught courses on the topic. I founded the Advanced Solutions Lab at Google where we helped data scientists working for Google Cloud customers (who already knew ML) become ML engineers capable of building reliable ML models. The first two are the books I’d recommend today to newcomers and the last three to folks attending the ASL. 

Valliappa's book list on to become a machine learning engineer

Why did Valliappa love this book?

This recommendation is a bit of a cheat — I’m not recommending this exact book, but one of the books in the series that this book is part of.

Once you have the first two books under your belt, you’ll know how to solve ML problems. But you will keep reinventing the wheel. What you need next is a book on common “ML tricks” — best practices and common techniques when doing ML in production.

The problem is that these tricks are specific to the type of data that you will be processing. If you are going to be processing images or time series, read the corresponding books in the same series instead.

By Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta

Why should I read it?

1 author picked Practical Natural Language Processing as one of their favorite books, and they share why you should read it.

What is this book about?

Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.

Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You'll learn how to…


Human + Machine

By Paul R. Daugherty, H. James Wilson,

Book cover of Human + Machine: Reimagining Work in the Age of AI

Steve Finlay Author Of Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies

From the list on machine learning for managers and business leaders.

Who am I?

I have worked in the field of machine learning and predictive analytics for many years. Having started out as a technical specialist, I have become increasingly interested in the legal, ethical, and social aspects of these subjects. This is because it is these “soft issues” that often determine how successful these technologies are in practice and if they are viewed as a force for good or evil in wider society. This has led me to write several books focusing on the practical and cultural aspects of these subjects and how best to apply them for the benefit of business, individuals, and wider society.

Steve's book list on machine learning for managers and business leaders

Why did Steve love this book?

Many writers have discussed the dangers that artificial intelligence and machine learning represent to our livelihoods, and how clever computers and autonomous robots will supplant us all in the workplace. What I like about this book is that it provides an alternative, and very optimistic, view of how these new technologies are being deployed. The authors present a future based on a partnership, in which artificial intelligence-based tools work in tandem with human workers, enhancing what individuals can do in the workplace rather than replacing them.

By Paul R. Daugherty, H. James Wilson,

Why should I read it?

1 author picked Human + Machine as one of their favorite books, and they share why you should read it.

What is this book about?

AI is radically transforming business. Are you ready?

Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on?

In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show…


Genius Makers

By Cade Metz,

Book cover of Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World

Art Kleiner Author Of The AI Dilemma: 7 Principles for Responsible Technology

From the list on understanding AI and its effect on people.

Who am I?

I’m a storyteller writing on business and technology. I specialize in clear views of complex systems. When Juliette showed me her research on tech companies and AI responsibility, I saw the power of a book – the book that ultimately became The AI Dilemma. The core dilemma is that in the right hands the technology is needed, and in the wrong hands it’s dangerous. When Juliette asked me to coauthor it, I jumped at the chance. As we worked, I realized that the topic brought into focus all the research and thinking I’d ever done about human, organizational, and machine behavior. 

Art's book list on understanding AI and its effect on people

Why did Art love this book?

If ever a subject deserved the sweeping hand of a highly skilled journalist/historian, it’s generative AI and machine learning. The field is shaped by its founders’ idiosyncratic and fascinating personalities.

NYTimes reporter Cade Metz observed many events first-hand. We read about Go Grandmaster Lee Sedol recovering from losing to Google’s AI by mastering the machine’s logic. We see Geoffrey Hinton flying supine because of his back problems, and the origins of Joy Buolamwini’s famous Gender Shades project.

We get the backstory to the most serious issues: like how well can AI developers be trusted to manage risk? As a journalist-historian myself, I deeply appreciate being immersed in contemporary history. 

By Cade Metz,

Why should I read it?

1 author picked Genius Makers as one of their favorite books, and they share why you should read it.

What is this book about?

