Summary
In this chapter, we described various unsupervised learning, supervised learning, semi-supervised learning, and anomaly detection problems and presented concrete methods that are used for them. You need to understand these concrete methods and the problems that they address to be able to understand when and how to apply them. Even though we rarely implement these methods from scratch, knowing how these methods work will enable you to save time and cost when applying the wrong tools to the job. Furthermore, it will enable you to gain a better understanding of the results you’ll see when applying these methods and help you explain them to your project stakeholders.
In the next chapter, you will learn where these methods fit into an artificial intelligence project, how the workflow works, and how to get optimal results by using the available tools.
