update

Author: zhengruifeng

docs/ml-collaborative-filtering.md

@@ -59,7 +59,7 @@ This approach is named "ALS-WR" and discussed in the paper
 It makes `regParam` less dependent on the scale of the dataset, so we can apply the
 best parameter learned from a sampled subset to the full dataset and expect similar performance.
 
-## Examples
+**Examples**
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">

docs/ml-tuning.md

@@ -62,7 +62,7 @@ To help construct the parameter grid, users can use the [`ParamGridBuilder`](api
 
 After identifying the best `ParamMap`, `CrossValidator` finally re-fits the `Estimator` using the best `ParamMap` and the entire dataset.
 
-## Examples: model selection via cross-validation
+**Examples: model selection via cross-validation**
 
 The following example demonstrates using `CrossValidator` to select from a grid of parameters.
 
@@ -102,7 +102,7 @@ It splits the dataset into these two parts using the `trainRatio` parameter. For
 
 Like `CrossValidator`, `TrainValidationSplit` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
 
-## Examples: model selection via train validation split
+**Examples: model selection via train validation split**
 
 <div class="codetabs">