[doc][streaming] Fixed broken link in mllib section

The commit message is pretty self-explanatory.

Author: BenFradet <benjamin.fradet@gmail.com>

Closes #5600 from BenFradet/master and squashes the following commits:

108492d [BenFradet] [doc][streaming] Fixed broken link in mllib section

1 files changed, 1 insertions, 1 deletions

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 262512a639..2f2fea5316 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1588,7 +1588,7 @@ See the [DataFrames and SQL](sql-programming-guide.html) guide to learn more abo
 ***
 
 ## MLlib Operations
-You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. (Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details.
+You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. [Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details.
 
 ***