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# Machine Learning in .NET

ML.NET provides state-of-the-art machine learning (ML) algorithms, transforms, and components, and powers ML pipelines in many Microsoft products. Developed and used internally at Microsoft for over 5 years, the goal is to make ML.NET useful for all developers, data scientists, and information workers and helpful in all products, services, and devices.

### Build Status
# Machine Learning for .NET

Coming soon
[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.

### Installation
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.

You can install ML.NET NuGet from the CLI using:
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](https://github.com/dotnet/core) or later is available.

The current release is 0.1. Check out the [release notes](https://github.com/dotnet/machinelearning/blob/master/Documentation/release-notes/0.1/release-0.1.md).

First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) 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 ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
or from the package manager:
```
Install-Package Microsoft.ML
```
For an example of getting started with .NET Core, see [here](https://www.microsoft.com/net/learn/get-started).

### Building
To build ML.NET from source go to [developers guide](https://github.com/dotnet/machinelearning/blob/master/Documentation/project-docs/developer-guide.md)
Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager.

## Building

To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).

Live build status is coming soon.

## Contributing

We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md).

### Example
## Community

Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/corefx](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/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](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):

Simple snippet to train a model for sentiment classification (See the complete sample [here](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):
```C#
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader<SentimentData>(dataPath, separator: ","));
Expand All@@ -33,7 +61,7 @@ pipeline.Add(new FastTreeBinaryClassifier());
var model = pipeline.Train<SentimentData, SentimentPrediction>();
```

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

```C#
SentimentData data = new SentimentData
Expand All@@ -46,11 +74,6 @@ SentimentPrediction prediction = model.Predict(data);
Console.WriteLine("prediction: " + prediction.Sentiment);
```

### Code of Conduct

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).

## License

ML.NET is licensed under the [MIT license](LICENSE).
Expand All@@ -61,4 +84,5 @@ ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.

There are many .NET related projects on GitHub.

- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.
- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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# Machine Learning in .NET

ML.NET provides state-of-the-art machine learning (ML) algorithms, transforms, and components, and powers ML pipelines in many Microsoft products. Developed and used internally at Microsoft for over 5 years, the goal is to make ML.NET useful for all developers, data scientists, and information workers and helpful in all products, services, and devices.

### Build Status
# Machine Learning for .NET

Coming soon
[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.

### Installation
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.

You can install ML.NET NuGet from the CLI using:
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](https://github.com/dotnet/core) or later is available.

The current release is 0.1. Check out the [release notes](https://github.com/dotnet/machinelearning/blob/master/Documentation/release-notes/0.1/release-0.1.md).

First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) 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 ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
or from the package manager:
```
Install-Package Microsoft.ML
```
For an example of getting started with .NET Core, see [here](https://www.microsoft.com/net/learn/get-started).

### Building
To build ML.NET from source go to [developers guide](https://github.com/dotnet/machinelearning/blob/master/Documentation/project-docs/developer-guide.md)
Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager.

## Building

To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).

Live build status is coming soon.

## Contributing

We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md).

### Example
## Community

Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/corefx](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/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](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):

Simple snippet to train a model for sentiment classification (See the complete sample [here](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):
```C#
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader<SentimentData>(dataPath, separator: ","));
Expand All@@ -33,7 +61,7 @@ pipeline.Add(new FastTreeBinaryClassifier());
var model = pipeline.Train<SentimentData, SentimentPrediction>();
```

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

```C#
SentimentData data = new SentimentData
Expand All@@ -46,11 +74,6 @@ SentimentPrediction prediction = model.Predict(data);
Console.WriteLine("prediction: " + prediction.Sentiment);
```

### Code of Conduct

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).

## License

ML.NET is licensed under the [MIT license](LICENSE).
Expand All@@ -61,4 +84,5 @@ ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.

There are many .NET related projects on GitHub.

- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.
- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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# Machine Learning in .NET

ML.NET provides state-of-the-art machine learning (ML) algorithms, transforms, and components, and powers ML pipelines in many Microsoft products. Developed and used internally at Microsoft for over 5 years, the goal is to make ML.NET useful for all developers, data scientists, and information workers and helpful in all products, services, and devices.

