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.
Understand the framework, components, and APIs of ML.NET using C#
Develop regression models using ML.NET for employee attrition and file classification
Evaluate classification models for sentiment prediction of restaurant reviews
Work with clustering models for file type classifications
Use anomaly detection to find anomalies in both network traffic and login history
Work with ASP.NET Core Blazor to create an ML.NET enabled web application
Integrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detection