Supervised Learning Essentials
A practical tour of regression, classification, and the evaluation metrics — accuracy, precision, recall, and overfitting checks — that reveal whether a model truly works.
Predicting numbers: regression
Regression predicts a number Supervised learning comes in two flavours, and the first is regression: predicting a continuous number. Think house price, next month’s demand, tomorrow’s temperature, or the delivery time for an order. The answer is not a yes/no or a category — it is a value on a scale. The line-of-best-fit intuition Imagine […]
Feature engineering, plainly
The model sees only what you put in front of it A supervised model does not understand your business. It sees columns of numbers. Feature engineering is the work of turning what you know into columns the model can use — and on most real problems it moves the result more than swapping algorithms ever […]
A model card for the model you built
The document that makes a model safe to hand over A trained model on its own is an artefact nobody else can judge. A model card is the short, honest document that travels with it — what it does, on whom, how well, and where it should not be used. The practice comes out of […]