What is the difference between supervised and unsupervised machine learning ?
SUPERVISED LEARNING : What do we understand about Supervision? Supervision is learning something under the guidance of a teacher or some supervisor, who can judge us whether we are doing things right or not. Similarly, in supervised learning, we have a set of labeled data while we are training an algorithm. Now what do we understand by labeled data?
Labeled data mean that the target data is tagged with the answer, that the algorithm on which we are working, should come up with. So, for example, a labeled dataset of bikes would tell the algorithm to tell us about R15, Pulsar, Splendor. And whenever a new image is being shown, the algorithm compares it to the training data set to come up with a result and to predict the correct label.
UNSUPERVISED LEARNING: This technique is used where deep learning model is handed the data set with no explicit instructions, that what to do with it. The model then tries to automatically find the structure in data by extracting the features and analyzing the structure.
It can organize the data in various ways like Clustering, Anomaly Detection, Association, Auto-Encoders.
It’s difficult to calculate the accuracy of the algorithm under unsupervised learning.