Introduction to machine learning / Etienne Bernard

Includes index.

What is machine learning?-- Machine learning paradigms-- Classification-- Regression-- How it works-- Clustering-- Dimensionality reduction-- Distribution learning-- Data preprocessing-- Classic supervised learning methods-- Deep learning methods-- Bayesian inference-- Going further

"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"-- Provided by publisher.