What are the main differences between supervised learning and unsupervised learning within the confines of machine learning? Explain your answer in 3-5 sentences

Answer 1

Supervised learning involves training a model on labeled data, where the input data is provided along with the correct output. The model learns the relationship between the input and output during the training process. In contrast, unsupervised learning involves training a model on unlabeled data, where the model tries to find patterns or hidden structures in the data without any guidance on the correct output. Supervised learning is typically used for tasks such as classification and regression, while unsupervised learning is used for tasks such as clustering and dimensionality reduction. Additionally, supervised learning requires more human intervention and labeling of data, while unsupervised learning can be more exploratory and can be used to discover new insights in the data.