Book Image

Hands-On Machine Learning with ML.NET

By : Jarred Capellman
Book Image

Hands-On Machine Learning with ML.NET

By: Jarred Capellman

Overview of 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.
Table of Contents (19 chapters)
1
Section 1: Fundamentals of Machine Learning and ML.NET
4
Section 2: ML.NET Models
10
Section 3: Real-World Integrations with ML.NET
14
Section 4: Extending ML.NET
Using ML.NET with UWP

Now that we have established how to create a production-grade .NET Core console application, in this chapter, we will deep dive into creating a fully functional Windows 10 application with the Universal Windows Platform (UWP) framework. This application will utilize an ML.NET binary classification model to make web-page-content classifications, in order to determine if the content is benign or malicious. In addition, we will explore breaking your code into a component-based architecture, using a .NET Standard Library to share between our desktop application and the console application that will train our model. By the end of the chapter, you should have a firm grasp of designing and coding a production-grade UWP desktop application with ML.NET.

The following topics will be covered in this chapter:

  • Breaking down the UWP application
  • Creating the web browser...