Learn how to use ML.NET, an open-source and cross-platform machine learning framework for building custom machine learning solutions and integrating them into .NET applications. Tutorials, code examples, API reference, and other documentation show you how.
To learn how a machine learning application is built with ML.NET, read What is ML.NET and how does it work? Or get started by adding the Microsoft.ML NuGet package to your application.
Learn how to create common solutions with ML.NET:
- Analyze sentiment using binary classification
- Categorize GitHub issues using multiclass classification
- Predict NYC taxi fares using regression
- Categorize flowers by characteristics using clustering
- Create a movie recommender with matrix factorization
- Build a custom image classifier with TensorFlow
- Detect objects in images with ONNX
Reference and Resources
The ML.NET API has two sets of packages: release components and preview components. The release API contains components for data handling, algorithms for binary classification, multiclass classification, regression, anomaly detection, time series and forecasting, ranking, model saving and loading, ONNX and TensorFlow model handling, and much more! The preview API contains algorithms for recommendation tasks, and support for standard deep learning models.