What are the best machine learning textbooks for students and practitioners? How can you find a machine learning book that answers your questions on the vast array of machine learning applications? If you are worried about any of the above questions, this is the right article for you. Machine learning is increasingly becoming popular for its ability to equip humans to run tasks in an automated manner. Learning this subject can come with so much stress; however, the right materials will help make the walk easier.
This article features some of the best machine learning books that will help you properly understand the subject. You’ll also learn about a few questions that will point you in the right direction of choosing rightly. Just like machine learning itself, this article generates so many possibilities for your study. Read on to find out all that’s on offer.
If you want to just get back to studying, get our top pick for machine learning books here.
Last update: 2024-05-06
Our Favorite for Best Machine Learning Books
The Hundred-Page Machine Learning Book by Andriy Burkov
The Hundred-Page Machine Learning Book is an excellent introduction to machine learning for practitioners and students. Its provision of an excellent practical guide makes it worth buying for learning the basics of machine learning execution within a few days. The book's introductory part gets you started on the subject, while its other parts walk readers through advanced topics. The book’s structure and the details it contains make it a beautiful book for engineers looking to incorporate machine learning in their daily works without needing to go through any formal degree program.
The Hundred-Page Machine Learning Book can best be described as a compact “how-to” science guide that teaches the essential topics in machine learning and data science. Over time, it has been adopted as a go-to resource for both academicians and practitioners that want to refresh their memory on the subject. Despite the small size of this book, it still covers all the major machine language approaches, including deep learning, modern support vector machines, random forests, etc.
What Makes It Unique:
- The Hundred-Page Machine Learning Book does not need a level of statistical and mathematical approaches or even prior experience. This makes it one of the few accessible books to everyone willing to learn new machine learning approaches.
- It is an excellent way to get introduced into the world of machine learning.
- Instead of just blowing over a few hard to crack topics, this book manages to find a balance between easy-to-understand explanations, intuitive visualization, and algorithms, making it beneficial for newcomers.
- Compresses several essential machine learning topics into hundred pages to aid easier coverage.
Why Choose this Machine Learning Book:
- The Hundred-Page Machine Learning Book is clear and concise, with enough details to help you understand all the basic machine learning principles.
- It offers a good combination of the theoretical and practical parts of studying machine learning.
- A precise blend of the introductory and advanced aspects of machine learning makes it an excellent option to start the subject with.
- Burkov, Andriy (Author)
- English (Publication Language)
- 160 Pages - 01/13/2019 (Publication Date) - Andriy Burkov (Publisher)
Last update: 2024-05-06
Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop
Aimed at senior undergraduates and entry graduate students, Pattern Recognition and Machine Learning (Information Science and Statistics) provide a look into an authoritative presentation of many statistical techniques that have been incorporated into machine learning of pattern recognition. Apart from being a good practical guide, this book will also serve as an excellent reference point for personal studies. Its extensive coverage, coherent viewpoint, and detailed explanations combine to make it a must-have in the library of every machine learning practitioner.
A strong feature of this beautifully produced book is the author’s use of intuition and geometric illustrations to make explanations as clear as possible. This is why many professionals recommend the book as an impressive book that can serve as the basis for further statistical courses. Unlike most other books on this subject, this one uses graphical models for describing probability distribution. It doesn’t assume any previous knowledge of pattern recognition. Instead, it assumes familiarity with basic linear algebra and multivariate calculus, hence building on such knowledge.
What Makes it Unique:
- Pattern Recognition and Machine Learning (Information Science and Statistics) use over 700 pages of clearly illustrated texts to develop a statistical framework that encompasses machine learning.
- As a textbook, it comes with a wide range of instructions, examples, and exercises, all targeted at helping the reader develop a full understanding of the subject.
- It reflects all the recent developments in the field of machine learning and pattern recognition.
Why Choose this Machine Learning Book:
- Apart from the incredible clarity offered by this book, it is also well organized and features other materials that will smoothen your learning journey.
- Pattern Recognition and Machine Learning (Information Science and Statistics) features very detailed explanations and does not shy away from the deeper maths.
- It contains several clear examples and exercises to make self-study easier.
- Hardcover Book
- Bishop, Christopher M. (Author)
- English (Publication Language)
Last update: 2024-05-06
Natural Language Processing with Python by Steven Bird and Ewan Klein
Steven Bird‘s Natural Language Processing with Python is a complete natural language toolkit written to introduce readers to python programming languages. The book assumes that readers would have some working knowledge about how python works, so it skips some introductory aspects of the subject. However, it features an accessible introduction to natural language processing, which supports several language technologies as a field, from email filtering to predictive text and even translation.
This book helps to guide readers through the process of writing their first Python programs that are workable with an extensive collection of unstructured text. It also uses a comprehensive range of linguistic data structures to offer access to a variety of datasets. With the information provided in this book, you’ll have a proper understanding of the algorithms that would help you analyze any written communication structure and content.
What Makes it Unique:
- Natural Language Processing with Python is packed with tons of exercises, illustrations, and examples to help make your walk by learning natural language programming easily.
- It offers direct access to some of the most popular linguistic databases, such as treebanks and WordNet.
