NOTE: the methods introduced here are all based on RDD-based API. As of Spark 2.0, the RDD-based APIs in the spark.mllib package have entered maintenance mode. The primary Machine Learning API for Spark is now the DataFrame-based API in the spark.ml package. I would strongly suggest NOT use this repo for your learning anymore (please refer to https://spark.apache.org/docs/2.1.0/ml-guide.html).
In this repo, I try to introduce some basic machine learning usages of PySpark. The contents I'm going to cover would be quite simple. But I guess it would be helpful for some people since I would cover some questions I encountered myself from the perspective of a person who's used to more "normal" ML settings (like R language).
For the basic PySpark operations (Tranformations and Actions), you may refer to my another GitHub repo, Spark Practice.
Some of the examples are from the official examples given by Spark. But I will give more details.
Please note this repostory is under the Creative Commons Attribution-ShareAlike License[https://creativecommons.org/licenses/by-sa/3.0/].