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
β€‹βœοΈβ€‹
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—‹
​

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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