We saw how to use jug, a little Python framework, to manage computations in a way that takes advantage of multiple cores or multiple machines. Although this framework is generic, it was built specifically to address the data analysis needs of its author (who is also an author of this book). Therefore, it has several aspects that make it fit in with the rest of the Python machine learning environment.

We also learned about AWS, the Amazon cloud. Using cloud computing is often a more effective use of resources than building an in-house computing capacity. This is particularly true if your needs are not constant, but changing. Starcluster even allows for clusters that automatically grow as you launch more jobs and shrink as they terminate.

This is the end of the book. We have come a long way. We learned how to perform classification when we have labeled data and clustering when we do not. We learned about dimensionality reduction and topic modeling to make sense of large datasets. Towards the end, we looked at some specific applications, such as music genre classification and computer vision. For implementations, we relied on Python. This language has an increasingly expanding ecosystem of numeric computing packages built on top of NumPy. Whenever possible, we relied on scikit-learn but also used other packages when necessary. Due to the fact that they all use the same basic data structure (the NumPy multidimensional array), it is possible to mix functionality from different packages seamlessly. All of the packages used in this book are open source and available for use in any project.

Naturally, we did not cover every machine learning topic. In the Appendix A, Where to Learn More About Machine Learning, we provide pointers to a selection of other resources that will help interested readers learn more about machine learning.