Add release notes for ML.NET 0.2 (#301)

* Add release notes for ML.NET 0.2

* Adding release note about TextLoader changes and additional issue/PR references

* Addressing comments: fixing typos, changing formatting, and adding references

docs/release-notes/0.2/release-0.2.md

@@ -0,0 +1,95 @@
+# ML.NET 0.2 Release Notes
+
+We would like to thank the community for the engagement so far and helping us
+shape ML.NET.
+
+Today we are releasing ML.NET 0.2. This release focuses on addressing
+questions/issues, adding clustering to the list of supported machine learning
+tasks, enabling using data from memory to train models, easier model
+validation, and more.
+
+### Installation
+
+ML.NET supports Windows, MacOS, and Linux. See [supported OS versions of .NET
+Core
+2.0](https://github.com/dotnet/core/blob/master/release-notes/2.0/2.0-supported-os.md)
+for more details.
+
+You can install ML.NET NuGet from the CLI using:
+```
+dotnet add package Microsoft.ML
+```
+
+From package manager:
+```
+Install-Package Microsoft.ML
+```
+
+### Release Notes
+
+Below are some of the highlights from this release.
+
+* Added clustering to the list of supported machine learning tasks
+
+    * Clustering is an unsupervised learning task that groups sets of items
+      based on their features. It identifies which items are more similar to
+      each other than other items. This might be useful in scenarios such as
+      organizing news articles into groups based on their topics, segmenting
+      users based on their shopping habits, and grouping viewers based on
+      their taste in movies. 
+
+    * ML.NET 0.2 exposes `KMeansPlusPlusClusterer` which implements [K-Means++
+      clustering](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf)
+      with [Yinyang K-means
+      acceleration](https://www.microsoft.com/en-us/research/publication/yinyang-k-means-a-drop-in-replacement-of-the-classic-k-means-with-consistent-speedup/?from=http%3A%2F%2Fresearch.microsoft.com%2Fapps%2Fpubs%2Fdefault.aspx%3Fid%3D252149).
+      [This
+      test](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs)
+      shows how to use it (from
+      [#222](https://github.com/dotnet/machinelearning/pull/222)).
+
+* Train using data objects in addition to loading data from a file using
+  `CollectionDataSource`. ML.NET 0.1 enabled loading data from a delimited
+  text file. `CollectionDataSource` in ML.NET 0.2 adds the ability to use a
+  collection of objects as the input to a `LearningPipeline`. See sample usage
+  [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs#L133)
+  (from [#106](https://github.com/dotnet/machinelearning/pull/106)). 
+
+* Easier model validation with cross-validation and train-test
+
+    * [Cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics))
+      is an approach to validating how well your model statistically performs.
+      It does not require a separate test dataset, but rather uses your
+      training data to test your model (it partitions the data so different
+      data is used for training and testing, and it does this multiple times).
+      [Here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L51)
+      is an example for doing cross-validation (from
+      [#212](https://github.com/dotnet/machinelearning/pull/212)).
+
+    * Train-test is a shortcut to testing your model on a separate dataset.
+      See example usage
+      [here](https://github.com/dotnet/machinelearning/blob/78810563616f3fcb0b63eb8a50b8b2e62d9d65fc/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs#L36).
+
+    * Note that the `LearningPipeline` is prepared the same way in both cases.
+
+* Speed improvement for predictions: by not creating a parallel cursor for
+  dataviews that only have one element, we get a significant speed-up for
+  predictions (see
+  [#179](https://github.com/dotnet/machinelearning/issues/179) for a few
+  measurements).
+
+* Updated `TextLoader` API: the `TextLoader` API is now code generated and was
+  updated to take explicit declarations for the columns in the data, which is
+  required in some scenarios. See
+  [#142](https://github.com/dotnet/machinelearning/pull/142).
+
+* Added daily NuGet builds of the project: daily NuGet builds of ML.NET are
+  now available
+  [here](https://dotnet.myget.org/feed/dotnet-core/package/nuget/Microsoft.ML).
+
+Additional issues closed in this milestone can be found [here](https://github.com/dotnet/machinelearning/milestone/1?closed=1).
+
+### Acknowledgements
+
+Shoutout to tincann, rantri, yamachu, pkulikov, Sorrien, v-tsymbalistyi, Ky7m,
+forki, jessebenson, mfaticaearnin, and the ML.NET team for their contributions
+as part of this release!