AutoML in ML.NET: Automating Machine Learning for C# Developers

Integrating machine learning into software applications no longer requires a PhD in data science. This text-based course introduces you to automated machine learning (AutoML) using ML.NET, enabling you to build high-performing models with minimal manual tuning. You will transition from understanding core machine learning concepts to automatically training and evaluating models for classification, regression, and recommendation tasks. By studying clear explanations and written code examples, you will learn how to leverage the ML.NET AutoML API and CLI to automate the most time-consuming parts of the machine learning workflow. What you'll learn: - Understand foundational machine learning terminology and the core mechanics of AutoML. - Prepare and clean data automatically using ML.NET pipeline builders. - Configure AutoML experiments to search for the best classification and regression models. - Apply hyperparameter tuning techniques to optimize model performance. - Evaluate model metrics to select the most accurate solution for your application. - Deploy trained models seamlessly into modern .NET applications. The course begins with essential definitions and theoretical foundations of automated machine learning before guiding you through data preparation, experiment execution, and model evaluation. You will learn through structured text modules, step-by-step code walkthroughs, and conceptual explanations designed for software developers. This course is designed for beginners to machine learning and .NET developers who want to add predictive capabilities to their apps without deep statistical expertise; basic familiarity with C# is helpful but not required. Start exploring automated machine learning today and elevate your software projects.