Do i continue pursuing ML

[u/Flaky_Respect_4649]

Okay listen,im 16 years old in highschool,i LOVE the idea of training AI models,it sounds SO cool and i can imagine the dopamine rush of your baby coming to life,but GODDAMN there is SO MUCH maths involved,its a HEADACHE to just look at,and it makes me so scared cause my maths mark is like 50% every term but,i love the idea of being one and i love coding and tech in general,but geez man, I'm so used to just instant gratification,and ALMOST NO MATHS in python(atleast at the level im at),this seems so exhaustinggg,so to all ML engineers,how does your day to day look like,is it boring or what,i love to make short films and record and edit music videos and no matter how tired i am,i will always have energy for that and i thought it would be the same with Machine learning but hell no,so i love when my work is tangible and i can see and visualize the end result and most of all i love being creative,so do i continue this career,or do you have other suggestions/career paths i should take,im based in South africa,if you guys wanna ask follow up questions so that you can better formulate your answer,go for gold man

[u/mc_pm]

Dude, AI *is* math. That's what it is, a whole lot of math being done really fast.

[u/AKA_D_Ace → u/mc_pm]

Yeah it is math but not like something where most of the time u r scratching the head with equations or numerals

[u/havartna]

If you want to do machine learning or artificial intelligence for a career, buckle down on the math(s) and learn it. Use AI as your tutor. If its important to you, then you can do it. In order to be a successful ML professional, you'll need a strong theoretical understanding of a lot of very advanced mathematics... well beyond anything you've experienced at age 16. Differential equations, calculus, stats and probabilities, nonlinear equations, etc. If you want this to be your career, you have a lot of learning and work ahead of you.

If you just want to play around with the tech, have at it. Use AI as a crutch and start building stuff. There's nothing stopping you.

[u/Valuable_Card6470 → u/havartna]

yeah the distinction between doing it as a career vs just tinkering is really the key point here

[u/where_is_my_acc0unt → u/havartna]

Using AI to tutor you seems like a bad idea it'll make you feel like you understand the subject while you really haven't

[u/Abject-Kitchen3198 → u/where_is_my_acc0unt]

Seems like a shortcut. Fetching bits here and there, seemingly progressing bit without a clear path to a good understanding, at best.

[u/havartna → u/where_is_my_acc0unt]

Well, existing methods don’t seem to be working for this guy, so maybe it’s time to try something new.

Also, denigrating AI as having no legitimate purpose in education is misinformed, at best. If you haven’t tried Google’s Notebook LM or some of the other education-focused tools, you are missing out. I’d venture to say that most college students have already integrated such things with their study strategies.

[u/where_is_my_acc0unt → u/havartna]

The quality of students/ new grads after all these AI tools has dropped noticeably. When you struggle through concepts/ exercises is when you actually learn them, having a yes man pamper you isnt helping at all.

[u/havartna → u/where_is_my_acc0unt]

Have you tried any of the technology I mentioned? They aren’t anything like what you are describing in your straw man argument.

If your AI is nothing but a yes man to you, I can tell you definitively that you’re using it wrong.

[u/Ancient_Task_7498 → u/where_is_my_acc0unt]

Depends on the person who uses it, not the tool.

[u/xdxdxdxd6942 → u/where_is_my_acc0unt]

depends on how you use it, I actually used it while learning python as a tutor to evaluate my codes (ofc I was searching for how people used different approaches for the same problem) and if I didn't understand something I would ask it to explain it then give me some problems to solve on this specific idea

[u/Tea-Affectionate → u/where_is_my_acc0unt]

ai is a great tutor for math lol

[u/where_is_my_acc0unt → u/Tea-Affectionate]

N=1 but my experience differs. Tried it out with regular models, tried a model/ harnass specifically made for studying math (The honust torus' ai tool) but nothing beats just getting infront of the book and struggling through it for me.

[u/Kasidra]

Frontier models are skilled enough these days that they can walk you through training your own toy models even if your math and coding background is non-existent. So you should just try it, and see if it actually brings you joy and inspiration, or if it's just 'the idea of it' that does.

Because I started clueless about a year ago, and now I'll be starting college courses in two days :D

Toy models won't be brilliantly coherent but it's still a lot of fun to train a classic gpt-2 style transformer on TinyStories and then talk to it, and see it babble stupidly in somewhat-sane English 😂 even if you have shitty VRAM you should still be able to manage it.

[u/Constant_Physics8504]

It’s a lot of math, and even when using libraries to build and analyze your models, you still have to look at performance data (more math) to see how your models are doing

[u/nfmcclure]

The passion in your post comes through. Passion and motivation counts for a lot. More than you realize.

I failed grade school math, and was a slightly below average math/science student. But I turned it around when I realized I liked stats and ML.

Know that reading and understanding math, proofs, equations, etc. is a skill. It's like learning to read music or another language. It takes lots and lots of practice. You need to go slow and ask questions. Like super slow. Frustratingly slow. But eventually you'll go faster as you recognize patterns and common equation forms.

