What you'll learn?
Learners will use eigen-analysis to rank web pages, study quantum stability, validate engineering vibrations, and apply it to extract biological markers, detect hidden text topics, spot cyber-attacks, and compress 3D assets.
Description
Course Outcomes:
- Implement PCA and SVD from scratch using Python/NumPy.
- Compress images and reduce dataset dimensions efficiently.
- The math behind Covariance Matrices and Eigen decomposition.
- The geometry of Orthogonal Projection and Basis Changes.
- The connection between Eigenvalues, Singular Values, and Variance.
Course content
Total:
11 lectures
Total Duration:
3 hours, 6 minutes, and 25 seconds
the instructor
M SANJEEVA REDDY
Future-ready mathematics innovator, blending AI, machine learning, and timeless principles to make math accessible and empowering.