Looking for FREE Machine Learning Books to upgrade your Data Science skills?
Well, you've come to the right place. We've been collecting some of our favourite FREE ebooks on Machine Learning and organised them in this blog post for you.
In this post we bring you all the FREE books that combine Machine Learning with Data Science that we've found (so far), categorised by sub-topic so you can find what you're looking for easily.
We'll be adding new FREE Artificial Intelligence books as we find them, so bookmark the page so you can check out any new books we bring to you.
To get one of the FREE ML books we have for you, click on the image and you'll be taken to the page where you can download or read the book.
Some of these books are essential reading and we'd recommend reading them even if they weren't FREE.
And we know some of our Data Ninjas love having a paper copy of the books.
So we've added links when hard copies are available for those of you who want to add them to your library.
Disclosure: The FREE ebooks were free to download at the time of posting but other links in this post may contain affiliate links. As Amazon Associates we may earn from qualifying purchases.
You can find further details in our TCs
FREE General Machine Learning Books
These free artificial intelligence books are a great introduction to machine learning. So they're great choices for beginners in Data Science.
Demystifying Artificial Intelligence
Jeff Leek and Divya Narayanan
You should look at your data.
Graphs and charts let you explore and learn about the structure of the information you collect. Good data visualizations also make it easier to communicate your ideas and findings to other people. Beyond that, producing effective plots from your own data is the best way to develop a good eye for reading and understanding graphs - good and bad - made by others, whether presented in research articles, business slide decks, public policy advocacy, or media reports.
This book teaches you how to do it.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalka
This book is a general introduction to machine learning that can serve as a reference book for researchers and a textbook for students.
It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.
Practical Artificial Intelligence Programming With Java
This book for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical Artificial Intelligence (AI) programming and information processing techniques.
In the style of a “cook book,” the chapters can be studied in any order. Each chapter follows the same pattern: a motivation for learning a technique, some theory for the technique, and a Java example program that you can experiment with.
This book uses both best of breed open source software and the author's own libraries to introduce the reader to Artificial Intelligence (AI) technologies like genetic algorithms, neural networks, expert systems, machine learning, and statistical natural language processing (NLP).
Applied Artificial Intelligence: And Engineering Approach
Bernhard G. Humm
Why yet another book on Artificial Intelligence?
It is true that hundreds of publications on Artificial Intelligence (AI) have been published within the last decades - scientific papers and text books. Most of them focus on the theory behind AI solutions: logic, reasoning, statistical foundations, etc. However, little can be found on engineering AI applications.
Modern, complex IT applications are not built from scratch but by integrating off-the-shelf components: libraries, frameworks, and services. The same applies, of course, for AI applications. Over the last decades, numerous off-the-shelf components for AI base functionality such as logic, reasoning, and statistics have been implemented - commercial and open source. Integrating such components into user friendly, high-performance, and maintainable AI applications requires specific engineering skills. "Applied Artificial Intelligence - An Engingeering Approach" focuses on those skills.
Ian Goodfellow. Yoshua Bengio and Aaron Courville
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. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Understanding Machine Learning
Shai Shalev-Schwartz and Shai Ben-David
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
A Brief Introduction To Neural Networks
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning.
After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems.
And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment.
In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
A Course In Machine Learning
Hal Daumé III
CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).
Its focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.
Algorithms For Reinforcement Learning
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective.
In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions.
Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms’ merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering.
FREE Data Science and Statistics Machine Learning Books
Gaussian Processes For Machine Learning
C.E Rasmussen and C.K.I Williams
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines through a systematic and unified treatment of theoretical and practical aspects of GPs in machine learning.
Statistical Learning With Sparsity
Trevor Hastie, Robert Tibshirani and Martin Wainwright
During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. This book describes the important ideas in these areas in a common conceptual framework.
Evaluating Machine Learning Models
Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming. Now you have help. With this O’Reilly report, machine-learning expert Alice Zheng takes you through the model evaluation basics.
In this overview, Zheng first introduces the machine-learning workflow, and then dives into evaluation metrics and model selection. The latter half of the report focuses on hyperparameter tuning and A/B testing, which may benefit more seasoned machine-learning practitioners.
Interpretable Machine Learning
This book is about making machine learning models and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.
All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and natural language processing tasks. Reading the book is recommended for machine learning practitioners, data scientists, statisticians, and anyone else interested in making machine learning models interpretable.
Azure Machine Learning
This ebook introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services.
The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today.
It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes.
The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services.
Machine Learning, Neural And Statistical Classification
Edited by D. Michie, D.J. Spiegelhalter and C.C. Taylor
The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems.
As the book's title suggests, a wide variety of approaches has been taken towards this task. Three main historical strands of research can be identified: statistical, machine learning and neural network.
Data Mining: Practical Machine Learning Tools And Techniques
Ian H. Witten and Eibe Frank
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This book on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.
Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects.
Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods.
Artificial Intelligence: Foundations On Computational Agents
David Poole and Alan Macworth
This is a book about the science of artificial intelligence (AI).
