Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.
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ML.NET Samples

ML.NET is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers. In this GitHub repo, we provide samples which will help you get started with ML.NET and how to infuse ML into existing and new .NET apps.

There are two types of samples/apps in the repo:

  • Getting Started - ML.NET code focused samples for each ML task or area, usually implemented as simple console apps.

  • End-End apps - Real world examples of web, desktop, mobile, and other applications infused with Machine Learning using ML.NET

The official ML.NET samples are divided in multiple categories depending on the scenario and machine learning problem/task, accessible through the following table:

ML Task



Binary classification

Task of classifying the elements of a given set into two groups, predicting which group each one belongs to.

Sentiment analysis     C#     F#   Getting started icon

Fraud detection    C#    Getting started icon

Multi-class classification

Task of classifying instances into one of three or more classes, predicting which group each one belongs to.

Issues classification     C#    End-to-end app icon

Iris flowers classification    C#    F#    Getting started icon


The task is to predict a numeric value with given input variable data. It is widely used for forecasting and 'how much / how many' predictions.

Price prediction     C#     F#   Getting started icon

Sales forecast     C#    End-to-end app icon

Demand prediction     C#    Getting started icon


Recommender systems are typically based on content based and collaborative filtering methods. A collaborative method predicts what items/products a user might like based on his past actions/likes/ratings compared to other users.

Product recommender     Coming soon    End-to-end app icon

Movie recommender     Coming soon    End-to-end app icon


ML task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. It is an exploratory task. It does not classify items across particular labels.

Customer segmentation     C#    Getting started icon

Clustering Iris flowers     C#     F#   Getting started icon

Anomaly detection

Task's goal is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.Usually problems such as bank fraud, structural defects or medical problems

Coming soon


Construction of ranking models for information retrieval systems so the items are ordered/ranked based on user's input variables such as likes/deslike, context, interests, etc.

Coming soon

Deep Learning

Deep learning is a subset of machine learning. Deep learning architectures such as deep neural networks, are usually applied to fields such as computer vision (object detection, image classification, style transfer), speech recognition, natural language processing and audio recognition.

Object detection Coming soon    Getting started icon

Style Transfer Coming soon    End-to-end app icon

TensorFlow with ML.NET Coming soon    Getting started icon

ONNX with ML.NET - Coming soon    Getting started icon

Learn more

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

API reference

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


We welcome contributions! Please review our contribution guide.


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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.