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‎samples/Directory.Build.props

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<Project>
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<PropertyGroup>
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<MicrosoftMLVersion>1.2.0</MicrosoftMLVersion>
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<MicrosoftMLPreviewVersion>0.14.0</MicrosoftMLPreviewVersion>
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<MicrosoftMLVersion>1.1.0</MicrosoftMLVersion>
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<MicrosoftMLPreviewVersion>0.13.0</MicrosoftMLPreviewVersion>
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</PropertyGroup>
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</Project>

‎samples/csharp/end-to-end-apps/AnomalyDetection-Sales/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | WinForms app | .csv files | Spike and Change Point Detection of Product Sales | Anomaly Detection | IID Spike Detection and IID Change point Detection |
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| v1.1.0 | Dynamic API | Up-to-date | WinForms app | .csv files | Spike and Change Point Detection of Product Sales | Anomaly Detection | IID Spike Detection and IID Change point Detection |
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![Alt Text](./SpikeDetectionE2EApp/SpikeDetection.WinForms/images/productsales.gif)
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‎samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow model | DeepLearning model |
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| v1.1.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow model | DeepLearning model |
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## Problem

‎samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/TensorFlowImageClassification/TensorFlowImageClassification.csproj

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<ItemGroup>
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<PackageReference Include="Microsoft.AspNetCore.App" />
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<PackageReference Include="Microsoft.AspNetCore.Razor.Design" Version="2.2.0" PrivateAssets="All" />
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<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
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<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
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<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
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<PackageReference Include="Microsoft.ML.TensorFlow" Version="$(MicrosoftMLVersion)" />
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<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
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<PackageReference Include="Microsoft.ML" Version="1.1.0" />
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<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.1.0" />
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<PackageReference Include="Microsoft.ML.TensorFlow" Version="0.13.0" />
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<PackageReference Include="Microsoft.VisualStudio.Web.CodeGeneration.Design" Version="2.2.3" />
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</ItemGroup>
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‎samples/csharp/end-to-end-apps/DeepLearning_ObjectDetection_Onnx/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model |
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| v1.1.0 | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning | Tiny Yolo2 ONNX model |
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## Problem
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Object detection is one of the classical problems in computer vision: Recognize what objects are inside a given image and also where they are in the image. For these cases, you can either use pre-trained models or train your own model to classify images specific to your custom domain.

‎samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md

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| ML.NET version | API type | Status | App Type | Data sources | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier, AveragedPerceptronTrainer |
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| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier, AveragedPerceptronTrainer |
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This is a simple prototype application to demonstrate how to use [ML.NET](https://www.nuget.org/packages/Microsoft.ML/) APIs. The main focus is on creating, training, and using ML (Machine Learning) model that is implemented in Predictor.cs class.

‎samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj

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<PackageReference Include="Microsoft.AspNetCore.App" />
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<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
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<PackageReference Include="Microsoft.AspNetCore.Razor.Design" Version="2.2.0" PrivateAssets="All" />
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<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
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<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
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</ItemGroup>
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<ItemGroup>
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<Content Update="wwwroot\images\smileybob.png">

‎samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/README.md

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| ML.NET version | API type | Status | App Type | Data sources | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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|v1.2.0 | Dynamic API | up-to-date | End-End app | .csv | Movie Recommendation | Recommendation | Field Aware Factorization Machines |
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|v1.1.0 | Dynamic API | up-to-date | End-End app | .csv | Movie Recommendation | Recommendation | Field Aware Factorization Machines |
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![Alt Text](https://github.com/dotnet/machinelearning-samples/blob/master/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/wwwroot/images/movierecommender.gif)
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‎samples/csharp/end-to-end-apps/Regression-SalesForecast/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | ASP.NET Core web app and Console app | SQL Server and .csv files | Sales forecast | Regression | FastTreeTweedie Regression |
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| v1.1.0 | Dynamic API | Up-to-date | ASP.NET Core web app and Console app | SQL Server and .csv files | Sales forecast | Regression | FastTreeTweedie Regression |
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eShopDashboardML is a web app with Sales Forecast predictions (per product and per country) using [Microsoft Machine Learning .NET (ML.NET)](https://github.com/dotnet/machinelearning).

‎samples/csharp/end-to-end-apps/Regression-SalesForecast/src/eShopDashboard/eShopDashboard.csproj

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<PackageReference Include="Serilog.Sinks.Seq" Version="4.0.0" />
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<PackageReference Include="Swashbuckle.AspNetCore" Version="4.0.1" />
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<PackageReference Include="TinyCsvParser" Version="2.0.0" />
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<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
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<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
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</ItemGroup>
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‎samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/README.md

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| ML.NET version | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
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| v1.1.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
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**This posts explains how to optimize your code when running an ML.NET model on an ASP.NET Core WebAPI service.** The code would be very similar when running it on an ASP.NET Core MVC or Razor web app, too.