### Build Status
# Machine Learning for .NET

Coming soon
[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.

### Installation
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.

You can install ML.NET NuGet from the CLI using:
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](https://github.com/dotnet/core) or later is available.

The current release is 0.1. Check out the [release notes](https://github.com/dotnet/machinelearning/blob/master/Documentation/release-notes/0.1/release-0.1.md).

First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) 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 ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
or from the package manager:
```
Install-Package Microsoft.ML
```
For an example of getting started with .NET Core, see [here](https://www.microsoft.com/net/learn/get-started).

### Building
To build ML.NET from source go to [developers guide](https://github.com/dotnet/machinelearning/blob/master/Documentation/project-docs/developer-guide.md)
Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager.

## Building

To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).

Live build status is coming soon.

## Contributing

We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md).

### Example
## Community

Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/corefx](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/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](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):

Simple snippet to train a model for sentiment classification (See the complete sample [here](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):
```C#
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader<SentimentData>(dataPath, separator: ","));
Expand All@@ -33,7 +61,7 @@ pipeline.Add(new FastTreeBinaryClassifier());
var model = pipeline.Train<SentimentData, SentimentPrediction>();
```

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

```C#
SentimentData data = new SentimentData
Expand All@@ -46,11 +74,6 @@ SentimentPrediction prediction = model.Predict(data);
Console.WriteLine("prediction: " + prediction.Sentiment);
```

### Code of Conduct

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).

## License

ML.NET is licensed under the [MIT license](LICENSE).
Expand All@@ -61,4 +84,5 @@ ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.

There are many .NET related projects on GitHub.

- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.
- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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@@ -1,30 +1,58 @@
# Machine Learning in .NET

ML.NET provides state-of-the-art machine learning (ML) algorithms, transforms, and components, and powers ML pipelines in many Microsoft products. Developed and used internally at Microsoft for over 5 years, the goal is to make ML.NET useful for all developers, data scientists, and information workers and helpful in all products, services, and devices.

### Build Status
# Machine Learning for .NET

Coming soon
[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.

### Installation
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.

You can install ML.NET NuGet from the CLI using:
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](https://github.com/dotnet/core) or later is available.

The current release is 0.1. Check out the [release notes](https://github.com/dotnet/machinelearning/blob/master/Documentation/release-notes/0.1/release-0.1.md).

First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) 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 ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
or from the package manager:
```
Install-Package Microsoft.ML
```
For an example of getting started with .NET Core, see [here](https://www.microsoft.com/net/learn/get-started).

### Building
To build ML.NET from source go to [developers guide](https://github.com/dotnet/machinelearning/blob/master/Documentation/project-docs/developer-guide.md)
Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager.

## Building

To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).

Live build status is coming soon.

## Contributing

We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md).

### Example
## Community

Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/corefx](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/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](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):

Simple snippet to train a model for sentiment classification (See the complete sample [here](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):
```C#
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader<SentimentData>(dataPath, separator: ","));
Expand All@@ -33,7 +61,7 @@ pipeline.Add(new FastTreeBinaryClassifier());
var model = pipeline.Train<SentimentData, SentimentPrediction>();
```

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

```C#
SentimentData data = new SentimentData
Expand All@@ -46,11 +74,6 @@ SentimentPrediction prediction = model.Predict(data);
Console.WriteLine("prediction: " + prediction.Sentiment);
```

### Code of Conduct

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).

## License

ML.NET is licensed under the [MIT license](LICENSE).
Expand All@@ -61,4 +84,5 @@ ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.

There are many .NET related projects on GitHub.

- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.
- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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# Machine Learning in .NET

ML.NET provides state-of-the-art machine learning (ML) algorithms, transforms, and components, and powers ML pipelines in many Microsoft products. Developed and used internally at Microsoft for over 5 years, the goal is to make ML.NET useful for all developers, data scientists, and information workers and helpful in all products, services, and devices.

