Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.

With this first preview release ML.NET enables ML tasks like classification (e.g. support text classification, sentiment analysis) and regression (e.g. price-prediction).

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Installation

ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.

The current release is 0.5. Check out the release notes.

First ensure you have installed .NET Core 2.0 or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet package manager:

Install-Package Microsoft.ML

Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our MyGet feed:

https://dotnet.myget.org/F/dotnet-core/api/v3/index.json

Building

To build ML.NET from source please visit our developers guide.

	x64 Debug	x64 Release
Linux
macOS
Windows

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.

Examples

Here's an example of code to train a model to predict sentiment from text samples. (You can see the complete sample here):

varpipeline=newLearningPipeline();pipeline.Add(newTextLoader(dataPath).CreateFrom<SentimentData>(separator:','));pipeline.Add(newTextFeaturizer("Features","SentimentText"));pipeline.Add(newFastTreeBinaryClassifier());varmodel=pipeline.Train<SentimentData,SentimentPrediction>();

Now from the model we can make inferences (predictions):

SentimentDatadata=newSentimentData{SentimentText="Today is a great day!"};SentimentPredictionprediction=model.Predict(data);Console.WriteLine("prediction: "+prediction.Sentiment);

Samples

We have a repo of samples that you can look at.

License

ML.NET is licensed under the MIT license.

.NET Foundation

ML.NET is a .NET Foundation project.

There are many .NET related projects on GitHub.

.NET home repo - links to 100s of .NET projects, from Microsoft and the community.

Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.

With this first preview release ML.NET enables ML tasks like classification (e.g. support text classification, sentiment analysis) and regression (e.g. price-prediction).

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Installation

ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.

The current release is 0.5. Check out the release notes.

First ensure you have installed .NET Core 2.0 or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet package manager:

Install-Package Microsoft.ML

Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our MyGet feed:

https://dotnet.myget.org/F/dotnet-core/api/v3/index.json

Building

To build ML.NET from source please visit our developers guide.

	x64 Debug	x64 Release
Linux
macOS
Windows

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.

Examples

Here's an example of code to train a model to predict sentiment from text samples. (You can see the complete sample here):

varpipeline=newLearningPipeline();pipeline.Add(newTextLoader(dataPath).CreateFrom<SentimentData>(separator:','));pipeline.Add(newTextFeaturizer("Features","SentimentText"));pipeline.Add(newFastTreeBinaryClassifier());varmodel=pipeline.Train<SentimentData,SentimentPrediction>();

Now from the model we can make inferences (predictions):

SentimentDatadata=newSentimentData{SentimentText="Today is a great day!"};SentimentPredictionprediction=model.Predict(data);Console.WriteLine("prediction: "+prediction.Sentiment);

Samples

We have a repo of samples that you can look at.

License

ML.NET is licensed under the MIT license.

.NET Foundation

ML.NET is a .NET Foundation project.

There are many .NET related projects on GitHub.

.NET home repo - links to 100s of .NET projects, from Microsoft and the community.

Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.

With this first preview release ML.NET enables ML tasks like classification (e.g. support text classification, sentiment analysis) and regression (e.g. price-prediction).

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Installation

ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.

The current release is 0.5. Check out the release notes.

First ensure you have installed .NET Core 2.0 or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet package manager:

Install-Package Microsoft.ML

Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our MyGet feed:

https://dotnet.myget.org/F/dotnet-core/api/v3/index.json

Building

To build ML.NET from source please visit our developers guide.

	x64 Debug	x64 Release
Linux
macOS
Windows

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.

Examples

Here's an example of code to train a model to predict sentiment from text samples. (You can see the complete sample here):

varpipeline=newLearningPipeline();pipeline.Add(newTextLoader(dataPath).CreateFrom<SentimentData>(separator:','));pipeline.Add(newTextFeaturizer("Features","SentimentText"));pipeline.Add(newFastTreeBinaryClassifier());varmodel=pipeline.Train<SentimentData,SentimentPrediction>();

Now from the model we can make inferences (predictions):

SentimentDatadata=newSentimentData{SentimentText="Today is a great day!"};SentimentPredictionprediction=model.Predict(data);Console.WriteLine("prediction: "+prediction.Sentiment);

Samples

We have a repo of samples that you can look at.

License

ML.NET is licensed under the MIT license.

.NET Foundation

ML.NET is a .NET Foundation project.

There are many .NET related projects on GitHub.

.NET home repo - links to 100s of .NET projects, from Microsoft and the community.

Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.

With this first preview release ML.NET enables ML tasks like classification (e.g. support text classification, sentiment analysis) and regression (e.g. price-prediction).

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Installation

ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.

The current release is 0.5. Check out the release notes.