‎samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/src/Scalable.WebAPI/Scalable.WebAPI.csproj

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
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<PackageReference Include="Microsoft.ML.FastTree" Version="$(MicrosoftMLVersion)" />
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<PackageReference Include="Microsoft.AspNetCore.App" />
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</ItemGroup>

‎samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/README.md

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| ML.NET version | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
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| v1.0.0 | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
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**This posts explains how to optimize your code when running an ML.NET model on an ASP.NET Core WebAPI service.** The code would be very similar when running it on an ASP.NET Core MVC or Razor web app, too.

‎samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/BlazorSentimentAnalysis.Server/BlazorSentimentAnalysis.Server.csproj

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<ItemGroup>
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<PackageReference Include="Microsoft.AspNetCore.Blazor.Server" Version="3.0.0-preview6.19307.2" />
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<PackageReference Include="Microsoft.AspNetCore.Mvc.NewtonsoftJson" Version="3.0.0-preview6.19307.2" />
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<PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
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<PackageReference Include="Microsoft.ML.FastTree" Version="$(MicrosoftMLVersion)" />
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<PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
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<PackageReference Include="Microsoft.ML.FastTree" Version="1.1.0" />
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‎samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/README.md

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| ML.NET version | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Up-to-date | Blazor / ASP.NET Core 3.0 Preview 6 | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
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| v1.1.0 | Up-to-date | Blazor / ASP.NET Core 3.0 Preview 6 | Single data sample | Sentiment Analysis | Binary classification | Linear Classification |
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# Goal
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‎samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection |
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| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection |
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In this sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to detect anomalies in time series data.
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‎samples/csharp/getting-started/AnomalyDetection_Sales/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Product Sales Spike Detection| Time Series - Anomaly Detection | IID Spike Detection and IID Change point Detection |
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| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Product Sales Spike Detection| Time Series - Anomaly Detection | IID Spike Detection and IID Change point Detection |
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In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to detect **spikes** and **change points** in Product sales. In the world of machine learning, this type of task is called TimeSeries Anomaly Detection.
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‎samples/csharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification |
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| v1.1.0 | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification |
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In this introductory sample, you'll see how to use ML.NET to predict a credit card fraud. In the world of machine learning, this type of prediction is known as binary classification.
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‎samples/csharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
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| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
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In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict type of heart disease. In the world of machine learning, this type of prediction is known as **binary classification**.
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‎samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | up-to-date | Console app | .tsv files | Sentiment Analysis | Two-class classification | Linear Classification |
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| v1.1.0 | Dynamic API | up-to-date | Console app | .tsv files | Sentiment Analysis | Two-class classification | Linear Classification |
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In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict a sentiment (positive or negative) for customer reviews. In the world of machine learning, this type of prediction is known as **binary classification**.
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‎samples/csharp/getting-started/BinaryClassification_SpamDetection/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Might need to update project structure to match template | Console app | .tsv files | Spam detection | Two-class classification | Averaged Perceptron (linear learner) |
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| v1.1.0 | Dynamic API | Might need to update project structure to match template | Console app | .tsv files | Spam detection | Two-class classification | Averaged Perceptron (linear learner) |
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In this sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict whether a text message is spam. In the world of machine learning, this type of prediction is known as **binary classification**.
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‎samples/csharp/getting-started/Clustering_CustomerSegmentation/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ |
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| v1.1.0 | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ |
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## Problem
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‎samples/csharp/getting-started/Clustering_Iris/READMe.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ |
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| v1.1.0 | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ |
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In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to divide iris flowers into different groups that correspond to different types of iris. In the world of machine learning, this task is known as **clustering**.
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‎samples/csharp/getting-started/DatabaseIntegration/DatabaseIntegration/DatabaseIntegration.csproj

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<PackageReference Include="Microsoft.ML.LightGBM" Version="1.0.0-preview" />
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‎samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md

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| ML.NET version | API type | Status | App Type | Data type | Scenario | ML Task | Algorithms |
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|----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
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| v1.2.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inception5h | DeepLearning model |
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| v1.1.0 | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inception5h | DeepLearning model |
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## Problem

‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj

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<PackageReference Include="Microsoft.ML.OnnxTransformer" Version="$(MicrosoftMLPreviewVersion)" />
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