Everyone is talking about how AI has changed the world. AI is quickly becoming a standard tool in nearly every sector of the global economy. It has several potential uses, including automating processes, doing predictive analyses, detecting fraud, enhancing the user experience, etc. To begin your journey into the world of artificial intelligence (AI) and its concepts, we recommend reading some of the best Artificial Intelligence books on the subject.

People are looking to artificial intelligence (AI) to drive future economic and technical progress. Consequently, there will be a dramatic uptick in demand for AI programmers and engineers over the next several years. Imagine you are eager to learn all you can about artificial intelligence but have little background in the subject. So, for that we bring you 15 Artificial Intelligence books to read in 6 months that will take you from being AI newbie to AI genius

15 Artificial Intelligence books For Beginners:

Artificial Intelligence: The Basics

(Kevin Warwick)

This Artificial Intelligence book gives a general outline of several areas of artificial intelligence (AI) and the ways to apply them. The book delves into the past, present, and future of artificial intelligence. The Artificial Intelligence book provides fascinating descriptions of AI and robotics in the present day. In addition, it suggests reading materials that go into further depth on a certain topic.

As far as AI books go, this one is a quick read. The text delves into the core concerns surrounding the subject and offers readers a revealing experience.

Artificial Intelligence for Humans

(Jeff Heaton)

For a general introduction and comprehension of AI algorithms, this Artificial intelligence book is a great resource. Those without a strong mathematical background will be able to learn AI with its help. Readers should be familiar with basic concepts in algebra and computer programming.

There is extensive coverage of the fundamental techniques in artificial intelligence, including clustering, distance metrics, dimensionality, and linear regression. Through engaging examples and use scenarios, as well as numerical computations that readers may replicate on their own, the algorithms are described.

Machine Learning for Dummies

( John Paul Mueller and Luca Massaron)

For those seeking an introduction to Machine Learning, Machine Learning for Dummies is an excellent way to start. Practical applications of machine learning’s foundational ideas and theories are covered comprehensively. To train computers to analyze data and spot patterns, it provides an introduction to Python and R.

Readers may derive the value of machine learning from simple tasks and trends in areas such as online advertising, search engine optimization, fraud detection, and more. Written by two seasoned data scientists, this Artificial intelligence book simplifies the concept of machine learning and how to apply it to real-world scenarios, making it accessible to a wide audience.

Artificial Intelligence and Machine Learning

(Vinod Chandra S. S.)

Students majoring in computer science and engineering are the book’s intended audience. Finding common ground in the complex AI and ML landscapes is the goal of this Artificial intelligence book. Case studies and practical examples illustrate all the key points.

It includes not just supervised and unsupervised learning but also statistical learning, artificial intelligence, machine learning, and reinforcement learning. Beginners who want to start careers in artificial intelligence will find this Artificial intelligence book very beneficial because it covers each topic with well-explained algorithms and pseudo-codes.

Artificial Intelligence – A Modern Approach (3rd Edition)

(Stuart Russell & Peter Norvig)

For those new to the field of artificial intelligence, this Artificial intelligence book is highly recommended. It provides a review of the many AI-related subjects in a less technical way. There is no unnecessary complexity in the writing; all ideas and explanations are readily apparent.

Topics covered include local search planning approaches, statistical Natural Language Processing, multi-agent systems, search algorithms, game theory, and more. Additionally, the book briefly discusses advanced AI subjects. If you want to know more about artificial intelligence, this is the book for you.

Make Your Own Neural Network

(Tariq Rashid)

There is an Artificial intelligence book that walks its readers through the math behind Neural Networks absolutely step-by-step. It begins with the most fundamental concepts and works its way up to a more complex comprehension of neural networks. It suggests that people create their own neural networks using the Python programming language.

The text is organized into three sections. Part one covers the many mathematical concepts that form the basis of neural networks. The second part is more hands-on, instructing readers in Python and inspiring them to build their very own neural networks. Part three provides an insight into the neural network’s enigmatic inner workings. It also includes instructions on how to set up a Raspberry Pi and run the codes.

Machine Learning for Beginners

(Chris Sebastian)

This Artificial intelligence book is perfect for those who have never touched machine learning before, as the name suggests. It begins with the origins of machine learning and continues through its evolution into its current state. It explains why large data is crucial to ML and how developers utilize it to create algorithms that learn. The author provides a thorough explanation of several AI concepts, including neural networks, swarm intelligence, etc.

