Core

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Linear Algebra

Linear Algebra* ***Courses**

- 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

π

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

π

2017

This 4-part course goes through similar material as Trefor Bazett's course but goes through a lot more examples.

Easy

π

β

Resource

Year

Description

Use

2016

Beautiful visualizations and explanations to gain an intuitive understanding of Linear Algebra.

π₯

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.

π₯

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.

π

2016

A textbook that's a good complementary resource to keep at hand for references and definitions while going through a Linear Algebra course.

π

β

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