- Although newbies may find the first few chapters a bit messy, you’ll soon realize that the book offers more than illustrations and practical examples on the subject.
Why Choose this Machine Learning Book:
- Natural Language Processing with Python is a simple, elegant, and well-rounded book in natural language programming.
- It uses simple examples and illustrates how machine learning techniques can be applied to solve NLP problems.
- The organization of the book makes it a good read for personal studies.
- Used Book in Good Condition
- Bird, Steven (Author)
- English (Publication Language)
Last update: 2024-05-06
Runner Ups for Best Machine Learning Books
Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
Understanding Machine Learning: From Theory to Algorithms aims at introducing machine learning and all of its algorithmic paradigms in a straightforward and principled manner. The book starts by providing detailed theoretical accounts of the basics of machine learning. It then goes on in subsequent chapters to explain the mathematical derivations through which the principles get transformed into practical algorithms. Unlike most other machine learning books, this one delves in-depth into addressing a wide array of central topics. Some of such issues include stability concepts, computational complexity, and algorithmic paradigms.
Although written for beginning graduates and advanced undergraduates, the book still covers machine learning fundamentals, making it accessible to people with just a passing interest on the subject. The book takes a shot at explaining the basic principles behind the automated learning approach, while also discussing some of the considerations that underlie its usage. It explains all the whys and hows of several important machine learning algorithms.
What Makes it Unique:
- Understanding Machine Learning: From Theory to Algorithms is a classic book that efficiently blends machine learning’s theoretical and practical aspects.
- The presentation of such a broad range of analysis techniques makes it great for readers interested in machine learning’s mathematical aspects.
- Instead of merely brushing over the surface, this book does a proper coverage of several theoretical foundations, making it appropriate for classroom studies at advanced undergraduate stages.
Why Choose this Machine Learning Book:
- Instead of just going into a laundry list of techniques, the authors of this book provide a clear and concise introduction to help build your knowledge on the subject matter.
- Each chapter features a brief summary to give you a sense of what to expect throughout the chapter.
- Cambridge university press
- Language: english
- Binding: hardcover
Last update: 2024-05-06
Machine Learning For Dummies by John Paul Mueller and Luca Massaron
Written by two renowned data scientists, Machine Learning For Dummies serves as an entry point into a complicated field. The book covers all the introductory topics to help familiarize you with the basic concepts of the subject before moving you into the practical aspect. Here, the authors paid serious attention to help you quickly make sense of programming tools and languages that you need to handle machine learning-based tasks. Non-respective of the motivation driving you to study machine learning, you can be sure of this guide’s ability to aid easier understanding and implementation of the subject.
Machine Learning For Dummies features comprehensive information on how most daily activities are directly linked to machine learning. It also introduces readers to the technicalities of speaking specific languages like Python and R. It’s a book to consider if you are just starting to learn about machine learning since it walks you through all the basics you’ll need.
What Makes it Unique:
- Machine Learning For Dummies efficiently blends information on significant concepts of machine learning and their implementation patterns.
- It offers excellent details on important introductory machine learning topics.
- The book uses several good examples without launching you into statistical jargon like most other books would do.
Why Choose this Machine Learning Book:
- The examples featured in this book are self-explanatory and sufficient.
- Machine Learning For Dummies uses common terms to guide you through the technical aspects of machine learning.
- Mueller, John Paul (Author)
- English (Publication Language)
- 464 Pages - 01/22/2021 (Publication Date) - For Dummies (Publisher)
Last update: 2024-05-06
Value Picks for Best Machine Learning Books
Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) by Kevin P. Murphy
In a nutshell, Machine Learning: A Probabilistic Perspective is a book that provides a very cohesive and comprehensive framework for readers to understand machine learning models. Although not exactly great for starters, this book uses inference and probabilistic models as unifying approaches to introduce machine learning comprehensively. Murphy realizes the need to automate data analysis in today’s world of electronic data calls. Therefore, throughout this textbook, he offers a unique and comprehensive introduction to a complicated subject using a unified approach.
Many readers find the informal style adopted to write the book exciting. The author made the writing style accessible while incorporating pseudo-codes to explain essential algorithms. Each chapter features worked examples and for images drawn from applications like robotics, text processing, and biology. Most users believe the book to be suitable for higher-level undergraduates with a background in college maths.
What Makes it Unique:
- Instead of being a cookbook of varying heuristic methods, Machine Learning: A Probabilistic Perspective focuses more on providing a model-based approach that can be used in graphical models languages.
- It is targeted at practitioners in the field with the assumption that readers already know the basics.
Why Choose this Machine Learning Book
- It covers a wide range of topics, presenting each topic from the perspective of a computer scientist.
- The book offers technical explanations and a solid rationale for popular modeling tricks.
- The texts are easy-to-understand for anybody with a passing knowledge of the subject’s introduction.
