Update readme with latest feedback

README.md

@@ -6,11 +6,11 @@ ML.NET was originally developed in Microsoft Research and is used across many pr
 
 This package enables training ML.NET pipelines or integrating ML.NET components directly into Scikit-Learn pipelines (it supports  `numpy.ndarray`, `scipy.sparse_cst`, and `pandas.DataFrame` as inputs).
 
-Documentation can be found [here](https://docs.microsoft.com/en-us/NimbusML/overview) with additional [notebook samples](https://github.com/Microsoft/NimbusML-Samples).
+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).
 
 ## Installation
 
-`nimbusml` runs on Windows, Linux, and macOS - any platform where 64 bit .NET Core is available. It relies on .NET Core, and this is installed automatically as part of the package.
+`nimbusml` runs on Windows, Linux, and macOS. 
 
 `nimbusml` requires Python **2.7**, **3.5**, or **3.6**, 64 bit version only. Python 3.7 is not yet supported.
 
@@ -20,34 +20,57 @@ Install `nimbusml` using `pip` with:
 pip install nimbusml
 ```
 
-`nimbusml` has been tested on Windows 10, MacOS 10.13, Ubuntu 14.04, Ubuntu 16.04, Ubuntu 18.04, CentOS 7, and RHEL 7.
+`nimbusml` has been reported to work on Windows 10, MacOS 10.13, Ubuntu 14.04, Ubuntu 16.04, Ubuntu 18.04, CentOS 7, and RHEL 7.
 
 ## Examples
 
 Here is an example of how to train a model to predict sentiment from text samples (based on [this](https://github.com/dotnet/machinelearning/blob/master/README.md) ML.NET example). The full code for this example is [here](https://github.com/Microsoft/NimbusML-Samples/blob/master/samples/2.1%20%5BText%5D%20Sentiment%20Analysis%201%20-%20Data%20Loading%20with%20Pandas.ipynb).
 
 ```python
+from nimbusml import Pipeline, FileDataStream
+from nimbusml.datasets import get_dataset
+from nimbusml.ensemble import FastTreesBinaryClassifier
+from nimbusml.feature_extraction.text import NGramFeaturizer
+
+train_file = get_dataset('gen_twittertrain').as_filepath()
+test_file = get_dataset('gen_twittertest').as_filepath()
+
+train_data = FileDataStream.read_csv(train_file, sep='\t')
+test_data = FileDataStream.read_csv(test_file, sep='\t')
+
 pipeline = Pipeline([ # nimbusml pipeline
-    NGramFeaturizer(columns={'Features': ['SentimentText']}),
-    FastTreeBinaryClassifier(feature=['Features'], label='Sentiment')
+    NGramFeaturizer(columns={'Features': ['Text']}),
+    FastTreesBinaryClassifier(feature=['Features'], label='Label')
 ])
 
 # fit and predict
-pipeline.fit(data)
-results = pipeline.predict(data)
+pipeline.fit(train_data)
+results = pipeline.predict(test_data)
 ```
 
 Instead of creating an `nimbusml` pipeline, you can also integrate components into Scikit-Learn pipelines:
 
 ```python
+from sklearn.pipeline import Pipeline
+from nimbusml.datasets import get_dataset
+from nimbusml.ensemble import FastTreesBinaryClassifier
+from sklearn.feature_extraction.text import TfidfVectorizer
+import pandas as pd
+
+train_file = get_dataset('gen_twittertrain').as_filepath()
+test_file = get_dataset('gen_twittertest').as_filepath()
+
+train_data = pd.read_csv(train_file, sep='\t')
+test_data = pd.read_csv(test_file, sep='\t')
+
 pipeline = Pipeline([ # sklearn pipeline
     ('tfidf', TfidfVectorizer()), # sklearn transform
-    ('clf', FastTreeBinaryClassifier())]) # nimbusml learner
+    ('clf', FastTreesBinaryClassifier()) # nimbusml learner
 ])
 
 # fit and predict
-pipeline.fit(data)
-results = pipeline.predict(data)
+pipeline.fit(train_data["Text"], train_data["Label"])
+results = pipeline.predict(test_data["Text"])
 ```
 
 
@@ -57,11 +80,15 @@ Many additional examples and tutorials can be found in the [documentation](https
 
 ## Building
 
-To build `nimbusml` from source please visit our [developers guide](docs/developers/developer-guide.md).
+To build `nimbusml` from source please visit our [developer guide](docs/developers/developer-guide.md).
 
 ## Contributing
 
-We welcome [contributions](docs/project-docs/contributing.md)!
+The contributions guide can be found [here](docs/project-docs/contributing.md). Given the experimental nature of this project, support will be provided on a best-effort basis. We suggest opening an issue for discussion before starting a PR with big changes.
+
+## Support
+
+If you have an idea for a new feature or encounter a problem, please open an [issue](https://github.com/Microsoft/NimbusML/issues/new) in this repository or ask your question on Stack Overflow.
 
 ## License