[MINOR][DOCS] Fix typos

## What changes were proposed in this pull request?
Fix Typos.
This PR is the complete version of apache#23145.

## How was this patch tested?
NA

Closes apache#23185 from kjmrknsn/docUpdate.

Authored-by: Keiji Yoshida <kjmrknsn@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>

Author: kjmrknsn

docs/configuration.md

@@ -498,7 +498,7 @@ Apart from these, the following properties are also available, and may be useful
   <td>
     Reuse Python worker or not. If yes, it will use a fixed number of Python workers,
     does not need to fork() a Python process for every task. It will be very useful
-    if there is large broadcast, then the broadcast will not be needed to transferred
+    if there is a large broadcast, then the broadcast will not need to be transferred
     from JVM to Python worker for every task.
   </td>
 </tr>

docs/graphx-programming-guide.md

@@ -522,7 +522,7 @@ val joinedGraph = graph.joinVertices(uniqueCosts,
 
 A key step in many graph analytics tasks is aggregating information about the neighborhood of each
 vertex.
-For example, we might want to know the number of followers each user has or the average age of the
+For example, we might want to know the number of followers each user has or the average age of
 the followers of each user.  Many iterative graph algorithms (e.g., PageRank, Shortest Path, and
 connected components) repeatedly aggregate properties of neighboring vertices (e.g., current
 PageRank Value, shortest path to the source, and smallest reachable vertex id).
@@ -700,7 +700,7 @@ a new value for the vertex property, and then send messages to neighboring verti
 super step.  Unlike Pregel, messages are computed in parallel as a
 function of the edge triplet and the message computation has access to both the source and
 destination vertex attributes.  Vertices that do not receive a message are skipped within a super
-step.  The Pregel operators terminates iteration and returns the final graph when there are no
+step.  The Pregel operator terminates iteration and returns the final graph when there are no
 messages remaining.
 
 > Note, unlike more standard Pregel implementations, vertices in GraphX can only send messages to

docs/ml-datasource.md

@@ -5,7 +5,7 @@ displayTitle: Data sources
 ---
 
 In this section, we introduce how to use data source in ML to load data.
-Beside some general data sources such as Parquet, CSV, JSON and JDBC, we also provide some specific data sources for ML.
+Besides some general data sources such as Parquet, CSV, JSON and JDBC, we also provide some specific data sources for ML.
 
 **Table of Contents**
 

docs/ml-features.md

@@ -359,7 +359,7 @@ Assume that we have the following DataFrame with columns `id` and `raw`:
 ~~~~
  id | raw
 ----|----------
- 0  | [I, saw, the, red, baloon]
+ 0  | [I, saw, the, red, balloon]
  1  | [Mary, had, a, little, lamb]
 ~~~~
 
@@ -369,7 +369,7 @@ column, we should get the following:
 ~~~~
  id | raw                         | filtered
 ----|-----------------------------|--------------------
- 0  | [I, saw, the, red, baloon]  |  [saw, red, baloon]
+ 0  | [I, saw, the, red, balloon]  |  [saw, red, balloon]
  1  | [Mary, had, a, little, lamb]|[Mary, little, lamb]
 ~~~~
 
@@ -1302,15 +1302,15 @@ need to know vector size, can use that column as an input.
 To use `VectorSizeHint` a user must set the `inputCol` and `size` parameters. Applying this
 transformer to a dataframe produces a new dataframe with updated metadata for `inputCol` specifying
 the vector size. Downstream operations on the resulting dataframe can get this size using the
-meatadata.
+metadata.
 
 `VectorSizeHint` can also take an optional `handleInvalid` parameter which controls its
 behaviour when the vector column contains nulls or vectors of the wrong size. By default
 `handleInvalid` is set to "error", indicating an exception should be thrown. This parameter can
 also be set to "skip", indicating that rows containing invalid values should be filtered out from
 the resulting dataframe, or "optimistic", indicating that the column should not be checked for
 invalid values and all rows should be kept. Note that the use of "optimistic" can cause the
-resulting dataframe to be in an inconsistent state, me:aning the metadata for the column
+resulting dataframe to be in an inconsistent state, meaning the metadata for the column
 `VectorSizeHint` was applied to does not match the contents of that column. Users should take care
 to avoid this kind of inconsistent state.
 

docs/ml-pipeline.md

@@ -62,7 +62,7 @@ In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [
 
 A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`.  See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.
 
-Columns in a `DataFrame` are named.  The code examples below use names such as "text," "features," and "label."
+Columns in a `DataFrame` are named. The code examples below use names such as "text", "features", and "label".
 
 ## Pipeline components
 

docs/mllib-linear-methods.md

@@ -272,7 +272,7 @@ In `spark.mllib`, the first class $0$ is chosen as the "pivot" class.
 See Section 4.4 of
 [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) for
 references.
-Here is an
+Here is a
 [detailed mathematical derivation](http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297).
 
 For multiclass classification problems, the algorithm will output a multinomial logistic regression
@@ -350,7 +350,7 @@ known as the [mean squared error](http://en.wikipedia.org/wiki/Mean_squared_erro
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
-The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint.
+The following example demonstrates how to load training data, parse it as an RDD of LabeledPoint.
 The example then uses LinearRegressionWithSGD to build a simple linear model to predict label
 values. We compute the mean squared error at the end to evaluate
 [goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit).

docs/security.md

@@ -337,7 +337,7 @@ Configuration for SSL is organized hierarchically. The user can configure the de
 which will be used for all the supported communication protocols unless they are overwritten by
 protocol-specific settings. This way the user can easily provide the common settings for all the
 protocols without disabling the ability to configure each one individually. The following table
-describes the the SSL configuration namespaces:
+describes the SSL configuration namespaces:
 
 <table class="table">
   <tr>

docs/sparkr.md

@@ -296,7 +296,7 @@ head(agg(rollup(df, "cyl", "disp", "gear"), avg(df$mpg)))
 
 ### Operating on Columns
 
-SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.
+SparkR also provides a number of functions that can be directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.
 
 <div data-lang="r"  markdown="1">
 {% highlight r %}