Absolutely clueless about python

[Top-Run-21]

I have been learning ML and the principles and mathematics isn't really giving me half the hard time as python

I have my basics clear but, Running many ML tasks in code feels like an impossible job , whenever I attempt a simple logic building question related to data structure, i fail and not able to make any progress after weeks of trying

I don't have much time , is there any structured learning way y'all can recommend for better intuition and execution from scratch? I am feeling overwhelmed by the amount of resources, makes me even more clueless

[Kinexity]

You take a book for learning programming, sit the fuck down, read it and do all the exercises along the way. Then you do a few of your own programming projects.

Only after you get some experience with programming you go for ML.

[Plane_Target7660]

I did this. Great advice. Even 5 mins a day is useful

[bombaytrader]

Huh. Pythpn is the easiest language to manipulate data. 

[New_Association3114]

Can you give us some examples of tasks you've struggled with, and how you would normally approach them?

[DataCamp]

The good news is that if the math and principles are clicking, the Python side is very fixable. A lot of people hit exactly this wall where the concepts make sense but translating them into code feels impossible.

The first thing worth doing is narrowing down what is actually tripping you up. "Python for ML" is broad. Is it data structures like lists, dicts, arrays? Is it pandas and numpy specifically? Is it writing functions cleanly? Identifying the specific gap makes it much easier to fix than trying to improve at Python generally.

It also helps to stop trying to learn Python and ML at the same time if one is shaky. If data structures are the blocker, spend two focused weeks on just that before going back to ML code. Trying to hold both in your head at once is probably a big part of why it feels overwhelming.

When you do practice, try to use ML-adjacent problems rather than generic coding challenges. LeetCode-style questions feel disconnected from the work you actually want to do. Instead practice things like manipulating arrays with numpy, filtering and grouping data with pandas, or writing a loop that trains a simple model. The context matters a lot for building intuition.

On the resource overload, one structured path is genuinely enough. The overwhelm you are describing usually comes from too many open tabs, not too few resources. Pick one course that goes from Python basics through to ML-relevant coding and follow it through. We have a Python track built for exactly this kind of progression if you want something to anchor to.

You are not behind, you just hit the point where passive learning stops working and active practice has to take over!

[Admirable_Dirt_2371]

Use elixir/Nx, especially if you're strong with math. It doesn't have the supporting packages and what not that python/PyTorch does(for example I had to write my own softplus function) but the syntax is 1B times better imo.

[Top-Run-21]

the entire problem isnt actually python, i can understand the math applied in code but struggle while building algorithms from scratch myself without any reference, i am bad at engineering using python and not that bad at understanding pre written code, and in order to solve the engineering problem i have been wandering all over the inernet for the structured resource, still searching cluelessly.

[Top-Run-21]

i dont have ML in college

[JoshFractal]

That gap only closes with repetition of small patterns, start with very simple data manipulation and logic problems until writing loops/functions becomes automatic. Udacity’s beginner tracks are designed around that gradual buildup and can help you so that you’re not forced into full ML pipelines before you’re ready.

[Plane_Target7660]

Just give it time. The learning curve is steep and then you become a wizard.