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Artificial Intelligence and Books


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20+ Best Artificial Intelligence Books for Beginners & More

Posted in Artificial Intelligence, Books
Artificial Intelligence Books

Table of Contents

In 2022, Artificial Intelligence is the hottest and most in-demand field; most engineers want to make their careers in AI, Data Science & Data Analytics. Going through the best and most reliable resources is the best way to learn, so here is the list of the best AI books on the market today.

What is AI (Artificial Intelligence)?

Artificial Intelligence is the field of study that simulates the processes of human intelligence on computer systems. 

These processes include the acquisition of information, using them, and approximating conclusions. 

The research topics in AI include problem-solving, reasoning, planning, natural language, programming, and machine learning. Automation, robotics, and sophisticated computer software and programs characterize a career in Artificial Intelligence. 

Basic foundations in maths, technology, logic, and engineering can go a long way in kick-starting a career in Artificial Intelligence.

Best Artificial Intelligence Books

Here we have listed a few basic and advanced Artificial Intelligence books, which will help you find your way around AI.

Best AI Books for Beginners

1. Artificial Intelligence: Learning Automation Skills with Python

By – Chris Baker

This book explores the potential consequences of Artificial Intelligence and how it will shape the world in the coming years. It familiarizes how AI aims to aid human cognitive limitations. It covers:

  • A brief history of artificial intelligence
  • The state of machine learning
  • Artificial neural networks applied to machine learning
  • How to build an AI-ready culture
  • Effects of AI on our daily lives

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2. Artificial Intelligence Basics: A Non-Technical Introduction

By – Tom Taulli

This book equips you with a fundamental grasp of Artificial Intelligence and its impact. It provides a non-technical introduction to important concepts such as Machine Learning, Deep Learning, Natural Language Processing, Robotics, and more. Further, the author expands on the questions surrounding the future impact of AI on aspects that include societal trends, ethics, governments, company structures, and daily life.

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3. Artificial Intelligence: An Essential Beginner’s Guide to AI, Machine Learning, Robotics, The Internet of Things, Neural Networks, Deep Learning, Reinforcement Learning, and Our Future

By – Neil Wilkins

Have you ever wondered what the deal is with artificial intelligence and intelligent machines? In a few years, are they going to decide on cases such as DMCA and copyright infringements? Will they eventually become so intelligent that they can fully operate self-driving vehicles?

What kind of effect would these intelligent machines have on humanity and society? How will they affect human jobs — would the AI essentially take over?

This book will show you the basic concepts of artificial intelligence as well as many other topics surrounding AI and machine learning. It even delves into issues like AI and employment as well as AI and ethics. However, when it comes to truly learn about the subject, this book doesn’t impart all that much technical knowledge. Those looking to learn how might want to look elsewhere.

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4. Artificial Intelligence and Machine Learning

By – Chandra S.S.V

This book is primarily intended for undergraduate and postgraduate students of computer science and engineering. This textbook covers the gap between the difficult contexts of Artificial Intelligence and Machine Learning. It provides the most number of case studies and worked-out examples. In addition to Artificial Intelligence and Machine Learning, it also covers various types of learning like reinforced, supervised, unsupervised and statistical learning. It features well-explained algorithms and pseudo-codes for each topic which makes this book very useful for students.

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5. Python: Beginner’s Guide to Artificial Intelligence

By – Rahul Kumar, Ankit Dixit, Denis Rothman, Amir Ziai, Mathew Lamons

This book helps you to gain real-world contextualization using deep learning problems concerning research and application. Design and implement machine intelligence using real-world AI-based examples. This book offers knowledge in machine learning, deep learning, data analysis, TensorFlow, Python, and the fundamentals of AI, and will be able to apply your skills in real-world projects.

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6. A First Course in Artificial Intelligence

By – Deepak Khemani

This book follows a bottom-up approach exploring the basic strategies needed for problem-solving mainly on the intelligence part. Its main features include an introductory course on Artificial Intelligence, a knowledge-based approach using agents all across and detailed, well-structured algorithms with proofs.

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7. Basics of Artificial Intelligence & Machine Learning

By – Dr. Dheeraj Mehrotra 

This book delivers an understanding of Artificial Intelligence and Machine Learning with a better framework of technology.

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8. Life 3.0 Being Human in the Age of Artificial Intelligence

By – Max Tegmark

This book takes its readers to the heart of the latest AI thought process to explore the next phase of human existence. The author here explores the burning questions of how to prosper through automation without leaving people jobless, how to ensure that future AI systems work as intended without malfunctioning or getting hacked and how to flourish life with AI without eventually getting outsmarted by lethal autonomous machines.

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9. Artificial Intelligence – A Modern Approach (3rd Edition)

By – Stuart Russell and Peter Norvig

This edition covers the changes and developments in Artificial Intelligence since those covered in the last edition of this book in 2003. This book covers the latest development in AI in the field of practical speech recognition, machine translation, autonomous vehicles, and household robotics. It also covers the progress, in areas such as probabilistic reasoning, machine learning, and computer vision.

