10 Best AI Books to Build a Fruitful Career

Artificial Intelligence and Machine Learning aren’t going away anytime soon. Why? Because the bulk of industries worldwide is utilizing AI and ML for a brighter tomorrow and producing many career possibilities as a result.

In fact, AI will create nearly 97 million jobs worldwide, by 2025.

As an ambitious professional, you may now grasp these cutting-edge solutions and embark on a long and fruitful career.

Do you want to start a career in this field but aren’t sure where to begin? Do you want to understand AI but don’t know where to start? Do you want to learn AI and Machine Learning but don’t know where to start?

In this article, we will give you a list of 10 AI books that will help you learn AI.

Best AI Books to Build a Fruitful Career

1. Artificial Intelligence: A Modern Approach

Artificial Intelligence A Modern Approach, Global Edition

Author: Peter Norwig
Publisher: Pearson
Edition: 4th
Available in: Kindle, Paperback

About the book

The long-awaited rewrite of Artificial Intelligence: A Modern Approach delves into the field’s entire breadth and complexity (AI). Machine learning, deep learning, transfer learning, fairness, multi-agent systems, natural language processing, robotics, probabilistic programming, causality, privacy, and safe AI are all covered in the 4th Edition.

This brings learners up to date on the most recent technologies, provides concepts in a more homogeneous way, and includes greatly expanded coverage of deep learning, machine learning, transfer learning, multi-agent structures, mechatronics, language processing, cause and effect, probabilistic coding, confidentiality, honesty, and safe AI.

This new version is the best single-volume tome on AI. While there are many more technical guides available for people interested in becoming AI engineers, this book is thorough. It is suitable for both novices and specialists.

What you’ll learn

  • Intelligent agents
  • Solving problems by searching
  • Search in complex environments
  • Logical agents
  • Inference in first-order logic
  • Automated planning and more

You can buy this book from here.

2. Reinforcement Learning: An Introduction

Reinforcement Learning An Introduction (Adaptive Computation and Machine Learning series)

Author: Richard S. Sutton
Publisher: Bradford
Edition: 2nd
Available in: Kindle, Paperback

Reinforcement learning is a computational approach to learning in which an agent strives to maximize the overall amount of reward it gets while engaging with a complicated, uncertain environment. Richard Sutton and Andrew Barto present a clear and basic overview of the field’s fundamental theories and algorithms in Reinforcement Learning. This second version has been extensively enlarged and updated, with new topics introduced and existing themes updated.

This second edition, like the first, concentrates on key online learning techniques, with the more mathematical content separated into shaded boxes. Part I tries to cover as much reinforcement learning as feasible without going further than the tabular case, which has explicit solutions.

You can buy this book from here.

3. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems

Author: Aurelien Geron
Publisher: O’ Reilly
Edition: 3rd
Available in: Kindle, Paperback

A succession of recent improvements in deep learning has enhanced the overall field of machine learning. Even programmers with little or no experience with this technology may now construct data-driven programs using simple, efficient tools. This simple guide will show you how to do it.

Author Aurélien Géron utilizes basic examples, little theory, and two production-ready Python structures and Tensor Flow—to assist you to get a basic insight into the tools and methodologies for constructing ai applications.

You’ll look at a variety of strategies, beginning with simple linear regression and moving through deep learning models. All you require to get started are some programming skills and the activities in each section to enable you to put whatever you’ve studied into practice.

What you’ll learn

  • Investigate the landscape of machine learning, particularly neural networks.
  • Track an example machine-learning project from start to finish with Scikit-Learn.
  • Investigate various training models, such as support vector machines, random forests, decision trees, and ensemble approaches.
  • Investigate neural net designs such as recurrent nets, convolutional networks, and deep reinforcement learning
  • Learn how to train and scale deep neural networks.

You can buy this book from here.

4. The Master Algorithm

The Master Algorithm How the Quest for the Ultimate Learning Machine Will Remake Our World

Author: Petro Domingoes
Publisher: Basic books
Edition: Reprint
Available in: Kindle, Paperback

A wide-ranging and thought-provoking look at machine learning and the quest to create artificial intelligence that are as adaptable as our own.

The race to build the ultimate learning algorithm is on in the world’s best research labs and universities: one capable of uncovering any knowledge from data and accomplishing everything we want before we ask.

Pedro Domingos raises the curtain on the learning engines that run Google, Amazon, and your smartphone in his book The Master Algorithm. He constructs the Master Algorithm, a design for the future universal learner, and analyzes the implications for business, science, and society. This book is the bible of data-ism, today’s philosophy.

What you’ll learn

  • Machine learning revolution
  • Master algorithm
  • Hume’s problem induction
  • Pieces of puzzle

You can buy this book from here.

5. Prediction Machines: The Simple Economics of Artificial Intelligence

Prediction Machines The Simple Economics of Artificial Intelligence

Author: Ajay Agarwal
Publisher: Harvard Business review press
Edition: 1st
Available in: Kindle, Paperback

Artificial intelligence achieves the seemingly impossible, bringing machines to life and allowing them to do things like drive automobiles, trade stocks, and teach children. However, the enormity of the change that AI will bring can be paralyzing. How should businesses develop plans, governments craft rules, and individuals plan their lives in a world so different from our own?