### Build Status
# Machine Learning for .NET

Coming soon
[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.

### Installation
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.

You can install ML.NET NuGet from the CLI using:
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](https://github.com/dotnet/core) or later is available.

The current release is 0.1. Check out the [release notes](https://github.com/dotnet/machinelearning/blob/master/Documentation/release-notes/0.1/release-0.1.md).

First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) 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 ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
or from the package manager:
```
Install-Package Microsoft.ML
```
For an example of getting started with .NET Core, see [here](https://www.microsoft.com/net/learn/get-started).

### Building
To build ML.NET from source go to [developers guide](https://github.com/dotnet/machinelearning/blob/master/Documentation/project-docs/developer-guide.md)
Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager.

## Building

To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).

Live build status is coming soon.

## Contributing

We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md).

### Example
## Community

Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/corefx](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/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](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):

Simple snippet to train a model for sentiment classification (See the complete sample [here](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):
```C#
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader<SentimentData>(dataPath, separator: ","));
Expand All@@ -33,7 +61,7 @@ pipeline.Add(new FastTreeBinaryClassifier());
var model = pipeline.Train<SentimentData, SentimentPrediction>();
```

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

```C#
SentimentData data = new SentimentData
Expand All@@ -46,11 +74,6 @@ SentimentPrediction prediction = model.Predict(data);
Console.WriteLine("prediction: " + prediction.Sentiment);
```

### Code of Conduct

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).

## License

ML.NET is licensed under the [MIT license](LICENSE).
Expand All@@ -61,4 +84,5 @@ ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.

There are many .NET related projects on GitHub.

- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.
- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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# Machine Learning in .NET

ML.NET provides state-of-the-art machine learning (ML) algorithms, transforms, and components, and powers ML pipelines in many Microsoft products. Developed and used internally at Microsoft for over 5 years, the goal is to make ML.NET useful for all developers, data scientists, and information workers and helpful in all products, services, and devices.

### Build Status
# Machine Learning for .NET

Coming soon
[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.

### Installation
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.

You can install ML.NET NuGet from the CLI using:
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](https://github.com/dotnet/core) or later is available.

The current release is 0.1. Check out the [release notes](https://github.com/dotnet/machinelearning/blob/master/Documentation/release-notes/0.1/release-0.1.md).

First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) 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 ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
or from the package manager:
```
Install-Package Microsoft.ML
```
For an example of getting started with .NET Core, see [here](https://www.microsoft.com/net/learn/get-started).

### Building
To build ML.NET from source go to [developers guide](https://github.com/dotnet/machinelearning/blob/master/Documentation/project-docs/developer-guide.md)
Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager.

## Building

To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).

Live build status is coming soon.

## Contributing

We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md).

### Example
## Community

Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/corefx](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/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](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):

Simple snippet to train a model for sentiment classification (See the complete sample [here](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):
```C#
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader<SentimentData>(dataPath, separator: ","));
Expand All@@ -33,7 +61,7 @@ pipeline.Add(new FastTreeBinaryClassifier());
var model = pipeline.Train<SentimentData, SentimentPrediction>();
```

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

```C#
SentimentData data = new SentimentData
Expand All@@ -46,11 +74,6 @@ SentimentPrediction prediction = model.Predict(data);
Console.WriteLine("prediction: " + prediction.Sentiment);
```

### Code of Conduct

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).

## License

ML.NET is licensed under the [MIT license](LICENSE).
Expand All@@ -61,4 +84,5 @@ ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.

There are many .NET related projects on GitHub.

- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.
- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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# Machine Learning in .NET

ML.NET provides state-of-the-art machine learning (ML) algorithms, transforms, and components, and powers ML pipelines in many Microsoft products. Developed and used internally at Microsoft for over 5 years, the goal is to make ML.NET useful for all developers, data scientists, and information workers and helpful in all products, services, and devices.

### Build Status
# Machine Learning for .NET

Coming soon
[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.

### Installation
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.

You can install ML.NET NuGet from the CLI using:
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](https://github.com/dotnet/core) or later is available.

