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

Creating an anomaly detection application

As mentioned earlier, the application we will be creating is a login anomaly detector. Given a set of attributes relating to the login, the application will use that data to find anomalies such as unusual login times. As with other applications, this is not meant to power the next ML login anomaly detection product; however, it will show you how to use anomaly detection in ML.NET.

As with previous chapters, the completed project code, sample dataset, and project files can be downloaded here: https://github.com/PacktPublishing/Hands-On-Machine-Learning-With-ML.NET/tree/master/chapter06.

Exploring the project architecture

Building on the project architecture and code we created in previous chapters, the bulk of the changes in this example are in the training of the model.

In the following screenshot, you will find the Visual Studio Solution Explorer view of the project. The new additions to the solution are the LoginHistory and LoginPrediction files...