Machine learning is a special branch of computer science and artificial intelligence (AI), primarily involved in using data and algorithms and focuses on simulating the process of human learning and gradually improving its accuracy.
For beginners with no prior knowledge about this field, it can be challenging to understand and grasp the basics of Machine learning. Under the vast umbrella of machine learning, there are several research topics, fields, and business use cases that can make the learning journey overwhelming for beginners. Most learners don’t know where to start. This is where textbooks come in.
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Top 10 Machine Learning Books to Read
This article highlights some of the best machine learning books for beginners that university professors and AI experts also recommend. They are also helpful to professionals in this field to refer to specific topics to refresh their memory.
1. ‘Artificial Intelligence: A Modern Approach’ by Stuart J. Russel and Peter Norvig
This book perfectly covers ML and AI with great attention to detail and in a comprehensible language to make it easy for beginners. This book by Russell and Norvig is highly recommended by university-level professors and experts in the industry. An excellent choice for beginners, this book covers the basics of Artificial Intelligence and provides a thorough introduction to the field.
It also has an overview of many key research topics. It is a good choice for a book on Machine Learning because it also has a problem-solving approach. It is a de-facto textbook for beginners in Machine Learning as it serves as the cornerstone of introductory and in-depth courses in this field ever since it was published in 1994. Newer editions of this book cover topics on newer technologies and trends.
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2. ‘Deep Learning’ by Ian Goodfellow, Yoshoua Bengio, and Aaron Courville
If you are looking for a reliable book on deep learning, this is the best choice. This textbook combines general knowledge on deep learning and the mathematical approach one needs to get started with this topic. It consists of useful information on the prominent figures in this field, like Geoffrey Hinton, Yann LeCun, and the like.
If you focus on the knowledge in this book on deep learning and the advanced lectures in a university course, there is no stopping you from gaining the most reliable information and knowledge in this field. Researchers and professionals swear by the usefulness of this book.
3. ‘The Hundred-Page Machine Learning Book’ by Andriy Burkov
For beginners looking for a fun and compact, easily-comprehensible guide to machine learning, this textbook is undoubtedly a great choice. One fun thing to note is that it began as a simple LinkedIn challenge for the writer Andriy Burkov and led to one of the finest guides in machine learning. Despite being just a hundred-page learner’s guide to this field, it is a succinct textbook that focuses on the basics of machine learning, complex theories, and practical problems.
4. ‘The Elements of Statistical Learning: Data Mining, Inference, and Prediction’ by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
This textbook is usually used to introduce machine learning to beginners. It has been written in a unique style that motivates students and readers to analyse, speculate and experiment with things on their own that help in their career later on. It is a great book not only for basic information but also for skill development.
Because of its detailed theoretical framework and variegated range of topics, this textbook has gained a name for itself in the Machine learning community. It has a great introduction to the topic and can be used as a book for reference material later on for anyone.
5. ‘Applied Predictive Modelling’ by Max Kuhn and Kjell Johnson
This book provides a detailed introduction to modelling processes and predictive models. It is highly popular amongst data science students because of the detailed breakdown of the modelling process. It covers essential topics like predictive modelling processes such as data preprocessing, classification methods, and regression. It is an excellent book for skill building as it provides problems that require to be solved with code.
6. ‘Pattern Recognition and Machine Learning’ by Christopher M. Bishop
This was first published in 2006 and has been the go-to textbook for all university students of machine learning. It is a great book for beginners already pursuing this course because it has multivariate calculus and linear algebra that they can practice later on. Go for this book if you want to start with pattern recognition.
7. ‘Python Machine Learning’ by Sebastian Raschka and Vahid Mirjalili
For beginner’s level programmers, this deep learning textbook mainly focuses on applying popular machine learning algorithms. It has an in-depth chapter on the usage of scikit-learn and is the preferred textbook for students who have a knack for algorithms.
8. ‘Machine Learning’ by Tom M. Mitchell
This machine learning textbook is the perfect guide for students and professionals in this field. Its simple language makes it easy for learners to comprehend and grasp the concept of machine learning better, thereby making this challenging concept easier to understand. This book also acts as a fantastic textbook to brush up on one’s knowledge of the basics of machine learning.
9. ‘Speech and Language Processing’ by Daniel Jurafsky and James H. Martin
This book is considered one of the best machine learning books amongst most of the ones available because of its detailed introduction to the basics of machine learning. Industry experts and professors in AI/ML regard this book as their bible, especially as reference material for Natural Language Processing. Its detailed information about language technology covers a vast range of topics and courses. It also greatly emphasizes practical applications, making it a great guide for students interested in language processing.
10. ‘Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems’ by Aurélien Géron
This is a unique book on machine learning. It implements the practical application of machine learning, making it one of the best guides to programmers. Students who want to gain a comprehensive and in-depth knowledge implementation of programs for machine learning via TensorFlow and scikit-learn frameworks can refer to these books. The easily digestible explanations combined with exercises give readers a comprehensive understanding and skill development.
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Textbooks are primary and viable sources of knowledge and information in a particular field. All published deep learning textbooks are written by professionals in AI and are trustworthy, and can be relied upon by students pursuing machine learning.
Speaking of which, a career in Machine Learning and AI is quite a prospective choice if you are in a dilemma about which path to choose. If you have a knack for reading Machine learning and deep learning books, you can boost this passion further by enrolling in a course. upGrad has a top-tier Advanced Certificate Programme in Machine Learning & Deep Learning that will provide you with great Machine learning textbook recommendations and in-depth training in the field of AI.
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Are textbooks on deep learning and machine learning viable sources of information?
Yes. Textbooks on this topic are reliable sources of information because professionals and experts in this field write them. However, if you want an in-depth understanding of this subject to pursue a career, enrolling in a professional course is always advisable.
How can I become a master in machine learning?
A career in Machine Learning and AI is quite a prospective choice if you are in a dilemma about which path to choose. If you have a knack for reading Machine learning and deep learning books, you can boost this passion further by enrolling in a course.