Add info to README.md

README.md

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 `nimbusml` is a Python module that provides Python bindings for [ML.NET](https://github.com/dotnet/machinelearning). 
 
-ML.NET was originally developed in Microsoft Research and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and others. `nimbusml` was built to enable data science teams that are more familiar with Python to take advantage of ML.NET's functionality and performance. 
+ML.NET was originally developed in Microsoft Research and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel, and others. `nimbusml` was built to enable data science teams that are more familiar with Python to take advantage of ML.NET's functionality and performance. 
 
-This package enables training ML.NET pipelines or integrating ML.NET components directly into [scikit-learn](https://scikit-learn.org/stable/) pipelines (it supports  `numpy.ndarray`, `scipy.sparse_cst`, and `pandas.DataFrame` as inputs).
+`nimbusml` enables training ML.NET pipelines or integrating ML.NET components directly into [scikit-learn](https://scikit-learn.org/stable/) pipelines. It adheres to existing `scikit-learn` conventions, allowing simple interoperability between `nimbusml` and `scikit-learn` components, while adding a suite of fast, highly optimized, and scalable algorithms, transforms, and components written in C++ and C\#.
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+See examples below showing interoperability with `scikit-learn`. A more detailed example in the [documentation](https://docs.microsoft.com/en-us/nimbusml/tutorials/b_c-sentiment-analysis-3-combining-nimbusml-and-scikit-learn) shows how to use a `nimbusml` component in a `scikit-learn` pipeline, and create a pipeline using only `nimbusml` components.
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+`nimbusml` supports `numpy.ndarray`, `scipy.sparse_cst`, and `pandas.DataFrame` as inputs. In addition, `nimbusml` also supports streaming from files without loading the dataset into memory with `FileDataStream`, which allows training on data significantly exceeding memory.
 
 Documentation can be found [here](https://docs.microsoft.com/en-us/NimbusML/overview) and additional notebook samples can be found [here](https://github.com/Microsoft/NimbusML-Samples).