Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Eigenvalues, Eigenvectors, and Diagonalization with NumPy

Go to class Write review

Overview

This course introduces students to eigenvalues, eigenvectors, and matrix diagonalization. Focusing on matrix transformations, students will explore practical applications of these concepts using the `numpy.linalg` library to solidify their understanding of advanced linear algebra principles.

Syllabus

Unit 1: Introduction to Eigenvalues and Eigenvectors with NumPy

Eigenvalues and Eigenvectors in NumPy
Explore Matrix Changes Impact
Fix the Matrix Calculation Errors
Calculate Eigenvalues and Eigenvectors
Write Your First NumPy Matrix Code

Unit 2: Diagonalization of Matrices with NumPy

Matrix Diagonalization in Action
Modify the Matrix for Diagonalization
Fix the Python Matrix Code
Unlock Matrix Diagonalization Skills
Write Code for Matrix Diagonalization

Unit 3: Power of a Matrix Using Eigen Decomposition

Matrix Power with Eigen Decomposition
Matrix Power Exploration Challenge
Fix Matrix Power Calculation
Matrix Power Calculation Challenge
Mastering Matrix Powers with Python

Unit 4: Singular Value Decomposition (SVD) with NumPy

Singular Value Decomposition Practice
Exploring SVD Through Matrix Change
Fix the Singular Value Decomposition
Fill in the Missing Code
Mastering Singular Value Decomposition

Unit 5: Solving Systems of Equations with NumPy

Solve Linear Equations with NumPy
Changing Coefficients in Equations
Fixing Error with NumPy
Solve Linear Equations with NumPy
Mastering NumPy Equations from Scratch

Reviews

Start your review of Eigenvalues, Eigenvectors, and Diagonalization with NumPy