2 Types of machine learning

This chapter covers:

Three main different types of machine learning.
The difference between labelled and unlabelled data.
What supervised learning is and what it’s useful for.
The difference between regression and classification, and what are they useful for.
What unsupervised learning is and what it’s useful for.
What reinforcement learning is and what it’s useful for.

As we learned in Chapter 1, machine learning is common sense, but for a computer. It mimics the process in which humans make decisions based on experience, by making decisions based on previous data. Of course, this is challenging for computers, as all they do is store numbers and do operations on them, so programming them to mimic human level of thought is difficult. Machine learning is divided into several branches, and they all mimic different types of ways in which humans make decisions. In this chapter, we overview some of the most important of these branches.

ML has applications in many many fields. Can you think of some fields in which you can apply machine learning? Here is a list of some of my favorites:

2.1 What is the difference between labelled and unlabelled data?

2.2 What is supervised learning?

2.2.1 Regression models predict numbers

2.2.2 Classification models predict a state

2.3 What is unsupervised learning?

2.3.1 Clustering algorithms split a dataset into similar groups

2.3.2 Dimensionality reduction simplifies data without losing much information

2.3.3 Matrix factorization and other types of unsupervised learning

2.4 What is reinforcement learning?

2.5 Summary