- Hardcover Book
- Murphy, Kevin P. (Author)
- English (Publication Language)
Last update: 2024-05-06
Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas Müller and Sarah Guido
Written by two machine learning experts, Introduction to Machine Learning with Python is set up to take readers through a step-by-step understanding of machine learning from bottom-up. It does not assume a programming background but builds from the introduction, gradually reaching a climax on the subject’s practicalities. In summary, the book teaches several advanced topics, sandwiched with the necessary basics, while maintaining a logical structure. The authors realize the importance of building unique machine learning solutions for commercial applications; therefore, it dives deep into explaining the practical steps involved.
A passing familiarity on matplotlib and NumPy libraries will help you gain more from the book. Apart from covering the fundamental concepts of the subject, the book also explains the advantages of popular machine learning algorithms and their shortcomings. This book contains instructions that will allow you to set up an excellent development environment for people who will like to self-study.
What Makes it Unique:
- Introduction to Machine Learning with Python offers a detailed explanation of how readers can represent the data process created by machine learning.
- Throughout this book, the authors turn their attention to machine learning’s practicalities instead of the mathematics behind it.
Why Choose this Machine Learning Book:
- Introduction to Machine Learning with Python features an intuitive structure with each chapter elaborating on the core of machine learning concepts to help walk you through the basics.
- The book features a well-organized structure that’s easy for starters to follow.
- The author uses well-explained illustrations and examples to explain complex topics.
- Müller, Andreas (Author)
- English (Publication Language)
- 398 Pages - 11/15/2016 (Publication Date) - O'Reilly Media (Publisher)
Last update: 2024-05-06
How to Choose the Best Machine Learning Books for You
When it comes to choosing a good machine learning textbook, nothing beats knowing where to look. The first section of this article contains several options that you can pick up, irrespective of your learning stage. However, there might be times when the options in the list will not match your specific requirements, and you’ll need to get an additional book to broaden your knowledge of a particular topic.
At such times, you’ll need to search deep for the best options for your needs. Although challenging, it is possible to find top-quality books with good content when you search deeper. We’ve highlighted some questions that you’ll need to ask yourself and answer sincerely to help you through this rigorous process. Answering the following questions will guide you on the right path of finding the best machine learning books for your needs.
Are You Doing a Research on Machine Learning?
If you are researching machine learning, you’ll need a book that focuses on your research area. Most people in this category require advanced machine learning books to handle their research successfully. If you are thinking of starting an academic or professional study, you should go for a book focusing on your area of interest. The only problem with such books is that they assume a passing knowledge of the introductory parts of the basic topics.
Are You a Self Learner or an Absolute Beginner?
Most beginners do not possess the ability to learn by themselves. This is why they are often advised to go for books that are written for starters. Advanced learners, on the other hand, would do well studying by themselves. However, self-studying would mean getting textbooks for it. Such books feature tons of illustrations and examples to help walk learners through difficult topics. If you are new to it, then you can look through the contents of the options available to you to know, which helps you to self-study. If you are not sure about your ability to comprehend quickly when studying alone, then you can opt for other options that suit classroom situations.
What Machine Learning Books Do We Recommend for Intermediate and Experts?
After learning the basics, you would naturally expect to move to advanced topics and machine learning practicalities. These aspects would pose unique challenges of their own, and unless you are ready to handle such challenges, they may soon become intimidating to you. Thankfully, tons of advanced machine learning books can help walk you through this state. Some deep learning books for beginners can also help you get in touch with practicalities of it. Generally, such books focus on introducing you to the application of the knowledge you might have gained from reading machine learning books for beginners. Ask for recommendations from your book vendor or an experienced machine learning expert if it’s difficult to spot the differences.
Are You Looking for More Comprehensive Topics?
Most advanced learners are faced with the challenge of demystifying challenging topics. Tackling comprehensive topics requires finding the appropriate materials that will help. Whether it’s in the classroom or a professional setting, you’ll appreciate owning a book that walks you through such topics. When in the market, specifically lookout for such books that offer you what you need in this capacity. Such books are typically more expensive, but the knowledge you’ll get from them is definitely worth the price.
Wrapping Things Up: The Best Machine Learning Books
Here’s a helpful table to consolidate every machine learning book we went over:
Last update: 2024-05-06
That sums up a comprehensive list, reviewing the best machine learning books available in today’s market. These books guarantee enough information to help you learn the different aspects of machine learning, and you’ll want it. Whether you are a beginner, an intermediate learner, or a machine learning professional, you can rest assured that you’ll find a book that will suit your needs from our list above.
However, we understand that you may have a specific need or may not be interested in any of the recommended machine learning books reviewed. In that case, we advise that you make your selection by yourself. If you still find the whole process of choosing the right book for your need confusing, you can refer to our buying guide. The buying guide provided in this article covers a few questions that will guide you to the path of making a good choice.
Why go through the stress of sorting through tons of information when you can just pick some of our recommended texts. Our favorite machine learning book, The Hundred-Page Machine Learning Book by Andriy Burkov, is a concise book that leads you through machine learning execution basics. Our runners up, Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David, goes a step further to explain the algorithmic paradigms of machine learning in a straightforward manner. Overall, these books will help hasten your learning process while increasing your interest in the subject matter.
Looking for more IT-related textbooks? Check them out below:
> The Best Database Design Books
> The Best Computer Networking Books