The current release is 0.1. Check out the [release notes](https://github.com/dotnet/machinelearning/blob/master/Documentation/release-notes/0.1/release-0.1.md).

First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) 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 ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
or from the package manager:
```
Install-Package Microsoft.ML
```
For an example of getting started with .NET Core, see [here](https://www.microsoft.com/net/learn/get-started).

### Building
To build ML.NET from source go to [developers guide](https://github.com/dotnet/machinelearning/blob/master/Documentation/project-docs/developer-guide.md)
Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager.

## Building

To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).

Live build status is coming soon.

## Contributing

We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md).

### Example
## Community

Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/corefx](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/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](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):

Simple snippet to train a model for sentiment classification (See the complete sample [here](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):
```C#
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader<SentimentData>(dataPath, separator: ","));
Expand All@@ -33,7 +61,7 @@ pipeline.Add(new FastTreeBinaryClassifier());
var model = pipeline.Train<SentimentData, SentimentPrediction>();
```

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

```C#
SentimentData data = new SentimentData
Expand All@@ -46,11 +74,6 @@ SentimentPrediction prediction = model.Predict(data);
Console.WriteLine("prediction: " + prediction.Sentiment);
```

### Code of Conduct

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).

## License

ML.NET is licensed under the [MIT license](LICENSE).
Expand All@@ -61,4 +84,5 @@ ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.

There are many .NET related projects on GitHub.

- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.
- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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# Machine Learning in .NET

ML.NET provides state-of-the-art machine learning (ML) algorithms, transforms, and components, and powers ML pipelines in many Microsoft products. Developed and used internally at Microsoft for over 5 years, the goal is to make ML.NET useful for all developers, data scientists, and information workers and helpful in all products, services, and devices.

### Build Status
# Machine Learning for .NET

Coming soon
[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.

### Installation
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.

You can install ML.NET NuGet from the CLI using:
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](https://github.com/dotnet/core) or later is available.

The current release is 0.1. Check out the [release notes](https://github.com/dotnet/machinelearning/blob/master/Documentation/release-notes/0.1/release-0.1.md).

First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) 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 ML.NET NuGet from the .NET Core CLI using:
```
dotnet add package Microsoft.ML
```

From package manager:
or from the package manager:
```
Install-Package Microsoft.ML
```
For an example of getting started with .NET Core, see [here](https://www.microsoft.com/net/learn/get-started).

### Building
To build ML.NET from source go to [developers guide](https://github.com/dotnet/machinelearning/blob/master/Documentation/project-docs/developer-guide.md)
Or alternatively you can add the Microsoft.ML package from within Visual Studio's NuGet package manager.

## Building

To build ML.NET from source please visit our [developers guide](Documentation/project-docs/developer-guide.md).

Live build status is coming soon.

## Contributing

We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md).

### Example
## Community

Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/corefx](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/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](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):

Simple snippet to train a model for sentiment classification (See the complete sample [here](https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/Scenarios/Scenario3_SentimentPrediction.cs)):
```C#
var pipeline = new LearningPipeline();
pipeline.Add(new TextLoader<SentimentData>(dataPath, separator: ","));
Expand All@@ -33,7 +61,7 @@ pipeline.Add(new FastTreeBinaryClassifier());
var model = pipeline.Train<SentimentData, SentimentPrediction>();
```

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

```C#
SentimentData data = new SentimentData
Expand All@@ -46,11 +74,6 @@ SentimentPrediction prediction = model.Predict(data);
Console.WriteLine("prediction: " + prediction.Sentiment);
```

### Code of Conduct

This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community.
For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct).

## License

ML.NET is licensed under the [MIT license](LICENSE).
Expand All@@ -61,4 +84,5 @@ ML.NET is a [.NET Foundation](http://www.dotnetfoundation.org/projects) project.

There are many .NET related projects on GitHub.

- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.
- [.NET home repo](https://github.com/Microsoft/dotnet) - links to 100s of .NET projects, from Microsoft and the community.