Types of ML
There are many ways to segment ML and dive deeper. In Chapter 1, Data Science Terminology, I mentioned statistical and probabilistic models. These models utilize statistics and probability, which we’ve seen in the previous chapters, in order to find relationships between data and make predictions. In this chapter, we will implement both types of models. In the following chapter, we will see ML outside the rigid mathematical world of statistics/probability. You can segment ML models by different characteristics, including the following:
- The types of data organic structures they utilize (tree, graph, or neural network (NN))
- The field of mathematics they are most related to (statistical or probabilistic)
- The level of computation required to train (deep learning (DL))
Branching off from the top level of ML, there are the following three subsets: