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

The book "Machine Learning and C# (.NET)" is the first practical guide of its kind, showing how to apply machine learning algorithms in the .NET ecosystem without resorting to Python. If you are a C# developer and want to integrate intelligent features into your applications — from sales forecasting to image recognition — this book will be your reliable companion.

What the book is about

• Fundamentals of machine learning: regression, classification, clustering, ensemble methods.
• Working with the ML.NET framework: data loading, building pipelines, training and evaluating models.
• Real-world projects: recommendation system, anomaly detector, text sentiment analysis.
• Integrating neural networks: using TensorFlow and ONNX in .NET applications.
• Deploying models: publishing via web API, embedding in desktop and mobile applications.
• Best practices: handling missing values, normalization, metric selection, combating overfitting.

Who this book is for

• C# developers who want to learn machine learning without changing their stack.
• .NET backend engineers looking to add AI functionality to web services.
• Students and junior specialists studying data science on the Microsoft platform.
• Team leads and architects evaluating ML.NET capabilities for commercial projects.

What you will learn

• How to design and train a machine learning model using only C# and Visual Studio.
• How to prepare data: from CSV and databases to streaming sources.
• How to choose the right algorithm: linear regression, decision trees, SVM, neural networks.
• How to evaluate model quality using cross-validation and a confusion matrix.
• How to export a model to ONNX format and use it in other environments.
• How to build an end-to-end pipeline from raw data to a production solution.

Each chapter contains ready-to-run code examples that can be executed immediately in .NET Core / .NET 8+. The book is written in simple language, without excessive mathematics — the focus is on practice. You will learn not only how to run algorithms but also understand why they work and how to tune them for a specific task.

Whether you are building a chatbot, a spam filtering system, or predictive analytics — this book will give you the tools to implement machine learning on the .NET platform.