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Data Science

6 learners

Basic Linear Algebra with SciPy

This course introduces participants to SciPy’s linear algebra capabilities, focusing on solving linear equations, understanding eigenvalues and eigenvectors, matrix decomposition, and working with sparse matrices. By the end of the course, learners will gain a practical understanding of applying linear algebra solutions effectively in Python.

MatPlotLib

Python

SciPy

See path

4 lessons

15 practices

1 hour

Mathematics and Statistics

Lessons and practices

Lesson 1: Vector Norm, Matrix Determinant, and Inverse Matrix with SciPy

Comparing Vector Magnitudes with Norms

Calculating L1 Norm in SciPy

Determinant for System Solvability

Calculate the Inverse of a Matrix

Lesson 2: Solving Linear Equation Systems with SciPy

Represent Linear Equations with Matrices

Verify Your Linear Equation Solution

Solving Linear Equations Made Simple

Lesson 3: Eigenvalues and Eigenvectors with SciPy

Visualizing Eigenvectors with Plotting

Complete the Eigenvalue Challenge

Exploring Eigenvectors with Python

Lesson 4: Matrix Decomposition with SciPy

Matrix Decomposition Mastery

Break Down the Matrix

Matrix Decomposition Validation Task

Matrix Mastery with SVD Decomposition

Recompose a Matrix with SVD

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