We are reader supported and may earn a commission when you buy through links on our site
AI is the science and engineering of making intelligent machines, especially intelligent computer programs. The full form of AI is Artificial Intelligence. Artificial intelligence exists when a machine has a cognitive ability. The benchmark for AI is the human level concerning reasoning, speech, and vision.
Here is a curated list of Top AI Books that should be part of any beginner to advanced Data Science Learner’s library.
This Artificial Intelligence reference book is a step-by-step journey through the mathematics of neural networks and making your own using the Python computer language.
This reference book takes you on a fun and unhurried journey. The book starts with very simple ideas, and gradually building up an understanding of how neural networks work. In this book, you will also learn to code in Python and make your neural network to offering professionally developed networks.
Artificial Intelligence is a book written by John Paul Mueller and Luca Massaron. The book provides a clear introduction to AI and how it’s being used today.
Inside this book, you will get an overview of the technology. It also talks about the common misconceptions surrounding it. The book explores the use of AI in computer applications, scope, and history of AI.
Machine Learning For Absolute Beginners is a book written by Oliver Theobald. The book covers chapters like What is machine learning, types of machine learning, the machine learning toolbox, data scrubbing setting up your data, regression analysis. The book also covers clustering, support vector machines, artificial neural networks, Building a model in Python, etc. It includes algorithms like Cross-Validation, Ensemble Modelling, Grid Search, Feature Engineering, and One-hot Encoding.
Superintelligence is an ideal reference book written by Stuart Russell and Peter Norvig. This book is the most comprehensive, up-to-date introduction to the theory and practice of the AI subject.
This AI book brings readers up to date on the latest technologies, presents concepts in a more unified manner. The book also offers machine learning, deep learning, transfer learning multi-agent systems, robotics, etc.
This book offers a basic conceptual theory of artificial intelligence. It acts as complete reference material for beginners. It helps students in undergraduate or graduate-level courses in Artificial Intelligence.
This edition gives you detailed information about the changes that have taken place in the field of artificial intelligence from its last edition. There are many important applications of AI technology like deployment of practical speech recognition, machine translation, household robotic that are explained in detailed.
Artificial Intelligence Engines is a book written by James V Stone. The book explains how AI algorithms, in the form of deep neural networks. It is rapidly eliminating that advantage. Deep neural networks use for many business applications like a cancer diagnosis, object recognition, speech recognition, robotic control, chess, poker, etc.
In this book, key neural network learning algorithms are explained, followed by detailed mathematical analyses.
Life 3.0: Being Human in the Age of Artificial Intelligence is a book written by Max Tegmark. The book talks about the rise of AI how it has the potential to transform our future more than any other technology.
This book also cover full range of viewpoints or the most controversial issues. It talks about the meaning, consciousness, and the ultimate physical limits on life in the cosmos.
Deep Learning Illustrated is an AI book written by Jon Kohn, Grant Beyleveld, and Aglae Basens. This book talks about many powerful new artificial intelligence capabilities and algorithm performance. Deep Learning Illustrated and offers a complete introduction to the discipline’s techniques.
This book can serve as a practical reference guide for developers, researchers, analysts, and students who want to apply it.
Predictive Analytics For Dummies is a book written by Anasse Bari, Mohamed Chaouchi, and Tommy Jung. With the help of this reference book, you will learn about the core of predictive analytics.
The book offers some common use cases to help you get started. It also covers details on modeling, k-means clustering. The book also provides tips on business goals and approaches.
Data Science from Scratch is a book written by Joel Gurus. This book helps you to learn math and statistics that is at the core of data science. You will also learn hacking skills you need to get started as a data scientist.
The books include topics like implement k-nearest neighbors, naïve bayes, linear and logistic regression, decision trees, and clustering models. You will also able to explore natural language processing, network analysis, etc.
Hands-On Machine Learning is a book written by Aurélien Géron. The book helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.
