[sin7]

I've been doing the data thing for a while. During one of my defenses of R, someone brought up that R was a black hole. That if you programmed in R, you were a user who just filled in the correct function arguments and it just spit out the answer. And that was when my thoughts on machine learning changed.

The vast majority of us are users. We massage the data to be in a certain shape, then feed it through a machine that someone else created. We can change the parameters. We can change the data. But few of us are going to look in to the code of a random forest function.

I've switched tracks and started doing web development. Playing with the hyper parameters in machine learning is no different than changing the feel of a drop down by changing the colors, fonts and other things to fit a certain aesthetic.

I could be wrong, but I have yet to meet anyone that has done anything besides use packages created by others to call themselves data scientists. I think that opens it up to becoming just another tool no different than Excel.

[ishjoh]

years ago on the first ML hype wave I completed the excellent MOOC by Andrew Ng. In that course, he did go through the math and it was helpful to me to understand what was going on under the hood, but even then the value of ML wasn't understanding what it was doing, but understanding if your model was doing something well. I think your take that using packages created by others will be mostly what we do moving forward, and that's also true of pretty much all software development.

[itronitron]

I consider R to be one of the lower-level ML/DS languages, in that people that use R typically are fairly intentional about what they are doing.

I've been working in this space for a long time and recently started reading up on a particular ML technique which gained a lot of popularity over the past five years. What strikes me about 95% of the material available is how over-hyped and uninformative it is, to the point of just being wrong.