Machine learning is based on complicated arithmetic and probability statistics, but this Artificial intelligence book simplifies these concepts by providing readers with simple examples. Practical examples of the positive effects of machine learning algorithms on human life are also present.

Machine Learning: The New AI

(Ethem Alpaydin)

Machine Learning provides a brief introduction to the topic. Here, you will find a description of its development, an analysis of key learning algorithms, and some examples of its use. It provides historical context for the current machine learning boom by detailing the evolution of digital technology from desktop computers to mobile phones.

This Artificial intelligence book showcases real-life applications of machine learning and how it has become an integral part of our lives. Data privacy and security and the ethical and legal considerations surrounding machine learning’s future are also covered. Even if you don’t have a degree in computer science, you should have no trouble understanding and enjoying this Artificial intelligence book.

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

(John D. Kelleher, Brian Mac Namee, Aoife D’Arcy)

All the essentials of machine learning, as well as its real-world uses, case studies, and practical examples, are covered in this Artificial intelligence book. Important machine learning techniques used in predictive analytics are also described in detail.

There is minimal technical jargon and a straightforward explanation of the four primary approaches. With the use of algorithms and mathematical models backed by extensive examples, each method is outlined. Readers with a foundational understanding of mathematics, statistics, computer science, or engineering will find this book to be an excellent resource.

The Hundred-Page Machine Learning Book

(Andriy Burkov)

Many professionals in the field consider “The Hundred-Page Machine Learning Book” by Andriy Burkov to be the definitive resource for machine learning. An excellent starting point for anyone unfamiliar with machine learning, it covers all the bases. Experts in the subject will find useful advice from the author’s extensive background in artificial intelligence.

All the main schools of thought in machine learning are covered in the book. They span from traditional logistic and linear regression to more cutting-edge methods like random forests, Deep Learning, boosting, and support vector machines. Anyone interested in learning more about the mathematics underlying machine learning algorithms would benefit greatly from reading this Artificial intelligence book.

Machine Learning for Absolute Beginners: A Plain English Introduction

(Oliver Theobald)

This Artificial intelligence book is rare among AI resources for its accessible treatment of both the theory and practice of machine learning. To save newcomers from being bewildered by technical terms, it uses simple English. It explains the various methods in a way that anyone can understand, with plenty of graphic examples.

There are many facets of AI that enthusiasts should be familiar with beyond only the technical aspects used in business. These include philosophical, social, ethical, humanitarian, and other ideas.

Human Compatible – Artificial Intelligence and the Problem of Control

(Stuart Russell)

Stuart Russell, an AI researcher, discusses the potential downsides and short-term advantages of AI. It offers a hopeful and compassionate perspective on humanity’s fate in the era of AI. Additionally, the author stresses the need of starting over with artificial intelligence (AI) in order to create a system that serves human needs and goals.

Applied Artificial Intelligence: A Handbook for Business Leaders

(Mariya Yao, Adelyn Zhou, Marlene Jia)

Anyone working in business with an interest in boosting productivity and societal well-being through the application of AI will find Applied Artificial Intelligence to be an invaluable resource.

Making smart, data-driven business decisions is the main subject of this Artificial intelligence book, which delves into the topic of machine learning and AI. For C-suite executives looking to get the most out of Machine Learning, this is a top pick among practical AI publications.

Superintelligence: Paths, Dangers, Strategies

(Nick Bostrom)

The book, which Elon Musk and Bill Gates both highly recommended, is about navigating the uncharted territory of artificial intelligence. Nick Bostrom is a polymath and philosopher from Sweden who wrote this book. This magnificent work of literature is based on his extensive background and expertise in the fields of computational neuroscience and artificial intelligence.

Life 3.0

(Max Tegmark)

Max Tegmark’s Artificial Intelligence book is a must-read for everyone interested in learning more about AI. It delves into the more general questions and facets of AI, such as artificial intelligence’s physical boundaries, machine consciousness, and superintelligence. Additionally, it delves into the topic of automation and the social concerns that stem from AI.

Final Words

So, those are a few of the books on AI that we think would be good places to start. Among the many topics covered by the umbrella term “artificial intelligence,” you’ll want to familiarize yourself with machine learning, deep learning, computer vision, neural networks, and a host of others. Python is also introduced to help put machine learning into context. No prior knowledge of mathematics or computer programming is required to read and comprehend these Artificial Intelligence books.