This reference material also teaches you techniques, starting with simple linear regression and progressing to deep neural networks. In this book, you will also explore several training models, including support vector machines, decision trees, random forests, and ensemble methods. You can also learn techniques for training and scaling deep neural networks.
Applied Artificial Intelligence is a book written by Mariya Yao, Adelyn Zhou, and Marlene Jia. This book is a practical guide for business leaders who are passionate about leveraging machine intelligence. This helps you to enhance the productivity of their organizations and the incase the quality of life in their communities. The book also helps you to take business decisions through applications of AI and machine learning.
Prediction Machines is a book written by Ajay Agrawal, Joshua Gans, and Avi Goldfarb. The book talks about the heart of making decisions under uncertainty. It also explains how prediction tools increase productivity– operating machines, handling documents, communicating with customers. In the end, the book discusses how better prediction creates opportunities for new business structures.
Human + Machine: Reimagining Work in the Age of AI is a book written by Paul R. Daugherty and H. James Wilson. The book talks about the essence of the AI paradigm, which helps you to shift is the transformation of all business processes inside a single organization.
The book explains how companies are using the new rules of AI to leap ahead on innovation. It also describes six entirely new types of hybrid human + machine roles that every company must develop.
Architects of Intelligence contain a series of in-depth, one-to-one interviews where the author, Martin Ford, reveals the truth behind these questions. He has given thoughts of the brightest minds in the Artificial Intelligence community.
This AI book helps collects the opinions of the luminaries of the AI business, Like Stuart Russell, Rodney Brooks, Demis Hassabis, and Yoshua Bengi. You should read this book to get in-depth knowledge and the future of the AI field.
Artificial Intelligence for Humans is a book written by Jeff Heaton. In this AI book, you will learn about the basic Artificial Intelligence algorithms. Like dimensionality, clustering, error calculation, hill climbing, Nelder Mead, and linear regression.
This Artificial Intelligence book explains all algorithms using actual numeric calculations that you can perform yourself. Every chapter in this book includes a programming example. Examples are currently provided in Java, C#, Python, and C. Other languages planned.
HBR’s 10 Must Reads on AI, Analytics, and the New Machine Age is a book written by Micheal E. Porter, Thomas H. Davenport, Paul Daugherty, H. James Wilson.
The book combed through hundreds of Harvard Business Review articles and selected the most important ones. This book helps you to understand various AI consent and how to adopt them.
In this book, you will learn data science, driven by artificial intelligence and machine learning. It also covers chapters about the blockchain and Augmented reality.
TensorFlow is the most popular Deep Learning Library available in the market. It has a most authentic graph computations feature which helps you to visualize and designed neural network. This useful Machine learning book offers both convolutions as well as Recurrent Neural network.
Machine learning models supported by TensorFlow like Deep Learning Classification, Boston Tree, and wipe & deep layer methods are covered in the book. The book includes complete professional deep learnings practices with detailed examples.
This deep learning book offers a mathematical and conceptual background, and relevant concepts in linear algebra, probability and information theory, and machine learning.
The book describes many important deep learning techniques widely used in industry, which includes regularization, optimization algorithms, sequence modeling. This book also offers research-related information like linear factor models, autoencoders, structured probabilistic models, the partition function, etc.
Python Machine Learning book gives you access to the world of predictive analytics. It helps you to learn the best practices and methods to improve and optimize machine learning systems and algorithms.
Wants to find out how to use Python? Then you should pick up Python Machine Learning. The book helps you to get started from scratch, or helps you to extend your data science knowledge.
Deep Learning with R introduces you to a universe of deep learning using the Keras library and its R language interface. It is written for Python as Deep Learning with Python by Keras creator and Google.
The books help you set up your deep-learning environment. You can also practice your new skills with R-based applications in computer vision, natural language processing, and generative models. Moreover, to learn this course, you don’t need any previous experience of machine learning or deep learning.