Skip to main content
Apress
Book cover

ML.NET Revealed

Simple Tools for Applying Machine Learning to Your Applications

  • Book
  • © 2021

Overview

  • Provides full-spectrum coverage of ML.NET
  • Uses developer-to-developer language
  • Shows you how to leverage ML.NET to use other popular frameworks such as TensorFlow

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (10 chapters)

Keywords

About this book



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 outscenarios 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


Authors and Affiliations

  • Bangalore, India

    Sudipta Mukherjee

About the author

Sudipta Mukherjee is an electronics engineer by education and a computer scientist by profession. He holds a degree in electronics and communication engineering. He is passionate about data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning. He is the author of several technical books. He has presented at @FuConf and other developer events, and he lives in Bangalore with his wife and son.

Bibliographic Information

Publish with us