Singular Value Decomposition Courses and Certifications

Serrano.Academy

Singular Value Decomposition and Image Compression

Learn how rotations and scalings in singular value decomposition enable dimensionality reduction and adjustable image compression.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

Data Science Dojo

Singular Value Decomposition with R - Introduction to Text Analytics with R Part 8

Use truncated SVD in R to project TF-IDF document vectors into a latent semantic space, then compare and tune random forest models.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Steve Brunton

Singular Value Decomposition

2 ratings

Explore singular value decomposition for matrix approximation, data processing, image compression, regression, PCA, and randomized algorithms.

• YouTube
• 7 hours
• Self-Paced
• Free Video

UofU Data Science

Singular Value Decomposition and Rank-K Approximation - Lecture 19

Dive into Singular Value Decomposition (SVD) concepts, exploring vector path tracing and rank-k approximation techniques for advanced matrix analysis and dimensionality reduction.

• YouTube
• 1 hour 24 minutes
• Self-Paced
• Free Video

Steve Brunton

SVD Part 2

A lecture on the second part of Singular Value Decomposition (SVD), a linear algebra method.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

UofU Data Science

Principal Component Analysis and Singular Value Decomposition - Spring 2023

Master dimensionality reduction techniques through PCA and SVD, exploring multicollinearity, vector basis, and practical applications with hands-on demonstrations for effective data analysis.

• YouTube
• 1 hour 23 minutes
• Self-Paced
• Free Video

Eigenvalues, Eigenvectors, and Diagonalization with NumPy

Apply NumPy to eigenvalues, eigenvectors, matrix diagonalization, matrix powers, SVD, and systems of equations.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Eigenvalues, Eigenvectors, and Diagonalization with R

Practice eigenvalues, eigenvectors, diagonalization, matrix powers, singular value decomposition, and linear systems through hands-on R computations.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Matrix Methods

249 ratings at Coursera

Learn matrix multiplication, linear equation solving, orthogonality, and least squares approximation, and use the singular value decomposition for dimensionality reduction and noise reduction, with optional Python examples.

• Coursera
• 5 weeks, 1 hour a week
• Self-Paced
• Paid Course

High-Dimensional Data Analysis

15 ratings at edX

A focus on several techniques that are widely used in the analysis of high-dimensional data.

• edX
• 4 weeks, 2-4 hours a week
• Self-Paced
• Free Online Course (Audit)

Integral Calculus and Numerical Analysis for Data Science

111 ratings at Coursera

Build the math foundation for data science: compute integrals and area under a curve, apply root-finding methods, matrix diagonalization, SVD, and partial derivatives.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course

Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD

5 ratings at edX

This course takes you through roughly five weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology.

• edX
• 3 weeks, 5-6 hours a week
• Self-Paced
• Free Online Course (Audit)

Career Certificate

Applications of Linear Algebra

1 rating

Apply determinants, eigenvalues and eigenvectors to discrete dynamical systems and Markov chains, then use least-squares approximation, orthogonality, symmetric matrices and the singular value decomposition.

• edX
• 6 weeks, 5-6 hours a week
• Self-Paced
• $498.00
• 2 courses

StatQuest with Josh Starmer

Principal Component Analysis - PCA, Step-by-Step

A step-by-step walkthrough of PCA, showing how SVD reveals patterns, important variables, and the accuracy of lower-dimensional plots.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

Linear Algebra II: Matrices and Linear Transformations

5 ratings at edX

This course provides an overview of bachelor-level linear algebra. You will review all the concepts and practice and refresh the skills related to matrices and linear transformations.

• edX
• 6 weeks, 4-6 hours a week
• Self-Paced
• Free Online Course (Audit)