ML Techniques in Miniature

Machine learning, in a nutshell: give a system some data to learn a pattern from, then apply what it learned to new data it hasn't seen before to make a decision. The four small, hands-on demos below each show a different piece of that process, grounded in a tea-brewing or tea-shop scenario — not a survey of the field, just a chance to see each technique actually run on data you can watch and control.

Pick a tab below to try one.

What this teaches: Run four demos on data you control: overfitting, k-nearest neighbours classification, k-means clustering and decision boundaries. It teaches how a model learns a pattern from training data and why a model that fits its training data too closely does poorly on new data.

ML Techniques in Miniature covers the same overfitting, kNN, k-means, and decision-boundary concepts taught in DAVHILL’s Intro to Data Science (opens in a new tab) course.