Description:

Cluster analysis is a vital component of unsupervised learning and data science. In this short session, we will explore the couple of unsupervised learning techniques – perform data clustering, reduce data dimensionality.

Setup:

Because this is an abbreviated session, attendees MUST install Anaconda software https://www.anaconda.com/ and have a basic understanding of using Jupyter Notebook.

Course Code/Duration:

BDT114 / 90 Minutes

Learning Objectives:

We will learn the following about the recommendation systems:

• Build a model to create clusters from data. Understand the intuition behind what principal component analysis (PCA).
• Build a simple PCA model to reduce dimensionality of data