ML.NET Revealed - Simple Tools for Applying Machine Learning to Your Applications (Paperback, 1st ed.)

Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible. Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary "plumbing" that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not. Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try out scenarios and code samples that can be used in many real-world situations. What You Will Learn Create a machine learning model using only the C# language Build confidence in your understanding of machine learning algorithms Painlessly implement algorithms Begin using the ML.NET library software Recognize the many opportunities to utilize ML.NET to your advantage Apply and reuse code samples from the book Utilize the bonus algorithm selection quick references available online Who This Book Is For Developers who want to learn how to use and apply machine learning to enrich their applications

General

Imprint:	Apress
Country of origin:	United States
Release date:	December 2020
First published:	2021
Authors:	Sudipta Mukherjee
Dimensions:	254 x 178mm (L x W)
Format:	Paperback
Pages:	174
Edition:	1st ed.
ISBN-13:	978-1-4842-6542-0
Categories:	Books > Computing & IT > Computer programming > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-4842-6542-4
Barcode:	9781484265420

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