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Artificial intelligence or as it has come to be known among the masses, AI is fast gaining popularity in the world. Only a few years ago, AI was scoffed at and it merely reminded people of science fiction movies such as Star Trek. Now it has become a major topic of discussion, dominating conversations across the world. One of the biggest reasons Artificial Intelligence is gaining this amount of traction is because of its use in business. Businesses are always looking to improve their product, maximize revenue, minimize costs, and thereby, earn as much profit as possible. Intellectual capital is an incredibly important part of the development of new products, and more efficient methods of production. It requires incredible skill and intellect to maximize efficiency, and constantly come up with ideas for newer products. It has become a conundrum for companies around the world to keep innovating as they look to stay competitive in what is becoming an increasingly competitive market.

Artificial intelligence promises to make this conundrum much easier. AI’s ability to think as fast as, if not faster than most humans makes it a unique opportunity that businesses around the world should look to capitalize on as soon as possible. Use of AI could quickly help firms improve the quality and efficiency operations to a great extent.

An issue that most people face, however, is that they simply do not know Artificial intelligence and if they do, they do not properly understand it. Artificial intelligence is a complex technology that does require some time to properly comprehend. How can this be helped? Fortunately for people who know nothing about AI and wish to change that, there have been many books written over the years that are specifically targeted towards people who wish to grasp a basic understanding of AI.

Some of the best of those books are listed as follows:

Originally published in 1974, and released as second edition in 1985, this book has become a classic for those wishing to enter the field of Artificial Intelligence. It happens to be one of the first books ever written on Artificial intelligence. The book, a comprehensive survey of artificial intelligence, attempts to enter fascinating questions, ranging from “Can computers think?” to “Can they develop their own concepts?” It acts as introduction to the basic science of the process of reasoning in computers, and analyzes the approaches taken by AI and the results achieved in two decades leading up to the publishing of the book. It is an easy to read compilation of robot systems, artificial intelligence accomplishments, problem-solving methods, automated understanding of natural languages, and many of the other areas that come under the broad umbrella of Artificial intelligence.

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For anyone who wishes to enter the Artificial intelligence space, but has limited understanding of the concept would do well to read this book. It allows the reader to grasp a much deeper understanding of the subject, and sets up a perfect base for further exploration of the subject.

The name gives away what the book is all about. If you are a person who knows absolutely nothing about machine learning, this is the book to start with. It is an extremely credible book which has been written by two authors who happen to be data scientists with years of experience. It is the perfect book for someone who cannot grasp artificial intelligence and machine learning at all.

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Even though the book uses Python code throughout, one really does not need any knowledge of Python to understand what is being taught. You will be learning a deep history of machine learning and the differences between machine learning and AI. The authors have taken their time and written with great detail to try and explain every single point in as much detail as possible.

All you need to pick up this book is a basic background in Mathematics and logic with no prior hard core programming experience required. The only slight hitch might come if you have never encountered algorithms before but you can still get by that hurdle if you are willing to put in the time and do some complementary research.

While there are many differences between machine learning and AI, they do happen to share many concepts that can help beginner programmers embark on their journey to master these topics. It is important for one to understand how to approach these problems so that they can solve & scale projects in machine learning. This is why Artificial Intelligence: A Modern Approach can be just the perfect introduction. The book is over 1100 pages and covers a wide variety of techniques.

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People who have even an intermediate level of expertise with programming may scoff at the simplicity with which the book approaches artificial intelligence but this book is not meant for anyone who happens to have any prior experience. It is well and truly made for the person who just wants to get started, and this can be perfect to satisfy your appetite for AI and the basics of machine learning.

This is one of the best artificial intelligence books for beginners. This book has been written for simply understanding the fundamental building block of artificial intelligence and its algorithms. This is a good starting point for anyone interested in learning AI from core. These algorithms are not only a great foundation for the rest of the series, but are very useful in their own right.

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The book has explained all the algorithms with the use actual numeric calculations that anyone can perform themselves. Not only this book, but the entire Artificial Intelligence for Humans series is meant to teach Artificial intelligence to those without an extensive mathematical background.

The above are some of the best books that one can refer to in order to get a base for Artificial intelligence and machine learning. There are certainly many other books that will help but it is highly recommended to start off with these and slowly build towards other books that may feature more complex matter.

You might want to read:
Difference between Artificial Intelligence, Machine learning, Deep learning, Predictive Analytics, Data Science and Big data

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