Explain the differences and use-cases of supervised, unsupervised, and reinforcement learning in AI.

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∻Differences and Use-Cases of AI Learning Paradigms∻ ‖ ▪ ⋮ Supervised Learning: This involves training a model on a labeled dataset, which means that each training example is paired with an output label. ‖ ‖ ▪ ⋮ Use-Case for Supervised Learning: It is commonly used in applications where historical data predicts future events, such as in fraud detection, spam filtering, and risk assessment. ‖ ‖ ▪ ⋮ Unsupervised Learning: This type of learning involves training a model on data that does not have labeled responses. The system tries to learn the patterns and the structure from the data without external guidance. ‖ ‖ ▪ ⋮ Use-Case for Unsupervised Learning: It is used for clustering and association problems, such as market basket analysis, customer segmentation, and anomaly detection. ‖ ‖ ▪ ⋮ Reinforcement Learning: In this paradigm, an agent learns to make decisions by performing actions and receiving rewards or penalties. It is about taking suitable action to maximize reward in a particular situation. ‖ ‖ ▪ ⋮ Use-Case for Reinforcement Learning: It is widely used in areas such as robotics, gaming, navigation, and real-time decisions, where the model needs to make a sequence of decisions. ‖ ⚹ Each of these learning paradigms has its unique characteristics and is suited for different types of problems in the field of artificial intelligence. ⚹

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