Summary
It is certainly an exciting time to be studying machine learning. Ongoing work on the relatively uncharted frontiers of parallel and distributed computing offers great potential for tapping the knowledge found in the deluge of big data. And the burgeoning data science community is facilitated by the free and open-source R programming language, which provides a very low barrier for entry—you simply need to be willing to learn.
The topics you have learned, both in this chapter as well as previous chapters, provide the foundation for understanding more advanced machine learning methods. It is now your responsibility to keep learning and adding tools to your arsenal. Along the way, be sure to keep in mind the no free lunch theorem—no learning algorithm rules them all, and they all have varying strengths and weaknesses. For this reason, there will always be a human element to machine learning, adding subject-specific knowledge and the ability to match the appropriate algorithm...
