Linear Algebra for Machine Learning and Data Science
Completed by John Martin Patterson
February 18, 2026
34 hours (approximately)
John Martin Patterson's account is verified. Coursera certifies their successful completion of Linear Algebra for Machine Learning and Data Science
What you will learn
Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
Apply common vector and matrix algebra operations like dot product, inverse, and determinants
Express certain types of matrix operations as linear transformation, and apply concepts of eigenvalues and eigenvectors to machine learning problems
Skills you will gain
- Category: Applied Mathematics
- Category: Algebra
- Category: Linear Algebra
- Category: Image Analysis
- Category: Mathematical Software
- Category: Machine Learning
- Category: Data Manipulation
- Category: Data Transformation
- Category: Machine Learning Methods
- Category: Dimensionality Reduction
- Category: Python Programming
