Update some Python MLlib parameters to use camelCase, and tweak docs

We've used camel case in other Spark methods so it felt reasonable to
keep using it here and make the code match Scala/Java as much as
possible. Note that parameter names matter in Python because it allows
passing optional parameters by name.

1 files changed, 9 insertions, 0 deletions

diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index c977bc4f35..1a5c640d10 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -21,6 +21,8 @@ depends on native Fortran routines. You may need to install the
 if it is not already present on your nodes. MLlib will throw a linking error if it cannot 
 detect these libraries automatically.
 
+To use MLlib in Python, you will also need [NumPy](http://www.numpy.org) version 1.7 or newer.
+
 # Binary Classification
 
 Binary classification is a supervised learning problem in which we want to
@@ -316,6 +318,13 @@ other signals), you can use the trainImplicit method to get better results.
 val model = ALS.trainImplicit(ratings, 1, 20, 0.01)
 {% endhighlight %}
 
+# Using MLLib in Java
+
+All of MLlib's methods use Java-friendly types, so you can import and call them there the same
+way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
+Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
+calling `.rdd()` on your `JavaRDD` object.
+
 # Using MLLib in Python
 Following examples can be tested in the PySpark shell.