We have casually said "use cross-validation" for three days now. Today we finally explain it properly, along with the related question of how to find ...
Parathan ThiyagalingamDay 2 introduced overfitting. Day 3 framed it as a variance problem. Today we meet the cleanest, most asked-about tool to fix it in linear models: Reg...
Parathan ThiyagalingamThere is a saying in ML: "garbage in, garbage out." The fanciest model in the world cannot save us from bad inputs. Today we cover the most underrated...
Parathan ThiyagalingamWe have been saying "the model learns the best parameters" for six days now. Today we open that black box. Gradient Descent is the engine that powers ...
Parathan ThiyagalingamWe have casually used "accuracy" and "R²" for the last two days without thinking too carefully about them. Today we take that question seriously. Pick...
Parathan ThiyagalingamYesterday we fit a line to predict numbers. Today, we adapt that same idea for a different kind of question: yes or no. Logistic Regression is the wor...
Parathan ThiyagalingamThree days of fundamentals and we are finally meeting our first real algorithm. Linear Regression is the simplest, oldest, and still one of the most u...
Parathan ThiyagalingamOn Day 2, we met overfitting and underfitting as the two failure modes of ML. Today we put both of them inside one mental model that explains every mo...
Parathan ThiyagalingamOn Day 1, we said a model "learns" from examples. But how do we actually know if it learned, or if it just memorised? That single question is what tra...
Parathan Thiyagalingam