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CONTRIBUTING.md

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## Uploading datasets
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* Only datasets that allowed for public use for all purposes (including redistribution) can be uploaded to this repository.
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* Only datasets that allowed for public use for all purposes (including redistribution) can be uploaded to this repository.
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* To avoid the repository growing too large that it's not convenient to work with, the limit for an uploaded dataset file is 5 MB. Everything that is bigger should be downloaded programmatically on the first run of the app.
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* All datasets should be stored in [datasets](https://github.com/dotnet/machinelearning-samples/tree/master/datasets) folder to allow reusing them by other examples.
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* All datasets should be stored in [datasets](https://github.com/dotnet/machinelearning-samples/tree/main/datasets) folder to allow reusing them by other examples.
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* If you are uploading a dataset, please add a section in datasets [README](datasets/README.md) file describing the original source and license.

README.md

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# ML.NET Samples
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[ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers.
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[ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers.
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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.
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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.
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**Note:** Please open issues related to [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) framework in the [Machine Learning repository](https://github.com/dotnet/machinelearning/issues). Please create the issue in this repo only if you face issues with the samples in this repository.
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The official ML.NET samples are divided in multiple categories depending on the scenario and machine learning problem/task, accessible through the following tables:
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<table align="middle" width=100%>
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<table align="middle" width=100%>
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<td align="middle" colspan="3">Binary classification</td>
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</tr>
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<td align="middle"><img src="images/sentiment-analysis.png" alt="Binary classification chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>Sentiment Analysis<br><a href="samples/csharp/getting-started/BinaryClassification_SentimentAnalysis">C#</a> &nbsp; &nbsp; <a href="samples/fsharp/getting-started/BinaryClassification_SentimentAnalysis">F#</a></b></td>
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<td align="middle"><img src="images/spam-detection.png" alt="Movie Recommender chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>Spam Detection<br><a href="samples/csharp/getting-started/BinaryClassification_SpamDetection">C#</a> &nbsp; &nbsp; <a href="samples/fsharp/getting-started/BinaryClassification_SpamDetection">F#</a></b></td>
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<td align="middle"><img src="images/anomaly-detection.png" alt="Power Anomaly detection chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>Credit Card Fraud Detection<br>(Binary Classification)<br><a href="samples/csharp/getting-started/BinaryClassification_CreditCardFraudDetection">C#</a> &nbsp;&nbsp;&nbsp;<a href="samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection">F#</a></b></td>
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</tr>
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</tr>
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<td align="middle"><img src="images/disease-detection.png" alt="disease detection chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>Heart Disease Prediction <br><a href="samples/csharp/getting-started/BinaryClassification_HeartDiseaseDetection">C#</a></td>
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<td></td>
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<td></td>
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</tr>
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</tr>
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<td align="middle" colspan="3">Multi-class classification</td>
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<img src="images/app-type-e2e-black.png" alt="End-to-end app icon">&nbsp;<a href="samples/csharp/end-to-end-apps/AnomalyDetection-Sales">C#</a><b></td>
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<td align="middle"><img src="images/spike-detection.png" alt="Spike detection chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>Power Anomaly Detection<br><a href="samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings">C#</a><b></td>
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<td align="middle"><img src="images/anomaly-detection.png" alt="Power Anomaly detection chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>Credit Card Fraud Detection<br>(Anomaly Detection)<br><a href="samples/csharp/getting-started/AnomalyDetection_CreditCardFraudDetection">C#</a><b></td>
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<td align="middle" colspan="3">Clustering</td>
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<td align="middle"><img src="images/image-classification.png" alt="Image Classification chart"><br><b>Image Classification Predictions<br>(Pretrained TensorFlow model scoring)<br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon">&nbsp;<a href="samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow">C#</a> &nbsp; <a href="samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow">F#</a>&nbsp;&nbsp&nbsp&nbsp&nbsp;&nbsp;
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<img src="images/app-type-e2e-black.png" alt="End-to-end app icon">&nbsp;<a href="samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow">C#</a><b></td><b></td>
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<td align="middle"><img src="images/image-classification.png" alt="Image Classification chart"><br><b>Image Classification Training<br> (TensorFlow Featurizer Estimator)<br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon">&nbsp;<a href="samples/csharp/getting-started/DeepLearning_TensorFlowEstimator">C#</a> &nbsp; <a href="samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator">F#</a><b></td>
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<td align="middle"><br><img src="images/object-detection.png" alt="Object Detection chart"><br><b>Object Detection<br> (ONNX model scoring)<br>
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<img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon">&nbsp;<a href="samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx">C#</a>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
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<img src="images/app-type-e2e-black.png" alt="End-to-end app icon">&nbsp;<a href="/samples/csharp/end-to-end-apps/ObjectDetection-Onnx">C#</a><b></td>
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</table>
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# Automate ML.NET models generation (Preview state)
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The previous samples show you how to use the ML.NET API 1.0 (GA since May 2019).
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The previous samples show you how to use the ML.NET API 1.0 (GA since May 2019).
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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.
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# Additional ML.NET Community Samples
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In addition to the ML.NET samples provided by Microsoft, we're also highlighting samples created by the community showcased in this separated page:
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[ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/master/docs/COMMUNITY-SAMPLES.md)
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[ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/main/docs/COMMUNITY-SAMPLES.md)
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Those Community Samples are not maintained by Microsoft but by their owners.
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If you have created any cool ML.NET sample, please, add its info into this [REQUEST issue](https://github.com/dotnet/machinelearning-samples/issues/86) and we'll publish its information in the mentioned page, eventually.

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