Core
Specializations
Linear Algebra
Linear Algebra Courses
Recommended path
- If you're looking for the most complete introduction to Linear Algebra (which will also take longest), go through the MIT course.
- If you want to build up an understanding of Linear Algebra more quickly (at a similar level of rigorousness, but at the expense of completeness), take the Princeton course.
- If you are looking for a course that dives straight into the computational side of Linear Algebra, give Rachel Thomas' course a try.
- If you already know some basic linear algebra, Gil Strang's Matrix Methods course might be worth watching.
- For a review of Linear Algebra, Khan Academy's and Trefor Bazett's videos are the way to go.
- For more examples, check out MathTheBeautiful's YouTube channel.
Prerequisites: High school algebra and trigonometry.
Course
Year
Description
Difficulty Level
Resources
2005
The most popular Linear Algebra course online (for a good reason). Excellent lectures by the great Gilbert Strang, covering the foundations of Linear Algebra in a bottoms-up fashion.
Medium
2008
Does not go as deep as MIT 18.06, but the explanations are easier to follow. Unfortunately the video quality is a bit rough (still manageable though).
Medium
🗋
2017
The only course that tackles Linear Algebra from such a computational and applied angle. Uses Python and libraries like NumPy and scikit-learn to cover topics like PageRank and SVD.
Medium
2014
Short-form videos in classic Sal-Khan-style with emphasis on building intuition with examples.
Easy
2018
Short-form videos with emphasis on simple explanations and examples. Fantastic resource to quickly cover ground or review basic concepts.
Easy
🗋
2017
This 4-part course goes through similar material as Trefor Bazett's course but goes through a lot more examples.
Easy
🗋
Supplementary Resources
Resource
Year
Description
Use
2016
Beautiful visualizations and explanations to gain an intuitive understanding of Linear Algebra.
🎥
2018
Another wonderful course taught by Gil Strang that focuses on the applications of Linear Algebra (with a bit of Calculus + Statistics) to Machine Learning. This is a great resource for anyone who already has a basic understanding of Linear Algebra and would like to explore its connection to ML and Signal Processing.
🎥
2018
A nicely written book by Stephen Boyd, who goes through the foundations of Linear Algebra and applies these to applied topics like population dynamics or the least-squares problem.
📚
2016
A textbook that's a good complementary resource to keep at hand for references and definitions while going through a Linear Algebra course.
📚
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