Lecture 14 – Feature Engineering
Presented by Raguvir Kunani
Content by Raguvir Kunani, Joseph Gonzalez, John DeNero, Josh Hug
Note: In this lecture you see the sklearn package being used to fit models. This video gives a quick guide on how the sklearn package works.
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.
| Video | Quick Check | |
|---|---|---|
| 14.0 Introduction |
||
| 14.1 Motivating Feature Engineering |
14.1 | |
| 14.2 Applying nonlinear transformations to quantiative features. Incorporating domain knowledge. |
14.2 | |
| 14.3 One-Hot Encoding |
14.3 | |
| 14.4 Imputing missing values |
14.4 | |
| 14.5 Bag of words encoding. N-gram encoding. |
14.5 | |
| 14.6 Feature functions |
14.6 | |
| 14.7 Implications of feature engineering on normal equations |
14.7 | |
| 14.8 Conclusion |