The following tutorials enable you to understand how to use ML.NET to build custom machine learning solutions and integrate them into your .NET applications:
- Sentiment analysis: demonstrates how to apply a binary classification task using ML.NET.
- GitHub issue classification: demonstrates how to apply a multiclass classification task using ML.NET.
- Price predictor: demonstrates how to apply a regression task using ML.NET.
- Iris clustering: demonstrates how to apply a clustering task using ML.NET.
- Recommendation: generate movie recommendations based on previous user ratings
- Image classification: demonstrates how to retrain an existing TensorFlow model to create a custom image classifier using ML.NET.
- Anomaly detection: demonstrates how to build an anomaly detection application for product sales data analysis.
- Detect objects in images: demonstrates how to detect objects in images using a pre-trained ONNX model.
- Classify sentiment of movie reviews: learn to load a pre-trained TensorFlow model to classify the sentiment of movie reviews.
For more examples that use ML.NET, check out the dotnet/machinelearning-samples GitHub repository.