I’m building my ML learning path in public — from NumPy to Transformers

#buildinpublic #learning #machinelearning #python

Hey everyone!

I've been learning machine learning by building ML-Foundations, a public collection of Jupyter notebooks documenting my learning journey.

Instead of keeping notes scattered across different places, I wanted to organize what I'm learning into a structured, hands-on path:

NumPy, Pandas, and data visualization

Statistics and machine learning fundamentals

Classical ML with libraries like scikit-learn

Deep learning concepts

NLP and Transformers

The goal is to make the learning process practical: study a concept, experiment with the code, and keep everything organized in one place.

This is a learning-in-public project, so I'm still improving the notebooks and the learning sequence.

I'd genuinely appreciate feedback from people who have learned or taught ML:

Does the progression make sense for a beginner?

Which foundational topics would you add?

What makes a learning repository genuinely useful to you?

If you're learning ML too, I hope you find something useful here!

#showdev #python #machinelearning #datascience

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