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ai-f20-m4's Introduction

Feature Engineering Module

Content for 2 weeks of class (10 days).

Topic of the day Things to cover
Mon 1. Robust ML Tree & Mult Models, Validation, ColumnTransformer, Pipelines
Tue 2. Data Cleaning Missings & Outliers (Drop vars, impute vars)
Wed 3. Numerical encodings MinMaxScaler, StandardScaler, BoxCox, QuantileTransformer
Thu 4. Categorical encodings Ordinal, Binary, OneHot, Mean Enc., CatBoost
Fri 5. Feature Selection & Dim Reduction PCA, tSNE, UMAP, VarianceThreshold
Mon 6. FE for NLP BoW, TFIDF, N-Grams
Tue 7. FE for Time Series Lag features, TSfresh
Wed 8. FE for Geographic data Lat, lon. population
Thu 9. FE for Several tables Manually merge & join, featuretools
Fri 10. Kaggle challenge

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