diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 702bcf748fc7..aea07be34cb8 100644
--- a/docs/ml-guide.md
+++ b/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.
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 75aea7060187..ac61df374cca 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -277,9 +277,7 @@ for details on the API.
 `ElementwiseProduct` multiplies each input vector by a provided "weight" vector, using element-wise
 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:
+between the input vector, `v` and transforming vector, `scalingVec`, to yield a result vector. Denoting the `scalingVec` as "`w`", this transformation may be written as:
 
 `\[ \begin{pmatrix}
 v_1 \\