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 to data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# and .NET is mandatory to grasp the concepts covered in this book effectively.
Hands-On Machine Learning with ML.NET
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Hands-On Machine Learning with ML.NET
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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)
Preface
Section 1: Fundamentals of Machine Learning and ML.NET
Free Chapter
Getting Started with Machine Learning and ML.NET
Setting Up the ML.NET Environment
Section 2: ML.NET Models
Regression Model
Classification Model
Clustering Model
Anomaly Detection Model
Matrix Factorization Model
Section 3: Real-World Integrations with ML.NET
Using ML.NET with .NET Core and Forecasting
Using ML.NET with ASP.NET Core
Using ML.NET with UWP
Section 4: Extending ML.NET
Training and Building Production Models
Using TensorFlow with ML.NET
Using ONNX with ML.NET
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