#14 - Dimensionality Reduction and Clustering. Understanding PCA, Kmeans and Autoencoders.

In this episode I talk about the problems of dimensionality reduction and clustering. I explain the applications of each one of these problems and also the most famous methods for solving them, such as PCA, KPCA, ICA and NNMF for the dimensionality reduction and the Kmeans for the clustering problems. In the end I also explain the autoencoders, which are powerfull neural networks that can be used for both problems.

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Code: https://github.com/filipelauar/projects/tree/main/dimensionality%20reduction%20and%20clustering