- Python Programming
- Mathematical Modeling
- Machine Learning
- Data Science
- Data Transformation
- Linear Algebra
- Applied Mathematics
- Data Manipulation
- Machine Learning Methods
- Dimensionality Reduction
- NumPy
Linear Algebra for Machine Learning and Data Science
Completed by Christopher Stanley Bates
November 13, 2024
34 hours (approximately)
Christopher Stanley Bates'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
