ML.NET is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers.

ML.NET samples are divided in three categories:

• Getting started (C#) - basic "hello world" samples for each ML task, in C#
• Getting started (F#) - basic "hello world" samples for each ML task, in F#
• Examples - examples of how you can use various ML.NET components (learners, transforms, ...).
• End-to-end (C#) - real world examples of web, desktop, mobile, and other applications infused with ML solutions via ML.NET APIs.

All samples in this repo are using the latest released Microsoft.ML NuGet package. If you would like to see the examples referencing the source code, check out scenario tests in ML.NET repository.

For VB.NET samples, check this external repo supported by the community (Kudos for Nukepayload2): https://github.com/Nukepayload2/machinelearning-samples/tree/master/samples/visualbasic

See ML.NET Guide for detailed information on tutorials, ML basics, etc.

Check out the ML.NET API Reference to see the breadth of APIs available.

We welcome contributions! Please review our contribution guide.

Please join our community on Gitter

This project has adopted the code of conduct defined by the Contributor Covenant to clarify expected behavior in our community. For more information, see the .NET Foundation Code of Conduct.

ML.NET Samples are licensed under the MIT license.