Udacity has a Linear Algebra review course, but I don't believe it is public for now. I had taken a linear algebra course before I took the GT ML class, but I wasn't a expert by any means. I don't believe you will need a deep understanding of linear algebra before taking this class. Singular value decomposition might come up. I think if you are familiar with everything in the following pdf you should be fine.
http://minireference.com/static/tutorials/linear_algebra_in_...
If you are motivated, you will do fine. Good luck!
One here: https://www.udacity.com/course/linear-algebra-refresher-cour...
And another here: https://www.udacity.com/course/intro-algebra-review--ma004
http://www.intmath.com/matrices-determinants/matrix-determin... - matrices
http://www.intmath.com/vectors/vectors-intro.php - vectors
Linear Algebra using Python:
https://www.coursera.org/course/matrix - Coursera: Coding the Matrix
http://codingthematrix.com/ - Website: Coding the Matrix
The simplest software for linear algebra would be GeoGebra, http://www.geogebra.org/. For instance, to enter a matrix just have the spreadsheet view open, enter the numbers, highlight the cells, then choose the option "Create Matrix". To enter a vector start writing "vec" in the input bar at the bottom and intellisense gives you the option to choose "Vector[<Start Point>, <End Point>]". Choose this. Fill it in, for example, "Vector[(-3, 4), (1, 2)]" (Hint: Use Tab to move between options in the input formula, here to move between "<Start Point>" and "<End Point>]".) Voila the vector is drawn! You can even draw a vector with just two clicks in Graphics view, if you first select the "Vectors" tab at the top (the symbol is a line with an arrowhead). The GeoGebra software is really incredible for learning/doing Linear Algebra, Calculus and Statistics. A real godsend.
Some visual matrix operations are here: http://setosa.io/ev/ If you want a beginner textbook, I recommend http://www.matrixanalysis.com/
Again, it is simple, so maybe you can even take course and look up Wikipedia when needed (but for me it is hard to guess you level, current knowledge, etc).
In any case, this ML course assumes some ML knowledge.