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⇱ Fundamental Linear Algebra Concepts with Python | Coursera


Fundamental Linear Algebra Concepts with Python

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Fundamental Linear Algebra Concepts with Python

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Gain insight into a topic and learn the fundamentals.
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1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
4.5

18 reviews

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

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Assessments

11 assignments¹

AI Graded see disclaimer
Taught in English

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This course is part of the Linear Algebra for Data Science Using Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

In this course, you'll be introduced to finding inverses and matrix algebra using Python. You will also practice using row reduction to solve linear equations as well as practice how to define linear transformations. Let's get started!

In module 1, you’ll learn how to define linear equations, how to use Python to find the determinant of matrices and how to perform different commands using Python. We will cover the following learning objectives.

What's included

8 videos1 reading3 assignments1 discussion prompt

8 videosTotal 39 minutes
  • Specialization Introduction 4 minutes
  • Course Introduction3 minutes
  • Instrutor Introduction4 minutes
  • Review of Linear Equations4 minutes
  • Determinants7 minutes
  • Find the Determinant of Any Matrix Using Python6 minutes
  • Inverse of a 2 x 2 Matrix5 minutes
  • Inverses of a 3 x 3 Matrix6 minutes
1 readingTotal 10 minutes
  • Welcome to Fundamentals of Linear Algebra and Data Science Concepts with Python10 minutes
3 assignmentsTotal 90 minutes
  • Finding Inverses30 minutes
  • Finding Determinants30 minutes
  • Find the Inverse 30 minutes
1 discussion promptTotal 10 minutes
  • Meet and Greet Forum10 minutes

Let’s recap! In module 1, you learned how to define linear equations, how to use Python to find the determinant of matrices and how to perform different commands using Python. In module 2, you’ll learn how to explain different matrix algebra functions, perform matrix algebra on large data sets using Python. We will cover the following learning objectives.

What's included

5 videos2 assignments1 discussion prompt

5 videosTotal 33 minutes
  • Review of Matrix Arithmetic6 minutes
  • Review Matrix Algebra in Python7 minutes
  • Using Python to Find the Transpose of a Matrix5 minutes
  • Using Python to Find the Inverse of a Matrix5 minutes
  • Using Matrix Algebra in Python10 minutes
2 assignmentsTotal 60 minutes
  • Matrix Algebra Using Python30 minutes
  • Matrix Algebra Functions Using Python30 minutes
1 discussion promptTotal 10 minutes
  • Using Python to Perform Matrix Algebra10 minutes

Let’s recap! In module 2, you learned how to explain different matrix algebra functions and perform matrix algebra on large data sets using Python. In module 3, you will learn how to solve systems of linear equations using several methods. We will cover the following learning objectives.

What's included

5 videos3 assignments1 discussion prompt

5 videosTotal 35 minutes
  • Row Reduction (Infinitely Many Solutions)9 minutes
  • Row Reduction (No Solutions)7 minutes
  • Finding Inverses of a Higher Dimension Matrices10 minutes
  • Solving Systems Using the Inverse6 minutes
  • Cramer's Rule4 minutes
3 assignmentsTotal 90 minutes
  • Solving Systems of Linear Equations30 minutes
  • Using Row Reduction to Solve Linear Equations30 minutes
  • Using the Inverse and Cramer's Rule30 minutes
1 discussion promptTotal 10 minutes
  • Solving Systems of Linear Equations10 minutes

Welcome to the final module of this course! Over the past 3 modules, you have been introduced to and gained knowledge on the following topics: determinants, inverses, matrix algebra with Python, row reduction and, systems of linear equations. In the final module of the course, you’ll apply what you’ve learned to concrete, real-world examples. You’ll practice using linear transformation, Eigenvalues and Eigenvectors, and solving applications. We will cover the following learning objectives.

What's included

11 videos3 assignments1 peer review1 discussion prompt

11 videosTotal 87 minutes
  • Linear Transformations4 minutes
  • Examples of Linear Transformations7 minutes
  • Matrix Representations of Transformations6 minutes
  • Eigenvalues6 minutes
  • Finding Eigenvalues7 minutes
  • Eigenspace6 minutes
  • Diagonalizable Matrix9 minutes
  • Diagonalizable Matrices II11 minutes
  • Diagonalization of a Matrix Using Python3 minutes
  • Real World Examples13 minutes
  • More Real World Examples - PCA Step by Step16 minutes
3 assignmentsTotal 90 minutes
  • Linear Transformations30 minutes
  • Diagonalization of a Matrix Using Python30 minutes
  • Eigenvalues and Eigenvectors30 minutes
1 peer reviewTotal 60 minutes
  • Eigenvalues and Eigenvectors60 minutes
1 discussion promptTotal 10 minutes
  • Analyzing Real-World Examples: Eigenvalues and Eigenvectors10 minutes

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Howard University
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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.