# ML Club Video: Dimensionality Reduction
Date: 2023-11-18
Tags: Dimensionality Reduction, Principal Component Analysis, t-SNE

> Update: There is a new video on this topic from 2024.  Please refer to the new video [here](https://karthikvedula.com/posts/ml-club-video-24-25-dimensionality-reduction/) as it is updated with more info.
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[Interactive: Interactive visualization](https://drive.google.com/file/d/1pFSbEyLe64mXvXm3CLhUp2utG7P106Nl/preview)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!