I5 · Machine Learning Foundations

Types of Machine Learning

Machine learning is usually divided by what the model learns from. Supervised learning uses labelled examples. Unsupervised learning finds structure with no labels. Semi-supervised learning mixes a few labels with much unlabelled data. Self-supervised learning creates its own labels from the data. Reinforcement learning learns from rewards.

10 min Intermediate

What you need: The earlier topics of this module.

Definition

Machine learning is usually divided by what the model learns from. Supervised learning uses labelled examples. Unsupervised learning finds structure with no labels. Semi-supervised learning mixes a few labels with much unlabelled data. Self-supervised learning creates its own labels from the data. Reinforcement learning learns from rewards.

In simple words

Supervised is a teacher who gives answers while you practise. Unsupervised is being handed a pile of photos and sorting them into groups by yourself. Reinforcement is learning to ride a cycle: nobody gives you answers, you just fall or don't.

Why it matters

The first decision in any ML project is which type fits. It depends on one thing above all: do you have labels? Labels are often the most expensive part of a project, so this choice shapes the cost.

How it works

TypeLearns fromTypical taskAlgorithms
SupervisedInputs with right answersPredict a label or a numberLinear regression, trees, SVM
UnsupervisedInputs onlyGrouping, compression, anomaliesK-means, PCA, DBSCAN
Semi-supervisedFew labels, much unlabelled dataReduce labelling costSelf-training, label propagation
Self-supervisedLabels made from the data itselfLearn representationsContrastive learning, BERT, GPT
ReinforcementRewards from actionsDecisions over timeQ-learning, PPO

Supervised has two kinds

  • Regression predicts a number, such as a house price.
  • Classification predicts a category, such as spam or not spam.

Common mistakes

  • Confusing clustering with classification. Classification has known labels, and clustering finds its own groups.
  • Treating a number that stands for a category as regression, like predicting a city code.

Check yourself

Question 1

1. Predicting tomorrow's temperature in °C is:

Question 2

2. You have no labels and want to group customers. Which type fits?

Question 3

3. How are LLMs mainly pre-trained?

Summary

  • The types differ by what the model learns from.
  • Supervised splits into regression (numbers) and classification (categories).
  • Do you have labels? Start there.

Read it. Now try it yourself.

Open the interactive activity

Finished reading?

Sources: Google Machine Learning Crash Course