I read this in conjunction with MIT's OCW Scholar class and did all the problem sets and took the tests. Took me some time to finish the course, but it was surprising to find how fun linear algebra can be. I mean matrices and vectors? They weren't nearly sexy as curvy double integrals (multivariable calculus) or kinky Markov chains (probability theory). And I took the course only because linear algebra was indispensable for making a headway into mathematical statistics, probability theory, and other higher branches of math. But I have to admit, I kind of started liking this dowdy branch of math for its plain elegance. You have to admit, it's SO plain. Just look at the cover. Squares and rectangles? Are we in elementary geometry here? Euclid is, like, so played out. BUT there's some sexiness here if you look closely: Gaussian elimination, projections, eigenvalues and eigenvectors, diagonalization, and the crux of it all, single value decomposition (yes, I know, I'm shamelessly showing off a little here because I genuinely think these are cool-sounding and actually practical concepts).
True to their reputation, Professor Strang's video lectures are golden—there was one with a bad camera and another that was hard to follow, but that's like 2 out of 33 lectures. The rest were crystal clear and made the subject sound a bit too easy. The book, on the other hand, was less clear and less useful, as it keeps bringing up new concepts ahead of time and doesn't give motivating explanations for the material (I'm still a little iffy on why diagonalization is useful, how the "particular" solution is related to "special" solutions in solving Ax = b, or how single value decomposition can be applied, for example). The biggest problem with this book is, well, the problem sets. They are really not all that helpful. He should have at least included questions from other sections (for greater learning) and more basic questions just to let the students practice the mechanics of the concepts. They were also just...disappointingly easy. Compared to, say, Blitzstein's excellent but difficult probability theory online class, the questions were either trivially abstract or too basic to be of any challenge to contribute to solid learning experience.
Overall, though, I would definitely recommend this textbook along with his lectures, but I'd complement it with a more rigorous textbook and/or online course (which I'm planning on doing with Sheldon Axler's Linear Algebra Done Right and Paul Halmos's Linear Algebra Problem Book). I did get a good grasp of the field (though far from mastery) and it was well worth the effort.