The most recommended data mining books

Who picked these books? Meet our 11 experts.

11 authors created a book list connected to data mining, and here are their favorite data mining books.
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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 my list on machine learning for beginners.

Why am I passionate about this?

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

Yuxi (Hayden) Liu 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…


Book cover of Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies

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

From my list on machine learning for beginners.

Why am I passionate about this?

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

Yuxi (Hayden) Liu Why did Yuxi love this book?

Another practical book that I highly recommend. Its intuitive structure is the first thing I like about it. It gives you a comprehensive walkthrough of the ML workflow, from data exploration to learning. It covers abundant practical guides that get you prepared for real world challenges, such as how to handle outliers and to impute missing data. As a ML practitioner, I appreciate the dedicated case studies throughout the entire book. They really excite learners for future real world applications.

By John D. Kelleher, Brian Mac Namee, Aoife D'Arcy

Why should I read it?

1 author picked Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition as one of their favorite books, and they share why you should read it.

What is this book about?

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. 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…


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 my list on mathematics for quant finance.

Why am I passionate about this?

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

Chris Conlan 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…


Book cover of Programming Collective Intelligence: Building Smart Web 2.0 Applications

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

From my list on machine learning for beginners.

Why am I passionate about this?

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

Yuxi (Hayden) Liu Why did Yuxi love this book?

This was my favorite book when I started my career. It talks about how information is processed, in an intelligent way, in the internet age. It acts as a tutorial to teach developers how to code our own ML programs, from online dating services, to document analyzer, and search engine. The author did an excellent job of explaining abstract ML algorithms with clear examples. His coding style in Python reads clearly, which makes the book more beginner-friendly.

Don’t get disappointed when you know this book is more than a decade old. It was a visionary book back in the day and it is still relevant today.

By Toby Segaran,

Why should I read it?

1 author picked Programming Collective Intelligence as one of their favorite books, and they share why you should read it.

What is this book about?

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing,…


Book cover of Calling Bullshit: The Art of Skepticism in a Data-Driven World

Olivier Sibony Author Of You're About to Make a Terrible Mistake: How Biases Distort Decision-Making and What You Can Do to Fight Them

From Olivier's 3 favorite reads in 2024.

Why am I passionate about this?

Author

Olivier's 3 favorite reads in 2024

Olivier Sibony Why did Olivier love this book?

There are lots of book that claim to help you think. There is even a "smart thinking" category in some bookstores, presumably to distinguish the books it contains from the ones that promote dumb thinking. I have read many such books (and written a couple myself). This one is my favorite. It is packed with concrete examples and fun to read. You will genuinely be smarter after reading this book.

By Carl T. Bergstrom, Jevin D. West,

Why should I read it?

4 authors picked Calling Bullshit as one of their favorite books, and they share why you should read it.

What is this book about?

Bullshit isn’t what it used to be. Now, two science professors give us the tools to dismantle misinformation and think clearly in a world of fake news and bad data.
 
“A modern classic . . . a straight-talking survival guide to the mean streets of a dying democracy and a global pandemic.”—Wired

Misinformation, disinformation, and fake news abound and it’s increasingly difficult to know what’s true. Our media environment has become hyperpartisan. Science is conducted by press release. Startup culture elevates bullshit to high art. We are fairly well equipped to spot the sort of old-school bullshit that is based…


Book cover of Competing on Analytics: The New Science of Winning

Jeremy Adamson Author Of Minding the Machines: Building and Leading Data Science and Analytics Teams

From my list on for data science and analytics leaders.

Why am I passionate about this?

I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector.  I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics. 

Jeremy's book list on for data science and analytics leaders

Jeremy Adamson Why did Jeremy love this book?

This is a foundational book on analytics and data science as a business function and helped to shape the development of the practice. It provides a view of the discipline through a business lens and avoids deep technical examinations. Though much has changed in the 15 years since it was originally published, it is still essential reading for a leader in the field. No book since has captured as well the competitive differentiation that analytics provides.

By Thomas H. Davenport, Jeanne G. Harris,

Why should I read it?

1 author picked Competing on Analytics as one of their favorite books, and they share why you should read it.

What is this book about?

You have more information at hand about your business environment than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new…


Book cover of Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All

Peter J. Bentley Author Of Artificial Intelligence and Robotics: Ten Short Lessons

From my list on no hype and no nonsense artificial intelligence.

