[MINOR][DOC] Fix a few markdown typos

## What changes were proposed in this pull request?

Easy fix in the markdown.

## How was this patch tested?

jekyII build test manually.

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: lemonjing <932191671@qq.com>

Closes #20897 from Lemonjing/master.

(cherry picked from commit 8020f66)
Signed-off-by: hyukjinkwon <gurwls223@apache.org>

Author: lemonjing

docs/ml-guide.md

@@ -111,7 +111,7 @@ and the migration guide below will explain all changes between releases.
 * The class and trait hierarchy for logistic regression model summaries was changed to be cleaner
 and better accommodate the addition of the multi-class summary. This is a breaking change for user
 code that casts a `LogisticRegressionTrainingSummary` to a
-` BinaryLogisticRegressionTrainingSummary`. Users should instead use the `model.binarySummary`
+`BinaryLogisticRegressionTrainingSummary`. Users should instead use the `model.binarySummary`
 method. See [SPARK-17139](https://issues.apache.org/jira/browse/SPARK-17139) for more detail 
 (_note_ this is an `Experimental` API). This _does not_ affect the Python `summary` method, which
 will still work correctly for both multinomial and binary cases.

docs/mllib-feature-extraction.md

@@ -278,8 +278,8 @@ for details on the API.
 multiplication. In other words, it scales each column of the dataset by a scalar multiplier. This
 represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29)
 between the input vector, `v` and transforming vector, `scalingVec`, to yield a result vector.
-Qu8T948*1#
-Denoting the `scalingVec` as "`w`," this transformation may be written as:
+
+Denoting the `scalingVec` as "`w`", this transformation may be written as:
 
 `\[ \begin{pmatrix}
 v_1 \\

docs/mllib-pmml-model-export.md

@@ -7,15 +7,15 @@ displayTitle: PMML model export - RDD-based API
 * Table of contents
 {:toc}
 
-## `spark.mllib` supported models
+## spark.mllib supported models
 
 `spark.mllib` supports model export to Predictive Model Markup Language ([PMML](http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language)).
 
 The table below outlines the `spark.mllib` models that can be exported to PMML and their equivalent PMML model.
 
 <table class="table">
   <thead>
-    <tr><th>`spark.mllib` model</th><th>PMML model</th></tr>
+    <tr><th>spark.mllib model</th><th>PMML model</th></tr>
   </thead>
   <tbody>
     <tr>