Building, Tuning, and Deploying Models

Abstract

A very popular saying in the Machine Learning community is "70% of Machine Learning is data processing" and going by the structure of this book, the quote seems quite apt. In the preceding chapters, you saw how you can extract, process, and transform data to convert it to a form suitable for learning using Machine Learning algorithms. This chapter deals with the most important part of using that processed data, to learn a model that you can then use to solve real-world problems. You also learned about the CRISP-DM methodology for developing data solutions and projects—the step involving building and tuning these models is the final step in the iterative cycle of Machine Learning.