Section 1
Supervised Learning
Every supervised model, no matter how sophisticated, is answering the same question: given labeled examples, what function best predicts the label from the features? This section builds that function three different ways — a straight line, a probability curve, and a tree of yes/no questions — so the shape of "learning" stops feeling abstract.
Chapters
6
Lesson Reading
2 hr 50 min
Labs
6
Interview Sets
6
Chapter Path
Chapter 1The Supervised Learning Workflow40 minutesAcademy PassChapter 2Linear Regression25 minutesAcademy PassChapter 3Logistic Regression25 minutesAcademy PassChapter 4Decision Trees25 minutesAcademy PassChapter 5Ensemble Methods25 minutesAcademy PassChapter 6Model Evaluation & the Bias-Variance Tradeoff30 minutesAcademy Pass