Machine learning is a technology that enables computers to learn from data and make decisions or predictions without being explicitly programmed for each task. Here’s a quick look at its different types:

1. Supervised Learning

• Definition: The model is trained on a dataset where each example is labeled with the correct answer. The goal is for the model to learn patterns that map inputs to outputs.
• Example: Teaching a computer to recognize spam emails using a dataset of emails labeled as “spam” or “not spam.”

2. Unsupervised Learning

• Definition: The model is trained on data without labels. It tries to find hidden patterns or groupings in the data on its own.
• Example: Grouping customers based on their buying behavior without pre-labeled categories.

3. Semi-Supervised Learning

• Definition: Combines a small amount of labeled data with a large amount of unlabeled data during training. Useful when labeling data is expensive.
• Example: Using a few labeled images to help classify a large set of unlabeled images.

4. Reinforcement Learning

• Definition: The model learns by interacting with an environment and receiving feedback in the form of rewards or penalties. It learns through trial and error.
• Example: Training a robot to navigate a maze by rewarding it for finding the exit and penalizing it for hitting walls.

These types help solve different kinds of problems and make computers smarter.