Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Python and ML proficiency
·
Module 6
Linear Algebra Foundations
1
NumPy Vectors and Matrices: Basics
Represent vectors and matrices in NumPy and perform basic operations (addition, dot product)
2
NumPy Norms: Vectors and Matrices
Compute vector and matrix norms (e.g., L1, L2, Frobenius) using NumPy
3
Matrix Operations: Multiplication and Transposition
Perform matrix multiplication and transposition, and understand their properties
4
Understanding Linear Transformations and Their Matrix Representations
Describe linear transformations (scaling, rotation, shear) and their matrix representations
5
Matrix Operations with NumPy
Compute matrix determinants and inverses using numpy.linalg, and explain their geometric significance
6
Eigenvalues and Eigenvectors Explained
Explain the concepts of eigenvalues and eigenvectors
7
Eigenvalues and Eigenvectors with NumPy
Calculate eigenvalues and eigenvectors for a matrix using numpy.linalg
Previous module
Data Visualization and Preparation
Next module
Calculus for Machine Learning