Unsupervised Learning for Clustering, and Dimensionality Reduction

Designed hard (k-means), and soft (expectation maximization) clustering algorithms, and evaluated their performance using different metrices (Rand Index, Completeness Score and Homogeneity Score) [Python, Scikit-learn]

Developed different dimensionality reduction algorithms (principal component analysis-PCA, independent component analysis-ICA, random projection-RP, and linear discriminant analysis-LDA) on MNIST, and HAR datasets [Python, Scikit-learn]