Session 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
Training material provided: Yes (Digital format)
- This session is designed for anyone who is familiar with basic steps involved in machine learning and are familiar with tools involved in building machine learning models.
- Learn basic understanding of python language, pandas library and understanding of how to use Juypter Notebook.
