Creating a Machine Learning Architecture

In this chapter, we're going to summarize many of the concepts discussed in the book with the purpose of defining a complete machine learning architecture that is able to preprocess the input data, decompose/augment it, classify/cluster it, and eventually, show the results using graphical tools. We're also going to show how scikit-learn manages complex pipelines and how it's possible to fit them, and search for the optimal parameters in the global context of a complete architecture.