Multi-Field Machine Learning Models in ML.NET

Real-world machine learning problems rarely rely on a single source of information. To build truly effective models, you must learn how to combine diverse data inputs—such as raw text, sentiments, and categorical variables—into a unified pipeline. This text-based course guides you through the process of expanding your ML.NET models to handle multiple input fields. You will move from understanding foundational data schemas to building, training, and evaluating robust multi-feature models using modern C# development patterns. What you'll learn: Learn the core concepts of feature pipelines and data transformation in ML.NET. Define strongly typed input and output schemas using modern C# records and classes. Combine text, numerical, and categorical fields into a single feature vector. Configure advanced data estimators to preprocess diverse data types simultaneously. Train and evaluate multi-field models to ensure high prediction accuracy. Apply best practices for maintaining clean, scalable machine learning code in .NET. You will begin by exploring basic machine learning terminology and data pipeline architectures. From there, you will read through step-by-step code demonstrations showing how to structure, load, and transform multi-field datasets for practical scenarios. This course is designed for beginner .NET developers and aspiring data engineers who want to expand their machine learning capabilities, with no prior machine learning experience required. Start reading today to unlock the full potential of multi-input data in your .NET applications.