Hands-On Machine Learning with ML.NET

Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core

Key Features

• Get well-versed with the ML.NET framework and its components and APIs using practical examples
• Learn how to build, train, and evaluate popular machine learning algorithms with ML.NET offerings
• Extend your existing machine learning models by integrating with TensorFlow and other libraries

Book Description

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.

What you will learn

• Understand the ML.NET framework, components, and APIs using C# code
• Develop regression models using ML.NET for housing price predictions
• Train and evaluate classification models for sentiment prediction of your website comments
• Work with clustering models for customer segmentation of your product for sales
• Display the real-time-saved predicted results in your web application using ASP.NET Core Blazor and SignalR
• Work with Entity Framework (EF) and Web APIs in ASP.NET Core Blazor
• Discover how to integrate TensorFlow in an ML.NET application

Who This Book Is For

If you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.