'This colourful page-turner puts artificial intelligence into a human perspective . . . Metz explains this transformative technology and makes the quest thrilling.' Walter Isaacson, author of Steve Jobs
____________________________________________________

This is the inside story of a small group of mavericks, eccentrics and geniuses who turned Artificial Intelligence from a fringe enthusiasm into a transformative technology. It's the story of how that technology became big business, creating vast fortunes and sparking intense rivalries. And it's the story of breakneck advances that will shape our lives for many decades to come - both for good and for ill.
________________________________________________

'One day…


Deep Learning

By Ian Goodfellow, Yoshua Bengio, Aaron Courville

Book cover of Deep Learning

Jakub Langr Author Of GANs in Action: Deep Learning with Generative Adversarial Networks

From the list on applied deep learning.

Who am I?

I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.

Jakub's book list on applied deep learning

Why did Jakub love this book?

This is a very technical, truly academic book. I’d recommend reading it 4th or 5th if you felt that prior books did not have sufficient rigour. While this book is academic, it has been lauded by many for its clarity. This is a great book to really think about fundamentals and see what sorts of things can go wrong—even in applied settings.

By Ian Goodfellow, Yoshua Bengio, Aaron Courville

Why should I read it?

1 author picked Deep Learning as one of their favorite books, and they share why you should read it.

What is this book about?

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“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

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…


Computer Vision

By Simon J. D. Prince,

Book cover of Computer Vision: Models, Learning, and Inference

Mark S. Nixon Author Of Feature Extraction and Image Processing for Computer Vision

From the list on computer vision from a veteran professor.

Who am I?

It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.

Mark's book list on computer vision from a veteran professor

Why did Mark love this book?

This fine book is about learning the relationships between what is seen in an image, and what is known about the world. It’s a counterpart to our book on feature extraction and it shows you what can be achieved with the features. It’s not for those who shy from maths, as is the case for all of the books here. So that you can build the techniques, Simon’s book also includes a wide variety of algorithms to help you on your way.

By Simon J. D. Prince,

Why should I read it?

1 author picked Computer Vision as one of their favorite books, and they share why you should read it.

What is this book about?

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build…


Book cover of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

Art Kleiner Author Of The AI Dilemma: 7 Principles for Responsible Technology

From the list on understanding AI and its effect on people.

Who am I?

I’m a storyteller writing on business and technology. I specialize in clear views of complex systems. When Juliette showed me her research on tech companies and AI responsibility, I saw the power of a book – the book that ultimately became The AI Dilemma. The core dilemma is that in the right hands the technology is needed, and in the wrong hands it’s dangerous. When Juliette asked me to coauthor it, I jumped at the chance. As we worked, I realized that the topic brought into focus all the research and thinking I’d ever done about human, organizational, and machine behavior. 

Art's book list on understanding AI and its effect on people

Why did Art love this book?

Remember How to Lie With Statistics? Here is: How to abuse big data. And how to stop the abuse.

O’Neil is a good corrective for my own habit of seeing all sides. A former math professor and quant, she is harsh on AI because it systematically punishes the vulnerable and impoverished – and because companies resist change and manipulate the laws to keep their profits.

“Right now, the burden of proof is on us,” she told Juliette and me in an interview, “to prove that these algorithms harm us. I want that burden to be on the companies who profit from using them.” Weapons of Math Destruction argues that we can and should hold the perpetrators – including some of the world’s biggest tech companies – accountable. 

By Cathy O’Neil,

Why should I read it?

10 authors picked Weapons of Math Destruction as one of their favorite books, and they share why you should read it.

What is this book about?

'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times

'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year

In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric.

We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made…


The Elements of Statistical Learning

By Trevor Hastie, Robert Tibshirani, Jerome Friedman

Book cover of The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Chris Conlan Author Of Algorithmic Trading with Python: Quantitative Methods and Strategy Development

From the list on mathematics for quant finance.

Who am I?

I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.

Chris' book list on mathematics for quant finance

Why did Chris love this book?

This book might as well be called Introduction to machine learning, and it is probably one of the only books truly deserving of the title. Did you know neural networks have been used for decades to scan checks at the bank? They are called Boltzman Machine. Have you ever heard of how decision trees were used in old-school data mining? You could only get them from proprietary software packages from the early 2000s.

In quant trading, you will constantly face compute power constraints, so it is invaluable to understand the mathematical foundations of the most old-school machine learning methods out there. Researchers 20 years ago used to do a lot of impressive work with a lot less computing power.

