Introduction to Machine Learning by Etienne Bernard instant download

Introduction to Machine Learning by Etienne Bernard instant download

Machine learning—a computer’s ability to learn—is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better decisions. Whatever your industry or hobby, chances are that these modern artificial intelligence methods will be useful to you as well.

Introduction to Machine Learning weaves reproducible coding examples into explanatory text to show what machine learning is, how it can be applied, and how it works. Perfect for anyone new to the world of AI or those looking to further their understanding, the text begins with a brief introduction to the Wolfram Language, the programming language used for the examples throughout the book. From there, readers are introduced to key concepts before exploring common methods and paradigms such as classification, regression, clustering, and deep learning. The math content is kept to a minimum to focus on what matters—applying the concepts in useful contexts. This book is sure to benefit anyone curious about the fascinating field of machine learning.

eTextbook version includes links to web content.

From the Preface

"Machine learning—which roughly refers to computers learning to do things by themselves—is one of the most transformative domains in today's world and its use is growing. I joined Wolfram Research in 2012 and led the early development of the machine learning tools that are now part of the Wolfram Language. . . . I decided to write this book to share my understanding of machine learning as it is after these eight years of design and development. I hope that it will be useful to you."

About the Author

Etienne Bernard is a physicist turned software developer and entrepreneur in the field of machine learning. His goal is to simplify the practice of machine learning in order to spread its usage. During his career as a physicist, he worked on Markov chain Mont