# ML Club Video (2024-25): Dimensionality Reduction
Date: 2024-11-04
Tags: Dimensionality Reduction, Principal Component Analysis, t-SNE

[Interactive: Interactive visualization](https://www.youtube.com/embed/LbGzh4nbpI0?si=kjHPmnGxGERM9xX7)

In this ML Club session, we’ll learn how to visualize 1000-dimensional data!

High dimensional data is everywhere!

How do we do this? We have to represent a 1000 dimensions in 2 dimensions such that the meaning of the data is still preserved. In the session we talk about two very different approaches – Principal Component Analysis and t-Distributed Stochastic Neighbor Embedding.

How do these approaches work? Watch the video to find out!

Thank you to the Statquest video: [https://www.youtube.com/watch?v=NEaUSP4YerM](https://www.youtube.com/watch?v=NEaUSP4YerM) for helping me with this lecture.  Highly recommend the channel!