Page 37 - AI & Machine Learning for Beginners: A Guided Workbook

Types of Machine Learning

Supervised Learning:

The algorithm learns from labeled data (data with known answers).

Example: Email spam filters learn from emails that

humans have already marked as "spam" or "not spam."
Real-Life Analogy: Like learning with a teacher who gives
you problems with correct answers, then grades your work.
Unsupervised Learning:
The algorithm finds patterns in unlabeled data without predefined
categories.

Example: Customer segmentation that groups similar

customers based on purchasing behavior.
Real-Life Analogy: Like being given a pile of different
objects and asked to sort them however makes sense to
you.
Reinforcement Learning (RL) – Learning Through Experience
Reinforcement Learning (RL) is a type of Machine Learning
where an agent learns by interacting with an environment and
receiving rewards or penalties based on its actions. The goal is to
maximize cumulative rewards over time by discovering the best
strategy (policy).

Analogy: A Robot Navigating a Maze

The algorithm learns by receiving rewards or penalties for actions.

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