By Trevor Hastie, Robert Tibshirani, Jerome Friedman

Why should I read it?

2 authors picked The Elements of Statistical Learning as one of their favorite books, and they share why you should read it.

What is this book about?

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major…


Introduction to Algorithms

By Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein

Book cover of Introduction to Algorithms

Chris Zimmerman Author Of The Rules of Programming: How to Write Better Code

From the list on programming for people who want to be good at it.

Who am I?

I’ve spent most of my life writing code—and too much of that life teaching new programmers how to write code like a professional. If it’s true that you only truly understand something after teaching it to someone else, then at this point I must really understand programming! Unfortunately, that understanding has not led to an endless stream of bug-free code, but it has led to some informed opinions on programming and books about programming.

Chris' book list on programming for people who want to be good at it

Why did Chris love this book?

Yes, it’s a textbook, albeit a particularly well-written one. You may already have it on your shelf, if you’ve taken a programming class or two.

I’m way too old to have used CLRS as a textbook, though! For me, it’s an effectively bottomless collection of neat little ideas—an easy-to-describe problem, then a series of increasingly clever ways to solve that problem. How often do I end up using one of those algorithms? Not very often! But every time I read the description of an algorithm, I get a nugget of pure joy from the “aha” moment when I first understand how it works.

By Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein

Why should I read it?

3 authors picked Introduction to Algorithms as one of their favorite books, and they share why you should read it.


Deep Learning with Python

By Francois Chollet,

Book cover of Deep Learning with Python

Jakub Langr Author Of GANs in Action: Deep Learning with Generative Adversarial Networks

From the list on applied deep learning.

Who am I?

I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.

Jakub's book list on applied deep learning

Why did Jakub love this book?

This is a fantastic book to get you started. It is written by the author of a leading deep learning framework Keras, which makes even Tensorflow very easy to use. Chollet is a true leader of the deep learning craft and the Manning team always does an excellent job of forcing authors to make the subject matter accessible. Highly recommended!

By Francois Chollet,

Why should I read it?

2 authors picked Deep Learning with Python as one of their favorite books, and they share why you should read it.

What is this book about?

"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook

The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher Francois Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You'll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning…


AI 2041

By Kai-Fu Lee, Chen Qiufan,

Book cover of AI 2041: Ten Visions for Our Future

Art Kleiner Author Of The AI Dilemma: 7 Principles for Responsible Technology

From the list on understanding AI and its effect on people.

Who am I?

I’m a storyteller writing on business and technology. I specialize in clear views of complex systems. When Juliette showed me her research on tech companies and AI responsibility, I saw the power of a book – the book that ultimately became The AI Dilemma. The core dilemma is that in the right hands the technology is needed, and in the wrong hands it’s dangerous. When Juliette asked me to coauthor it, I jumped at the chance. As we worked, I realized that the topic brought into focus all the research and thinking I’d ever done about human, organizational, and machine behavior. 

Art's book list on understanding AI and its effect on people

Why did Art love this book?

Ten Visions for Our Future took a long time to read – and was worth it. Each of these futures is an immersion in a way of life that the technology makes possible.

Kai-fu Lee is an AI legend in the US (Google and Apple) and China; Stanley Chan (Qiufan’s other name) is a haunting, award-winning science fiction writer who portrays the personal impact of machine technology. Their stories stayed with me, because they go so far beyond the usual scenarios and have such emotional depth.

People are struggling not with an impersonal robotic technology, but with intensely personal worlds that leave them frightened, lonely, hopeful, and unsure of whom to trust. The tech leads them – as it will lead us – across the emotional thresholds of the future.  

By Kai-Fu Lee, Chen Qiufan,

Why should I read it?

1 author picked AI 2041 as one of their favorite books, and they share why you should read it.

What is this book about?

A WALL STREET JOURNAL, WASHINGTON POST, AND FINANCIAL TIMES BEST BOOK OF THE YEAR

In this ground-breaking blend of imaginative storytelling and scientific forecasting, a pioneering AI expert and a leading writer of speculative fiction join forces to answer an imperative question: How will artificial intelligence change our world within twenty years?

