Autoencoder
What autoencoders are, how they learn compressed data representations, and practical applications in anomaly detection and dimensionality …
Dimensionality-Reduction
Dimensionality Reduction
What dimensionality reduction is, common techniques including PCA and t-SNE, and when to reduce feature dimensions in your ML pipeline.
PCA - Principal Component Analysis
What PCA is, how it identifies principal components, and when to use it for dimensionality reduction in ML pipelines.
t-SNE
Non-linear dimensionality reduction technique for visualizing high-dimensional data in two or three dimensions.
UMAP
Uniform Manifold Approximation and Projection for faster dimensionality reduction that preserves both local and global structure.
Unsupervised Learning
What unsupervised learning is, how it discovers patterns without labels, and practical enterprise applications.