Top 10 Books on Machine Learning and AI

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Machine learning (ML) and artificial intelligence (AI) are foundational technologies in today’s technologically evolved and hyper-digital era. 

Computers changed the world, but ML and AI are changing computers by equipping them with the ability to process complex information at unprecedented scale and speed. Intellligent, human-like AIs are becoming more common, and we’re not even close to the peak yet. 

This is a guide to the best books on machine learning and AI. So, whether you’re an aspiring data scientist or data engineer, want to get into ML and AI, or would simply like to foster an interest in these fascinating subjects, read on!

1: Artificial Intelligence: A Guide for Thinking Humans - Melanie Mitchell

This superb guide to AI and ML provides a pragmatic, fun, and engaging insight into the discipline without weighing readers down with overbearingly technical detail. With that said, the book still covers the core AI algorithms, the past and future of AI, and its primary impacts on life now and life in the future. 

The book also contains information on neural networks, computer vision (CV), and natural language processing (NLP) to provide readers with a strong foundational and overarching knowledge of AI.

This very well-reviewed book is also inexpensive in paperback form and is a handy length at roughly 336 pages. Written in 2020, this book is certainly up-to-date and draws upon real industry insights to describe the kind of things that are happening right now in AI research. 

  • Easy to read but packed with good detail 
  • Excellent overview of AI and ML algorithms 
  • Numerous industry insights
  • Clear, lucid, and well-written for a diverse audience 

2: The Hundred-Page Machine Learning Book - Andriy Burkov

This 100-page book provides a superb must-have overview of machine learning for a wide audience. It packs plenty of detail into its small frame, including equations, diagrams, and decent technical detail. 

This short book provides a solid overview of algorithms and their purpose, covering many crucial topics like classical linear and logistic regression, modern support vector machines, deep learning, boosting, and random forests. 

While the book doesn’t shy away from technical subjects, it doesn’t assume any serious level of AI or ML knowledge from the reader. Instead, it’s accessible, clear, and well-written and will suit anyone involved either AI, ML, or IT in general. 

  • Short 100-page book 
  • Contains plenty of technical detail 
  • Suitable for a wide audience 
  • Excellent reviews by top AI and ML professionals 

3: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Aurelien Geron

This practical guide teaches techniques in three of ML’s most important tools: Scikit-Learn, Keras, and TensorFlow. Basic-to-good knowledge of Python is crucial here - Python is the premiere machine learning programming language and forms the basis of many well-known ML tools and libraries. 

This guide is targeted at those who want to code and build models, including in computer vision and NLP. The book uses practical examples of important AI and ML models that are used every day. It also aims to help readers optimize and deploy their models over the internet. 

An excellent book for getting hands-on with code without going through the formalities of learning math and theory.  

  • A hands-on guide for Python coders
  • Doesn’t require high-level coding knowledge
  • Aimed at building useful real-world models 
  • Very clearly written and well-reviewed 

4: Advances in Financial Machine Learning - Marcos Lopez de Prado

This well-reviewed, much-respected book is aimed at the economics and finance industry. Specifically, it teaches the application of AI and ML to finance and is ideal for anyone who wants to get involved with modern quantitative finance. 

While finance offers the type of high-volume, detailed data that ML demands and thrives upon, there are numerous challenges to processing financial data with ML techniques. This book addresses many of those challenges, ranging from issues with data ingestion to data cleaning, processing, and deploying models.
The author is extremely well-known in his field, and this book has been rated and reviewed by some influential voices across the global finance industry. 

  • Specifically for the finance industry 
  • Contains solutions to challenges in using ML in quantitative finance 
  • Highly-respected author
  • A well-reviewed, comprehensive book 

5: Deep Learning (Adaptive Computation and Machine Learning Series) - Ian Goodfellow , Yoshua Bengio, Aaron Courville

This book was written by top researchers in the field and was given an appraisal by Elon Musk. 

It covers all major topics in deep learning, including tons of theory and historical underpinnings. The book then turns its attention to topics like deep feedforward networks, regularization, optimization algorithms, convolutional networks, and sequence modeling. 

Models are described in the context of NLP, CV, bioinformatics, speech recognition, and video gaming. Complex topics are covered, like Monte Carlo modeling and deep generative models.

This is a very large 800-page book that contains huge volumes of detail about modern machine learning. It’s not the simplest introduction to the field, but serves as an excellent go-to for anyone who wants to get seriously involved in AI and ML. 

