What is the difference between supervised, unsupervised, and reinforcement learning?

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Supervised, unsupervised, and reinforcement learning are three main types of machine learning, each differing in how models learn from data:

Supervised Learning: This involves training a model on a labeled dataset, where both input data and the correct output (labels) are provided. The model learns to map inputs to outputs and is evaluated based on its accuracy on new, unseen data. Common tasks include classification (e.g., spam detection) and regression (e.g., predicting housing prices).

Unsupervised Learning: Here, the data has no labels. The model tries to find patterns or structure in the data without explicit instruction. Common tasks include clustering (e.g., customer segmentation) and dimensionality reduction (e.g., PCA for visualizing high-dimensional data). It’s used to explore data and uncover hidden structures.

Reinforcement Learning (RL): In RL, an agent learns to make decisions by interacting with an environment. It receives rewards or penalties based on its actions and learns a policy to maximize cumulative reward over time. RL is commonly used in robotics, gaming (e.g., AlphaGo), and autonomous systems.

In summary:

• Supervised: Learn from labeled data.

• Unsupervised: Find structure in unlabeled data.

• Reinforcement: Learn by interacting and receiving feedback.

Each type suits different problems and data scenarios, and sometimes they are combined in hybrid approaches.

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