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Top 10 books on Artificial Intelligence

Top 10 books on Artificial Intelligence

In this post, you will discover the top 10 books available right now on Artificial Intelligence. Currently, there aren’t many books at the moment in deep learning because it’s still an area of research. There are quite a few available online in which you may purchase.

1. Artificial Intelligence: A Modern Approach (3rd Edition)


by Peter Norvig, Stuart J. Russel

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.

2. Pattern Recognition and Machine Learning (Information Science and Statistics)

By – Christopher M. Bishop

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

3. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

By – Denis Rothman

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

4. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

by Trevor Hastie, Robert Tibshirani  Jerome Friedman 

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.

5. The Hundred-Page Machine Learning Book

by Andriy Burkov

The Hundred-Page Machine Learning Book. This book covers supervised and unsupervised learning, support vector machines, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning.

6. Programming Collective Intelligence: Building Smart Web 2.0 Applications

by Toby Segaran 

This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning

7. Make Your Own Neural Network

by Tariq Rashid 

This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won’t need any mathematics beyond secondary school, and an accessible introduction to calculus is also included.

8. Machine Learning for Dummies

By – John Mueller and Luca Massaron

This offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. This book makes it easy to understand and implement machine learning seamlessly. It explains how day to day activities are powered by Machine Learning, explores Python, R and how to perform pattern-oriented tasks and data analysis, explains Coding in R using R studio, explains coding in Python using Anaconda.

9. Grokking Algorithms: An illustrated guide for programmers and other curious people

by Aditya Bhargava

Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You’ll start with sorting and searching and, as you build up your skills in thinking algorithmically, you’ll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python.

10. 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.