I like this article a lot. I think it's important and instructive, especially for beginners, to think about machine learning from this high level perspective, because that's what gets drowned out in books and papers, and it's what's required to be able to come up with your own solutions.
If I didn't like the article I wouldn't come here to say this: The text needs urgent proofreading. And I mean some of the sloppiness like extra words or missing blanks on the left-hand side of braces, not necessarily the kind of grammar errors that non-native speakers are bound to make.
Yeah, I felt the same way. Most papers do not mention about features, why they chose them, what they learned about the dataset. Only, a high level architecture of the system, and possibly some comparisons. One of the reasons why I wrote about the post is because I wanted to shed some light on the parts that books and papers do not focus on.
I get some feedback around proofreading and grammar for the post. I will definitely try to make it better next time, possibly making someone do the review. I guess I cannot correct non-native speaking, but at least next time I could be more careful about sloppiness and the things that are related to grammar.
If I didn't like the article I wouldn't come here to say this: The text needs urgent proofreading. And I mean some of the sloppiness like extra words or missing blanks on the left-hand side of braces, not necessarily the kind of grammar errors that non-native speakers are bound to make.