Machine Learning and C# (.NET): A Practical Guide

The book "Machine Learning and C# (.NET)" is a practical guide for developers who want to master artificial intelligence technologies without leaving the .NET ecosystem. Unlike many books focused on Python, this edition focuses on the specifics of working with ML.NET, TensorFlow.NET, and other libraries available in the C# world.

What is this book about?

The book covers the complete cycle of creating machine learning models: from data preparation and algorithm selection to deploying a ready-made solution in a production environment. You will learn how to:

• Work with the ML.NET framework — the native tool for machine learning in .NET
• Build classification, regression, clustering, and recommendation system models
• Integrate neural networks via TensorFlow.NET and ONNX
• Process text, images, and time series
• Deploy models as REST APIs, desktop, and web applications
• Optimize model performance in the .NET environment

Who is this book for?

This edition is intended for C# developers with basic programming experience who want to expand their skills in Data Science and machine learning. The book will be useful for both beginners and experienced professionals looking to integrate AI into their .NET platform projects.

What will you learn?

• ML Basics: how algorithms work, what overfitting is, quality metrics
• ML.NET in Action: from data loading to model export
• Deep Learning: creating neural networks in C#
• Practical Examples: chatbots, recommendation systems, text sentiment analysis, image recognition
• DevOps for ML: model version management, A/B testing, monitoring

All code examples are available for download and are written following .NET best practices (dependency injection, async/await, Entity Framework). The book includes step-by-step instructions, screenshots, and explanations of complex concepts.

Take a step towards creating intelligent applications in C# — discover the world of machine learning without switching to other programming languages!