Lec 1 | MIT 18.06 Linear Algebra, Spring 2005

I did Strang's linear algebra course[0] (link goes to Youtube playlist of lectures) several years after graduating and recommend it highly. I was looking for refresher but I gained a deeper understanding of several important concepts; in particular it's fair to say I barely understood, or perhaps even misunderstood, SVD until Strang. If you're not sure if you need something like that, I suggest doing something like testing yourself on this video[1] or the MIT problem sets[2] it's easy to tell yourself that you "know" linear algebra when it would be closer to the truth to say that you used to know linear algebra, but can't answer even basic questions today. After Strang, Golub's book on Matrix Computations is also really incredible.[3] [0]: ▶ this video [1]: link [2]: link [3]: link

Love Gilbert Strang's original linear algebra lectures on YouTube [1]. They were a huge help while I took the course. In my opinion, he takes great care to motivate each topic and express his train of thought when working through problems. [1] ▶ this video

Gilbert Strang's Linear Algebra course from MIT, available on youtube (3 million views!), and his textbook of the same name are awesome. What a great teacher. ▶ this video

Useful list. I would recommend adding one more resource for linear algebra/machine learning: I absolutely enjoyed learning Linear Algebra from these beautiful lectures by Prof. Gilbert Strang (MIT): ▶ this video Seriously, I gained a new found appreciation for Linear algebra after going through these lectures. You should go over some of these lectures even if you already know linear algebra - it might give you insights you never had before (it did, for me). Absolute must-watch if you are into machine learning or related areas.

I highly recommend the MIT 18.06 open course on linear algebra - the lectures are first rate. I've been going through this as a refresher, in prep for the Stanford machine learning class. Link to the course: link Link to the YouTube videos of the lectures: ▶ this video

It depends on who wants to hire you. For the top jobs, this is for sure not enough. I always tell people to please make sure they know all the contents from here ▶ this video (Gilbert Strang's Linear Algebra course) before they make any claims about being a data scientist. I bet I can teach a monkey to open a CSV in pandas and call .fit() in sklearn. But do the people really understand the underlying assumptions. Most self-proclaimed data scientists don't I am sure. I still have a hard time calling myself a data scientist. And I am three years into a relevant PhD. The more I study, the less I feel I truly know.

Does your degree program not include at least one Linear Algebra class? Or are you just trying to get a head start? In either case, you might find this course[1] and accompanying videos[2] useful. Also, of course, is 3blue1brown[3] on Youtube. And then there are the Gilbert Strange lectures[4] on Youtube. [1]: link [2]: link [3]: link [4]: ▶ this video

I've mentioned this on HN before but I think its still relevant to people interested in learning ML who feel they are behind on the math. If, like me, you can't sit thru lots of pages of mathematics text and instead prefer that a human explains it to you via videos that you can replay, here is a list of courses that take you from basic algebra and pre-calculus math all the way to the concepts you need to understand the principles behind the most advanced ML algorithms. All explained by very energetic people who are experts in their fields, and starting from the very basics. This covers calculus, linear algebra, probability, statistics, convex optimization and a math for ML course thrown in for the HN audience: (The first two are "MOOCs" recorded in the 1970s! probably the first ever recorded MOOC, even before the internet, and the lecturer is absolute gold) Calculus Revisited: Single Variable Calculus | MIT link . Calculus Revisited: Multivariable Calculus | MIT link . Complex Variables, Differential Equations, and Linear Algebra | MIT link . Linear Algebra | MIT - ▶ this video . Introduction to Linear Dynamical Systems |Stanford link Probability | Harvard link . Intermediate Statistics | CMU link . Convex Optimization I | Stanford link Math Background for ML | CMU link .

If you find it easier to keep at it and learn from lecture videos instead of from textbooks, here's a math curriculum of lecture videos I've curated. This covers calculus, linear algebra, probability, statistics, convex optimization and a math for ML course thrown in for the HN audience: Calculus Revisited: Single Variable Calculus | MIT link Calculus Revisited: Multivariable Calculus | MIT link Complex Variables, Differential Equations, and Linear Algebra | MIT link Linear Algebra | MIT - ▶ this video Introduction to Linear Dynamical Systems |Stanford link Probability | Harvard link Intermediate Statistics | CMU link Convex Optimization I | Stanford link Math Background for ML | CMU link

For anyone who wants to learn linear algebra, I highly recommend Gilbert Strang's book and course from MIT: ▶ this video .

Gilbert Strang's linear algebra ▶ this video Walter Lewin's Classical Mechanics link for the content and the delivery

Ah but the beauty is that you can read for pleasure knowing that you won't have an exam at the end and trying to work out what your professor thinks is important. If you want to enjoy linear algebra without the pressure of doing any, I can heartily recommend MIT's open coursewear videos featruring Gilbert Strang ▶ this video In fact all the MIT stuff I have tried is fun to watch