I'm taking the edX series of courses on Machine Learning with Apache Spark. It is a pretty good class and covers linear algebra and the basic ETL workflow, plus selection of models, model parameters tweaking parameters etc., study cases involving user ad clicks prediction and PCA analysis of neural synapse data of jellyfishes.
However, my biggest fear is that re-learning different matrix manipulations, walk-through of logistics regression, PCA and SVM will be similar to learning Spanish in high school without immersion, learning guitar scales without improvising with it, learning mappings of different Madden key-combo's; I remember learning all of these mathematical syntactical operations in high school, and have an eerie feeling of once grasping these concepts concretely once and yet only knowing it vaguely. It is nice to re-learn these things, like refreshing myself with a rolodex of Spanish verb conjugation, only to go back to the CRUD work I deal with on a daily basis and fade back to oblivion.
Not sure about the Coursera class, the structure of the edX courses are presented in Python Juypter notebooks where you fill in the code snippets. It is fun and addictive to solve each exercise as a mini-puzzle; but not sure how much it'll stick vs. if one had to take an concrete problem and take its pieces and puzzles from start to finish, without the hands-holding.
I think perhaps Cousera courses have "capstone" projects - curious if anyone have had experience doing one? In lieu of one on the edX course, I think I plan to grok some papers with some large genomic dataset and/or financial time-series and try to replicate their result.