reordered doc

docs/_data/menu-ml.yaml

@@ -6,19 +6,5 @@
   url: ml-classification-regression.html
 - text: Clustering
   url: ml-clustering.html
-- text: Collaborative filtering
-  url: ml-collaborative-filtering.html
-- text: Chaining transforms with pipelines
-  url: ml-pipelines.html
 - text: Advanced topics
   url: ml-advanced.html
-- text: Feature extraction, transformation, and selection
-  url: ml-features.html
-- text: Decision trees for classification and regression
-  url: ml-decision-tree.html
-- text: Ensembles
-  url: ml-ensembles.html
-- text: Linear methods with elastic-net regularization
-  url: ml-linear-methods.html
-- text: Multilayer perceptron classifier
-  url: ml-ann.html

docs/ml-features.md

@@ -1,7 +1,7 @@
 ---
 layout: global
 title: Feature Extraction, Transformation, and Selection - SparkML
-displayTitle: <a href="ml-guide.html">ML</a> - Features
+displayTitle: Features
 ---
 
 This section covers algorithms for working with features, roughly divided into these groups:

docs/mllib-guide.md

@@ -66,15 +66,18 @@ We list major functionality from both below, with links to detailed guides.
 
 # spark.ml: high-level APIs for ML pipelines
 
-**[spark.ml programming guide](ml-guide.html)** provides an overview of the Pipelines API and major
-concepts. It also contains sections on using algorithms within the Pipelines API, for example:
-
-* [Feature extraction, transformation, and selection](ml-features.html)
+* [Overview: estimators, transformers and pipelines](ml-intro.html)
+* [Building and transforming features](ml-features.html)
+* [Classification and regression](ml-classification-regression.html)
 * [Clustering](ml-clustering.html)
-* [Decision trees for classification and regression](ml-decision-tree.html)
-* [Ensembles](ml-ensembles.html)
-* [Linear methods with elastic net regularization](ml-linear-methods.html)
-* [Multilayer perceptron classifier](ml-ann.html)
+* [Advanced topics](ml-advanced.html)
+
+Some techniques are not available yet in spark.ml, most notably:
+ - clustering
+ - collaborative filtering
+ - dimensionality reduction
+
+Users can seemlessly combine the implementation of these techniques found in `spark.mllib` with the rest of the algorithms found in `spark.ml`.
 
 # Dependencies