Fundamental Linear Algebra Concepts with Python
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Fundamental Linear Algebra Concepts with Python
This course is part of Linear Algebra for Data Science Using Python Specialization
Instructors: Dennis Davenport
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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 videos•Total 39 minutes
- Specialization Introduction •4 minutes
- Course Introduction•3 minutes
- Instrutor Introduction•4 minutes
- Review of Linear Equations•4 minutes
- Determinants•7 minutes
- Find the Determinant of Any Matrix Using Python•6 minutes
- Inverse of a 2 x 2 Matrix•5 minutes
- Inverses of a 3 x 3 Matrix•6 minutes
1 reading•Total 10 minutes
- Welcome to Fundamentals of Linear Algebra and Data Science Concepts with Python•10 minutes
3 assignments•Total 90 minutes
- Finding Inverses•30 minutes
- Finding Determinants•30 minutes
- Find the Inverse •30 minutes
1 discussion prompt•Total 10 minutes
- Meet and Greet Forum•10 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 videos•Total 33 minutes
- Review of Matrix Arithmetic•6 minutes
- Review Matrix Algebra in Python•7 minutes
- Using Python to Find the Transpose of a Matrix•5 minutes
- Using Python to Find the Inverse of a Matrix•5 minutes
- Using Matrix Algebra in Python•10 minutes
2 assignments•Total 60 minutes
- Matrix Algebra Using Python•30 minutes
- Matrix Algebra Functions Using Python•30 minutes
1 discussion prompt•Total 10 minutes
- Using Python to Perform Matrix Algebra•10 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 videos•Total 35 minutes
- Row Reduction (Infinitely Many Solutions)•9 minutes
- Row Reduction (No Solutions)•7 minutes
- Finding Inverses of a Higher Dimension Matrices•10 minutes
- Solving Systems Using the Inverse•6 minutes
- Cramer's Rule•4 minutes
3 assignments•Total 90 minutes
- Solving Systems of Linear Equations•30 minutes
- Using Row Reduction to Solve Linear Equations•30 minutes
- Using the Inverse and Cramer's Rule•30 minutes
1 discussion prompt•Total 10 minutes
- Solving Systems of Linear Equations•10 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 videos•Total 87 minutes
- Linear Transformations•4 minutes
- Examples of Linear Transformations•7 minutes
- Matrix Representations of Transformations•6 minutes
- Eigenvalues•6 minutes
- Finding Eigenvalues•7 minutes
- Eigenspace•6 minutes
- Diagonalizable Matrix•9 minutes
- Diagonalizable Matrices II•11 minutes
- Diagonalization of a Matrix Using Python•3 minutes
- Real World Examples•13 minutes
- More Real World Examples - PCA Step by Step•16 minutes
3 assignments•Total 90 minutes
- Linear Transformations•30 minutes
- Diagonalization of a Matrix Using Python•30 minutes
- Eigenvalues and Eigenvectors•30 minutes
1 peer review•Total 60 minutes
- Eigenvalues and Eigenvectors•60 minutes
1 discussion prompt•Total 10 minutes
- Analyzing Real-World Examples: Eigenvalues and Eigenvectors•10 minutes
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