Lecture 16 – Feature Engineering
Presented by Alvin Wan
Content by Alvin Wan, Andrew Bray, Suraj Rampure, Ani Adhikari
A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding. Currently, this lecture has no quick checks. We will be adding some shortly.
| Video | Quick Check | |
|---|---|---|
| 16.1 Introduction. |
16.1 | |
| 16.2 Numpy: Coding a Linear Model |
16.2 | |
| 16.3 Sklearn: Coding a Linear Model |
16.3 | |
| 16.4 Where a Linear Model Struggles |
16.4 | |
| 16.5 Benefit #1: Enhancing your linear model |
16.5 | |
| 16.6 Sklearn: Imputing Data |
16.6 | |
| 16.7 Benefit #2: Applying Domain Knowledge |
16.7 | |
| 16.8 Benefit #3: Non-numeric and Categorical Data |
16.8 | |
| 16.9 Conclusion: Overfitting and Next Steps |
16.9 |