Time Series Forecasting with ML.NET

Every modern organization relies on historical data to predict future trends, whether forecasting product sales, inventory demand, or system resource utilization. ML.NET allows developers to build and integrate powerful machine learning models directly into their existing C# applications without needing a background in Python or R. This text-only course provides a clear path to understanding and implementing predictive analytics on sequential data. By following this structured guide, you will transition from understanding basic data patterns to implementing robust forecasting models. You will learn how to handle time-dependent data, configure training pipelines, and generate actionable future predictions. What you'll learn: - Understand the foundational concepts of time series data, seasonality, and trend analysis. - Prepare and clean historical sequential datasets for machine learning pipelines. - Implement Singular Spectrum Analysis (SSA) in ML.NET to extract patterns and predict future values. - Configure and train forecasting models using modern C# and .NET standards. - Evaluate model performance using key metrics like mean absolute error and root mean squared error. - Integrate trained forecasting models into production-ready .NET applications. The course begins with essential terminology and the mathematical foundations of sequential data before moving into practical code implementations. You will work through detailed written explanations and C# code snippets that build your skills step by step. This course is designed for .NET developers and beginners eager to explore machine learning. No prior data science experience is required, though a basic familiarity with C# and the .NET ecosystem is recommended. Start reading today to bring the power of predictive forecasting to your software projects.