Introduction to Machine Learning

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.

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Information and Media Inquiries

December 20, 2021 Publication
Publicity and Interviews: publishing@wolfram.com
Translation Rights Requests: info@dropcap.com
Trim Size: 7" x 10"
Non-Fiction

Distribution by Ingram, Amazon and Baker & Taylor

UK Distribution: Turnaround Publisher Services

Contents

• Preface
• Short Introduction to the Wolfram Language
• 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
• Index

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 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.

Etienne joined Wolfram Research in 2012 to develop machine learning tools and applications for the Wolfram Language and Wolfram|Alpha. From 2014 to 2021, he led the machine learning group at Wolfram Research, developing a user-friendly neural network framework and applications such as topic detection and named entity recognition.

In 2021, Etienne cofounded and became the CEO of NuMind, a startup providing user-friendly machine learning solutions for companies.