[SPARK-14615][ML][FOLLOWUP] Fix Python examples to use the new ML Vector and Matrix APIs in the ML pipeline based algorithms

## What changes were proposed in this pull request?

This PR fixes Python examples to use the new ML Vector and Matrix APIs in the ML pipeline based algorithms.

I firstly executed this shell command, `grep -r "from pyspark.mllib" .` and then executed them all.
Some of tests in `ml` produced the error messages as below:

```
pyspark.sql.utils.IllegalArgumentException: u'requirement failed: Input type must be VectorUDT but got org.apache.spark.mllib.linalg.VectorUDTf71b0bce.'
```

So, I fixed them to use new ones just identically with some Python tests fixed in https://github.com/apache/spark/pull/12627

## How was this patch tested?

Manually tested for all the examples listed by `grep -r "from pyspark.mllib" .`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #13393 from HyukjinKwon/SPARK-14615.

1 files changed, 1 insertions, 1 deletions

diff --git a/examples/src/main/python/ml/elementwise_product_example.py b/examples/src/main/python/ml/elementwise_product_example.py
index 41727edcdb..598deae886 100644
--- a/examples/src/main/python/ml/elementwise_product_example.py
+++ b/examples/src/main/python/ml/elementwise_product_example.py
@@ -19,7 +19,7 @@ from __future__ import print_function
 
 # $example on$
 from pyspark.ml.feature import ElementwiseProduct
-from pyspark.mllib.linalg import Vectors
+from pyspark.ml.linalg import Vectors
 # $example off$
 from pyspark.sql import SparkSession