Where textbook theory meets the messy reality of real-world data science and machine learning.
Data science courses show you a perfect world. Clean datasets, simple patterns, textbook examples that fit neatly into basic statistical models. But step into the real world, and you'll face the "scare" – data that's messy, mixed, and refuses to behave like your coursework.
This is where theory meets reality. Where your carefully learned models encounter distributions that are mixtures of multiple patterns, outliers that break assumptions, and edge cases that textbooks conveniently ignore.
Code Ramblings explores the gap between academic ML and real-world implementation. Through hands-on notebooks and experiments, we test concepts, push boundaries, and show you what happens when data doesn't cooperate with your models.
We don't just show you the problems – we dissect them, understand their components, and build solutions that work with messy, real-world data.
When your data is a blend of multiple distributions and simple statistics fail you.
Hierarchical data structures and random effects in practice.
Understanding baselines and what "beating random" really means.
We're constantly exploring new ways data can surprise and challenge us.
Stop assuming your data will be clean and well-behaved. Start with the mixture models example and see what real data looks like.
Start with Mixture Models