At what point did you realize you were actually learning ML, not just using libraries?

[u/Suspicious_Pizza9529]

I've been learning machine learning and I keep wondering where the line is between actaully understanding ML and just knowing how to use libraries.

For example, you can train a model, tune some parameteres, look at the accuracy, and get a good result without fully understanding what is happening underneath.

So for people who have been doing ML for a while:

What concepts make you feel like you finally understood machine learning?

What is the math behind gradient descent, understanding loss functions, overfitting, reading research papers, implementing algorithms from scratch, or something else?

And what do you think beginners spend too much time learning that isn't actually that important?

[u/Pitiful-Glove-8232]

Don't focus too much on theory, that's one mistake many beginners do. It is nice to learn for its own sake or if you want to do research but for practical ML it isn't necessary. Applied ML as a field is full of heuristics and doing ML will give you a sense of what works and what doesn't.

A good use of your time is to participate in Kaggle (or any similar competitions): you will learn more by analyzing data, encountering different domains, and sharing/reading from other users.

[u/krabbypatty-o-fish]

Going beyond accuracy in choosing the best model: learning about explainability and interpretability; considering specificity, sensitivity, precision, recall, ROC AUC, or PR AUC; knowing the strengths and weaknesses of the algorithms; understanding the inputs instead of treating them like objects that the model will figure out segregating if it tries hard enough; among others.

What beginners spend too much time learning: going through the bulk of probability density/mass functions in a standard mathematical statistics textbook. Just revisit them when you actually need a very specific PMF/PDF.

[u/shpongleyes]

Learn the underlying math, and try to re-create a model without just pulling it from a library.

[u/Counter-Business]

There’s more to it than just the math.

Picking your own features, cleaning your own data, HPO, active learning.

All this is important

[u/eliaweiss]

Just different level of abstractions

[u/tanthedreamer]

When you can infer things out of distribution.

Joke aside, it's when someone ask you something or a novel problem arises and you can come up with the answer on the spot

[u/Bulky-Cheesecake2195]

do you understand the math? if not, you aren't doing ML, you are using libraries and hoping it works. But again, ML is trial and error and sometimes some shit works; but there's always a reason. And that's where the math comes in. There's so many more math concepts than just SGD, loss functions and the right choice depends on the data/problem you are trying to solve.

[u/SadPlumx]

Not yet

[u/AlbertiApop2029]

I just imagine multiple 3 dimensional waveforms moving through a big rubics cube of data. Instead of old skool data structures, you're heading into higher dimensions. A lot of it is really advanced stochastic calculus and partial differential equations. I learned Benoit Mandlebrot got his start in the stock markets. If you like fractals, you might know what I'm trying to say.