Must Read Books for Beginners on Machine Learning and Artificial Intelligence
As a data scientist, are you eager to unlock new doors and level up your skills? We’ve meticulously handpicked best AI books to kickstart your enthralling journey into the fascinating realm of ML and AI. Our selection covers books that break down complex concepts into easily digestible nuggets, keeping you engaged with relatable examples and real-world applications. Whether you’re a Python or R aficionado, there’s something intriguing in store for everyone. Checkout the best book for machine learning and artificial intelligence in here!
‘Machine learning is a core, transformative way by which we’re rethinking everything we’re doing. We’re thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. We’re in the early days, but you’ll see us in a systematic way think about how we can apply machine learning to all these areas.’
– Sundar Pichai, CEO, Google
Table of contents
- 1. Machine Learning Yearning
- 2. The Hundred-Page Machine Learning Book
- 3. Programming Collective Intelligence
- 4. Machine Learning for Hackers
- 5. Machine Learning by Tom M Mitchell
- 6. The Elements of Statistical Learning
- 7. Learning from Data
- 8. Pattern Recognition and Machine Learning
- 9. Natural Language Processing with Python
- 10. Artificial Intelligence: A Modern Approach
- 11. Artificial Intelligence for Humans
- 12. Paradigm of Artificial Intelligence Programming
- 13. Artificial Intelligence: A New Synthesis
- 14. Superintelligence
- 15. The Singularity is Near
- 16. Life 3.0 – Being Human in the Age of Artificial Intelligence
- 17. The Master Algorithm
- End Notes
1. Machine Learning Yearning
Machine Learning Yearning by Andrew Ng is a highly-regarded guide and one of the best books for machine learning enthusiasts who want to understand practical aspects of ML in real-world applications. This book focuses on teaching you how to make effective decisions while structuring machine learning projects. Ng, a renowned AI expert, shares valuable insights and strategies for creating successful ML systems that can make accurate predictions.
The book emphasizes choosing the right approach to handle issues like bias, variance, and overfitting, and it delves into the importance of error analysis and data collection. As you progress through the chapters, you’ll learn how to prioritize the most promising directions for your ML projects.
Machine Learning Yearning is an essential resource for developers, data scientists, and professionals seeking to unlock the true potential of machine learning and create systems that can genuinely learn from experience.
2. The Hundred-Page Machine Learning Book
The Hundred-Page Machine Learning Book by Andriy Burkov is a concise yet comprehensive guide to the fundamentals of machine learning and one of the best AI books available. Written in a straightforward manner, this book aims to make ML accessible to a broad audience, including software developers, data scientists, and business professionals. It covers essential topics such as supervised and unsupervised learning, neural networks, deep learning, reinforcement learning, and support vector machines. Additionally, the book discusses feature engineering, model evaluation, and selection techniques. Each chapter is structured to provide both a high-level overview and practical guidance, making it the perfect resource for those looking to quickly grasp the core concepts of machine learning without being overwhelmed by excessive details.
3. Programming Collective Intelligence
Programming Collective Intelligence, PCI as it is popularly known, is one of the best books to start learning machine learning. If there is one book to choose on machine learning – it is this one. I haven’t met a data scientist yet who has read this book and does not recommend to keep it on your bookshelf. A lot of them have re-read this book multiple times.
The book was written long before data science and machine learning acquired the cult status they have today – but the topics and chapters are entirely relevant even today! Some of the topics covered in the book are collaborative filtering techniques, search engine features, Bayesian filtering and Support vector machines. If you don’t have a copy of this book – order it as soon as you finish reading this article! The book uses Python to deliver machine learning in a fascinating manner.
4. Machine Learning for Hackers
Machine Learning for Hackers, authored by Drew Conway and John Myles White, is an engaging and practical guide, ranking among the best AI books for those interested in applying machine learning techniques to real-world problems. Targeted primarily at programmers with a background in data analysis, this book utilizes the R programming language to explore and implement various machine learning algorithms.
