Hands-On Machine Learning with ML.NET

By Jarred Capellman
  • Instant online access to over 7,500+ books and videos
  • Constantly updated with 100+ new titles each month
  • Breadth and depth in over 1,000+ technologies
  1. Section 1: Fundamentals of Machine Learning and ML.NET

About this book

Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code.

The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR.

By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET.

Publication date:
March 2020

Section 1: Fundamentals of Machine Learning and ML.NET

This section gives an overview of this book's audience and a short introduction to machine learning and the importance of learning how to utilize machine learning. In addition, this section introduces the reader to ML.NET.  It also talks about the tools and framework needed to build the applications and gives a step-by-step explanation of how to work with ML.NET. 

This section comprises the following chapters:

  • Chapter 1, Getting Started with Machine Learning and ML.NET
  • Chapter 2Setting Up the ML.NET Environment

About the Author

  • Jarred Capellman

    Jarred Capellman is a Director of Engineering at SparkCognition, a cutting-edge artificial intelligence company located in Austin, Texas. At SparkCognition, he leads the engineering and data science team on the industry-leading machine learning endpoint protection product, DeepArmor, combining his passion for software engineering, cybersecurity, and data science. In his free time, he enjoys contributing to GitHub daily on his various projects and is working on his DSc in cybersecurity, focusing on applying machine learning to solving network threats. He currently lives just outside of Austin, Texas, with his wife, Amy.

    Browse publications by this author

Recommended For You

Python Machine Learning - Third Edition

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.

By Sebastian Raschka and 1 more
ASP.NET Core 3 and React

Build modern, scalable, and cloud-ready single-page applications using ASP.NET Core, React, TypeScript, and Azure

By Carl Rippon
Mastering Machine Learning Algorithms - Second Edition

Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems

By Giuseppe Bonaccorso
Mastering Go - Second Edition

Dive deep into the Go language and become an expert Go developer

By Mihalis Tsoukalos