Practical Unsupervised Learning in ML.NET

Unlocking patterns in unlabeled data is one of the most powerful capabilities in modern software development. This text-based course introduces you to the essentials of unsupervised machine learning using ML.NET, designed specifically for C# developers. You will transition from understanding core machine learning terminology to confidently implementing clustering and anomaly detection algorithms. You will learn to prepare data, train models, and extract valuable insights from time-series and unstructured datasets using native .NET tools. What you will learn: - Understand the foundational principles of unsupervised learning and how it differs from supervised methods. - Group complex datasets using ML.NET clustering algorithms like K-Means to discover hidden patterns. - Detect anomalies, spikes, and change points in time-series data to predict unusual system behavior. - Prepare and clean raw data using modern C# data structures and pipeline transformations. - Evaluate model performance using unsupervised metrics to ensure reliable prediction quality. - Integrate trained ML.NET models into modern .NET applications using dependency injection. The course begins with foundational machine learning definitions and core concepts before guiding you through step-by-step written tutorials. You will read clear explanations, analyze structured C# code snippets, and complete practical written exercises to reinforce your learning. This course is designed for beginner .NET developers and software engineers new to machine learning, with no prior data science experience required. Start building intelligent C# applications today.