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.
Learn how to create common solutions with ML.NET:
Develop with the ML.NET API:
- Analyze sentiment using binary classification
- Categorize GitHub issues using multiclass classification
- Predict prices 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
- Classify sentiment of movie reviews using TensorFlow
Use Model Builder, a graphical Visual Studio extension:
Run the ML.NET CLI:
See the samples repository on GitHub for applications in C# and F#.
The ML.NET API
- The Release ML.NET 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 ML.NET API contains algorithms for recommendation tasks, and support for standard deep learning models.
The AutoML API
ML.NET CLI reference