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Best Advanced Books in Artificial Intelligence

10. Python: Advanced Guide to Artificial Intelligence

By – Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani

This book is a complete guide to learning popular machine learning algorithms. You will learn how to extract features from your dataset and perform dimensionality reduction by using Python-based libraries. Then you will be learning the advanced features of Tensorflow and how to implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this book, you will have an in-depth knowledge of Tensorflow and will be the go-to person for solving AI problems.

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

By – James V Stone

In this book, key neural network learning algorithms are explained, followed by detailed mathematical analyses. Online computer programs collated from open source repositories give hands-on experience of neural networks. It is an ideal introduction to the algorithmic engines of modern-day artificial intelligence.

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12. Artificial Intelligence By Example

By – Denis Rothman

This book serves as a starting point for understanding how Artificial Intelligence works with the help of real-life scenarios. You will be able to understand the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotients in chatbots using neural networks. By the end of this book, you will have understood the fundamentals of AI and worked through a number of case studies that will help you develop the business vision. This book will help you develop your adaptive thinking to solve real-life AI cases. Prior experience with Python and statistical knowledge is essential to make the most out of this book.

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13. Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp

By – Peter Norvig

This book teaches advanced Common Lisp techniques in the context of building major AI systems. It reconstructs authentic, complex AI programs using state-of-the-art Common Lisp, and builds and debugs robust practical programs while demonstrating superior programming style and important AI concepts. It is a useful supplement for general AI courses and an indispensable reference for a professional programmer.

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Best AI Books for Other Industries

14. Applied Artificial Intelligence: A Handbook for Business Leaders

By – Mariya Yao, Adelyn Zhou, Marlene Jia

Applied Artificial Intelligence is a practical guide for business leaders who are passionate about leveraging machine intelligence to enhance the productivity of their organizations and the quality of life in their communities. This book focuses on driving concrete business decisions through applications of artificial intelligence and machine language. It is one of the best practical guides for business leaders looking to get true value from the adoption of Machine Learning Technology.

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15. Artificial Intelligence in Healthcare

By – Mahajan MD, Parag Suresh

This book explores the role of Artificial Intelligence in Healthcare, how it is revolutionizing all aspects of healthcare, and guides you through the current state and future applications of AI in healthcare, including those under development. It also discusses the ethical concerns related to the use of AI in healthcare, the principles of AI & how it works, the vital role of AI in all major medical specialties, & the role of start-ups and corporate players in AI in healthcare.

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16. Machine Learning – The New AI

By – Ethem Alpaydin

It is a concise overview of machine learning which underlies applications that include recommendation systems, face recognition, and driverless cars. The author offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.

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17. Fundamentals of Machine Learning for Predictive Data Analytics – Algorithms, Worked Examples and Case Studies (The MIT Press)

By – John D. Kelleher, Brian Mac Namee

It is a comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution.

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18. Machine Learning for Beginners

By – Chris Sebastian

This book traces the development of Machine Learning from the early days of computer learning to machines being able to beat human experts. It explains the importance of data and how massive amounts of it provide ML programmers with the information they need to develop learning algorithms. This book explores the relationship between Artificial Intelligence and Machine Learning. Machine Learning for Beginners may just be the best book for AI if you’re a complete newbie to the subject.

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19. Applied Analytics through Case Studies Using SAS and R: Implementing Predictive Models and Machine LearningTechniques

By – Deepti Gupta

It is a Data Science Bool with an effective understanding of ML Algorithms on R and SAS. This book provides real-time industrial data sets. It covers the Role of Analytics in various Industries with case studies in Banking, Retail, Telecommunications, Healthcare, Airlines, and FMCG along with Analytical Solutions.

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20. Advances in Financial Machine Learning

By – Lopez de Prado, Marcos

This book teaches readers how to structure Big Data in a way that is amenable to Machine Language Algorithms, how to conduct research on that data with ML algorithms, how to use supercomputing methods, and how to backtest discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples.

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21. Human Compatible – Artificial Intelligence and the Problem of Control

By – Stuart Russel

In this book, the author explores the idea of intelligence in humans and machines. He describes the near-time benefits that can be expected from intelligent personal assistants to vastly accelerated scientific research. The author suggests that AI can be built on a new foundation by which machines will be designed where they will be uncertain about the human preference they are required to satisfy. Such machines would be humble, altruistic, and committed to pursuing human objectives.

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How to Learn Artificial Intelligence?

If you’re wondering how to learn artificial intelligence from scratch, the steps below may help.

If you do not know how to code or do not know anything about Python, many often recommend starting there. Learn Python through self-study or by purchasing courses on websites like Udemy. You can also sign up for a coding bootcamp or an online program. 

After you learn Python, the generally recommended next step is to learn about data science. Again, you can learn about this topic on your own or through Udemy courses or online programs. Once you know enough about data science, you can then start learning more about machine learning and artificial intelligence.

