Lectures related to Diagonalization of Matrices

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Explores Runge Kutta and multistep methods for solving ODEs, including Backward Euler and Crank-Nicolson.