Regression Models, Clustering and Dimensionality Reduction

This repository is a hub to store my notes and assignments on the basics of machine learning concepts and modeling from a Machine Learning course I took in my MS-CS program at Santa Clara University

Topics Covered

1. Multivariate linear regression, K-means clustering

File: HW2.ipynb - a notebook that implements multivariate linear regression and ridge regression models, compares K-means with spectral and hierarchical clustering models.

2. Solving LR via Gradient Descent

File: LR_and_GD.ipynb - a notebook that implments a Logistic Regression (LR) model and illustrates how to solve LR via Gradient Descent (GD).

3. Dimensionality Reduction: PCA vs t-SNE

File: HW3(Q1)-PCA-TSNE-1.ipynb - a notebook that compares t-SNE with PCA and explains why t-SNE could lead to a better visualization than PCA