First ensure you have installed .NET Core 2.0 or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet package manager:

Install-Package Microsoft.ML

Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our MyGet feed:

https://dotnet.myget.org/F/dotnet-core/api/v3/index.json

Building

To build ML.NET from source please visit our developers guide.

	x64 Debug	x64 Release
Linux
macOS
Windows

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.

Examples

Here's an example of code to train a model to predict sentiment from text samples. (You can see the complete sample here):

varpipeline=newLearningPipeline();pipeline.Add(newTextLoader(dataPath).CreateFrom<SentimentData>(separator:','));pipeline.Add(newTextFeaturizer("Features","SentimentText"));pipeline.Add(newFastTreeBinaryClassifier());varmodel=pipeline.Train<SentimentData,SentimentPrediction>();

Now from the model we can make inferences (predictions):

SentimentDatadata=newSentimentData{SentimentText="Today is a great day!"};SentimentPredictionprediction=model.Predict(data);Console.WriteLine("prediction: "+prediction.Sentiment);

Samples

We have a repo of samples that you can look at.

License

ML.NET is licensed under the MIT license.

.NET Foundation

ML.NET is a .NET Foundation project.

There are many .NET related projects on GitHub.

.NET home repo - links to 100s of .NET projects, from Microsoft and the community.

Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.

With this first preview release ML.NET enables ML tasks like classification (e.g. support text classification, sentiment analysis) and regression (e.g. price-prediction).

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Installation

ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.

The current release is 0.5. Check out the release notes.

First ensure you have installed .NET Core 2.0 or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet package manager:

Install-Package Microsoft.ML

Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our MyGet feed:

https://dotnet.myget.org/F/dotnet-core/api/v3/index.json

Building

To build ML.NET from source please visit our developers guide.

	x64 Debug	x64 Release
Linux
macOS
Windows

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.

Examples

Here's an example of code to train a model to predict sentiment from text samples. (You can see the complete sample here):

varpipeline=newLearningPipeline();pipeline.Add(newTextLoader(dataPath).CreateFrom<SentimentData>(separator:','));pipeline.Add(newTextFeaturizer("Features","SentimentText"));pipeline.Add(newFastTreeBinaryClassifier());varmodel=pipeline.Train<SentimentData,SentimentPrediction>();

Now from the model we can make inferences (predictions):

SentimentDatadata=newSentimentData{SentimentText="Today is a great day!"};SentimentPredictionprediction=model.Predict(data);Console.WriteLine("prediction: "+prediction.Sentiment);

Samples

We have a repo of samples that you can look at.

License

ML.NET is licensed under the MIT license.

.NET Foundation

ML.NET is a .NET Foundation project.

There are many .NET related projects on GitHub.

.NET home repo - links to 100s of .NET projects, from Microsoft and the community.

Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.

With this first preview release ML.NET enables ML tasks like classification (e.g. support text classification, sentiment analysis) and regression (e.g. price-prediction).

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Installation

ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.

The current release is 0.5. Check out the release notes.

First ensure you have installed .NET Core 2.0 or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet package manager:

Install-Package Microsoft.ML

Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our MyGet feed:

https://dotnet.myget.org/F/dotnet-core/api/v3/index.json

Building

To build ML.NET from source please visit our developers guide.

	x64 Debug	x64 Release
Linux
macOS
Windows

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.

Examples

Here's an example of code to train a model to predict sentiment from text samples. (You can see the complete sample here):

varpipeline=newLearningPipeline();pipeline.Add(newTextLoader(dataPath).CreateFrom<SentimentData>(separator:','));pipeline.Add(newTextFeaturizer("Features","SentimentText"));pipeline.Add(newFastTreeBinaryClassifier());varmodel=pipeline.Train<SentimentData,SentimentPrediction>();

Now from the model we can make inferences (predictions):

SentimentDatadata=newSentimentData{SentimentText="Today is a great day!"};SentimentPredictionprediction=model.Predict(data);Console.WriteLine("prediction: "+prediction.Sentiment);

Samples

We have a repo of samples that you can look at.

License

ML.NET is licensed under the MIT license.

.NET Foundation

ML.NET is a .NET Foundation project.

There are many .NET related projects on GitHub.

.NET home repo - links to 100s of .NET projects, from Microsoft and the community.

Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.

With this first preview release ML.NET enables ML tasks like classification (e.g. support text classification, sentiment analysis) and regression (e.g. price-prediction).

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Installation

ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.

The current release is 0.5. Check out the release notes.

First ensure you have installed .NET Core 2.0 or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet package manager:

Install-Package Microsoft.ML

Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our MyGet feed:

https://dotnet.myget.org/F/dotnet-core/api/v3/index.json

Building

To build ML.NET from source please visit our developers guide.

	x64 Debug	x64 Release
Linux
macOS
Windows

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.