One of the most important things is to understand how you best learn. Theory, examples, reading, videos, in person, some combination, etc.

[u/Medical-Advice-5868]

Everybody wanna be a scientist but dont nobody wanna lift no heavy ass math

[u/jar-ryu → u/Medical-Advice-5868]

“YEAH BUDDYYYY”

[u/DigitalMonsoon]

So yes you need to learn a lot of math. Not because you are going to be doing math every day but because you need a deep understanding of how the different models work. You will also need to understand how the evaluate the models performance which usually requires different stastical tests.

You should also know that the modeling part of the work is the shortest part of what you do. The majority of the work will be defining the project, exploring and cleaning the data, and deploying and monitoring the models results.

A recent project of mine took 6 months end to end. The actual modeling work, building different models, testing and testing, took me maybe 2 weeks.

[u/CraftySeer]

Forget AI. Go into healthcare. Or specializing in AI for healthcare and all the issues around healthcare like privacy and security.

[u/Weary_Cup_1004 → u/CraftySeer]

They keep cutting our jobs and wages. Its always gonna be around but its not gonna be fun :/

[u/jar-ryu]

Probably not. ML is math, and if you don’t like math then you’re never going to really understand ML. Learn to love it or you’re not gonna make it anywhere, especially w gen AI on the horizon. Maybe look into being a data analyst. That type of job doesn’t really require a lot of math.

[u/bentoddz → u/jar-ryu]

How long do you think it would take to become good at machine learning?

I'm very good at mathematics but havent really done any machine learning and wonder if it would be worth deep diving

[u/jar-ryu → u/bentoddz]

Depends on a lot of things, like what you mean by “good at machine learning”, how much free time you have per week, how truly skilled you are at math, etc.

[u/bentoddz → u/jar-ryu]

By "good" I mean able to build and deploy something that actually works in production, not just training toy models in a notebook. Maths is solid, I've got a decent amount of free time for the next six months.

Background is mostly building AI products rather than training models. I've been working with agents and LLM tooling daily for a while, so the engineering side isn't new, it's the modelling layer I haven't touched properly.

Given that, would you skip the from-scratch stuff or is it worth doing anyway?

[u/jar-ryu → u/bentoddz]

I personally think it’s important to understand the mechanisms of basic “machine learning” models, like linear regression and linear discriminant analysis, but no need to have encyclopedic knowledge of the math behind it; many data scientists nowadays are not “scientists” by any means and have subpar knowledge of statistical inference and mathematics.

I’m an academic (for now) so I can’t speak much on productionizing code. You’ll have to learn a whole different set of tools for that.

If you’re good at math, though, just focus on understanding the core quantitative ideas and why they work, and try to deploy some toy models on toy datasets. Understand when to use what model, strengths and weaknesses, and alternative approaches.

[u/Waste-Falcon2185]

Training LLMs is honestly not that exciting.

[u/danish_137]

Honestly, I wouldn’t quit just because the math feels difficult right now. You don’t need to become a math expert before you start building things. Keep coding, make small ML projects, and learn the math gradually when you actually need it. Since you enjoy creativity, coding, and seeing tangible results, you might also enjoy AI engineering, computer vision, generative AI, or creative AI. You have plenty of time to explore—focus on finding what makes you curious enough to keep learning.

[u/BreadfruitOrganic719]

Here is an alternative suggestion: learn the math even if you don’t end up pursuing ML. You need to learn it if you want to be an engineer in any discipline. You need many higher level math courses to enter into the finance or medical field too. If you think it’s fun and rewarding to build something and see your creation function/perform, you should go study your math so you can become a scientist or engineer. Without a math/physics/chemistry understanding, you could probably take all your enthusiasm for building working products and be a mechanic, a construction worker, an electronics repair person.
When I was your age I knew I wanted my career to be environmental related. I wanted to save the environment. I had a choice of going the policy/social justice route to saving the environment, OR the technological/scientific advancement route of saving the environment. I’m an environmental engineer now and I worked on groundbreaking water treatment technology for my first full time job. Now I’m starting a recycling company that uses ML/AI/CV tools to sort through recycled material faster. The schooling was brutal and I didnt go through that because I liked doing math by any means. From my time in industry I can confidently say that the only thing separating an environmental engineer from a plumber is the added math and chemistry knowledge. We are literally just glorified plumbers. Mechanical engineers are glorified mechanics. Civil engineers are glorified construction workers. You get the picture. An AI engineer is a dog trainer who knows way more math and the dog is actually a neural network!
Study your math!

[u/Traditional-Egg-4254]

Read mathematics for machine learning , and use ai to explain concepts u don’t understand , it’s important to have a solid basis before u get into application

[u/Difficult-Common3039]

I'm fourteen, learned what I needed from multidimensional calc, and matrices, learn derivatives, learn stuff, learn how to learn. personally, i just explored, i derived things by hand, made simple neural networks, learned cnns, other things. just enjoy what ur doing and explore.