machinelearning and machinelearning-samples
ML.NET is the core framework library, while machinelearning-samples provides accompanying code examples and tutorials that demonstrate how to use ML.NET—making them complements designed to be used together.
About machinelearning
dotnet/machinelearning
ML.NET is an open source and cross-platform machine learning framework for .NET.
Provides AutoML capabilities for automated model selection and hyperparameter tuning, plus native support for consuming pre-trained TensorFlow and ONNX models. Built on a modular pipeline architecture that handles data loading from files/databases, feature transformations, and algorithm composition—enabling scenarios like classification, regression, forecasting, and anomaly detection. Runs on .NET Core and .NET Framework across Windows, Linux, macOS, and ARM64 architectures.
About machinelearning-samples
dotnet/machinelearning-samples
Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.
Covers binary/multiclass classification, regression, time series forecasting, anomaly detection, and recommendation systems using matrix factorization and field-aware factorization machines. Organized as both minimal console getting-started samples and full end-to-end web/desktop applications demonstrating model integration into production .NET workflows. Available in C# and F#, with concrete domain examples like sentiment analysis, fraud detection, and demand prediction.
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