There is a lot of literature in books and in the web around the details of machine learning algorithms, for example, on how to calculate the centroid of k-means, or the distance of k-NN, or the coefficients of linear regressions, however there isn’t a lot of material around why and how to pick the best algorithm for a particular use-case.
I find this to be a bit of an irony, as one of the goals of ML is to allow you to see the big picture, yet the procedures available today for selecting a ML technique focus on the tree, rather than on the forrest.
I tried to summarize some of these decisions in the following slides:
This is by no means complete, particularly the unsupervised models section, and really just an initial effort. All feedback is very welcomed, and will be considered.