ML.NET tutorials
The following tutorials help you understand how to use ML.NET to build custom machine learning solutions and integrate them into your .NET applications:
- Sentiment analysis: Apply a binary classification task using ML.NET.
- GitHub issue classification: Apply a multiclass classification task using ML.NET.
- Price predictor: Apply a regression task using ML.NET.
- Iris clustering: Apply a clustering task using ML.NET.
- Recommendation: Generate movie recommendations based on previous user ratings
- Image classification: Retrain an existing TensorFlow model to create a custom image classifier using ML.NET.
- Anomaly detection: Build an anomaly detection application for product sales data analysis.
- Detect objects in images: Detect objects in images using a pre-trained ONNX model.
- Categorize an image from Custom Vision ONNX model: Detect objects in images using an ONNX model trained in the Microsoft Custom Vision service.
- Classify sentiment of movie reviews: Load a pretrained TensorFlow model to classify the sentiment of movie reviews.
Next steps
For more examples that use ML.NET, see the dotnet/machinelearning-samples GitHub repository.
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