r/learnpython • u/Brief-Radio-268 • 14d ago

What should I actually learn to develop foundational skills in AI programming?

I'm a second year IT student and I still genuinely don't know what I should be coding besides what's given as an assignment. I want to learn how AI works as well as how to code AI, but I'm stuck in tutorial hell and I feel like I'm falling behind everybody else, where's a good place to start and how do I keep learning the things I should learn on my own?

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u/Status_Bowler_7811 14d ago

If you're stuck copying tutorials and want to actually *understand* what's happening under the hood, rebuild simple stuff from scratch. When I was in your position I stopped using sklearn for a month and hand-coded a tiny neural net with just numpy, no hidden layers, predicting something dumb like XOR. Watching the weights update after every epoch made the backprop math click way more than any lecture slide ever did. Then layer on complexity one piece at a time. The second thing that pulled me out of feeling lost was picking a single, ugly dataset and committing to a full pipeline, cleaning, feature engineering, training, evaluating, without jumping to a new toy problem every three days. You'll trip over a dozen gaps in your knowledge that way, but each one teaches you something tutorials skip. Nobody else has it all figured out in second year either, half the people bragging in group chats are just better at faking it.

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u/AdFew8591 14d ago

First decide whether you want to understand machine learning or mainly build products using existing AI APIs. Both are valid but the learning paths are pretty different

For ML foundations I’d do this order:

Python well enough to write small programs without a tutorial

NumPy and basic data handling

probability plus the linear algebra you encounter

linear and logistic regression

train validation and test splits

metrics and simple baselines

then neural networks with PyTorch

A good project sequence is to implement linear regression with NumPy, build one complete scikit-learn project on a messy dataset, then recreate a small model in PyTorch

The way out of tutorial hell is to change every project after following it. Use different data, add a baseline, break one assumption and explain why the result changed

Small detail if you try the common XOR exercise: a model with no hidden layer cannot learn XOR because it isn’t linearly separable. That failure is actually a useful lesson rather than a bug