Reviewing Machine Learning in X

Reviewing some classic promises.

  ·   2 min read

Machine Learning in a week/year

This is a classic medium post, in fact, I do think it is the quintessential data science medium post: some useful information, a clickbait-y title, and definitely some over-promising. Still the post is pretty good to structuring a self learning path. I do think there are two major things of note:

  1. The path is too aggressive. It’s fairly odd to me that it’s a very deep-end method of learning. The idea is application as soon as possible, which is admirable, but the lack of review and seeking out mentorship and collaboration seems off.
  2. The path is very light on statistics and mathematics basis. Which is fine, if you view doing data science and machine learning as a purely engineering endeavour (which it often can be), but I would think that a good grasp of statistics and mathematics is important for someone who wants to do cutting edge research.

As an aside I have started a unscheduled post of learning path and resources compiled in the blog, hopefully I will update it from time to time.

A good data science profile

Speaking of the “learning data science” side of things, I have heard about a framework of 0portfolios (more on this some other time):

  1. Demonstration
  2. Narrative
  3. Product

This is just a fantastic example of demonstrative profiles. Readable, clear and concise.