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

Summary

Over the course of this chapter, we have discussed what goes into a production-ready ASP.NET Core Blazor web application architecture, using the work performed in previous chapters as a foundation. We also created a brand new file classification web application utilizing the FastTree binary classifier from ML.NET. Lastly, we also discussed some ways to further enhance an ASP.NET Core application (and production applications in general).

In the next chapter, we will deep dive into creating a production web browser using the content of a web page to determine if the content is malicious or not, using ML.NET's sentiment analysis and the UWP framework.