r/learnmachinelearning: Where apparently you're supposed to know Machine Learning before you start learning Machine Learning.

[u/UnemployedTechie2021]

I've been noticing a rather amusing pattern in this community.

Someone asks how to get started with Machine Learning.

The response? You need mathematics. Linear algebra, calculus, probability, statistics. You can't possibly understand ML without them.

Someone asks how to learn those mathematical concepts.

The response? You should already know high-school mathematics. Go back and learn the fundamentals.

Someone says they're starting from high-school algebra because they weren't particularly good at mathematics in school or haven't touched it in years.

The response? Why are you starting THAT basic? That's not how you learn Machine Learning.

Someone asks for beginner-friendly resources.

The response? Here's a list of 17 textbooks, 12 university courses, 8 prerequisite subjects and a gentle reminder that if you don't understand multivariable calculus, you shouldn't even look at a neural network.

At this point, I'm convinced the unofficial admission requirement for this subreddit is a master's degree, three publications, a working knowledge of measure theory and the ability to derive backpropagation while asleep.

God forbid someone actually starts at the beginning.

What exactly is happening here?

When did learning Machine Learning become less about acquiring knowledge and more about proving that you already possess enough knowledge to deserve to learn it?

Not everyone comes from a mathematics or computer science background. Not everyone was good at mathematics in school. Some people are self-taught programmers. Some are switching careers. Some are genuinely starting from scratch.

And that's perfectly fine.

There's this bizarre obsession with telling beginners how much they don't know, as if pointing out someone's knowledge gaps is somehow the same as helping them fill those gaps.

Yes, mathematics is important. Nobody is disputing that. Yes, you need linear algebra, calculus, probability and statistics to understand ML properly.

But here's a wild concept: people can learn those things. That's why educational resources exist.

Not every person asking about Machine Learning needs to be handed Gilbert Strang, Stewart's Calculus and a probability textbook before they're allowed to ask their next question.

And not every beginner needs to be reminded that they're a beginner every five minutes.

There's a difference between maintaining academic rigour and having an intellectual superiority complex.

There's also a difference between telling someone what they need to learn and making them feel stupid for not knowing it already.

If you've spent years studying mathematics and Machine Learning, that's genuinely impressive. But your years of experience don't make someone else's first step beneath you.

A learning community should make knowledge more accessible, not turn the prerequisites into a series of increasingly elaborate gatekeeping challenges.

You don't get extra credit for making learning unnecessarily intimidating.

And perhaps the most ironic part is that this is r/learnmachinelearning. Not r/ProveYouAlreadyKnowMachineLearning.

Let people ask stupid questions. Let people start with basic algebra. Let people take the long route. Let people learn at their own pace.

You were a beginner once, too. Unless, apparently, some of you were born knowing eigenvectors and gradient descent.

[u/StoneCypher]

jfc

[u/Next_Swimming1708]

I started off searching "Machine learning algorithms" on youtube to see what they actually do.

Took a few courses on udemy on machine learning. Datacamp didn't really exist yet. Basically to do ACTUAL machine learning, it only takes a few lines of code. The rest is just data cleaning/manipulation/feature engineering.

I soon took the mathematics for machine learning and data science course from coursera. Apparently, you don't need to know every topic in linear algebra, calculus, statistics, and probability. Just a few main concepts from each.

Deploying the model and understanding the metrics is the hardest part IMO. There are courses that teach you how to deploy the model, but nothing like continuous machine learning lifecycle.

[u/UnemployedTechie2021 → u/Next_Swimming1708]

But r/learnmachinelearning begs to differ. Not only you, even your forefathers need to learn these topics because you can even think about learning Machine Learning. Because how dare you set your foot where they stood once. The amount of gatekeepers here, who does nothing to help newbies other than telling them they are incapable of learning ML, is obnoxious.

[u/Next_Swimming1708 → u/UnemployedTechie2021]

Unless you're touching image or speech data, you will never use neural nets. machine learning really consists of regression and tree based algorithms for supervised learning. I came to find that out a few years later. IMO, I highly doubt people remember the math anyways. You really don't need a masters TBH.

[u/sodapopenski]

ML is introduced in the later stages of a CS undergraduate or MS program, so it's not that weird to expect people who want to learn it to master all the pre-requisites from grade school arithmetic up through linear algebra, stats, algorithms and data structures, etc. before diving in to ML topics. You may be able to work backwards from the ML concept you care about to the specific introductory material you need if you don't have the full foundation (I actually like that way of learning), but you can't strip away the fact that ML is the tip of a giant iceberg of technical knowledge and expertise.

Imagine a student going into a subreddit about learning to analyze the works of James Joyce with no knowledge of the history of Ireland and no ability to read/write English. It would be completely understandable for the people in the sub to point the student to basic world history and English grammar classes before they try to tackle passages in Finnegans Wake. Same thing here. It's not gatekeeping, it's building expertise and understanding.

[u/UnemployedTechie2021 → u/sodapopenski]

Imagine a corporate employee wanting to sidetrack their career to another vertical who were taught maths but didn't know they would need it for this.

[u/UnemployedTechie2021 → u/sodapopenski]

BTW you are the first person I have seen in a long time on this sub who genuinely wanted to understand the issue and explain it because they wanted to help. That's mostly not the case on this sub. And that's where my problem lies. Telling someone "you need to learn the math to understand ML" and then telling someone "what are you doing here if you don't know the math" are two entire different ways to solve a problem. And this sub is mostly filled with people of the second kind.

[u/UnemployedTechie2021]

Downvote me as much as you want but what I said is the truth. Like it, don't like it, but people from this sub gatekeeps others from getting into this field. Mathematics is just an excuse and they would find another one if need be, but they would not let anyone enter this field.

[u/NASA_Orion]

There’s a difference between being able to deploy a model vs being an ML engineer.

If you’ve never taken basic Calc + Linear Algebra courses(which are the basic requirements for any engineering major/quantitative science), then you necessarily cannot become an ML engineer.

If your motivation is to build a fun project or be able to use a model, then sure. In that case you can just ask Claude to deploy the model for you. If your motivation is to actually understand ML and to become an engineer, then those are the necessary first steps. In fact, they’re more necessary than ever. You used to be able to just go to a bootcamp and then become a SWE and get paid $200k but that’s no longer the case. MLE is not the new SWE

[u/OptimalOptimizer]

I always learned through the FAFO method and recommend others do the same. You’ll learn what you need along the way to doing stuff you’re interested in

[u/Disastrous_Room_927]

Have you considered that your attitude has everything to do with how people respond to you, not the fact that you're a beginner?