The authors provide hands-on examples, walking readers through each step of data preprocessing, feature selection, and model training to demonstrate how machine learning can solve complex issues. From sentiment analysis to recommendation systems, Machine Learning for Hackers serves as an excellent starting point for programmers and data enthusiasts seeking a comprehensive introduction to AI-driven problem-solving.
5. Machine Learning by Tom M Mitchell
After you’ve read the above books, you are good to dive into the world of machine learning. And this is a great introductory book to start your journey. It provides a nice overview of ML theorems with pseudocode summaries of their algorithms. Apart from case studies, Tom has used basic examples to help you understand these algorithms easily.
Most of the experts you ask in this field never fail to mention this book which helped them at the start of their careers. It’s such a well-written and explained book that we feel it should be made mandatory in every machine learning course!
6. The Elements of Statistical Learning
This is quite a popular book. It was written by Trevor Hastie, Robert Tibshirani and Jerome Friedman. This book aptly explains various machine learning algorithms mathematically from a statistical perspective. It provides a powerful world created by statistics and machine learning. This books lays emphasis on mathematical derivations to define the underlying logic behind an algorithm. Keep in mind that you need to have a rudimentary understanding of linear algebra before picking this up.
There’s a beginner friendly version of these concepts in a book by some of the same authors, called ‘Introduction to Statistical Learning’. Make sure you check that out if this one is too complex for you right now.
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7. Learning from Data
Learning from Data by Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin is a highly-regarded resource and one of the best AI books for understanding the theoretical foundations and practical aspects of machine learning. This book aims to strike a balance between theory and practice by providing a comprehensive introduction to the key concepts, techniques, and algorithms in machine learning.
With its focus on the process of learning from data, the book covers topics such as linear models, regularization, neural networks, support vector machines, and unsupervised learning. It also delves into the broader context of machine learning, exploring its impact on various fields and applications.
Learning from Data is ideal for students, researchers, and professionals seeking to gain a solid understanding of machine learning’s core principles and the essential knowledge required to apply AI techniques effectively in a wide range of scenarios.
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8. Pattern Recognition and Machine Learning
Pattern Recognition and Machine Learning by Christopher M. Bishop is a classic and one of the best books for machine learning enthusiasts who want to dive deep into the subject matter. This comprehensive textbook provides an introduction to the field of pattern recognition, covering both probabilistic and Bayesian approaches to machine learning.
The book presents the theory and techniques essential for understanding a wide range of machine learning algorithms, including supervised and unsupervised learning methods, graphical models, and neural networks. With its strong emphasis on real-world applications, the book demonstrates the relevance and importance of machine learning in fields such as computer vision, speech recognition, and data mining.
Richly illustrated and featuring numerous examples and exercises, Pattern Recognition and Machine Learning is an indispensable resource for students, researchers, and professionals who want to explore and master the principles and methods of modern machine learning.
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9. Natural Language Processing with Python
Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper is a practical and comprehensive guide, earning its place among the best AI books for those interested in exploring the fascinating world of natural language processing (NLP). The book introduces readers to NLP techniques using the powerful and versatile Python programming language.
The authors cover essential topics such as building linguistic data structures, working with corpora, part-of-speech tagging, parsing, and semantic interpretation. Through engaging examples and hands-on exercises, the book demonstrates how to create custom Python tools to analyze and manipulate natural language text, opening up new opportunities for text analysis and NLP applications.
Ideal for researchers, developers, and linguists, Natural Language Processing with Python serves as an invaluable resource for anyone looking to understand and harness the potential of NLP to process and derive insights from large volumes of unstructured text data.
10. Artificial Intelligence: A Modern Approach
Who better to learn AI from than the great Peter Norvig? You have to take a course from Norvig to understand his style of teaching. But once you do, you will remember it for a long, long time.
This book is written by Stuart Russell and Peter Norvig. It is best suited for people new to A.I. More than just providing an overview of artificial intelligence, this book thoroughly covers subjects from search algorithms and reducing problems to search problems, working with logic, planning, and more advanced topics in AI, such as reasoning with partial observability, machine learning and language processing. Make it the first book on A.I in your book shelf.
