Effective immediately in response to COVID-19, all Technology Training classes will be delivered online until further notice.
In advance of each session, Tech Training will provide you with a Zoom link to your class, along with any required class materials.
Cluster analysis is a vital component of unsupervised learning and data science. In this short session, we will explore the two unsupervised learning techniques -- perform data clustering and reduce data dimensionality.
Abstract
Prerequisite: Learners should have a basic understanding of Python language, pandas library and understanding of how to use Juypter Notebook.
Audience: 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.
Learning Objectives
By the end of this course, learners will have the opportunity to:
- Learn 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
University IT Technology Training classes are only available to Stanford University staff, faculty, students and Stanford Hospitals & Clinics employees. A valid SUNet ID is needed in order to enroll in a class.
