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From m...@apache.org
Subject spark git commit: [doc][streaming] Fixed broken link in mllib section
Date Mon, 20 Apr 2015 20:47:07 GMT
Repository: spark
Updated Branches:
  refs/heads/master 97fda73db -> 517bdf36a


[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


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/517bdf36
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/517bdf36
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/517bdf36

Branch: refs/heads/master
Commit: 517bdf36aecdc94ef569b68f0a96892e707b5c7b
Parents: 97fda73
Author: BenFradet <benjamin.fradet@gmail.com>
Authored: Mon Apr 20 13:46:55 2015 -0700
Committer: Xiangrui Meng <meng@databricks.com>
Committed: Mon Apr 20 13:46:55 2015 -0700

----------------------------------------------------------------------
 docs/streaming-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/517bdf36/docs/streaming-programming-guide.md
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diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 262512a..2f2fea5 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.
 
 ***
 


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