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11. Artificial Intelligence for Humans
This book is written by Jeff Heaton. It teaches basic artificial intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. It explains these algorithms using interesting examples and cases. Needless to say, this book requires good commands over mathematics. Otherwise, you’ll have tough time deciphering the equations.
12. Paradigm of Artificial Intelligence Programming
Artificial Intelligence for Humans by Jeff Heaton is a highly accessible and informative series that ranks among the best AI books for individuals looking to grasp the fundamentals of artificial intelligence. The series covers a wide range of topics, from genetic algorithms and swarm intelligence to machine learning and neural networks, providing a comprehensive understanding of AI concepts.
Heaton’s engaging writing style makes complex AI principles easy to comprehend, even for readers without a strong mathematical background. Each volume focuses on a specific area of AI, using practical examples and real-world applications to demonstrate how the techniques and algorithms can be employed to solve problems and develop intelligent systems.
Ideal for students, developers, and anyone curious about AI, Artificial Intelligence for Humans offers a valuable introduction to the diverse and ever-evolving world of artificial intelligence, equipping readers with the knowledge needed to navigate and excel in this exciting field.
13. Artificial Intelligence: A New Synthesis
This book is written by Nils J Nilsson. After reading the above 3 books, you’d like something which could challenge your mind. Here’s what you are looking for. This books covers topics such as Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks and explains them with great ease. I wouldn’t recommend this book for a beginner. However, it’s a must read for advanced level user.
Nick Bostrom has authored (or co-authored) over 200 publications, including this book called Superintelligence. Most of the world is enthralled and captivated by what AI can do and it’s potential to change the world.
But how many of us stop to think about how AI will affect our society? Are we considering the human aspect at all when building AI products and services? If not, we really should. In this thought-provoking book, Nick Bostrom lays down a future scenario where machines reach the superintelligent stage and deliberately or accidentally lead to the extinction of humans.
This might sound like a sci-fi movie plot, but the way Mr. Bostrom has laid down his arguments and the thinking behind them will definitely sway you and make you take him seriously. We consider this a must-read for everyone working in the AI space.
15. The Singularity is Near
Similar to the above idea propounded by Nick Bostrom, Ray Kurzweil’s ‘Singularity is Near’ delves into the thick depths of superintelligent machines. It is a slightly long read, but well worth it in the end. The way Mr. Ray has described the Singularity is breathtaking and will make you stop in your tracks.
Singularity, as Ray Kurzweil has described it, is the point where humans and the intelligence of machines will merge. Once this happens, machines will be far more intelligent than all of the human species combined. It’s NOT science fiction but a truly poignant description of what might happen in the future if we aren’t careful with what and how we work with AI.
16. Life 3.0 – Being Human in the Age of Artificial Intelligence
When Stephan Hawking endorses a book, one sits up and listens. This book by Max Tegmark is an international bestseller and deals with the topic of superintelligence.
Some of the basic questions this book asks (and answers) are (taken from Amazon’s summary): How can we grow our prosperity through automation, without leaving people lacking income or purpose? How can we ensure that future AI systems do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will AI help life flourish as never before, or will machines eventually outsmart us at all tasks, and even, perhaps, replace us altogether?
17. The Master Algorithm
This is one of our favorite books in this list. Can there be just one algorithm that deals with all the aspects of technology? Instead of building AI products for specific functions, can we build one single algorithm for all functions? This thought is quite similar to what Albert Einstein spent the latter years of his life trying to discover.
Pedro Domingos is a masterful writer, and he deals with the intricacies of his subject extremely well. Make sure you add this to your reading list!
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Check out these 10 Free E-books on Machine Learning as well which are a great starting point for anyone in this field.
This is just the tip of the iceberg. Books are a wonderful source of knowledge for anyone willing to learn from them. This collection spans various aspects of AI and ML – from the mathematics and statistics side to the intangible factors like ethics and impact of society. All of these should be considered together when working on an AI and ML project.
Having said that, there is truly no substitute for experience. Once you’ve devoured all these books can provide, always apply your learning to real-world problems and challenges. And as always, if you have any questions or suggestions for us on this article, feel free to share them in the comments section below. We look forward to connecting with you!