MACHINE LEARNING

Detailed explanation-1: -Unsupervised learning uses unlabeled datasets and more difficult algorithms. Since the datasets are not labeled and very little information has been collected, the outcomes or predictions are also unlabeled. However, unlabeled datasets used in unsupervised learning could still reveal useful information.

Detailed explanation-2: -Unsupervised methods usually assign data points to clusters, which could be considered algorithmically generated labels. We don’t “learn” labels in the sense that there is some true target label we want to identify, but rather create labels and assign them to the data.

Detailed explanation-3: -Labeled data is used in supervised learning, whereas unlabeled data is used in unsupervised learning .

Detailed explanation-4: -Unsupervised learning is used for more complex tasks as compared to supervised learning because, in unsupervised learning, we don’t have labeled input data.