1Foundational Skills & Classical Machine Learning 1.4Data Science Fundamentals
1.4.6Feature Engineering
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This module is on the syllabus, but nobody has written it yet. If you'd like to take it on, say so on Discord or start the file on GitHub.
| Part | 1 Foundational Skills & Classical Machine Learning |
|---|---|
| Topic | Data Science Fundamentals |
| Subtopic | Feature Engineering |
| Practice | Contestants should develop practical skills necessary to implement AI methods in code. This includes knowing how to use library functions effectively, call the method on a particular data, and interpret outputs. |
| Scope | Feature Engineering involves transforming raw, potentially high-dimensional data, categorical data, time series, or ragged data into a compact set of informative features. Techniques involve sliding windows, pooling operations, one-hot encoding, statistical moment-based features (average, standard deviation), PCA and neural-network-based embeddings. |