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Eigenvalues, Eigenvectors, and Diagonalization with NumPy

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.

Numpy

Python

See path

5 lessons

25 practices

3 hours

Mathematics and Statistics

Lessons and practices

Lesson 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

Lesson 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

Lesson 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

Lesson 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

Lesson 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

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