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Module 11 5 concepts
Feature Engineering
Feature creation, selection, and transformation techniques.
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Automated Feature Engineering
AutoML, Featuretools, and neural feature learning – when manual engineering doesn’t scale.
Feature Extraction and Transformation
Creating new informative features from raw data through domain knowledge, mathematical transforms, and automated methods.
Feature Selection Methods
Filter, wrapper, and embedded approaches for identifying the most informative features – removing noise to improve generalization.
Handling High-Cardinality Features
Target encoding, hashing, and embedding approaches for categorical features with thousands of unique values.
Time-Series Feature Engineering
Lags, rolling statistics, seasonality decomposition, and calendar features – encoding temporal patterns for ML models.