The AI books on this list are an excellent way to start learning AI. Look for some of the AI books for beginners to help you get started. If the best books on artificial intelligence are not the best method for you, there are also courses on Udemy and programs online that you can participate in.

Finally, after you’ve learned enough about theory, you can use that knowledge and gain some practical experience by working through these steps:

  1. Find a topic that interests you. It must be something that you like or enjoy since a topic like that can help in keeping you motivated so you stay involved as you learn. Within this topic, focus on a specific problem and find a solution for it.
  2. Find a solution. Don’t worry if it’s super basic since the point is to find a solution that can cover your perceived problem to the fullest extent. From here, you will need an algorithm that can process data and make it understandable and straightforward for machine learning. You can then train a basic model, establish a result, and then evaluate performance.
  3. Continue improving upon your solution. Once you have a foundation, it becomes possible to get creative and build upon your solution. You can try to improve parts of it and evaluate the changes you made to see whether they were worth the time.
  4. Get feedback. The best way to continue learning is by getting feedback from people who already know what they are doing. Share your work in forums and spaces where you can get constructive feedback. This feedback can also come in the form of valuable advice.

Once you’ve completed steps one to four, you can keep repeating them until you become more comfortable with artificial intelligence. It may be a good idea to develop solutions for different problems while also working with other components and aspects of machine learning.

You can also join a Kaggle competition, which tests your knowledge and skills. This competition has you solving the same problems as other engineers. As a result, you’ll see different possible approaches that you may not have thought of otherwise.

Conclusion

A career in Artificial Intelligence can be realized in a variety of spheres which include private organizations, public undertakings, education, arts, health care, government services, and the military. The extent of artificial intelligence continues to advance every day. Hence, those with the ability to translate those digital bits of data into meaningful human conclusions will be able to sustain a very rewarding career in this field. 

You can check out many AI courses and certifications provided online. The courses will definitely be promising, and a whole lot of opportunities will show up on your way.

Frequently Asked Questions

1. Which book is best for learning AI?

The best book for artificial intelligence will depend on your skill level and your starting point. Additionally, it can also depend on your particular interests. On this list are some of the best artificial intelligence books currently available on the market. It may benefit you to check them out and see whether any of them suit your needs most.

2. Can I learn AI on my own?

Yes! Even without any prior knowledge or experience in engineering, it is entirely possible to learn AI on your own and from the comfort of your home. You can certainly learn about concepts and theories around AI, but that alone isn’t enough. You can create some simple machine learning projects to put your knowledge to the test and work with artificial intelligence in practice. Hands-on experience is key to being able to fully grasp a subject, after all.

It may be challenging to learn artificial intelligence by yourself if you have no knowledge at all of some topics that can be necessary for the process. If you are starting from scratch as a complete beginner, it might benefit you to start with some intro courses to Python. After learning more about Python, it might help you to begin moving into data science which leads to artificial intelligence and machine learning.

3. How do I start learning about AI?

Getting started with AI doesn’t have to be difficult. There are clear and simple steps you can take, such as reading some of the best AI books on this list. However, don’t just pick up any artificial intelligence book — make sure you look for one that suits your level and interests. For example, if you’re new to AI, start with what you think is the best book for artificial intelligence for beginners.

You can also follow the steps listed in the “How to Learn Artificial Intelligence” section above.

4. Can I learn AI without coding?

It is definitely possible, but it also depends on your idea of learning artificial intelligence. There are now some tools online that can let you start experimenting with machine learning and artificial intelligence without ever needing to know how to code. For example, there are services through which you can train AI tools on your own without knowledge of coding or programming, but the use cases are limited. Over time, companies might develop more tools that can broaden the scope of what these tools can do.

It’s also possible to learn AI and machine learning on your own by reading the best books on AI. However, it might be quite impossible to master artificial intelligence without knowing deeper math and coding. Thankfully, you don’t need to be an expert data scientist or a master computer scientist to start the process.

5. Is AI based on a book?

Yes and no. An unrelated A.I. is indeed based on a book. Artificial Intelligence (A.I.) is a science fiction and drama movie from 2001 that was not just written but also directed by the legendary Steven Spielberg. It was based on Ian Watson’s screen story, which in turn was based on a short story by Brian Aldiss from 1969 called “Supertoys Last All Summer Long.”

The artificial intelligence being spoken about in this article, on the other hand, is a term coined by Stanford computer science professor John McCarthy.

6. How can I prepare for an AI job interview? 

We highly recommend reading the AI interview questions before applying for jobs if you feel you may need to brush up on some questions asked. 

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Barnali Chanda

Barnali Chanda

Barnali is a software developer, who eventually transformed into a technical documentation writer with her continuous research and development skills. She is an expert in C, C++, PHP, Python and RDBMS. She makes sure to evolve with technology. Thus, trained in BI, she is a Data Science enthusiast and is on the verge to pursue a career in Data Science. View all posts by the Author

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