machine learning notes
09/04/2018
My machine learning notes is a compilation of fifty-five Jupyter notebooks covering beginner to intermediate level topics in traditional ML. The notebooks were all published between late 2017 and early 2018.
A few of the notebooks that have proven to be popular are undersampling and oversampling, L1/L2 norms, leakage, dimensionality analysis, feature selection, missing data imputation, classification probability calibration, and linear discriminant analysis.