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
Princeton - Linear Algebra​
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
πŸ—‹
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
Khan Academy - Linear Algebra​
2014
Short-form videos in classic Sal-Khan-style with emphasis on building intuition with examples.
Easy
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Trefor Bazett - Linear Algebra​
2018
Short-form videos with emphasis on simple explanations and examples. Fantastic resource to quickly cover ground or review basic concepts.
Easy
πŸ—‹
MathTheBeautiful - Linear Algebra​
2017
This 4-part course goes through similar material as Trefor Bazett's course but goes through a lot more examples.
Easy
πŸ—‹
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Supplementary Resources

Resource
Year
Description
Use
3Blue1Brown - Essence of Linear Algebra​
2016
Beautiful visualizations and explanations to gain an intuitive understanding of Linear Algebra.
πŸŽ₯
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.
πŸŽ₯
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.
πŸ“š
2016
A textbook that's a good complementary resource to keep at hand for references and definitions while going through a Linear Algebra course.
πŸ“š
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Last modified 8mo ago