- Machine Learning Methods
- Data Preprocessing
- Advanced Mathematics
- Mathematical Modeling
- Python Programming
- NumPy
- Dimensionality Reduction
- Applied Mathematics
- Linear Algebra
- Machine Learning
- Algebra
- Data Manipulation
Linear Algebra for Machine Learning and Data Science
Completed by Randika Prabashwara
January 31, 2024
34 hours (approximately)
Randika Prabashwara'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
