Types of machine learning

There are many ways to segment machine learning and dive deeper. In Chapter 1, How to Sound Like a Data Scientist, 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 machine learning outside the rigid mathematical world of statistics/probability. One can segment machine learning models by different characteristics, including:

• The types of data/organic structures they utilize (tree/graph/neural network)

• The field of mathematics they are most related to (statistical/probabilistic)

• The level of computation required to train (deep learning)

For the purpose of education, I will offer my own breakdown of machine learning models. Branching off of the top level of machine learning, there are the following three subsets:

• Supervised...