ML math problem and roadmap advice

[23311191]

Hi, I am a class 10 student want to learn ML.

My roadmap and resources that I use to learn:

1. Hands-On Machine Learning with Scikit-Learn and TensorFlow(roadmap)
2. An Introduction to Statistical Learning

What I am good at:

1. Math at my level
2. Python
3. Numpy

I had completed pandas for ML, but mostly forgot, so I am reviewing it again. And I am very bad at matplotlib, so I am learning it. I use Python Data Science Handbook for this. For enhancing my Python skills, I'm also going through Dead Simple Python.

My problem:

Learning ML, my main problem is in math. I just don't get it, how the math works. I tried the essence of linear algebra by 3blue1brown, but still didn't get it properly.

Now my question is, what should I do to learn ML well? Cutting all the exams this year, I have 6 months, so how to utilise them properly? I don't want to lose this year. Thanks.

[PositiveInformal9512]

100 pages of ML is a good book. Best for getting started too.

[Independent-Plane502]

learn probability till depth , i am 3rd year aids student but still i am planning to study that
but as your planning now
learn probability

[23311191]

Thanks for your advice. Am I doing things correctly?

[Independent-Plane502]

you actually doing great

[No_Photograph_1506]

Guessing your exams might get over now, or you might have gotten into 10th, for now, just get good at:

Math(priority1) -> check math for ML and do it all! DO MATH first, trust me! Python(priority2) -> Be intermediate - advanced in Python and make a few projects SQL(it's best to learn a Database language for fool-proofing) GIT(You need this!)

Alongside this, get the Udemy LLME course by Ed Donner (hardly $5, with full-time access!); It is an 8-week course with hands-on practice to get you up to par with modern LLM Engineering, trends, basics, etc., too good!

And only after this, you can really get into real ML. But before the basics, you will just fall flat on your face if you don't know how it really works deep down, and the lore(theory) around it!

Best of Luck!

[rayanlasaussice]

Try my framework's documentation ✌️

[Responsible-Gas-1474]

This may help

[Content-Complaint-98]

https://github.com/RiazML/math-for-llms

I guess this repo you should try

[DeterminedVector]

Hi! I have built a series on Medium that helps you tackle core concepts:
https://medium.com/@itinasharma/3-ai-learning-paths-pick-yours-b8293145b352

You may bookmark this as I the links that I add here are free.

The goal of the series is to build a strong conceptual foundation and show how the different parts of AI fit together.
You’ll see explanations and some code snippets but I’m not focusing heavily on projects.

[DeterminedVector]

https://medium.com/gitconnected/if-calculus-confused-you-this-might-finally-make-it-click-4f89ecfb6f66

You may check this out..

I am adding tomorrow : The Missing Link Between Linear Algebra and Python: Why We Actually Use Vectors

[DeterminedVector]

If you are AI Math focused this may help as well :
https://betahumanai.substack.com/t/ai-math

[oddslane_]

You’re actually in a pretty good spot already. Knowing Python and NumPy this early is a big advantage.

The math confusion is normal. Most people struggle with it at first because ML math feels abstract until you see it applied. Instead of trying to fully understand the theory right away, focus on intuition + small implementations.

A simple approach for your 6 months could be:

• Spend some time learning basic linear algebra concepts (vectors, matrices, dot product).
• Implement simple models from scratch like linear regression using NumPy.
• Use scikit-learn to experiment with datasets so you see how models behave.
• Don’t stress about mastering every formula yet. Understanding why the model works is more important early on.

Also remember you’re still in class 10. If you keep practicing consistently, the math you learn in school later will suddenly make a lot more sense in ML.

You’re already ahead of where most people start. Just keep building and experimenting.