  • Long, detailed book by MIT researchers 
  • Superbly reviewed
  • 800 pages
  • Covers plenty of complex topics in deep learning 

6: Mathematics for Machine Learning - Marc Peter Deisenroth

Mathematics is fundamental in machine learning and will remain so as ML develops into the future. 

This book seeks to consolidate numerous mathematical ML-related topics and concepts, including algebra, matrix decompositions, vector calculus, analytic geometry, probability, optimization, and statistics. It applies concepts to four fundamental models; linear regression, Gaussian mixture models, principal component analysis, and support vector machines.

This book is an excellent exposition of the math that lies at the center of ML. It’s best suited to those who wish to develop their fundamental knowledge of AI and ML’s mathematical systems. 

  • Math-oriented book for ML
  • Teaches the math fundamental to modern machine learning
  • Clear and well-written 
  • Written by top academics and professionals in the field 

7: Approaching (Almost) Any Machine Learning Problem - Abhishek Thakur

This has been described as a ‘rare’ book on ML that cuts through the noise. This book takes a thoroughly code-first approach to teach the reader how to solve ML problems. While transcribing code from a book may not seem ideal, this is a vital part of the learning process.

Everything is provided in Python with Python tools and libraries, which is where most ML models are built anyway. Readers must have started some of their own basic projects and will understand key ML tools in Python, like Scikit and PyTorch.

The books cover supervised vs unsupervised learning, feature engineering and feature selection, hyperparameter optimization, image classification and segmentation, text classification, model ensembling, and testing. There are lots of supportive explanations and justifications for decisions. 

  • Pragmatic and clear code-first book
  • Requires Python knowledge 
  • Covers the start-to-finish process of creating popular ML models
  • Very practical 

8: Introduction to Machine Learning with Python: A Guide for Data Scientists - Sarah Guido and Andreas C. Mueller

Another Python-focussed book that is designed specifically for data science. Covers the entire machine learning workflow, including data preprocessing and cleaning, training algorithms, evaluating results, and deploying models into production. 

Requires knowledge in Scikit-Learn, but knowledge in NumPy and Matplotlib is useful. 

This is your typical O’Reilly machine learning book that takes you through fundamental, intermediate, and advanced concepts logically, clearly, and comprehensively. 

  • Machine learning for data scientists
  • Requires Python and Scikit-Learn knowledge
  • Written for modern applications 
  • Good for business-oriented ML

9: Homo Deus: A Brief History of Tomorrow - Yuval Noah Harari

While it’s far from a strictly ML and AI-related book, Homo Deus: A Brief History of Tomorrow, serves as a highly influential futurist tour de force. It covers the full spectrum of futurist possibilities, ranging from beating death to creating highly sophisticated artificial intelligence. Harari’s message is that humanity will lose its dominance and, eventually, its meaning as machines start to take over our identities. 

This is an undeniably provocative and well-written book that appeals to a wide readership. While technical detail is sparse, it’s a great warning of what ML and AI might turn into, and how humans can work together to mitigate the risks. 

  • Influential book on AI, ML, and futurism in general 
  • Warns as to the dangers of unbridled AI development 
  • Powerful, evocative writing 
  • The follow-up to Sapiens by the same author

10: Human Compatible: AI and the Problem of Control - Stuart Russell

This book essentially describes the pros and cons of AI in great detail. It establishes AI’s potential for good but mainly investigates how humanity should exercise caution when training increasingly complex AI systems. This is something Aya Data has explored in relation to bias, crime, and sentience, which all incite controversial debate around the use of complex AI. 

By focusing on humans as the ultimate end user and beneficiary of AI, Russell argues we can remain in control of complex AI - or at least give ourselves the best chance. 

  • Great book about political and social debates surrounding AI
  • Good theory for how we should stay in control of AI
  • A warning of contemporary AI-related issues 
  • Influential author with plenty of experience 

Summary: Top 10 Books on Machine Learning and AI

The authors here represent a highly established group of top professionals working in elite companies and influential academic positions. They all have one thing in common: they wrote books for aspiring data scientists, engineers, and ML practitioners.

Some people simply love books and prefer them as a learning medium to other options. To others, exploring AI and ML through the medium books might seem unsatisfactory compared to the internet.

In defense of books, they’re coherent and focussed and cover the topics you expect them to cover from cover-to-cover. This builds a strong but continuous understanding. Then, you can bring a book to places you can’t bring a laptop or ereader. Books combine with other forms of learning to help readers gain a more complete understanding of a topic - get stuck in and we’re sure you’ll agree. 

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