In this chapter, we jumped right into using IBM Watson Studio's various features to accomplish various data preprocessing and setup objectives, such as using built-in R libraries for data preprocessing, dimensional reduction, and data fusion. We then offered a number of recommendations to save you time when preparing an ML project.

In the next chapter, we'll examine the machine learning paradigm and focus on various approaches and algorithms. The chapter will start by giving a practical background to model evaluation, model selection, and algorithm selection in machine learning and will then cover supervised learning.