AI will be the defining development of the twenty-first century. Within two decades, aspects of daily human life will be unrecognizable. AI will generate unprecedented wealth, revolutionize medicine and education through human-machine symbiosis, and create brand new forms of communication and entertainment. In liberating us from routine work,…


Advanced Methods and Deep Learning in Computer Vision

By E.R. Davies (editor), Matthew Turk (editor),

Book cover of Advanced Methods and Deep Learning in Computer Vision

Mark S. Nixon Author Of Feature Extraction and Image Processing for Computer Vision

From the list on computer vision from a veteran professor.

Who am I?

It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.

Mark's book list on computer vision from a veteran professor

Why did Mark love this book?

The advances of deep learning have been awesome, and fast. It’s been hard for the textbooks to keep up, so it’s good to include one that describes the advances and state of art very well. It seems appropriate that it’s edited by two leading researchers who are Roy – who described computer vision systems implementations in a long series of excellent books – and Matt, whose work on face recognition revolutionised and transformed the progress of face recognition in the 1990s. This book gives you an image of where we are now in computer vision, and where we are going. 

By E.R. Davies (editor), Matthew Turk (editor),

Why should I read it?

1 author picked Advanced Methods and Deep Learning in Computer Vision as one of their favorite books, and they share why you should read it.

What is this book about?

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection.

This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as…


Book cover of Computer Age Statistical Inference, Algorithms, Evidence, and Data Science

Ron S. Kenett Author Of The Real Work of Data Science: Turning Data into Information, Better Decisions, and Stronger Organizations

From the list on how numbers turn into information.

Who am I?

I was trained as a mathematician but have always been motivated by problem-solving challenges. Statistics and analytics combine mathematical models with statistical thinking. My career has always focused on this combination and, as a statistician, you can apply it in a wide range of domains. The advent of big data and machine learning algorithms has opened up new opportunities for applied statisticians. This perspective complements computer science views on how to address data science. The Real Work of Data Science, covers 18 areas (18 chapters) that need to be pushed forward in order to turning data into information, better decisions, and stronger organizations

Ron's book list on how numbers turn into information

Why did Ron love this book?

The text covers classic statistical inference, early computer-age methods, and twenty-century topics. This puts a unique perspective on current analytic technologies labeled machine learning, artificial intelligence, and statical learning. The examples used provide a powerful description of the methods covered and the compare and contrast sections highlight the evolution of analytics. This book by Efron and Hastie is a natural follow-up source for readers interested in more details.

By Bradley Efron, Trevor Hastie,

Why should I read it?

2 authors picked Computer Age Statistical Inference, Algorithms, Evidence, and Data Science as one of their favorite books, and they share why you should read it.

What is this book about?

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic…


Book cover of Machine Learning For Absolute Beginners: A Plain English Introduction

Yuxi (Hayden) Liu Author Of Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

From the list on machine learning for beginners.

Who am I?

I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.  

Yuxi's book list on machine learning for beginners

Why did Yuxi love this book?

This could be the first stop of your brand new machine learning journey. I personally like how the technical concept is translated into plain English – each chapter starts with a high-level overview of a ML algorithm or methodology, concise and clear, followed by lots of visual examples and real world scenarios. I can guarantee you won’t get lost halfway. The book focuses on getting you introduced to ML with minimal math. But if you want to grasp some more of math, the next book I recommend is waiting for you. 

By Oliver Theobald,

Why should I read it?

1 author picked Machine Learning For Absolute Beginners as one of their favorite books, and they share why you should read it.

What is this book about?

NOTICE: To buy the newest edition of this book (2021), please search "Machine Learning Absolute Beginners Third Edition" on Amazon. The product page you are currently viewing is for the 2nd Edition (2017) of this book.

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."

Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?

Well, hold on there...

Before you embark on your epic journey, there are some high-level theory and statistical principles to weave through first.
But rather than spend…


How to Measure Anything

By Douglas W. Hubbard,

Book cover of How to Measure Anything: Finding the Value of Intangibles in Business

Jakub Langr Author Of GANs in Action: Deep Learning with Generative Adversarial Networks

From the list on applied deep learning.