Examples

Here's an example of code to train a model to predict sentiment from text samples. (You can see the complete sample here):

varpipeline=newLearningPipeline();pipeline.Add(newTextLoader(dataPath).CreateFrom<SentimentData>(separator:','));pipeline.Add(newTextFeaturizer("Features","SentimentText"));pipeline.Add(newFastTreeBinaryClassifier());varmodel=pipeline.Train<SentimentData,SentimentPrediction>();

Now from the model we can make inferences (predictions):

SentimentDatadata=newSentimentData{SentimentText="Today is a great day!"};SentimentPredictionprediction=model.Predict(data);Console.WriteLine("prediction: "+prediction.Sentiment);

Samples

We have a repo of samples that you can look at.

License

ML.NET is licensed under the MIT license.

.NET Foundation

ML.NET is a .NET Foundation project.

There are many .NET related projects on GitHub.

.NET home repo - links to 100s of .NET projects, from Microsoft and the community.

Machine Learning for .NET

ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers.

ML.NET allows .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models, all in .NET.

ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.

With this first preview release ML.NET enables ML tasks like classification (e.g. support text classification, sentiment analysis) and regression (e.g. price-prediction).

Along with these ML capabilities this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework such as learning algorithms, transforms, and ML data structures.

Installation

ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core or later is available.

The current release is 0.5. Check out the release notes.

First ensure you have installed .NET Core 2.0 or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process.

Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:

dotnet add package Microsoft.ML

or from the NuGet package manager:

Install-Package Microsoft.ML

Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager or via Paket.

Daily NuGet builds of the project are also available in our MyGet feed:

https://dotnet.myget.org/F/dotnet-core/api/v3/index.json

Building

To build ML.NET from source please visit our developers guide.

	x64 Debug	x64 Release
Linux
macOS
Windows

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.

Examples

Here's an example of code to train a model to predict sentiment from text samples. (You can see the complete sample here):

varpipeline=newLearningPipeline();pipeline.Add(newTextLoader(dataPath).CreateFrom<SentimentData>(separator:','));pipeline.Add(newTextFeaturizer("Features","SentimentText"));pipeline.Add(newFastTreeBinaryClassifier());varmodel=pipeline.Train<SentimentData,SentimentPrediction>();

Now from the model we can make inferences (predictions):

SentimentDatadata=newSentimentData{SentimentText="Today is a great day!"};SentimentPredictionprediction=model.Predict(data);Console.WriteLine("prediction: "+prediction.Sentiment);

Samples

We have a repo of samples that you can look at.

License

ML.NET is licensed under the MIT license.

.NET Foundation

ML.NET is a .NET Foundation project.

There are many .NET related projects on GitHub.

.NET home repo - links to 100s of .NET projects, from Microsoft and the community.

Name		Name	Last commit message	Last commit date
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docs		docs
pkg		pkg
src		src
test		test
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.gitattributes		.gitattributes
.gitignore		.gitignore
.vsts-dotnet-ci.yml		.vsts-dotnet-ci.yml
BuildToolsVersion.txt		BuildToolsVersion.txt
CONTRIBUTING.md		CONTRIBUTING.md
Directory.Build.props		Directory.Build.props
Directory.Build.targets		Directory.Build.targets
DotnetCLIVersion.txt		DotnetCLIVersion.txt
ISSUE_TEMPLATE.md		ISSUE_TEMPLATE.md
LICENSE		LICENSE
Microsoft.ML.sln		Microsoft.ML.sln
PULL_REQUEST_TEMPLATE.md		PULL_REQUEST_TEMPLATE.md
README.md		README.md
ROADMAP.md		ROADMAP.md
build.cmd		build.cmd
build.proj		build.proj
build.sh		build.sh
config.json		config.json
dir.traversal.targets		dir.traversal.targets
init-tools.cmd		init-tools.cmd
init-tools.msbuild		init-tools.msbuild
init-tools.sh		init-tools.sh
run.cmd		run.cmd
run.sh		run.sh

Name		Name	Last commit message	Last commit date
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docs		docs
pkg		pkg
src		src
test		test
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.gitattributes		.gitattributes
.gitignore		.gitignore
.vsts-dotnet-ci.yml		.vsts-dotnet-ci.yml
BuildToolsVersion.txt		BuildToolsVersion.txt
CONTRIBUTING.md		CONTRIBUTING.md
Directory.Build.props		Directory.Build.props
Directory.Build.targets		Directory.Build.targets
DotnetCLIVersion.txt		DotnetCLIVersion.txt
ISSUE_TEMPLATE.md		ISSUE_TEMPLATE.md
LICENSE		LICENSE
Microsoft.ML.sln		Microsoft.ML.sln
PULL_REQUEST_TEMPLATE.md		PULL_REQUEST_TEMPLATE.md
README.md		README.md
ROADMAP.md		ROADMAP.md
build.cmd		build.cmd
build.proj		build.proj
build.sh		build.sh
config.json		config.json
dir.traversal.targets		dir.traversal.targets
init-tools.cmd		init-tools.cmd
init-tools.msbuild		init-tools.msbuild
init-tools.sh		init-tools.sh
run.cmd		run.cmd
run.sh		run.sh

