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author | BenFradet <benjamin.fradet@gmail.com> | 2015-04-20 13:46:55 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-04-20 13:46:55 -0700 |
commit | 517bdf36aecdc94ef569b68f0a96892e707b5c7b (patch) | |
tree | 8925bbba5d137c515a349724c7d9ccebab0690f1 /docs | |
parent | 97fda73db4efda2ba5b12937954de428258a5b56 (diff) | |
download | spark-517bdf36aecdc94ef569b68f0a96892e707b5c7b.tar.gz spark-517bdf36aecdc94ef569b68f0a96892e707b5c7b.tar.bz2 spark-517bdf36aecdc94ef569b68f0a96892e707b5c7b.zip |
[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
Diffstat (limited to 'docs')
-rw-r--r-- | docs/streaming-programming-guide.md | 2 |
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. *** |