Who am I?

I’ve been working in machine learning for about a decade. I’ve always been more interested in applied than theoretical problems and while blogs and MOOCs (Massive Online Open Courses) are a great way to learn, for certain deep topics only a book would do. I also teach at University of Oxford, University of Birmingham, and various FTSE100 companies. My machine learning has exposed me to many fascinating problems—from leading my own ML-focused startup through Y Combinator—to working at various companies as a consultant. I think there is currently no great curriculum for the practitioners really wanting to apply deep learning in practical cases, so I have given it my best shot.

Jakub's book list on applied deep learning

Why did Jakub love this book?

While technically not about deep learning, this book is fantastic for those interested in pursuing applied or practical machine learning problems. While the central thesis of a topic can be reduced to “Frequently, models are valuable simply by reducing uncertainty,” it is definitely worth a read as there’s a lot of deep thinking in this book!

By Douglas W. Hubbard,

Why should I read it?

1 author picked How to Measure Anything as one of their favorite books, and they share why you should read it.

What is this book about?

Now updated with new measurement methods and new examples, How to Measure Anything shows managers how to inform themselves in order to make less risky, more profitable business decisions This insightful and eloquent book will show you how to measure those things in your own business, government agency or other organization that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI. * Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfaction * Simplifies overall content while still making the…


Book cover of You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place

Michael L. Littman Author Of Code to Joy: Why Everyone Should Learn a Little Programming

From the list on computing and why it’s important and interesting.

Who am I?

Saying just the right words in just the right way can cause a box of electronics to behave however you want it to behave… that’s an idea that has captivated me ever since I first played around with a computer at Radio Shack back in 1979. I’m always on the lookout for compelling ways to convey the topic to people who are open-minded, but maybe turned off by things that are overly technical. I teach computer science and study artificial intelligence as a way of expanding what we can get computers to do on our behalf.

Michael's book list on computing and why it’s important and interesting

Why did Michael love this book?

So much of the public conversation around AI focuses on the extremes: "It's Going to Take Our Jobs And We'll Never Be Able To Work Ever Again!" or "It's Going To Create a Utopia And We'll Never Have To Work Ever Again!"

To be honest, I don't put a lot of credence into either of these perspectives. What I adore about this book is that it puts the technology in perspective in a concrete and laugh-out-loud funny way. Through detailed examples, it provides a glimpse into how the technology works, how it can be applied to real problems, and where it falls jaw-droppingly short. 

By Janelle Shane,

Why should I read it?

1 author picked You Look Like a Thing and I Love You as one of their favorite books, and they share why you should read it.

What is this book about?

“A deft, informative, and often screamingly funny primer on the ways that machine learning can (and often does) go wrong.” —Margaret Harris, Physics World

“You look like a thing and I love you” is one of the best pickup lines ever…according to an artificial intelligence trained by the scientist Janelle Shane, creator of the popular blog AI Weirdness. Shane creates silly AIs that learn how to name colors of paint, create the best recipes, and even flirt (badly) with humans—all to understand the technology that governs so much of our human lives.

We rely on AI every day, trusting it…


Introduction to Machine Learning with Python

By Andreas C. Müller, Sarah Guido,

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

Yuxi (Hayden) Liu Author Of Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

From the list on machine learning for beginners.

Who am I?

I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.  

Yuxi's book list on machine learning for beginners

Why did Yuxi love this book?

This book is more advanced than the first book I recommended. It presents ML theoretical and practical aspects step-by-step from the bottom up. Each chapter elaborates at length on a core building block in the ML life cycle. For example, feature engineering, supervised learning, and model evaluation have their own separate chapters, with intuitive discussions of how they work. Most of the concept is taught through the simple yet powerful Python Module Scikit-Learn so it won’t overburden you with heavy programming. This book will be perfect for practitioners with some understanding of statistics and linear algebra.

By Andreas C. Müller, Sarah Guido,

Why should I read it?

1 author picked Introduction to Machine Learning with Python as one of their favorite books, and they share why you should read it.

What is this book about?

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the…