Training Success Elements
- Self-paced training works well with your family and work responsibilities.
- Practice-based courses build your competency and confidence.
- Your personal academic advisor and an instructional support team assist you throughout your training.
- Upon graduation, you are a Linear Algebra subject matter expert.
Introduction
Linear Algebra is an essential skill set required in Data Science, Engineering, and many other areas. In this 120-hour hands-on college-level course you learn by doing, at your own pace, completing hands-on interactive coursework with realtime support of an AI-based teaching platform and the support of instructional staff.
Prerequisites
To succeed in this course, you should know basic high school math including arithmetic, working with fractions, relations, and functions. You should also know trigonometric, exponential, and logarithmic functions. Towards the end, the course uses inner products of polynomials which requires very basic knowledge of integration.
Time Commitment
Keep in mind that this is a full-semester college-level course whose completion requires a significant commitment. You will be working actively 100% of the time, not just watching some video tutorials.
Student Learning Outcomes (SLO)
Upon completing this course, you will be able to:
- Perform all standard vector and matrix operations.
- Transform linear system to matrix form and solve them.
- Create echelon and reduced echelon forms of matrices.
- Determine basic and free variables in linear systems.
- Express infinite solution sets in parametric vector form.
- Work with determinants and elementary matrices.
- Create and use LU and Cholesky matrix factorizations.
- Work with linear transformations associated with matrices.
- Calculate eigenvalues and eigenvectors.
- Work with linear spaces, bases, and coordinates.
- Perform orthogonal projections and decompositions.
- Diagonalize matrices.
- Perform spectral decomposition of matrices.
- Solve Least-Squares problems via normal equations and QR factorization.
- Perform Singular Value Decomposition (SVD) of matrices.
- Apply SVD to data cleaning and image compression.
Some of the more advanced material towards the end of the course are optional.
Equipment Requirements
Computer, laptop or tablet with Internet access, email, and one of the following browsers:
- Google Chrome
- Mozilla Firefox
- Microsoft Edge
- Safari
Syllabus and Course Details
Click here to access the syllabus.
More Information
To talk with one of our product specialists about this program, schedule a call below, fill in the Get-In-Touch form, or simply use the CHAT BOX at the bottom right of this screen.
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