88 books like Machine Learning For Absolute Beginners

By Oliver Theobald,

Here are 88 books that Machine Learning For Absolute Beginners fans have personally recommended if you like Machine Learning For Absolute Beginners. Shepherd is a community of 11,000+ authors and super readers sharing their favorite books with the world.

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Book cover of Mathematics for Machine Learning

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?

The book is a well-curated collection of the essential mathematical concepts that form ML. You may experience a cultural shock jumping to this book from the previous one, because the writing in this book is a bit formal. However, it is the missing but necessary piece for building solid foundations for practical ML. You will find it more valuable combining the intuition behind ML that you gained previously. And the explanations in the book are succinct and from the ML perspectives. For instance, partial derivatives are explained in terms of neural network weight optimization. I wish the concepts in Linear Algebra, Vector Calculus, and Probability courses back in college were introduced this way so I understand better how they are applied.  

By Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

Why should I read it?

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

What is this book about?

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these…


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 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 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 Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Tomasz Lelek Author Of Software Mistakes and Tradeoffs: How to make good programming decisions

From my list on big data processing ecosystem.

Why am I passionate about this?

I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.

Tomasz's book list on big data processing ecosystem

Tomasz Lelek Why did Tomasz love this book?

Apache Spark has a very high point of entry for newcomers to the Big Data ecosystem.

However, it is a key tool that almost everyone is using for running distributed processing. I recommend everyone to read this book before delving into production solutions based on Apache Spark.

This book will allow you to alleviate many spark problems, such as serialization, memory utilization, and parallelization of processing.

By Sandy Ryza, Uri Laserson, Sean Owen , Josh Wills

Why should I read it?

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

What is this book about?

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-classification, collaborative filtering, and anomaly detection among others-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find these patterns useful for…


Book cover of Information Quality: The Potential of Data and Analytics to Generate Knowledge

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

From my list on how numbers turn into information.

Why am I passionate about this?

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

Ron S. Kenett Why did Ron love this book?

A lightly technical introduction to a comprehensive framework defining and evaluating the quality of information generated by statistical analysis. It expands the role of analytics by including dimensions that affect information quality such as data resolution, data integration, operationalization, and generalizability of findings. This wide-angle perspective provides a practical checklist that has been found useful in applications. Multiple case studies enable the reader to connect to his favorite topic, but also learn from other areas.

By Ron S. Kenett, Galit Shmueli,

Why should I read it?

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

What is this book about?

Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance…


Book cover of Be Data Literate: The Data Literacy Skills Everyone Needs to Succeed

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?

Not everybody needs to be a data scientist, but everybody does need to be data literate. Without an intentional focus on evangelism and building a strong data culture in your organization it will be an uphill battle to make meaningful change. This book helps individuals and leaders to understand what data literacy is, and how we can build it like any other skill.

By Jordan Morrow,

Why should I read it?

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

What is this book about?

In the fast moving world of the fourth industrial revolution not everyone needs to be a data scientist but everyone should be data literate, with the ability to read, analyze and communicate with data. It is not enough for a business to have the best data if those using it don't understand the right questions to ask or how to use the information generated to make decisions. Be Data Literate is the essential guide to developing the curiosity, creativity and critical thinking necessary to make anyone data literate, without retraining as a data scientist or statistician. With learnings to show…


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…


5 book lists we think you will like!

Interested in data mining, machine learning, and algorithms?

Data Mining 13 books
Machine Learning 53 books
Algorithms 36 books