- Python Programming
- Data Manipulation
- NumPy
- Algebra
- Machine Learning Methods
- Machine Learning
- Mathematical Modeling
- Data Preprocessing
- Applied Mathematics
- Advanced Mathematics
- Linear Algebra
- Dimensionality Reduction
Linear Algebra for Machine Learning and Data Science
Completed by Nathaniel Sakyi Adibuer
March 25, 2023
34 hours (approximately)
Nathaniel Sakyi Adibuer'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
