Building AutoML Regression Pipelines with ML.NET

Integrating machine learning into .NET applications often feels daunting due to the complex math and endless trial-and-error of model tuning. Automated Machine Learning (AutoML) simplifies this process, allowing you to train high-performing regression models with minimal manual configuration. This written course guides you through the fundamentals of AutoML in ML.NET, showing you how to build a fully automated pipeline to solve regression problems, such as predicting real estate prices. You will transition from understanding core machine learning concepts to writing clean, modern C# code that automatically selects, trains, and evaluates the best model for your data. What you'll learn: - Understand foundational machine learning terminology, regression concepts, and how AutoML automates model selection. - Configure your .NET development environment and import the required ML.NET NuGet packages. - Prepare and clean structured datasets for regression analysis using modern C# data structures. - Initialize and run an AutoML experiment to automatically find the best regression algorithm for your dataset. - Evaluate model performance using key metrics like R-squared and Root Mean Squared Error (RMSE). - Deploy the trained model to make real-time predictions within a standard console application. The course starts with essential machine learning definitions and data preparation basics before moving into hands-on pipeline construction. You will progress logically from raw data loading to running automated experiments and consuming the finalized model in your code. This course is designed for C# and .NET developers who are complete beginners to machine learning. No prior data science or AI experience is required, though a basic familiarity with C# syntax is recommended. Start reading today and bring automated machine learning directly into your .NET development workflow.