In this chapter, we learned about various data transformations and preprocessing methods that are very much relevant in a machine learning pipeline. Preparing the attributes, cleaning the data, and making sure that the data is error free ensures that ML models learn the data correctly. Making the data noise free and generating good features assists a ML model in discovering the patterns in data efficiently.
The next chapter will focus on the techniques to AutoML algorithms. We will discuss various algorithm-specific feature transformations, automating supervised and unsupervised learning, and much more.
