Machine Learning with ML.NET: Model Training and Integration

Integrating machine learning into .NET applications no longer requires learning Python or managing complex external APIs. This text-based course provides a clear pathway for developers to build, train, and deploy predictive models directly within their existing C# environment. You will transition from understanding core machine learning concepts to preparing data, selecting algorithms, and training production-ready models. By reading through structured explanations and code examples, you will gain the confidence to integrate intelligence into your software. What you'll learn: Understand foundational machine learning concepts and the ML.NET pipeline architecture; Load and preprocess structured data using built-in estimators; Train classification, regression, and clustering models using C#; Evaluate model performance using standard metrics like accuracy and R-squared; Tune hyperparameters and optimize models using the AutoML API; Deploy and consume trained models within .NET applications. The course begins with essential machine learning terminology and pipeline structures before guiding you through data ingestion, transformation, model training, and evaluation. You will learn to work with datasets and integrate the final models seamlessly into your projects. This course is designed for C# developers and software engineers who want to learn machine learning without leaving their preferred ecosystem. No prior machine learning experience is required. Start reading today to bring the power of machine learning to your applications.