Price With GST: ₹23600/-Subject: Data Science Level: Beginner
Supervised learning requires training using labelled data. For example, in order to do classification, which is a supervised learning task, you’ll first need to label the data you’ll use to train the model to classify data into your labelled groups. Unsupervised learning, in divergence, does not require labeling data explicitly.
The crucial difference between both is, K-Nearest Neighbor is a supervised classification algorithm, whereas k-means is an unsupervised clustering algorithm. While the procedure may seem similar at first, what it really means is that in order to K-Nearest Neighbors to work, you need labelled data which you want to classify an unlabeled point into. In k-means clustering it requires set of unlabeled points and a threshold only. The algorithm will take that unlabeled data and will learn how to cluster them into groups by computing the mean of the distance between different points.