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.
- Taxi fare predictor: demonstrates how to apply a regression task using ML.NET.
- Iris clustering: demonstrates how to apply a clustering task using ML.NET.
The tutorials demonstrate how to use the ML.NET
LearningPipeline API introduced in ML.NET 0.1. For information about the new API introduced in ML.NET 0.6, see ML.NET high-level concepts and ML.NET Cookbook.
For more examples that use ML.NET, check the dotnet/machinelearning-samples GitHub repository.