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  • Book
  • © 2021

ML.NET Revealed

Simple Tools for Applying Machine Learning to Your Applications

Apress

Authors:

(view affiliations)
  • 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

Buying options

eBook
USD 44.99
Price excludes VAT (USA)
  • ISBN: 978-1-4842-6543-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 59.99
Price excludes VAT (USA)

This is a preview of subscription content, access via your institution.

Table of contents (10 chapters)

  1. Front Matter

    Pages i-xviii
  2. Meet ML.NET

    • Sudipta Mukherjee
    Pages 1-21
  3. The Pipeline

    • Sudipta Mukherjee
    Pages 23-36
  4. Handling Data

    • Sudipta Mukherjee
    Pages 37-51
  5. Regressions

    • Sudipta Mukherjee
    Pages 53-72
  6. Classifications

    • Sudipta Mukherjee
    Pages 73-91
  7. Clustering

    • Sudipta Mukherjee
    Pages 93-112
  8. Sentiment Analysis

    • Sudipta Mukherjee
    Pages 113-127
  9. Product Recommendation

    • Sudipta Mukherjee
    Pages 129-144
  10. Anomaly Detection

    • Sudipta Mukherjee
    Pages 145-158
  11. Object Detection

    • Sudipta Mukherjee
    Pages 159-170
  12. Back Matter

    Pages 171-174

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 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
This book is for developers who want to learn how to use and apply machine learning to enrich their applications.

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.

Keywords

  • ML.NET
  • Machine Learning
  • .NET
  • Microsoft Machine Learning
  • ML Models
  • Machine learning algorithms
  • C#
  • Python
  • machine learning and TensorFlow
  • machine learning and Onxx
  • deep learning models
  • deep learning framworks

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

Buying options

eBook
USD 44.99
Price excludes VAT (USA)
  • ISBN: 978-1-4842-6543-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 59.99
Price excludes VAT (USA)