ML.NET home page readme updated with getting started resources (dotne…

…t#4123)

* ML.NET home page readme updated with additional getting started resources for ML.NET users

ML.NET home page readme updated with additional getting started resources such as, Sample apps, community sample apps, ML.NET videos playlist at YouTube, Documentation, etc. plus some refactoring of the page so the info is clearer.

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README.md

@@ -2,28 +2,56 @@
 
 # Machine Learning for .NET
 
-[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.
+[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 while offering a production high quality. 
 
-ML.NET allows .NET developers to develop their own models and infuse custom machine learning into their applications, using .NET, even without prior expertise in developing or tuning machine learning models.
+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 while having a powerful end-to-end ML platform covering data loading from dataset files and databases, data transformations and many ML algorithms.
 
-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.
+ML.NET was originally developed in Microsoft Research and evolved into an Microsoft internal framework over the last decade being used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.
 
-ML.NET enables machine learning tasks like classification (for example: support text classification, sentiment analysis) and regression (for example, price-prediction).
+ML.NET enables machine learning tasks like classification (for example: support text classification, sentiment analysis), regression (for example, price-prediction) and many other ML tasks such as anomaly detection, time-series-forecast, clustering, ranking, etc.
 
-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.
+ML.NET also brings .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.
 
-## Documentation
+## Getting started with machine learning by using ML.NET
 
-Please check our documentation [here](https://docs.microsoft.com/en-us/dotnet/machine-learning/). See the [ML.NET API Reference Documentation](https://docs.microsoft.com/en-us/dotnet/api/?view=ml-dotnet).
+If you are new to machine learning, start by learning the basics from this collection of resources targeting ML.NET:
 
-## Installation
+[Learn ML.NET](https://dotnet.microsoft.com/learn/ml-dotnet)
+
+## ML.NET Documentation, tutorials and reference
+
+Please check our [documentation and tutorials](https://docs.microsoft.com/en-us/dotnet/machine-learning/). 
+
+See the [API Reference documentation](https://docs.microsoft.com/en-us/dotnet/api/?view=ml-dotnet).
+
+## Sample apps
+
+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. 
+
+In addition to the ML.NET samples provided by Microsoft, we're also highlighting many more samples created by the community showcased in this separated page [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/master/docs/COMMUNITY-SAMPLES.md)
+
+
+## ML.NET videos playlist at YouTube
+
+There a list of short videos each one focusing on a particular single topic of ML.NET at the [ML.NET videos playlist](https://aka.ms/mlnetyoutube) in YouTube.
+
+
+## Operating systems and processor architectures supported by ML.NET
+
+ML.NET runs on Windows, Linux, and macOS using [.NET Core](https://github.com/dotnet/core), or Windows using .NET Framework. 
+
+64 bit is supported on all platforms. 32 bit is supported on Windows, except for TensorFlow, LightGBM, and ONNX related functionality.
+
+## ML.NET Nuget packages status
 
 [![NuGet Status](https://img.shields.io/nuget/vpre/Microsoft.ML.svg?style=flat)](https://www.nuget.org/packages/Microsoft.ML/)
 
-ML.NET runs on Windows, Linux, and macOS using [.NET Core](https://github.com/dotnet/core), or Windows using .NET Framework. 64 bit is supported on all platforms. 32 bit is supported on Windows, except for TensorFlow, LightGBM, and ONNX related functionality.
+## Release notes
 
 Check out the [release notes](docs/release-notes) to see what's new.
 
+## Using ML.NET packages
+
 First, ensure you have installed [.NET Core 2.1](https://www.microsoft.com/net/learn/get-started) or later. ML.NET also works on the .NET Framework 4.6.1 or later, but 4.7.2 or later is recommended.
 
 Once you have an app, you can install the ML.NET NuGet package from the .NET Core CLI using:
@@ -42,7 +70,7 @@ Daily NuGet builds of the project are also available in our [MyGet](https://dotn
 
 > [https://dotnet.myget.org/F/dotnet-core/api/v3/index.json](https://dotnet.myget.org/F/dotnet-core/api/v3/index.json)
 
-## Building
+## Building ML.NET (For contributors building ML.NET open source code)
 
 To build ML.NET from source please visit our [developers guide](docs/project-docs/developer-guide.md).
 
@@ -69,7 +97,8 @@ Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet
 This project has adopted the code of conduct defined by the [Contributor Covenant](https://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
+
+## Code examples
 
 Here is a snippet code for training a model to predict sentiment from text samples. You can find complete samples in [samples repo](https://github.com/dotnet/machinelearning-samples).
 
@@ -101,13 +130,9 @@ Console.WriteLine("prediction: " + prediction.Prediction);
 ```
 A cookbook that shows how to use these APIs for a variety of existing and new scenarios can be found [here](docs/code/MlNetCookBook.md).
 
-## Samples
-
-We have a [repo of samples](https://github.com/dotnet/machinelearning-samples) that you can look at.
-
 ## License
 
-ML.NET is licensed under the [MIT license](LICENSE).
+ML.NET is licensed under the [MIT license](LICENSE) and it is free to use commercially.
 
 ## .NET Foundation