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.
Note: Please open issues related to ML.NET framework in the Machine Learning repository. Please create the issue in this repo only if you face issues with the samples in this repository.
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 tables:
| Binary classification | ||
 Sentiment analysis C# F#  |
 Spam Detection C# F# |
 Fraud detection C# F# |
 Heart Disease Prediction C# |
||
| Multi-class classification | ||
 Issues classification C# F#  |
 Iris flowers classification C# F# |
 MNIST C# |
| Recommendation | ||
 Product Recommendation C# |
 Movie Recommender C# |
Movie Recommender (E2E app) C# |
| Regression | ||
 Price Prediction C# F# |
 Sales ForeCasting C# |
 Demand Prediction C# F# |
| Clustering | ||
 Customer Segmentation C# F# |
 IRIS Flowers clustering C# F# |
|
| Anomaly Detection | ||
 Sales Spike Detection C#
C# |
 Power Anomaly Detection C# |
|
| Computer Vision | ||
 Image Classification (TensorFlow model scoring) C# F# |
Image Classification (TensorFlow Estimator) C# F# |
 Object Detection (ONNX model scoring) C#
C# |
| Cross Cutting Scenarios | ||
 Scalable Model on WebAPI C# |
 Training model with Database C# |
 Scalable Blazor web app C# |
The previous samples show you how to use the ML.NET API 1.0 (GA since May 2019).
However, we're also working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. The "best" model and the code for running it will be generated for you.
These additional technologies for automating model generation are in PREVIEW state and currently only support Binary-Classification, Multiclass Classification and Regression. In upcoming versions we'll be supporting additional ML Tasks such as Recommendations, Anomaly Detection, Clustering, etc..
The ML.NET CLI (command-line interface) is a tool you can run on any command-prompt (Windows, Mac or Linux) for generating good quality ML.NET models based on training datasets you provide. In addition, it also generates sample C# code to run/score that model plus the C# code that was used to create/train it so you can research what algorithm and settings it is using.
| CLI (Command Line Interface) samples |
|---|
| Binary Classification sample |
| MultiClass Classification sample |
| Regression sample |
ML.NET AutoML API is basically a set of libraries packaged as a NuGet package you can use from your .NET code. AutoML eliminates the task of selecting different algorithms, hyperparameters. AutoML will intelligently generate many combinations of algorithms and hyperparameters and will find high quality models for you.
| AutoML API samples |
|---|
| Binary Classification sample |
| MultiClass Classification sample |
| Regression sample |
| Advanced experiment sample |
In addition to the ML.NET samples provided by Microsoft, we're also highlighting samples created by the community shocased in this separated page: ML.NET Community Samples
Those Community Samples are not maintained by Microsoft but by their owners. If you have created any cool ML.NET sample, please, add its info into this REQUEST issue and we'll publish its information in the mentioned page, eventually.
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.