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.
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.
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.
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.
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.