The following tutorials help you understand how to useML.NET to build custom machine learning solutions and integrate them into your .NET applications:

• Sentiment analysis: Apply abinary classification task using ML.NET.
• GitHub issue classification: Apply amulticlass classification task using ML.NET.
• Price predictor: Apply aregression task using ML.NET.
• Iris clustering: Apply aclustering task using ML.NET.
• Recommendation: Generate movierecommendations 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 pretrained 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 thedotnet/machinelearning-samples GitHub repository.