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From the Publisher

“This is a terrific introduction . . . written in a non-technical style but doesn’t skimp on thoroughness. It illustrates the power of Wolfram Language in getting ML code running with very little effort.”

— Verified Purchaser

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.”

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.

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Page Count
424
376
112
368
572
220

Wolfram Language Programming Level
Intermediate
Beginner
Intermediate examples
Intermediate
Beginner
Intermediate

Publisher ‏ : ‎ Wolfram Media, Inc. (December 20, 2021)
Language ‏ : ‎ English
Paperback ‏ : ‎ 424 pages
ISBN-10 ‏ : ‎ 1579550487
ISBN-13 ‏ : ‎ 978-1579550486
Item Weight ‏ : ‎ 1.75 pounds
Dimensions ‏ : ‎ 7 x 0.86 x 10 inches

Introduction to Machine Learning

Machine learning is a rapidly evolving field that is revolutionizing various industries and processes. It is a subset of artificial intelligence that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions based on data.

Machine learning algorithms can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled data, where the input and output are known. Unsupervised learning, on the other hand, involves training a model on unlabeled data to discover patterns or relationships within the data. Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties.

Some common applications of machine learning include image and speech recognition, natural language processing, recommendation systems, and autonomous vehicles. Machine learning algorithms can also be used for predictive analytics, fraud detection, and personalized marketing.

In this post, we will explore the basics of machine learning, including key concepts, algorithms, and techniques. Stay tuned for more in-depth discussions on specific topics within the field of machine learning.
#Introduction #Machine #Learning