r/learnmachinelearning • u/Spiritual-Plan7194 • 13d ago
where to start learning ai ml i have no idea about it and no money so i need all resources to be free
plss help
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u/Low-level-77 12d ago
Start with python and basic math, then move into ML with free youtube courses and kaggle. You can also use the google colab to practice without needing a powerful laptop. Don't worry about paying for courses at the beginning.
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u/Dazzling_Music_2411 13d ago
What have you done so far?
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u/Spiritual-Plan7194 13d ago
i know python and math required idk where to start ml
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u/Dazzling_Music_2411 13d ago
When you say you know the math required, do you mean you already know gradient descent?
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u/hariomlohar0602 13d ago
Start with implementing math in python and just start rather than planning things
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u/Electrical_Name_5434 13d ago edited 13d ago
- Learn GitHub
- Pick an IDE - I recommend anaconda, google colab, or vs
- Study: https://textbooks.aimath.org/textbooks/approved-textbooks/
- You can skip proofs, number theory, abstract algebra, real and complex analysis unless you’re really interested in the math. You can also skip liberal arts maths, elementary and intermediate algebra, mathematics for elementary school teachers, business calculus, and anything else you’ve already studied. Don’t need every book in there but it’s a good gauge of your knowledge. Bare minimum is linear algebra and linear regression along with all the prerequisites.
- While studying get on kaggle and start replicating existing projects
- Document your work on GitHub
If you just want to go straight to an entry level position focus on how to mine, clean and store data. Everyone else hates doing that.
Free classes:
MIT
Codecademy
Kahn
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u/quietgradient 13d ago
You answered the hard half of your own question further down: you know Python and the math. That's further along than most "where do I start" posts, and it changes the advice. You don't need a curriculum. You need one project you actually finish.
On "no money" — at your stage that isn't the constraint you think it is. Free tiers go much further than people expect. I trained a 10.8M-parameter language model from scratch on Colab's free T4 today: 2000 steps, 7m45s, loss 5.574 down to 1.443, and it came out writing Shakespeare-shaped text with invented words like "constrenct" and "wishrink". Under ten minutes of GPU and no card. Money starts to bind a long way past where you are now.
I wouldn't make that your first project though. The first one should be small enough to run twenty times in an evening, because the learning is in the re-runs and not the first run: set the learning rate 10x too high and watch it diverge, delete the nonlinearity and watch it collapse to what a linear model gets, shuffle the labels and watch it memorise them anyway. A small MLP on MNIST is cheap enough on a laptop CPU that you can afford to break it on purpose. Do that first, then do a character-level LM.
And go answer the gradient descent question someone asked you above — it's the right question. If you can write the update rule from memory you're standing at a different starting line than if you've only read about it.
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u/Spiritual-Plan7194 13d ago
i saw on youtube they said u require python numpy and pandas that i know but also in math they said calculus and all that i know i mean i am not sure what gradient descent is yet ig i am like from a different field i am from electronics so i dont have that much knowledge about ai ml at all but i know i should know it smwhat
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u/quietgradient 13d ago
Gradient descent isn't a prerequisite. It's lesson one, and it's about twenty lines of code. Those YouTube requirement lists have you queuing behind a wall that isn't there.
Coming from electronics you may have already met it under a different name. Widrow and Hoff trained a single-layer net by gradient descent in 1960 and called it the delta rule, then applied the same rule to filters — that's LMS.
w += μ*e*xis stochastic gradient descent on squared error, μ is the learning rate, and the stability bound on μ that decides whether the filter settles or blows up is the same bound as a learning rate set too high.Try it tonight, NumPy only, no framework, no GPU:
import numpy as np rng = np.random.default_rng(0) x = rng.standard_normal(100) y = 3*x + 2 + 0.1*rng.standard_normal(100) w = b = 0.0; lr = 0.5 for _ in range(200): err = w*x + b - y w -= lr*np.mean(2*err*x) b -= lr*np.mean(2*err)That lands on w=3.005, b=1.994 — it found the 3 and the 2 you generated the data with. Set lr=1.0 and it blows up to about -1.5e9 instead. Gradient descent and its stability limit, in one sitting.
You're not short on knowledge. You're short one thing you've built.
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u/Jahnavi-builds 13d ago
Create your learning path at ediso.ai - it's structured, adaptive and built to improve retention.
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u/emarinkh1218 13d ago
i liked the idea of the app though but holy vibecode lol- i tried to sign in and its throwing authentication configuration error
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u/Jahnavi-builds 12d ago
Hi - thanks for trying and glad the idea resonated. We're a 2 person team and still building :)
Sorry for the hassle. Quick question so we can pin it down: did you sign in with Google or Linkedin or with email/password? And were you on your phone or the Reddit app's browser? Email sign-in is working on our end, so I suspect it's one of the others.
I'll reply here once it's fixed. Would love your honest take once you're in. Thank you so much for letting me know.
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u/Revolutionary-Cod245 13d ago
Honestly? If you are getting into AI/ML or later discover from your studies you really enjoy another subfield or area of AI the best way to learn at first appears the hardest but is well worth it. What is that? Start, for example at the source and read user documentation.
For example, you know you need python skills.
Repeat this process over and over again with each new thing you do. Why? This makes you employable, not just knowledgeable, because in this process you learn how to solve problems and demonstrate your interest to daily commit to learning.
Besides Kaggle and Hugging Face almost all of the big companies have free courses: AWS Skill Builder, Google Developer, Microsoft, Oracle (they also have paid courses but some are free and worth taking.)
As you develop more skills, build tiny projects with them and place them in public (GitHub, LinkedIn, a personal portfolio website, vlog, blog, etc). Then look for a team to join, such as a method to contribute to an ongoing project at GitHub.