Extract the maximum value from your data using feature engineering. Learn how to clean, normalize, and create features to improve the performance of your machine learning models.
Overview
Syllabus
Introduction
- Applied ML: Feature engineering
- What you should know
- Imputation
- Filling in missing values
- Binning
- Log transform
- Scaling
- Challenge: Basic techniques
- Solution: Basic techniques
- One hot encoding
- Hashing encoder
- Mean target encoding
- Challenge: Categorical
- Solution: Categorical
- PCA
- Feature aggregation
- TFIDF
- Text embeddings
- Challenge: Feature extraction
- Solution: Feature extraction
- Extracting date components
- Seasonality and trend decomposition
- Challenge: Temporal features
- Solution: Temporal features
- Importance and weights
- Recursive feature elimination
- Adding a random column
- Challenge: Feature selection
- Solution: Feature selection
- Next steps
Taught by
Derek Jedamski
