Supervised Learning:

  1. Definition: Supervised learning involves training a model on labeled data, where the input data is paired with the correct output (label).

  2. Objective: The goal is to learn a mapping from inputs to outputs to make predictions on new, unseen data.

  3. Examples: Classification (e.g., spam detection) and regression (e.g., predicting house prices).

  4. Advantages: High accuracy when sufficient labeled data is available.

  5. Limitations: Requires a large amount of labeled data, which can be expensive and time-consuming to obtain.

Unsupervised Learning:

  1. Definition: Unsupervised learning works with unlabeled data, where the goal is to find hidden patterns or structures within the data without predefined labels.

  2. Objective: The model tries to group or organize data based on inherent similarities or structures.

  3. Examples: Clustering (e.g., customer segmentation) and dimensionality reduction (e.g., PCA).

  4. Advantages: Does not require labeled data, making it easier to work with large datasets.

  5. Limitations: Results can be less interpretable and harder to evaluate compared to supervised learning.

Reinforcement Learning:

  1. Definition: Reinforcement learning involves training an agent to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions.

  2. Objective: The goal is to maximize the cumulative reward over time by learning a policy for action selection.

  3. Examples: Game playing (e.g., AlphaGo), robotics, and self-driving cars.

  4. Advantages: Effective for sequential decision-making problems.

  5. Limitations: Requires a lot of interactions with the environment, and training can be slow.