Introduction
Welcome to the module on ‘Unsupervised Learning’. In the previous modules, you learnt about several supervised learning techniques such as regression and classification. These techniques use a training set to make the algorithm learn, and then apply what is learnt to new, unseen data points.
In this module, you will be introduced to unsupervised learning.
In this session
You will start by learning what “clustering” is. It is an unsupervised learning technique, where you try to find patterns based on similarities in the data. Then, you will be introduced to a case study that shows the applicability of clustering in the industry.
You will learn the two most commonly used types of clustering algorithms – K-Means Clustering and Hierarchical Clustering, as well as their application in Python. Then, you will also look at what segmentation is and how it is different from clustering.
Guidelines for in-module questions
The in-video and in-content questions for this module are not graded. The graded questions are given in a separate segment labelled ‘Graded Questions’ at the end of the session. The questions in that segment will adhere to the following guidelines:
First Attempt Marks | Second Attempt Marks | |
Question with 2 Attempts | 10 | 5 |
Question with 1 Attempt | 10 | 0 |