Machine learning (ML) can be broadly divided into three main paradigms:

Supervised Learning (Option A):

Data includes labeled outputs (e.g., classification, regression).

Goal: Learn a mapping from input to output.

Unsupervised Learning (Option B):

Data has no labels.

Goal: Discover hidden patterns (e.g., clustering, dimensionality reduction).

Reinforcement Learning (Option C):

Agent interacts with an environment and learns by maximizing cumulative rewards through trial and error.

Used in robotics, game AI, and autonomous systems.

Since all three categories are valid, the correct answer is Option D (All of the above).