This repo is more than just notebooks, it reflects my dedication to learning, experimenting, and growing in the field of artificial intelligence and machine learning.
Each project, notebook, and line of code is part of a structured path that I'm building to master, data, math, and machine intelligence. Ultimately becoming one with the machine. Maybe even like Tony Stark (Iron Man) one day. 🤖⚡
I'm genuinely passionate about uncovering how machines learn, think, and evolve. This roadmap is how I'm turning that curiosity into real-world skills as I build and experiment. I’ve experimented with AI/ML in one of my classes before, but this is different, this is my journey. My grind. My progress.
I'm starting from the foundation, rebuilding the knowledge, and sharing everything here in public.
“Before building models, I will refresh my memory and build muscle.”
python/– NumPy, pandas, data viz basicssql/– Query practice & data analysis logicstatistics/– Probability, distributions, core theorylinear_algebra/– Vectors, matrices, math behind ML
- Student Grades Analyzer – NumPy project on matrix math
- Simulated Weather Analyzer – Temp/humidity logic & slicing
- BMI Calculator – Feature engineering & classification
- Titanic Dataset – Feature engineering & classification
- Heart Disease Dataset – Feature engineering, classification & model evaluation (ROC, PR curve)
Because I don’t just want to use technology, I want to understand it, bend it, and build with it.
I’m drawn to AI and machine learning because of the way they mimic thought, adapt, and evolve. That’s fascinating to me. The idea that we can train machines to see, hear, learn, and decide, that’s not just coding. That’s modern magic and I would like to sign up to be a wizard! 🧙🏽♂️
I don’t want to just watch from the sidelines while AI makes waves and shapes the future. I want to be in the game, building the systems that power it. That's why I'm here. That’s why this roadmap exists.
This repo documents that journey.
- Coursera Machine Learning Specialization
- Numpy, Pandas, Etc
- SQL Query practice & data analysis logic
- Statistics – Probability, distributions, core theory
- Linear_algebra – Vectors, matrices, math behind ML
- Model evaluation metrics