In this chapter, we laid some of the groundwork for data science, understood different types of models that can be built, and how they can be evaluated. First, we discussed several supervised learning algorithms, including regression and classification. Then, we discussed the unsupervised learning algorithm, including clustering and using text data to cluster them into different clusters using the MiniBatchKMeans algorithm. Finally, we briefly discussed reinforcement learning.
In the next chapter, we are going to use all the techniques we have learned so far to perform EDA on the Wine Quality dataset. Moreover, we will be using supervised learning algorithms to classify wine quality.
