| Name | First Aired | Runtime | Image | |
|---|---|---|---|---|
| S02E01 | Binary Classification | 15 | ||
| S02E02 | Logistic Regression | 15 | ||
| S02E03 | Logistic Regression Cost Function | 15 | ||
| S02E04 | Gradient Descent | 15 | ||
| S02E05 | Derivatives | 15 | ||
| S02E06 | More Derivative Examples | 15 | ||
| S02E07 | Computation graph | 15 | ||
| S02E08 | Derivatives with a Computation Graph | 15 | ||
| S02E09 | Logistic Regression Gradient Descent | 15 | ||
| S02E10 | Gradient Descent on m Examples | 15 | ||
| S02E11 | Vectorization | 15 | ||
| S02E12 | More Vectorization Examples | 15 | ||
| S02E13 | Vectorizing Logistic Regression | 15 | ||
| S02E14 | Vectorizing Logistic Regression's Gradient Output | 15 | ||
| S02E15 | Broadcasting in Python | 15 | ||
| S02E16 | A note on python numpy vectors | 15 | ||
| S02E17 | Quick tour of Jupyter iPython Notebooks | 15 | ||
| S02E18 | Explanation of logistic regression cost function - optional | 15 | ||
| S02E19 | Pieter Abbeel interview | 15 |
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