12 Best AI Books: What To Read If You Want to Learn & Earn

AI and machine-driven insights are more accessible than ever before. The best books on artificial intelligence (AI) make it possible for anyone to start using this technology to improve their workflows.

With the adoption of AI on the rise, knowing how to use these solutions is vital, and with prompt engineering roles reportedly paying up to $375,000 per year, it can also be a lucrative decision.

This article will look at some of the best books about AI on the market. This includes high-level overviews of foundational concepts like machine learning?and deep learning and?practical guides that break down coding basic solutions.

The list is based not only on customer reviews from sites including Amazon, Goodreads, and Audible but also on the opinions of Techopedia and the author. We recommend you research all titles independently before buying to ensure the best fit for your needs.

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The Best Books on AI: Editors Choice

12. Make Your Own Neural Work

Tariq Rashid’s Make Your Own Neural Work is a book that introduces readers to neural networks. It includes an overview of the mathematical concepts behind neural networks, practical guidance on how to use the Python programming language to build a basic neural network, and finally, how to deploy it to a Raspberry Pi.

This book is a great place to start for those who want to learn about neural networks and how they work in an accessible format. It’s also a good fit for those who want to experiment with building their own neural networks.

Reviews: 4.5 on Amazon, 4.3 on Goodreads?

11. Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning

James Stone’s Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning is one of the best books on how AI works and aims to offer a deep dive into the mathematical concepts behind neural networks and deep neural networks.

This includes a discussion of historical neural networks, including perceptrons, Hopfield nets, Boltzmann machines, back propagation networks, and modern deep neural networks like convolutional networks, generative adversarial networks, and reinforcement learning.

This book is recommended for readers who want to understand how deep learning works and provides a comprehensive introduction to the topic.

Reviews: 4.3 on Amazon

10. Artificial Intelligence for Dummies

Luca Massaron and John Mueller’s Artificial Intelligence for Dummies is a book designed to take beginners and introduce them to AI and deep learning.

It briefly introduces what AI is and isn’t, how it’s being applied in society (mainly in healthcare settings), and, ultimately, how readers can find an AI-proof job.

This book is recommended for those looking for a high-level overview of the role of AI in modern society in a format that’s easy to digest.

Reviews: 4.3 Amazon, 3.5 on Audible?

9. Python: Beginners’ Guide to Artificial Intelligence

Denis Rothman, Matthew Lamons, and Rahul Kumar’s Python: Beginner’s Guide to Artificial Intelligence is designed to help readers learn to use Python to build their own AI solutions. The book introduces machine learning and deep learning algorithms and how to build predictive models with TensorFlow and open-source Python libraries.

As such, it provides a valuable starting point for users who might understand the basic concepts of how AI and machine learning work but want to learn how to use Python to start building end-user solutions.

Reviews: Amazon 4.4

8. Artificial Intelligence Basics: A Non-Technical Introduction

Tom Taulli’s Artificial Intelligence Basics: A Non-Technical Introduction offers a beginner-friendly introduction to foundational AI concepts like machine learning, deep learning, and natural language processing. It also includes case studies on how industry-leading companies like Uber and Facebook have used AI in the past to enhance their operations.

This book is recommended for those requiring a high-level overview of AI basics who aren’t yet ready to develop their solutions.

Reviews: Amazon 4.3

7. The Hundred-Page Machine Learning Book

Andriy Burkov’s The Hundred-Page Machine Learning Book is a short book that breaks down machine learning and deep learning for beginners. Burkov outlines modern machine learning approaches, including classical linear regression and logistic regression.

This book is recommended to non-technicals new to AI and those already familiar with key concepts in the field who want to refresh their foundational knowledge.

Reviews: Amazon 4.5, Goodreads 4.3

6. Applied Artificial Intelligence: A Handbook for Business Leaders

Marina Yao, Adelyn Zhou, and Marlene Jia’s Applied Artificial Intelligence: A Handbook for Business Leaders is an ideal resource for executives,? managers, and team leaders who want to find new ways to use machine learning to improve their decision-making and their organization as a whole.

It also includes a brief look at potential use cases for AI, whether leaders should build or buy an AI platform, and how to find and recruit the top talent in the industry.

Reviews: Amazon 4.0, Goodreads 3.6

5. Superintelligence: Paths, Dangers, Strategies

Nick Bostrom’s Superintelligence: Paths, Dangers, Strategies aims to explore the concerns and dangers on the road to developing artificial general intelligence (AGI) if and when machine intelligence passes by human intelligence.

This book is mainly recommended to those who are new to AI and want to look at some of the high-level risks of AI and how these could be mitigated. It’s also worth considering from the perspective of supporting the development of responsible AI.

Reviews: Amazon 4.4, Goodreads 3.9

4. Human Compatible: Artificial Intelligence and the Problem of Control

Stuart Russell’s Human Compatible: Artificial Intelligence and the Problem of Control is another title that sets its sights on breaking down machine intelligence and the potential for misuse. Russell also outlines how humanity can work toward the development of responsible AI to minimize the potential for harm.

This book is suitable for those who want to delve into the concerns and possibilities of AI systems and want to consider how responsible AI could be used to mitigate potential danger.

Reviews: Amazon 4.5, Goodreads 4.0

3. Machine Learning (in Python and R) For Dummies

John Paul Mueller and Luca Massaron’s Machine Learning (in Python and R) for Dummies provides a detailed look into machine learning and how it can be used in practical situations. This includes an introduction to programming languages like Python and R and explaining how to teach machines pattern-oriented tasks.

This book is recommended for those readers who want to develop their ability to code machine learning solutions.

Reviews: 4.3 Amazon

2. Artificial Intelligence in Healthcare: AI Machine Learning and Deep and Intelligence Medicine Simplified for Everyone

Dr. Parag Suresh Mahajan’s Artificial Intelligence in Healthcare provides a detailed breakdown of how AI is being used to enhance the healthcare industry. The book provides a top-down perspective of current and potential use cases of AI technologies in healthcare and looks at the ethical debate around adopting these solutions.

This book is recommended for readers who want to better understand how healthcare providers across the industry use AI to enhance patient care and decision-making.

Review: Amazon 4.4, Audible 3.7, Goodreads 4.5

1. Human Compatible: Artificial Intelligence and the Problem of Control

Stuart Russel’s Human Compatible: Artificial Intelligence and the Problem of Control highlights the opportunities and ethical concerns over superhuman intelligence and AGI.

The book also explores how adverse effects can be mitigated by rethinking AI from the ground up to create humble and altruistic machines that defer to humans.

At a high level, the book is a good read for diving into the popular narrative around AI risk while putting forward a framework to work toward addressing these concerns through responsible development.

Reviews: Amazon 4.5, Goodreads 4.1

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