Back to Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors
Johns Hopkins University

Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors

This course is the second course in the Linear Algebra Specialization. In this course, we continue to develop the techniques and theory to study matrices as special linear transformations (functions) on vectors. In particular, we develop techniques to manipulate matrices algebraically. This will allow us to better analyze and solve systems of linear equations. Furthermore, the definitions and theorems presented in the course allow use to identify the properties of an invertible matrix, identify relevant subspaces in R^n, We then focus on the geometry of the matrix transformation by studying the eigenvalues and eigenvectors of matrices. These numbers are useful for both pure and applied concepts in mathematics, data science, machine learning, artificial intelligence, and dynamical systems. We will see an application of Markov Chains and the Google PageRank Algorithm at the end of the course.

Status: Linear Algebra
Status: Applied Machine Learning
Course15 hours

Featured reviews

BN

Reviewed Jun 6, 2024

Great course! Really explains every topic clearly. Go watch the 3Blue1Brown videos, they're the best

HK

Reviewed Oct 31, 2024

Good course content. Instructor is inspiring, quite able to view thing from different perspective and useful to increase understanding of concept.

YC

Reviewed May 30, 2025

yeah its very good to learm those from teachers,i enjoyed lot by learning and listioning

DY

Reviewed Dec 27, 2023

It helps me to advance my knowledge and the way of teaching is just to the point, that's what i was looking for. I would say it would be great if their are more examples included.

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