Many analysts either tremble in dread or forecast an unrealistically bright future in the face of such uncertainty.

Three famous economists recast the emergence of AI as a decrease in the cost of prediction in Prediction Machines. They dispel the AI-is-magic myth with a single, powerful stroke, demonstrating how simple economic tools provide clarity about the AI revolution and a foundation for action by CEOs, managers, and policymakers.

What you’ll learn

  • Prediction
  • Decision making
  • Tools
  • AI strategy

You can buy this book from here.

6. Applied Artificial Intelligence: A Handbook for Business Leaders

Artificial Intelligence The Insights You Need from Harvard Business Review

Author: Yoshua Bengio
Publisher: MIT
Edition: Illustrated edition
Available in: Kindle, Paperback, hardcover, audiobook

Harvard Business Review’s Artificial Intelligence: The Insights You Need outlines how to create the proper projects at your firm to profit from the machine intelligence revolution’s opportunity.

The business environment is evolving. Will you be left behind if you don’t adapt?

With the Insights series, you can get up to speed and learn more about the themes that are influencing your company’s future. Each book offers the basic fundamental intro and pragmatic examples your company needs to compete today, as well as the best research, personal interview, and assessment to get it ready for tomorrow.

The book also features HBR’s cleverest reasoning on fast-moving issues such as blockchain, data security, and AI. You can’t afford to overlook the impact these concerns will have on the corporate landscape.

You can buy this book from here.

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

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

Author: James V Stone
Publisher: MIT
Edition: 1st
Available in: Kindle, Paperback

This is a really easy-to-read book with a confident voice that makes you feel like you’re in the room with a very capable instructor guiding you through your maths route in ML.

What you’ll appreciate most about this book is its attention on all of the key components of ML’s theoretical foundation; it has lofty goals and, for the most part, achieves them.

This small book has a wealth of information, with a down-to-earth approach to explaining basic topics in a polite manner, presuming the reader is intelligent yet inexperienced in the area. This is a must-have book for any novice or even veteran research scientist.

What you’ll learn

  • Artificial neural networks
  • Backpropagation algorithm
  • Perception
  • Hopfield nets
  • Boltzmann machine

You can buy this book from here.

8. Artificial Intelligence Basics: A Non-Technical Introduction

Artificial Intelligence Basics A Non-Technical Introduction

Author: Yoshua Bengio
Publisher: MIT
Edition: 1st
Available in: Kindle, Paperback

Understanding AI and its implications for your business is critical for development and success in today’s world.

Artificial Intelligence Basics is here to provide you with a solid foundation in AI and its implications. Machine learning, natural language processing (NLP), deep learning, robots, and other vital subjects are introduced in an appealing, non-technical manner by author Tom Taulli.

Taulli uses his knowledge to elaborate on the larger topics surrounding AI, in addition to bringing you through real-world case studies and practical implementation processes. These include sociological trends, ethics, and the influence AI will have on international governments, business structures, and everyday life in the future.

You can buy this book from here.

9. AI and Machine Learning for Coders

AI and Machine Learning for Coders A Programmer's Guide to Artificial Intelligence

Author: Yoshua Bengio
Publisher: MIT
Edition: 1st
Available in: Kindle, Paperback

This is a great place to start if you want to advance your career from coder to AI specialist. This introductory book, based on Laurence Moroney’s very successful AI classes, takes a hands-on, code-first approach to help you gain confidence while learning fundamental subjects.

For the online, mobile, cloud, and embedded runtimes, you’ll learn how to implement the most prevalent situations in machine learning, such as natural language processing (NLP), computer vision, and sequence modeling. The majority of machine learning books start with a scary quantity of sophisticated mathematics. This guide is based on hands-on exercises that allow you to work with the code directly.

What you’ll learn

  • How to Create Models with TensorFlow Using Desired Employer Skills
  • Working with code examples to master the fundamentals of machine learning
  • How to Implement Computer Vision, including Image Feature Recognition
  • How to use Natural Language Processing (NLP) to tokenize and sequence words and phrases
  • Model embedding methods for Android and iOS
  • How to Use TensorFlow Serving to Serve Models Over the Web and in the Cloud

You can buy this book from here.

10. Atlas of AI

Atlas of AI Power, Politics, and the Planetary Costs of Artificial Intelligence

Author: Yoshua Bengio
Publisher: MIT
Edition: 1st
Available in: Kindle, Paperback

What occurs when artificial intelligence becomes pervasive in political discourse and depletes the planet’s resources? How is artificial intelligence (AI) influencing our perceptions of ourselves and our communities?

Award-winning academic Kate Crawford shows how AI is an extracting technology, from the minerals extracted from the soil to the labor extracted from low-wage data workers to the data extracted from every movement and expression, based on more than a decade of study.

This book explains how this global network is promoting a rise in inequalities and an undemocratic government. Crawford takes a practical and political approach to what it takes to build AI and how it centralizes power, rather than focusing just on code and algorithms. This is a critical examination of what’s at risk in the current political climate.

You can buy this book from here.

Conclusion

AI is the future and learning it will not only make you stand out in the crowd, but it will also give you a competitive edge in a job interview. No matter which book you choose, ensure that you read it thoroughly and understand the basics in detail. Is there a book that you think deserves to be on this list? Comment below and we will surely add it after reviewing.

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