ML Club Video: Dimensionality Reduction
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 for helping me with this lecture. Highly recommend the channel!
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