[MUSIC] In summary, in this module we
covered two very important and widely used algorithms,
k-means and MapReduce. We started the module by
presenting the goal of clustering. And since then, we've motivated
many applications of clustering. And then we dug into k-means as a specific
algorithm for performing clustering. And it really is the mostly
widely used algorithm out there. And then we took k-means as
an opportunity to present MapReduce, which is a general framework for
parallelizing algorithms. And we showed how you can apply
an iterative version of MapReduce to parallelize k-means. And like we almost always say,
what you learned in this module really generalizes to a wide range
of different applications, and different things you might
be interested in doing. But it's really, really,
really especially true for this module. K-means is a very generic tool for
clustering, and clustering is used in so many different areas. And MapReduce is a very, very powerful and
widely used framework for creating distributed implementations or
parallelizing algorithms. So, in summary, here are the set of things that you should
be able to do after finishing this module. [MUSIC]