There are generally 4 different types of machine learning, which can be defined as:-

1. Supervised Machine Learning:

• Trained on a “Labelled Dataset” with both input and output parameters.
• Learns to map inputs to correct outputs.
• Example: Image classification (differentiating between cats and dogs).

2. Unsupervised Machine Learning:

• Uses unlabeled data.
• Finds patterns, clusters, or structures in data.
• Example: Grouping similar customer profiles without predefined labels.

3. Semi-Supervised Machine Learning:

• Combines labeled and unlabeled data.
• Useful when fully labeled datasets are scarce.
• Example: Enhancing a model’s performance by leveraging both types of data.

4. Reinforcement Learning:

• Learns from feedback (rewards or penalties).
• Used in scenarios where an agent interacts with an environment.
• Example: Training an AI to play games or control robots.