[SPARK-18812][MLLIB] explain "Spark ML"
## What changes were proposed in this pull request? There has been some confusion around "Spark ML" vs. "MLlib". This PR adds some FAQ-like entries to the MLlib user guide to explain "Spark ML" and reduce the confusion. I check the [Spark FAQ page](http://spark.apache.org/faq.html), which seems too high-level for the content here. So I added it to the MLlib user guide instead. cc: mateiz Author: Xiangrui Meng <meng@databricks.com> Closes #16241 from mengxr/SPARK-18812.
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@ -35,6 +35,18 @@ The primary Machine Learning API for Spark is now the [DataFrame](sql-programmin
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* The DataFrame-based API for MLlib provides a uniform API across ML algorithms and across multiple languages.
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* DataFrames facilitate practical ML Pipelines, particularly feature transformations. See the [Pipelines guide](ml-pipeline.html) for details.
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*What is "Spark ML"?*
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* "Spark ML" is not an official name but occasionally used to refer to the MLlib DataFrame-based API.
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This is majorly due to the `org.apache.spark.ml` Scala package name used by the DataFrame-based API,
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and the "Spark ML Pipelines" term we used initially to emphasize the pipeline concept.
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*Is MLlib deprecated?*
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* No. MLlib includes both the RDD-based API and the DataFrame-based API.
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The RDD-based API is now in maintenance mode.
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But neither API is deprecated, nor MLlib as a whole.
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# Dependencies
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MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), which depends on
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