Supervised learning means training a model on labeled examples — data where you already know the answer — so it can predict the answer for new, unseen cases. This module covers the two core tasks, regression (predicting a number) and classification (predicting a category), through the algorithms almost everything else builds on: linear regression, logistic regression, and decision trees.
The first three lessons are short, visual explanations of the ideas themselves — no code, just intuition. The last video puts those ideas into practice: writing the actual scikit-learn code to split data into train and test sets, fit a model, and check how well it predicts.
Watch the lessons in order. If a video runs long, feel free to treat it as a reference you dip back into later rather than something to finish in one sitting.
StatQuest's visual walkthrough of fitting a line to data with least squares, and what R-squared actually tells you about how well that line fits.
Video 27minThe main ideas behind logistic regression — how it fits an S-shaped curve instead of a line, and why it's the go-to method for classification problems.
Video 8minHow a decision tree builds itself from raw data — choosing questions, measuring impurity, and splitting — to classify or predict outcomes.
Video 18minDLAcademy's hands-on scikit-learn course: train/test splits, fitting linear and logistic regression models in code, and checking how well they predict on real data.
Video 3h