Name		Name	Last commit message	Last commit date
build		build
docs		docs
pkg		pkg
src		src
test		test
tools-local/Microsoft.ML.InternalCodeAnalyzer		tools-local/Microsoft.ML.InternalCodeAnalyzer
.gitattributes		.gitattributes
.gitignore		.gitignore
.vsts-dotnet-ci.yml		.vsts-dotnet-ci.yml
BuildToolsVersion.txt		BuildToolsVersion.txt
CONTRIBUTING.md		CONTRIBUTING.md
Directory.Build.props		Directory.Build.props
Directory.Build.targets		Directory.Build.targets
DotnetCLIVersion.txt		DotnetCLIVersion.txt
ISSUE_TEMPLATE.md		ISSUE_TEMPLATE.md
LICENSE		LICENSE
Microsoft.ML.sln		Microsoft.ML.sln
PULL_REQUEST_TEMPLATE.md		PULL_REQUEST_TEMPLATE.md
README.md		README.md
ROADMAP.md		ROADMAP.md
build.cmd		build.cmd
build.proj		build.proj
build.sh		build.sh
config.json		config.json
dir.traversal.targets		dir.traversal.targets
init-tools.cmd		init-tools.cmd
init-tools.msbuild		init-tools.msbuild
init-tools.sh		init-tools.sh
run.cmd		run.cmd
run.sh		run.sh

Name		Name	Last commit message	Last commit date
build		build
docs		docs
pkg		pkg
src		src
test		test
tools-local/Microsoft.ML.InternalCodeAnalyzer		tools-local/Microsoft.ML.InternalCodeAnalyzer
.gitattributes		.gitattributes
.gitignore		.gitignore
.vsts-dotnet-ci.yml		.vsts-dotnet-ci.yml
BuildToolsVersion.txt		BuildToolsVersion.txt
CONTRIBUTING.md		CONTRIBUTING.md
Directory.Build.props		Directory.Build.props
Directory.Build.targets		Directory.Build.targets
DotnetCLIVersion.txt		DotnetCLIVersion.txt
ISSUE_TEMPLATE.md		ISSUE_TEMPLATE.md
LICENSE		LICENSE
Microsoft.ML.sln		Microsoft.ML.sln
PULL_REQUEST_TEMPLATE.md		PULL_REQUEST_TEMPLATE.md
README.md		README.md
ROADMAP.md		ROADMAP.md
build.cmd		build.cmd
build.proj		build.proj
build.sh		build.sh
config.json		config.json
dir.traversal.targets		dir.traversal.targets
init-tools.cmd		init-tools.cmd
init-tools.msbuild		init-tools.msbuild
init-tools.sh		init-tools.sh
run.cmd		run.cmd
run.sh		run.sh

Name		Name	Last commit message	Last commit date
build		build
docs		docs
pkg		pkg
src		src
test		test
tools-local/Microsoft.ML.InternalCodeAnalyzer		tools-local/Microsoft.ML.InternalCodeAnalyzer
.gitattributes		.gitattributes
.gitignore		.gitignore
.vsts-dotnet-ci.yml		.vsts-dotnet-ci.yml
BuildToolsVersion.txt		BuildToolsVersion.txt
CONTRIBUTING.md		CONTRIBUTING.md
Directory.Build.props		Directory.Build.props
Directory.Build.targets		Directory.Build.targets
DotnetCLIVersion.txt		DotnetCLIVersion.txt
ISSUE_TEMPLATE.md		ISSUE_TEMPLATE.md
LICENSE		LICENSE
Microsoft.ML.sln		Microsoft.ML.sln
PULL_REQUEST_TEMPLATE.md		PULL_REQUEST_TEMPLATE.md
README.md		README.md
ROADMAP.md		ROADMAP.md
build.cmd		build.cmd
build.proj		build.proj
build.sh		build.sh
config.json		config.json
dir.traversal.targets		dir.traversal.targets
init-tools.cmd		init-tools.cmd
init-tools.msbuild		init-tools.msbuild
init-tools.sh		init-tools.sh
run.cmd		run.cmd
run.sh		run.sh

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Machine Learning for .NET

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Machine Learning for .NET

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