Introduction to Machine Learning
by Etienne Bernard
Publisher
Wolfram Media
Publication
Dec 20, 2021
Format
Paperback/Softback
Illustrated
No
Price
34.95
Dimensions
7 x 10 in
ISBN
9781579550486
Pages
424
Age
Adult 18+
Category
General Nonfiction
Rights Sold
🌍 World Wide Rights Available
Description
Author(s)
Etienne Bernard
Related Titles
| Publisher | Wolfram Media |
| Publication | Dec 20, 2021 |
| Format | Paperback/Softback |
| ISBN | 9781579550486 |
| Pages | 424 |
| Age | 18 - 99 |
| Price | $34.95 |
Categories
General Nonfiction
BISAC Codes
COMPUTERS / Intelligence (AI) & Semantics
Introduction to Machine Learning
by Etienne Bernard
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.
Author

Etienne Bernard
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 Monte Carlo algorithms to solve physics problems. He obtained a PhD in physics from ENS Paris in 2011 and worked as a
postdoctoral scholar at MIT.
Current Licenses
🌍 World Wide Rights Available