We looked at 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.

You also learned about AWS and the Amazon cloud. Using cloud computing is often a more effective use of resources than building in-house computing capacity. This is particularly true if your needs are not constant and are changing. Furthermore cfncluster 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. You learned how to perform classification and clustering. You learned about dimensionality reduction and topic modeling to make sense of large datasets. Toward 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 used other packages when necessary. Due to the fact that they all use the same basic data structure (the NumPy multidimensional array), it's possible to mix functionalities 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, we provide a selection of other resources that will help interested readers learn more about machine learning.