Feature Engineering with Python

setup, tips, caching, regression target transforms
data imputation basics   (scikit-learn 0.24)
univariate, multivariate, nearest-neighbor, marking imputed values
datasets - simple examples   (scikit-learn 0.24)
iris, digits, cal housing, labeled faces, 20 newsgroups, (more)
feature engineering intro   (python DS handbook)
one-hot encoding, word counts, tf-idf, linear-to-polynomial, missing data, pipelines
feature extraction (text)   (scikit-learn 0.24)
bag of words, sparsity, vectorizers, stop words, tf-idf, decoding, applications, limits, the hashing trick, out-of-core ops
file i/o   (numeric python)
CSV, HDF5, h5py, pytables, hdfstore, JSON, serialization, pickle issues
preprocessing basics   (scikit-learn 0.24)
mean removal, variance scaling, sparse scaling, outlier scaling, distribution maps, normalization, category coding, binning, binarization, polynomial features.
random projections   (scikit-learn 0.24)