chapter two

2 Types of machine learning

 

In this chapter

  • three types of machine learning: supervised, unsupervised, and reinforcement learning
  • the difference between labeled and unlabeled data
  • the difference between regression and classification, and how they are used

As we learned in chapter 1, machine learning is common sense for a computer. Machine learning roughly mimics the process by which humans make decisions based on experience, by making decisions based on previous data. Naturally, programming computers to mimic the human thinking process is challenging, because computers are engineered to store and process numbers, not make decisions. This is the task that machine learning aims to tackle. Machine learning is divided into several branches, depending on the type of decision to be made. In this chapter, we overview some of the most important among these branches.

Machine learning has applications in many fields, such as the following:

What is the difference between labeled and unlabeled data?

What is data?

And what are features?

Labels?

Predictions

Labeled and unlabeled data

Supervised learning: The branch of machine learning that works with labeled data

Regression models predict numbers

Classification models predict a state

Unsupervised learning: The branch of machine learning that works with unlabeled data

Clustering algorithms split a dataset into similar groups

Dimensionality reduction simplifies data without losing too much information

Other ways of simplifying our data: Matrix factorization and singular value decomposition

Generative machine learning

What is reinforcement learning?

Summary

Exercises