Best Artificial Intelligence books

Here is the list of the Best Artificial Intelligence Books for Beginners and Advanced in 2022 for Data Science to learn. Read this list of best Artificial Intelligence books and if you found any Best Artificial Intelligence Book is missing please comment on the Best Artificial Intelligence books name so that we can add it and update the list.

9 Best Artificial Intelligence Books for Beginners and Advanced:

Artificial Intelligence: A Modern Approach, Global Edition

Rating: 4.7

The long-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi-agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

Pros:

• this is a seminal book covering most topics in modern AI to the extent that you will have a functional understanding of any of them.

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

Rating: 4.8

If you’re looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney’s extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

You’ll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code.

You’ll learn:

• How to build models with TensorFlow using skills that employers desire
• The basics of machine learning by working with code samples
• How to implement computer vision, including feature detection in images
• How to use NLP to tokenize and sequence words and sentences
• Methods for embedding models in Android and iOS
• How to serve models over the web and in the cloud with TensorFlow Serving

Pros:

• this book did a great job of explaining how tensorflow can be used to solve different types of problems, such as computer vision, natural language processing, and time series forecasting.
• You get the full tour in this book, from NLP coding to embedding your models in edge devices.

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

In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (perceptrons, Hopfield nets, Boltzmann machines and backpropagation networks), and modern deep neural networks (variational autoencoders, convolutional networks, generative adversarial networks, and reinforcement learning using SARSA and Q-learning).

Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching.

Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes’ theorem, maximum likelihood estimation), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.

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Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills

This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with.

You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs).

This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing.

By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.

What you will learn

• Apply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google Translate
• Understand chained algorithms combining unsupervised learning with decision trees
• Solve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graph
• Learn about meta learning models with hybrid neural networks
• Create a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data logging
• Building conversational user interfaces (CUI) for chatbots
• Writing genetic algorithms that optimize deep learning neural networks
• Build quantum computing circuits

Who this book is for

Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.

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Artificial Intelligence in Healthcare: AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

In this book,  Artificial Intelligence in Healthcare: AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone, you can discover the great improvements that AI is making, with chapters covering:

• The current applications and future of AI in healthcare and all major medical specialties
• The benefits and risks weighed up
• The ethics involved
• Machine learning and data science simplified
• AI’s role in medical research and education, health insurance, drug discovery, electronic health records, and the fight against COVID-19
• The roles that major corporations and start-up companies are playing
• The implementation of AI in clinical practice
• And lots more…

Quite simply the most authoritative text on the subject, Artificial Intelligence in Healthcare – 3rd Edition, is an absorbing and compelling read for anyone who wants to know more. It is packed with more updated information than any other book currently available, written in easy-to-understand language, and accessible to all.

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Artificial Intelligence and Machine Learning in Healthcare 

This book reviews the application of artificial intelligence and machine learning in healthcare.  It discusses integrating the principles of computer science, life science, and statistics incorporated into statistical models using existing data, discovering patterns in data to extract the information, and predicting the changes and diseases based on this data and models.

The initial chapters of the book cover the practical applications of artificial intelligence for disease prognosis & management.

Further, the role of artificial intelligence and machine learning is discussed with reference to specific diseases like diabetes mellitus, cancer, mycobacterium tuberculosis, and Covid-19.  The chapters provide working examples on how different types of healthcare data can be used to develop models and predict diseases using machine learning and artificial intelligence. 

The book also touches upon precision medicine, personalized medicine, and transfer learning, with the real examples.  Further, it also discusses the use of machine learning and artificial intelligence for visualization, prediction, detection, and diagnosis of Covid -19.

This book is a valuable source of information for programmers, healthcare professionals, and researchers interested in understanding the applications of artificial intelligence and machine learning in healthcare.

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Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.

This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; Best machine learning Books 2022 data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data.

Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.

What you will learn

• Understand what artificial intelligence, machine learning, and data science are
• Explore the most common artificial intelligence use cases
• Learn how to build a machine learning pipeline
• Assimilate the basics of feature selection and feature engineering
• Identify the differences between supervised and unsupervised learning
• Discover the most recent advances and tools offered for AI development in the cloud
• Develop automatic speech recognition systems and chatbots
• Apply AI algorithms to time series data

• Who this book is for

The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.

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Programming Game AI by Example (Wordware Game Developers Library)

Programming Game AI by Example provides a comprehensive and practical introduction to the “bread and butter” AI techniques used by the game development industry, leading the reader through the process of designing, programming, and implementing intelligent agents for action games using the C++ programming language.

Techniques covered include state- and goal-based behavior, inter-agent communication, individual and group steering behaviors, team AI, graph theory, search, path planning and optimization, triggers, scripting, scripted finite state machines, perceptual modeling, goal evaluation, goal arbitration, and fuzzy logic.

Pros:

• The basics are explained, like Cartesian coordinates, trigonometry, vectors, coordinate spaces, and some physics.
• This book is a must for anyone interested in the topic of AI in games, and it is full of examples that really drive home the lessons being taught.
• This is a great book for hobbiest game developers, and professionals new to AI.

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TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter

This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers.

The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico.

As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more.

Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios.

Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game.

By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.

What you will learn

• Understand the relevant microcontroller programming fundamentals
• Work with real-world sensors such as the microphone, camera, and accelerometer
• Run on-device machine learning with TensorFlow Lite for Microcontrollers
• Implement an app that responds to human voice with Edge Impulse
• Leverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE Sense
• Create a gesture-recognition app with Raspberry Pi Pico
• Design a CIFAR-10 model for memory-constrained microcontrollers
• Run an image classifier on a virtual Arm Ethos-U55 microNPU with microTVM

Who this book is for

This book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.

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Conclusion:

Up to now, we have discussed the 9 Best Artificial Intelligence Books for Beginners, and also some best Artificial Intelligence Books for Advanced AI Learners.

Still, if you find some of the Best Artificial Intelligence books are missing then please comment in the comment section. Thanks for reading the Best Artificial Intelligence books.