Binary Classification in ML.NET: Practical Supervised Learning

Integrating machine learning into C# applications often seems daunting, requiring you to learn new languages and complex toolchains. This text-based course shows you how to leverage your existing .NET skills to build and deploy powerful predictive systems using ML.NET. You will transition from a traditional software developer to a practitioner capable of implementing supervised machine learning workflows. You will start by understanding foundational terminology, data preparation, and the ML.NET context before progressing to training and deploying real-world models. What you'll learn: Understand key supervised learning concepts and the mechanics of binary classification; Configure data pipelines with strongly-typed C# classes and data loaders; Train predictive models using modern ML.NET algorithms such as FastTree and LightGbm; Evaluate model quality using metrics like AUC, F1-score, and confusion matrices; Save and load trained models to make real-time predictions in production environments; Integrate ML.NET models into modern .NET application architectures using dependency injection. The course begins with essential machine learning definitions and pipeline architectures, then guides you step-by-step through data loading, training, evaluation, and deployment. This course is designed for .NET developers who want to add machine learning to their toolkit. No prior data science or machine learning experience is required. Start reading today and build your first machine learning model using the C# tools you already know.