I'm e-learning Linear Algebra right now to have a good math foundation for Machine Learning.
I was a History and Sociology major in college - so I didn't take any math.
If you are like me, and working off an initial base of high school math, I would recommend the following (all free):
Linear Algebra Foundations to Frontiers (UT Austin) Course: https://www.edx.org/course/linear-algebra-foundations-to-fro... Comments: This was a great starting place for me. Good interactive HW exercises, very clear instruction and time-efficient.
Linear Algebra (MIT OpenCourseware) Course: https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra... Comments: This course is apparently the holy grail course for Intro Linear Algebra. One of my colleagues, who did an MS in EE at MIT, said Gilbert Strang was the best teacher he had. I started off with this but had to rewind to the UT class because I didn't have some of the fundamentals (e.g. how to calc a dot product). I'm personally 15% through this, but enjoying it.
Linear Algebra Review PDF (Stanford CS229) Link: http://cs229.stanford.edu/section/cs229-linalg.pdf Comments: This is the set of Linear Algebra review materials they go over at the beginning of Stanford's machine learning class (CS229). This is my workback to know I'm tracking to the right set of knowledge, and thus far, the courses have done a great job of doing so.
Author here. I made this demo and a related matrix-matrix multiplication demo  back in 2015 for Robert van de Geijn's Linear Algebra: Foundations to Frontiers MOOC class . In the light of Spectre attack and recent browsers' changes to reduce precision of timers, I remembered of this project, and decided to check if it still works now, 3 years later. Surprisingly, it still works well!
The source code is available on GitHub .
I agree. As far as calculus goes, I am more enamored with books like Spivak's ( http://www.amazon.com/Calculus-4th-Michael-Spivak/dp/0914098... ) that take a proof-centric approach to teach calculus from first principles.
Incidentally, for those who want to learn linear algebra for CS in a mooc setting there are 3 classes running at this very moment:
https://www.edx.org/course/linear-algebra-foundations-fronti... (from UT Austin)
https://www.edx.org/course/applications-linear-algebra-part-... (from Davidson)
http://coursera.org/course/matrix (from Brown)
The first 2 use matlab (and come with a free subscription to it for 6 months or so), the last python. One interesting part of the UT Austin class is that it teaches you an induction-tinged method for dealing with matrices that let you auto-generate code for manipulating them: http://edx-org-utaustinx.s3.amazonaws.com/UT501x/Spark/index... .
And of course there are Strang's lectures too, but those are sufficiently linked to elsewhere.
Learn linear algebra while writing a linear algebra library using the latest techniques (starts at the end of this month): https://www.edx.org/course/linear-algebra-foundations-fronti... I think there's also a non-mooc version at http://www.ulaff.net/
Unfortunately, I had to give up on the course last year because my math background is even more limited than yours (you'll need to know how to construct proofs). So time for me to learn calc, I guess. :)