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 .NET Core and Forecasting

Now that we have completed our deep dive into the various groups of algorithms ML.NET offers, we will begin to explore integrating ML.NET into a production application over the next few chapters. In this chapter, we will deep dive into a .NET Core console application building on the structure defined in previous chapters with a focus on hardening and error handling. The application we will be building uses forecasting to predict stock prices based on a series of trends. By the end of this chapter, you should have a firm grasp of designing and coding a production-grade .NET Core application with ML.NET.

In this chapter, we will cover the following topics:

  • Breaking down the .NET Core application architecture
  • Creating the forecasting application
  • Exploring additional production application enhancements