[SPARK-14817][ML][MLLIB][DOC] Made DataFrame-based API primary in MLlib guide

## What changes were proposed in this pull request?

Made DataFrame-based API primary
* Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html
* mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html
* ml-guide.html includes a "maintenance mode" announcement about the RDD-based API
  * **Reviewers: please check this carefully**
* (minor) Titles for DF API no longer include "- spark.ml" suffix.  Titles for RDD API have "- RDD-based API" suffix
* Moved migration guide to ml-guide from mllib-guide
  * Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides
  * **Reviewers**: I did not change any of the content of the migration guides.

Reorganized DataFrame-based guide:
* ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc.
* Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html
  * **Reviewers**: I did not change the content of these guides, except some intro text.
* Sidebar remains the same, but with pipeline and tuning sections added

Other:
* ml-classification-regression.html: Moved text about linear methods to new section in page

## How was this patch tested?

Generated docs locally

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #14213 from jkbradley/ml-guide-2.0.

1 files changed, 2 insertions, 2 deletions

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 2ee3b80185..de82a064d1 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -15,7 +15,7 @@ like Kafka, Flume, Kinesis, or TCP sockets, and can be processed using complex
 algorithms expressed with high-level functions like `map`, `reduce`, `join` and `window`.
 Finally, processed data can be pushed out to filesystems, databases,
 and live dashboards. In fact, you can apply Spark's
-[machine learning](mllib-guide.html) and
+[machine learning](ml-guide.html) and
 [graph processing](graphx-programming-guide.html) algorithms on data streams.
 
 <p style="text-align: center;">
@@ -1673,7 +1673,7 @@ See the [DataFrames and SQL](sql-programming-guide.html) guide to learn more abo
 ***
 
 ## MLlib Operations
-You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. [Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details.
+You can also easily use machine learning algorithms provided by [MLlib](ml-guide.html). First of all, there are streaming machine learning algorithms (e.g. [Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](ml-guide.html) guide for more details.
 
 ***