Stuck Between Data Analysis, Math, and Machine Learning—Need Clear Guidance

[ajwadode25]

Hi everyone,

I’m currently stuck between learning data analysis, mathematics, and machine learning. I want to quickly dive into machine learning, but I’m confused about which Pandas and NumPy topics are essential for ML.

The issue is that there are too many resources—when I start one, I find another better one and end up getting lost.

Can anyone suggest a clear learning path or a minimal list of topics in Pandas, NumPy, and Math that are absolutely necessary for starting ML without getting overwhelmed?

I’d really appreciate any structured advice or resource recommendations that focus only on what’s important for machine learning.

Thanks in advance!

[pkdvalis]

Here are two curriculums you can follow:

If you are specifically interested in ML I would say to try Kaggle first. I found the fCC source to be very out of date. The basics are probably good but the labs were too old to follow since many modules were too old and deprecated to use.

Kaggle:
https://www.kaggle.com/learn

The Scientific Computing with Python is newer and the data analysis material is good, although also getting outdated.

freeCodeCamp: Python, Data Analysis, Machine Learning, College Algebra
https://www.freecodecamp.org/learn/scientific-computing-with-python/