Sensei Learning Archive

Use Bayes' theorem plus a simplifying independence assumption to build a fast probabilistic classifier.

Classify a point by looking at the labels of its nearest examples under a chosen distance metric.

Understand the fundamental concept of algorithmic complexity and Big O notation for evaluating algorithm performance

Find the maximum-margin boundary that separates classes, with kernels for non-linear structure.

Reduce variance by averaging many decorrelated decision trees trained on bootstrap samples.

Make predictions through interpretable if-then splits chosen by information gain or impurity reduction.

Turn a linear score into a calibrated probability for binary classification.

Predict continuous values by fitting the best linear relationship between features and a target.

Map high-signal free AI education platforms to the Sensei algorithm curriculum.