Artificial Intelligence is currently one of the most trending topics in the tech industry. Many people want to know about Artificial Intelligence and Machine Learning. And people are learning about AI or Machine Learning through Youtube Videos, Articles, and Books. But when it comes to the reading of books, many people don't know that which book should we use to learn AI? So in this post, you will get a list of best books for AI and ML and these are for beginners and professionals.
What is Artificial Intelligence?
Artificial Intelligence(AI) is the simulation of human intelligence processes by machines, especially computer systems.
What is Machine Learning?
Machine Learning is an application of artificial intelligence(AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine Learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Here is the list of Best books for Artificial Intelligence
1.Life 3.0
- Author:-Max Tegmark
- Publisher:-Knopf
- Publication Date:-29 August 2017
- Language:-English
- Pages:-384 pages
In this book, Max Tegmark describes and illuminates the recent, path-breaking advances in Artificial Intelligence and how it is poised to overtake human intelligence. How will AI affect crime, war, justice, jobs, society, and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology. Max Tegmark also describes that how to keep AI beneficial. and how can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning, or getting hacked. This book covers the most controversial issues from superintelligence to meaning, consciousness, and the ultimate physical limits on life in the cosmos.
2.Artificial Intelligence For Humans Vol. 1 Fundamental Algorithms
- Author:-Jeff Heaton
- Publisher:-CreateSpace Publishing
- Publication Date:-26 November 2013
- Language:-English
- Pages:-224 Pages
This book teaches basic Artificial Intelligence algorithms such as Dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial intelligence for humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming anything more complicated than that is thoroughly explained.
3.Make your own neural Network
- Author:-Tariq Rashid
- Publisher:-CreateSpace
- Publication Date:-16 April 2016
- Language:-English
- Pages:-222 Pages
A gentle journey through the mathematics of neural networks, and making your own neural network using the python computer language is given in this book. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. yet too few really understand how neural networks actually work. This will guide you from very simple ideas, and gradually building up an understanding of how neural networks work. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible- there are enough texts for advanced readers already. You will learn to code in python and make your own neural network. In this book, Part 1 is all about mathematical ideas underlying the neural networks and Part 2 is all about practical.
4.Machine Learning(in Python & R) for dummies
- Author:-John Paul Mueller and Luca Massaron
- Publisher:-Wiley
- Publication Date:-1 January 2016
- Language:-English
- Pages:-432 Pages
The main purpose of this book is to help you understand what machine learning can and can't do for you today and what it might do for you in the future. You don't have to be a computer scientist to use this book, even though it does contain many coding examples. This book uses both python and R to perform various tasks. These two languages have special features that make them particularly useful in a machine learning setting. Machine learning for dummies helps you understand that both languages have their role to play and gives examples of when one language works a bit better than the other to achieve the goals you have in mind. You also discover some interesting techniques in this book. The most important is that you don't just see the algorithms used to perform tasks you also get an explanation of how the algorithms work.
5.Basics of Artificial Intelligence & Machine Learning
- Author:-Dr. Dheeraj Mehrotra
- Publisher:-Notion Press
- Publication Date:-1 January 2019
- Language:-English
- Pages:-80 Pages
The concept of Artificial Intelligence(AI) & Machine Learning (ML) has been in practice for over years with the advent of technological progress. Over time, it has blended our lives through nearly every narration of learning, teaching, enjoyment, normal routine operations, and whatnot. The aspect delivers a common understanding of the topics with reference to it making an impact on our lives, with a better framework of technology affecting our lives in particular. As we lead our lives, we come across the fact that AI, Robotics, and Learning Machines seem to be the household topic of discussion.
6.The Cambridge Handbook of Artificial Intelligence
- Author:-Keith Frankish and William M. Ramsey
- Publisher:-Cambridge University Press
- Publication Date:-12 June 2014
- Language:-English
- Pages:-365 Pages
Artificial Intelligence, or AI, is a cross-disciplinary approach to understanding, modeling, and creating intelligence of various forms. AI applications are transforming the way we interact with each other and with our environment, and work in artificially modeling intelligence is offering new insights into the human mind and revealing new forms mentality can take. The principal areas of research, and extensions of AI such as artificial life. with a focus on theory rather than technical and applied issues, the volume will be valuable not only to people working in AI but also to those who are new to this field.
- Author:-Stuart J. Russell and Peter Norvig
- Publisher:-Pearson
- Publication Date:-10 May 2021
- Language:-English
- Pages:-2120 Pages
The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. 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 multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, and safe AI.
8.Paradigms of Artificial Intelligence Programming.
- Author:-Peter Norvig
- Publisher:-Morgan Kaufmann
- Publication Date:-4 December 1991
- Language:-English
- Pages:-946 Pages
Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions.
9.Hands-On Machine Learning with Scikit-Learn & TensorFlow
- Author:-AurElien GEron
- Publisher:-Shroff/O'Reilly
- Publication Date:-1 January 2017
- Language:-English
- Pages:-568 Pages
Deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how by using concrete examples, minimal theory and two production-ready Python frameworks helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You will learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. with exercises in each chapter to help you apply what you have learned, all you need is programming experience to get started. Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods. Use the TensorFlow library to build and train neural nets.
10.Python Machine Learning 3rd Edition
- Author:-Sebastian Raschka & Vahid mirjalili
- Publisher:-Packt Publishing
- Publication Date:-12 December 2019
- Language:-English
- Pages:-1079 Pages
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with python. It acts as both a step-by-step tutorial and a reference you will keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest addition to scikit-learn. This book also explores a subfield of natural language processing(NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.
If you are looking for the best machine learning book then don't worry. There are a lot of books in the list of these books which tell about machine learning. But if you want a dedicated book about machine learning then there are some books on this list such as Python Machine Learning 3rd Edition, Machine Learning (in Python and R) for Dummies.
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