Feature engineering
Building and selecting features, the step that often does more for performance than the choice of model.
Course Duration: 5h
What You'll Learn
- Handle missing values without introducing bias
- Encode categorical variables according to their cardinality
- Create business features that carry genuinely new information
- Detect and eliminate data leakage before it distorts everything
- Select a subset of features and justify that choice
Prerequisites
- Python and pandas
- Notions of supervised learning
Course Modules
- Why features matter more than the algorithm
- Missing values: dropping, imputing, indicating
- Scaling: normalization and standardization
- Encoding: one-hot, ordinal, target
- Cyclical features, dates and durations
- Text features: bag of words and n-grams
- Features from aggregations and time windows
- Data leakage: the most frequent cases
- Feature selection: filters, wrappers, importance
- Reproducible pipelines with scikit-learn
Assessment and certificate
The course ends with a 40-question exam covering every module. On success, a certificate of completion is issued; its number is verifiable on the platform.
Free courses, by contrast, end with a 5-question quiz and a preview of the certificate, without certification.