It presents artificial intelligence as the study of the design of intelligent computational agents. The book is structured as a textbook, but it is accessible to a wide audience of professionals and researchers. In the last decades we have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. This book provides the first accessible synthesis of the field aimed at undergraduate and graduate students. It provides a coherent vision of the foundations of the field as it is today. It aims to provide that synthesis as an integrated science, in terms of a multi-dimensional design space that has been partially explored.
As with any science worth its salt, artificial intelligence has a coherent, formal theory and a rambunctious experimental wing. The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications.
Information Theory, Inference, And Learning Algorithms
David. J.C. MacKay
Information theory and inference, often taught separately, are here united in one entertaining textbook.
These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.
This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction.
A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.
Data-Intensive Text Processing With MapReduce
Jimmy Lin and Chris Dyer
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever.
MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance.
This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains.
This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
Theory and Applications for Advanced Text Mining
Edited by Shigeaki Sakurai
This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language.
This book will give new knowledge in the text mining field and help many readers open their new research fields.
C++ Neural Networks And Fuzzy Logic
The extensively revised and updated edition provides a logical and easy-to-follow progression through C++ programming for two of the most popular technologies for artificial intelligence - neural and fuzzy programming. The authors cover theory as well as practical examples, giving programmers a solid foundation as well as working examples with reusable code.
The hybridization of the technologies is demonstrated on architectures such as Fuzzy-Back-propagation Networks (NN-FL), Simplified Fuzzy ARTMAP (NN-FL), and Fuzzy Logic.
Machine Learning Yearning
AI, Machine Learning and Deep Learning are transforming numerous industries, and Andrew Ng has been writing a book, Machine Learning Yearning, to teach you how to structure Machine Learning projects.
This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. Some technical AI classes will give you a hammer; this book teaches you how to use the hammer. If you aspire to be a technical leader in AI and want to learn how to set direction for your team, this book will help.
The LION Way
Roberto Battiti and Mauro Brunato
This newly revised book presents two topics which are in most cases separated: machine learning (the design of flexible models from data) and intelligent optimization (the automated creation and selection of improving solutions). More and more innovative people (lionhearted?) can now master the source of power arising from LION techniques to solve problems, improve businesses, create new prescriptive analytics applications.
Powerful tools are not only for cognoscenti and this book does a serious effort to distinguish the paradigm shift brought about by machine learning and intelligent optimization methods from the fine details, and it does not refrain from presenting concrete examples and vivid sticky images (made to stick to your mind).
Free R Programing with Machine Learning Books
Practical Machine Learning
Kyriakos Chatzidimitriou, Themistoklis Diamantopoulos, Michail Papamichail, and Andreas Symeonidis
The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules.
The book is a compilation of the leaflets the authors give to their students during the practice labs, in the courses of Pattern Recognition and Data Mining, in the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki.
FREE Python Programming with Machine Learning Books
Data Science In The Cloud With Microsoft Azure Machine Learning And Python
Stephen F. Elston
Take time to explore Microsoft’s Azure machine learning platform, Azure ML - a production environment that simplifies the development and deployment of machine learning models.
In this O’Reilly report, Stephen Elston from Quantia Analytics uses a complete data science example (forecasting hourly demand for a bicycle rental system) to show you how to manipulate data, construct models, and evaluate models with Azure ML.
The report walks you through key steps in the data science process from problem definition, data understanding, and feature engineering, through construction of a regression model and presentation of results. You’ll also learn how to extend Azure ML with Python.
Elston uses downloadable Python code and data to demonstrate how to perform data munging, data visualization, and in-depth evaluation of model performance. At the end, you’ll learn how to publish your trained models as web services in the Azure cloud.
KB Neural Data Mining with Python Sources
The aim of this work is to present and describe in detail the algorithms to extract the knowledge hidden inside data using Python language, which allows us to read and easily understand the nature and the characteristics of the rules of the computing utilized, as opposed to what happens in commercial applications, which are available only in the form of running codes, which remain impossible to modify.
Bayesian Reasoning and Machine Learning
Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly.
People who know the methods have their choice of rewarding jobs.
This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus.
Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.
Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter.
Free Natural Language Processing Books
Natural Language Processing with Python
Steven Bird, Ewan Klein and Edward Loper
This book provides a highly accessible introduction to the field of NLP. It can be used for individual study or as the textbook for a course on natural language processing or computational linguistics, or as a supplement to courses in artificial intelligence, text mining, or corpus linguistics. The book is intensely practical, containing hundreds of fully-worked examples and graded exercises.
Free Computer Vision Books
Programming Computer Vision With Python
Jan Erik Solem
If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
More than just a source of “recipes”, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques.
I hope that these FREE Machine Learning books whet your appetite for more learning - especially as they're all free!
Is there a book that you'd like to recommend for this list? Are any of the links out of date? Leave a comment at the bottom and I'll jump right on it.
Bookmark this page so you can return to it and pick up more free books on AI, enjoy and don't forget to share with your friends on social media!
If you're looking for more FREE Data Science Books we also have the following posts.
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- Free Essential Python Books for Aspiring Data Scientists
- Free Books on R Programming That all Aspiring Data Scientists Should Read
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