Linear Algebra for Machine Learning and Data Science
Completed by Gede Timothy Lewis Keizi Amiano
March 20, 2024
34 hours (approximately)
Gede Timothy Lewis Keizi Amiano'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: Machine Learning
- Category: Data Manipulation
- Category: Linear Algebra
- Category: Dimensionality Reduction
- Category: Data Transformation
- Category: Machine Learning Methods
- Category: Algebra
- Category: Python Programming
- Category: Mathematical Software
- Category: Image Analysis
- Category: Applied Mathematics
