Machine Learning is, put simply, getting computers to generalize from examples. And that's what I try to do: put [seemingly complicated] things simply. My posts on Machine Learning (ML) consist primarily of beginner-focused introductions to common ML models or concepts. I felt like too many ML tutorials weren't accessible enough, so I strove to make my guides as clear and beginner-friendly as possible.
Unsure where to start? Here are some of my best / most popular posts:
What Gini Impurity is (with examples) and how it's used to train Decision Trees.Read
A simple explanation of how they work and how to implement one from scratch in Python.Read
Why existing libraries are uninspiring and how I built a better one.Read