Best Artificial Intelligence Books for Beginners

Artificial Intelligence is the field of study that simulates the processes of human intelligence on computer systems. 

These processes include the acquisition of information, using them, and approximating conclusions. 

The research topics in AI include problem-solving, reasoning, planning, natural language, programming, and machine learning. Automation, robotics, and sophisticated computer software and programs characterize a career in Artificial Intelligence. 

Basic foundations in maths, technology, logic, and engineering can go a long way in kick-starting a career in Artificial Intelligence.

1. Artificial Intelligence: Learning Automation Skills with Python

 

 

 

 

 

 

 

 

 

 

By – Chris Baker

Learn the potential consequences of Artificial Intelligence and how it will shape the world around us in the coming decades! Become familiar with how Artificial Intelligence aims to aid human cognitive limitations and how it is possible that in the future, the AI that humans create becomes inconceivable to humans themselves. And once you have an understanding of what AI is, you can move forward in your journey to create better informed industry-level business AI applications.

2. Artificial Intelligence Basics: A Non-Technical Introduction

 

 

 

 

 

 

 

 

 

 

By – Tom Taulli

Artificial Intelligence Basics  has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more. In addition to guiding you through real-world case studies and practical implementation steps, Taulli uses his expertise of him to expand on the bigger questions that surround AI. These include societal trends, ethics, and future impact AI will have on world governments, company structures, and daily life.

3. Artificial Intelligence: An Essential Beginner’s Guide to AI, Machine Learning, Robotics, The Internet of Things, Neural Networks, Deep Learning, Reinforcement Learning, and Our Futur

 

 

 

 

 

 

 

 

 

 

By – Neil Wilkins

This book will give you a glimpse into the most fascinating topic of the 21st century—artificial intelligence (AI), a hypothetical simulation of a living brain inside a computer.

4. Artificial Intelligence and Machine Learning

 

 

 

 

 

 

 

 

 

 

By – Chandra S.S.V

Primarily intended for the undergraduate and postgraduate students of computer science and engineering, this text bridges the gaps in knowledge of the seemingly difficult areas of artificial intelligence and machine learning.

This book promises to provide the most number of case studies and worked out examples than any other of its genre. The text is written in a highly interactive manner which makes for an avid reading. More into the text, the contents are well placed that it takes off from the introduction to AI, which is followed by heuristics searching and game playing. The machine learning section begins with the basis of learning, and the various association rule learning algorithms. Various types of learning like, reinforced, supervised, unsupervised and statistical are also included with numerous case studies and application exercises. The well explained algorithms and pseudo codes for each topic make this book useful for students.

5. Python: Beginner’s Guide to Artificial Intelligence

 

 

 

 

 

 

 

 

By – Rahul Kumar, Ankit Dixit, Denis Rothman, Amir Ziai, Mathew Lamons

This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries.

Throughout the Learning Path, you’ll learn how to develop deep learning applications for machine learning systems. Discover how to attain deep learning programming on GPU in a distributed way.

By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects.