Unsupervised Learning

In contrast to supervised learning, unsupervised learning deals with inputs only . This dataset is called unlabelled dataset. Since, we are dealing with inputs only the aim of unsupervised learning is to uncover the latent structure in data. For example, we want to know how many groups (classes) can we make out of the data?…

Supervised Learning

In supervised learning, we are given a training set consists of a set of input-output pairs, where is the number of samples. The inputs is a set of attributes or features which are stored in an matrix. In classification problems, the output where denotes the number of outputs. If , it is called binary classification…