Machine learning is an intimidating subject. Knowing where to develop mastery around such a massive subject that encompasses so many fields, research topics, and applications can be the hardest part of the journey. Anyone with a background in programming will attest to the value of a good textbook, especially when it comes to a subject as technical as machine learning.
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Whether you’re a complete novice or a distinguished mastermind in this field, we at iMerit have compiled the best field guides, icebreakers, and referential machine learning textbooks that will suit both newcomers and veterans alike who are looking to improve their understanding of machine learning.
Stuart J. Russel and Peter Norvig
Considered by many to be the de-facto machine learning textbook, Artificial Intelligence: A Modern Approach is a machine learning textbook that’s served as the cornerstone of highly-distinguished university-level AI programs since its inception in 1994. This book provides one of the most thorough introductions to this complicated field, and comes with intimate knowledge of key research. A 4th edition of the novel was published recently that focuses on the latest technologies and trends in the field, providing a fresh take to this timeless text.
Ian Goodfellow, Yoshoua Bengio, and Aaron Courville
This book is the undisputed perfect 101 lecture course of deep learning. It gives both a general context and knowledge regarding the mathematical foundation of Deep Learning, and more than enough to get you started on your own deep learning journey. The book has also been publicly acknowledged by some of the biggest names in machine learning due to its utility for both researchers and professionals.
This machine learning textbook is an easily digestibleWhat began as a simple LinkedIn challenge to Andriy Burkov became one of the finest crash-courses in machine learning. The Hundred-Page Machine Learning Book is commonly hailed as the most succinct machine learning textbook. Burkov doesn’t take any shortcuts either; the novel is practically brimming with complex theory combined with practical practice. All corresponding math equations are neatly tied into the work as well for those who care to go knee-deep. For those who find themselves wanting more after finishing the book, there are extended versions of various chapters for free online.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman
This machine learning textbook is commonly cited throughout the machine learning community for its conceptual framework and diverse range of topics. This textbook can function as both a casual intro to machine learning for a reference book for anyone needing to brush up on neural networks, testing methods, and more. The book actively encourages the reader every step of the way to conduct their own experiments and investigations on their own, making it useful for developing the skills and curiosities needed to make relevant breakthroughs within a machine learning capacity/career.
Max Kuhn and Kjell Johnson
Ideal for students or developers who need an introduction to modeling processes and/or predictive models, this machine learning textbook provides a comprehensive breakdown of the entire modeling process. Readers can expect to develop a journeyman understanding of things like the predictive modeling process across data preprocessing, regression, and classification techniques. The book gives hands-on examples of problems to solve with code in R for each stage, and also contains problems within each chapter to help the reader practice and apply what they’ve been learning.
Christopher M. Bishop
This book is a staple of many university courses since its initial publication back in 2006. But newbies beware, this book contains a litany of multivariate calculus and linear algebra that could scare off even an experienced mathematician or data scientist. It’s also the first machine learning textbook on pattern recognition that hails from the Bayesian viewpoint.
Sebastian Raschka and Vahid Mirjalili
This is the programmer’s machine learning textbook as it focuses exclusively on the implementation of a range of popular machine learning algorithms. The book places a special emphasis on using scikit-learn to implement these algorithms, and is a must for anyone looking to develop mastery around algorithm development.
Tom M. Mitchell
This compact machine learning textbook is the perfect field guide for the basics of machine learning. Students and experts alike can rejoice at the book’s brisk simplicity, a feat that’s nothing less than remarkable around such a challenging subject. The book is a fantastic reference guide for anyone looking to brush up on the basics while also not being too reductive. It’s for this same reason that the book is a no-brainer for new students as well. This machine learning textbook should serve as the foundation for which to conduct more in-depth research around.
If you find yourself interested in reading more, there’s some draft chapters that are free and published online for a possible second edition.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Daniel Jurafsky and James H. Martin
Perfect for anyone with a journeyman understanding of machine learning, this book is among the great machine learning textbooks thanks to its comprehensive introductions and detailed explanations of highly-specified machine learning fields. The book comes highly recommended from AI/ML experts worldwide as an NLP referential. The book provides detailed coverage around language technology, and unifies a diverse range of thoughts and theory across several distinct courses. The book’s emphasis on practical applications make it a perfect introduction to anyone interested in speech and language processing.
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Unique on this list due to its welcome practicality, this machine learning textbook is a mentor to programmers hoping to learn about the implementation of machine learning programs via scikit-learn and TensorFlow frameworks. The author’s explanations present strong evidence to aid in developing your understanding while also combining exercises to give readers a means to test said understanding. While the book is somewhat light in the theory it presents, it’s still a winner for anyone looking to quickly learn and understand how they’ll build a machine learning algorithm.
Any of these machine learning textbooks should serve you well in creating a foundation for your understanding and comprehension of machine learning. These textbooks have already stood the test of time in this rapidly developing field, and should continue as valuable referentials as you progress through your machine learning journey.
Once you’ve created your machine learning algorithm(s), you’ll be ready to start feeding it datasets and other kinds of information. For all data labeling and annotation needs, reach out to us at iMerit for a free consultation around how we tackle oceans of data everyday to help turn it into knowledge. For comprehensive annotations, come to iMerit.