- Dimensionality Reduction
- Data Preprocessing
- Advanced Mathematics
- Python Programming
- Machine Learning
- Linear Algebra
- Applied Mathematics
- Machine Learning Methods
- Algebra
- NumPy
- Mathematical Modeling
- Data Manipulation
Linear Algebra for Machine Learning and Data Science
Completed by Friskha Amellia Eddy
October 9, 2024
34 hours (approximately)
Friskha Amellia Eddy'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
