Differences between Supervised Learning and Unsupervised Learning !
Differences between Supervised Learning and Unsupervised Learning In machine learning, supervised learning uses labeled data to train models to predict specific outcomes, while unsupervised learning analyzes unlabeled data to discover patterns and relationships, and reinforcement learning learns through trial and error by receiving rewards or penalties for actions taken in an environment, without explicit labels on the data; essentially, supervised learning has a "teacher" providing correct answers, unsupervised learning explores data without guidance, and reinforcement learning learns by interacting with its environment and receiving feedback on its actions. 1. **Supervised Learning**: - **Definition**: Supervised learning is a type of machine learning where the algorithm learns from labeled data, which means the input data is paired with corresponding output labels. - **Objective**: The goal of supervised learning is to learn a mapping from input variables