Notes

Supervised Learning:
- Definition: In supervised learning, a machine learning algorithm learns to make predictions or classify data based on a labeled dataset. It's like having a teacher who guides the learning process by providing correct answers.
- Example: Predicting house prices based on features like square footage, number of bedrooms, and location, using a dataset of labeled house prices.

Unsupervised Learning:
- Definition: Unsupervised learning involves finding patterns or structures in data without explicit labels. It's like discovering hidden insights in data without a teacher's guidance.
- Example: Clustering customer data to group similar customers together based on their purchase behavior, without knowing in advance what those groups should be.

Reinforcement Learning:
- Definition: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. It receives feedback in the form of rewards or penalties for its actions, and its goal is to maximize the cumulative reward.
- Example: Training a computer program to play chess by allowing it to make moves on a chessboard and rewarding it for winning games or penalizing it for losing, helping it improve its game-playing strategy over time.