Data Science, Machine Learning, Unsupervised Learning, Dimensionality Reduction, Cruio.
Principal Component Analysis (PCA): PCA is a linear dimensionality reduction technique that aims to find the orthogonal axes (principal components) along which the data has the maximum variance. t-Distributed Stochastic Neighbor Embedding (t-SNE): t-SNE is a non-linear dimensionality reduction