Machine Learning and C# (.NET): From Basics to Production

The book "Machine Learning and C# (.NET)" is a practical guide for C# developers who want to master machine learning (ML) and integrate it into their .NET applications. Unlike many books focused solely on Python, this edition concentrates on the .NET ecosystem, including the ML.NET library, TensorFlow.NET, and other tools.

What this book is about

You will journey from basic machine learning concepts to creating full-fledged ML solutions ready for production deployment. The book covers:

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• Fundamentals of machine learning: types of tasks (regression, classification, clustering), quality metrics, overfitting, and regularization.
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• Working with data in C#: loading, cleaning, transforming, and visualizing data using LINQ and the Accord.NET library.
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• ML.NET — Microsoft's ML framework: creating models, automatic hyperparameter tuning (AutoML), exporting, and integrating into applications.
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• Deep learning: using TensorFlow.NET and TorchSharp to build neural networks (CNN, RNN, transformers).
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• Model deployment: creating APIs with ASP.NET Core, integrating with Azure ML cloud services, and Docker containerization.
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• Practical projects: sentiment analysis of texts, customer churn prediction, image recognition, and recommendation systems.
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Who this book is for

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• .NET developers looking to expand their skills in Data Science and AI.
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• C# programmers who want to implement machine learning in existing enterprise applications.
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• Students and aspiring ML engineers familiar with C# but without experience in machine learning.
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• Technical leaders evaluating .NET capabilities for artificial intelligence tasks.
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What you will learn

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• How to prepare data for ML models using C# and .NET capabilities.
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• How to train regression, classification, clustering, and ranking models on the .NET platform.
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• How to work with neural networks, including transformers and convolutional architectures, without switching to Python.
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• How to automate the process of selecting the best model using ML.NET AutoML.
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• How to integrate trained models into web applications, desktop programs, and microservices.
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• How to test and monitor ML models in a production environment.
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All code examples are available in the repository and are fully ready to run. The book is written in simple language, with an emphasis on practice and real-world use cases. After reading it, you will be able to confidently apply machine learning in your C# and .NET projects.