Why am I passionate about this?

I’ve been a geeky kid all my life. (I don’t think I’ve quite grown up yet.) Born in the 1970s, my childhood was a wonderful playground of building robots and software. I was awarded one of the early degrees in AI, and a PhD in genetic algorithms. I’ve since spent 25 years exploring how to make computers think, build, invent, compose… and I’ve also spent 20 years writing popular science books. I’m lucky enough to be a Professor in one of the world’s best universities for Computer Science and Machine Learning: UCL, and I guess I’ve written two or three hundred scientific papers over the years. I still think I know nothing at all about real or artificial intelligence, but then does anyone?

Peter's book list on no hype and no nonsense artificial intelligence

Peter J. Bentley Why did Peter love this book?

OK, I’m biased here because Rob is an old friend of mine. We first met at academic conferences and had several heated debates (arguments). But after spending a little time together at a workshop we realised each probably knew what they were talking about after all. Robert Elliott Smith, I should make clear it's not the Rob Smith who writes about “Artificial Superintelligence”. Those books definitely do not make this list.

Our Rob is a coherent, grounded scientist with bags of real-world experience, and he brings his knowledge to this title with gusto, telling us about how AI is affecting our lives in ways you never thought possible – and often not in a good way. If you want to understand what can go wrong with AI and what we should be doing to stop it, don’t read about singularities or other such nonsense, read this.

By Robert Elliott Smith,

Why should I read it?

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

What is this book about?

Shortlisted for the 2020 Business Book Awards

We live in a world increasingly ruled by technology; we seem as governed by technology as we do by laws and regulations. Frighteningly often, the influence of technology in and on our lives goes completely unchallenged by citizens and governments. We comfort ourselves with the soothing refrain that technology has no morals and can display no prejudice, and it's only the users of technology who distort certain aspects of it.

But is this statement actually true? Dr Robert Smith thinks it is dangerously untrue in the modern era.

Having worked in the field…


Book cover of R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Tilman M. Davies Author Of The Book of R: A First Course in Programming and Statistics

From my list on intro to programming and data science with R.

Why am I passionate about this?

I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.

Tilman's book list on intro to programming and data science with R

Tilman M. Davies Why did Tilman love this book?

For those intending to use R with an eye on the popular 'Tidyverse' suite of packages – which facilitate the handling, manipulation, and visualisation of data setsit's hard to go past this book. From the founding contributors of the RStudio/Tidyverse worlds, this is a great way to learn about this dialect of R against the overarching backdrop of statistical data analysis and data science.

By Hadley Wickham, Garrett Grolemund,

Why should I read it?

1 author picked R for Data Science as one of their favorite books, and they share why you should read it.

What is this book about?

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along…


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 my list on machine learning for beginners.

Why am I passionate about this?

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

Yuxi (Hayden) Liu 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…


Book cover of Interviewing Users: How to Uncover Compelling Insights

Gregg Bernstein Author Of Research Practice: Perspectives from UX researchers in a changing field

From my list on understanding user research.

Why am I passionate about this?

After a career that took me from designer to design professor, I’ve spent the past decade leading user research practices for growing product organizations. I’m excited about user research because it positions us closer to the people we design for, and challenges us to capture and explain complex scenarios in service to them. Though there are many books that teach user research, my list of recommendations is meant to demonstrate why we research, how we make sense of what we learn, and where research might take us.

Gregg's book list on understanding user research

Gregg Bernstein Why did Gregg love this book?

Listening to users is essential to product design and development, full stop. Interviews allow us to understand who uses our products and the contexts our products fit into, and Steve Portigal demonstrates how to do it like a pro in Interviewing Users. Steve breaks down every angle of the interview process, from planning to conducting to documentation. (I particularly love Steve’s approach to the interview field guide in chapter 3!)

By Steve Portigal,

Why should I read it?

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

What is this book about?

Interviewing is a foundational user research tool that people assume they already possess. Everyone can ask questions, right? Unfortunately, that's not the case. Interviewing Users provides invaluable interviewing techniques and tools that enable you to conduct informative interviews with anyone. You'll move from simply gathering data to uncovering powerful insights about people.


Book cover of Introduction to Machine Learning with Python: A Guide for Data Scientists
Book cover of Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies
Book cover of The Elements of Statistical Learning: Data Mining, Inference, and Prediction

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