Unsupervised Learning: Making Sense of Unstructured Data - Great Learning

Unsupervised Learning is a category of techniques that train computers to use a set of unlabeled / unseen data and learn by themselves. The algorithm is provided with a large volume of data and expected to identify hidden patterns. Since there's no prior information / label available for each data instance, there is no defined / correct outcome. The machines only need to determine if there are any patterns in the given data. Unsupervised Learning is utilised in the following use cases: Clustering: In clustering, the key idea is to divide the data into groups such that each group shares similar properties and each group is as dissimilar as possible to the other groups. Let us now try to understand clustering using a simple example: An organization wants to make a marketing strategy for its new product. They want to segment the customers into different groups in order to target the right audience. They have made segments: 1. based on customer demographics like occupation, age and gender

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• Sean Onamade