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Copy file name to clipboardexpand all lines: README.md
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# Machine Learning for .NET
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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 which makes machine learning accessible to .NET developers with the same code that powers machine learning across many Microsoft products, including Power BI, Windows Defender, and Azure.
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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 which makes machine learning accessible to .NET developers with the same code that powers machine learning across many Microsoft products, including Power BI, Windows Defender, and Azure.
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ML.NET allows .NET developers to develop/train their own models and infuse custom machine learning into their applications using .NET, even without prior expertise in developing or tuning machine learning models. It provides data loading from files and databases, enables data transformations and includes many ML algorithms.
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## ML.NET Documentation, tutorials and reference
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Please check our [documentation and tutorials](https://docs.microsoft.com/en-us/dotnet/machine-learning/).
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Please check our [documentation and tutorials](https://docs.microsoft.com/en-us/dotnet/machine-learning/).
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See the [API Reference documentation](https://docs.microsoft.com/en-us/dotnet/api/?view=ml-dotnet).
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## Sample apps
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We have a GitHub repo with [ML.NET sample apps](https://github.com/dotnet/machinelearning-samples) with many scenarios such as Sentiment analysis, Fraud detection, Product Recommender, Price Prediction, Anomaly Detection, Image Classification, Object Detection and many more.
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We have a GitHub repo with [ML.NET sample apps](https://github.com/dotnet/machinelearning-samples) with many scenarios such as Sentiment analysis, Fraud detection, Product Recommender, Price Prediction, Anomaly Detection, Image Classification, Object Detection and many more.
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In addition to the ML.NET samples provided by Microsoft, we're also highlighting many more samples created by the community showcased in this separate page [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/master/docs/COMMUNITY-SAMPLES.md)
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## Operating systems and processor architectures supported by ML.NET
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ML.NET runs on Windows, Linux, and macOS using [.NET Core](https://github.com/dotnet/core), or Windows using .NET Framework.
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ML.NET runs on Windows, Linux, and macOS using [.NET Core](https://github.com/dotnet/core), or Windows using .NET Framework.
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64 bit is supported on all platforms. 32 bit is supported on Windows, except for TensorFlow and LightGBM related functionality.
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To build ML.NET from source please visit our [developers guide](docs/project-docs/developer-guide.md).
# Instructions to build the binaries using Intel's MKL SDK
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ML.NET MKL implementation uses Intel MKL Custom Builder to produce the binaries for the functions that we select. Follow the instructions below to produce the binaries for each platform, which will then be added to the MlNetMklDeps nuget described on the previus section.
Copy file name to clipboardexpand all lines: docs/building/unix-instructions.md
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One way of obtaining CMake and other required libraries is via [Homebrew](https://brew.sh):
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```sh
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$ brew update && brew install cmake https://raw.githubusercontent.com/dotnet/machinelearning/master/build/libomp.rb mono-libgdiplus gettext && brew link gettext --force && brew link libomp --force
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$ brew update && brew install cmake https://raw.githubusercontent.com/dotnet/machinelearning/main/build/libomp.rb mono-libgdiplus gettext && brew link gettext --force && brew link libomp --force
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```
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Please note that newer versions of Homebrew [don't allow installing directly from a URL](https://github.com/Homebrew/brew/issues/8791). If you run into this issue, you may need to download libomp.rb first and install it with the local file instead.
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