GitHub - dotnet/machinelearning-samples: Samples for ML.NET, an open source and cross-platform machine learning framework for .NET. (original) (raw)
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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.
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
: End-user sample web and desktop apps infused with Machine Learning models based on 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 AnalysisC# F# |
 Spam DetectionC# F# |
 Credit Card Fraud Detection(Binary Classification)C# F# |
 Heart Disease Prediction C# |
||
| Multi-class classification | ||
  Issues Classification C# F# |
 Iris Flowers Classification C# F# |
 MNISTC# |
| Recommendation | ||
 Product RecommendationC# |
 Movie Recommender (Matrix Factorization)C# |
Movie Recommender (Field Aware Factorization Machines)C# |
| Regression | ||
 Price PredictionC# F# |
 Sales Forecasting (Regression)C# |
 Demand PredictionC# F# |
| Time Series Forecasting | ||
| Sales Forecasting (Time Series)C# |
||
| Anomaly Detection | ||
 Sales Spike Detection C# C# |
Power Anomaly DetectionC# |
Credit Card Fraud Detection(Anomaly Detection)C# |
| Clustering | ||
 Customer SegmentationC# F# |
 IRIS Flowers ClusteringC# F# |
|
| Ranking | ||
 Rank Search Engine ResultsC# |
||
| Computer Vision | ||
 Image Classification Training (High-Level API) C# F# |
Image Classification Predictions(Pretrained TensorFlow model scoring) C# F# C# |
Image Classification Training (TensorFlow Featurizer Estimator) C# F# |
 Object Detection (ONNX model scoring) C# C# |
| Cross Cutting Scenarios | | |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 
Scalable Model on WebAPIC# | Scalable Model on Razor web appC# | 
Scalable Model on Azure FunctionsC# |
| 
Scalable Model on Blazor web appC# | 
Large DatasetsC# | 
Loading data with DatabaseLoaderC# |
| Loading data with LoadFromEnumerableC# | 
Model ExplainabilityC# | 
Export to ONNXC# |
## Automate ML.NET models generation (Preview state)
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..
## CLI samples: (Preview state)
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 |
## AutoML API samples: (Preview state)
THESE SAMPLES USE THE 0.1.x VERSION OF THE AUTOML API. WHILE THESE APIS STILL WORK IN VERSION 0.2.x WE RECOMMEND USING THE NEW APIS INTRODUCED IN 0.2.x AND LATER. FOR 0.2.x SAMPLES, SEE ML.NET 2.0 Samples.
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 | | Ranking sample | | Regression sample | | Advanced experiment sample |
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## Additional ML.NET Community Samples
In addition to the ML.NET samples provided by Microsoft, we're also highlighting samples created by the community showcased 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.
## Translations of Samples:
*** Chinese Simplified**
## 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.
## Contributing
We welcome contributions! Please review our contribution guide.
## Community
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.
## License
ML.NET Samples are licensed under the MIT license.