Supervised Learning

Supervised learning is a Machine Learning technique in which the model is trained on a labeled training set.
The most popular supervised learning tasks are:

• Regression For example, predicting the price of a house.
  You will need a training set labeled with other house prices for that;
• Classification
  For example, classifying email as spam/ham.
  You will need a training set labeled as spam/ham for that.

Unsupervised Learning

Unsupervised learning is a Machine Learning technique in which the model is trained on an unlabeled training set.
The most popular unsupervised learning tasks are:

• Clusterization
  It is a process of grouping similar data points into clusters.
  You do not need to label the data for it. For example, a training set of emails without labels spam/ham will do;
• Anomaly Detection
  It is a process of detecting deviations from normal data behavior.
  For example, fraud detection in credit card transactions. No need to label fraud/not fraud. Simply give the transaction information to a model, which will determine if the transaction stands out;
• Dimensionality Reduction
  It is a process of reducing the number of dimensions while retaining as much relevant information as possible.
  It also does not require any labels.

Reinforcement Learning

Reinforcement Learning differs a lot from the previous two types. It is a technique used to train self-driving vehicles, robots, AI in gaming, etc.
Reinforcement Learning is a Machine Learning technique in which the agent(e.g., vacuum cleaner robot) learns by making decisions and getting a reward if the decision is correct and a penalty if the decision is wrong.
In the case of a vacuum cleaner robot, it would receive a reward if it moves to a dirty area and a penalty if it moves to an area already cleaned. Also, it would get a large reward once the whole area is cleaned.