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• 🥇 I'm proficient with:

  • torch, scikit-learn, imblearn, numpy/scipy/statsmodels, pandas, xgboost/catboost/lgbm, gplearn, albumentations, category_encoders

• 🥈 I know my way around:

  • spark, keras/tensorflow, scikit-optimize, OpenGL, OpenCV, transformers

• 🎖 Recently, I've learned how to:

  • never accept the null hypothesis;
  • stop misinterpreting p-values;
  • read impurity-based, permutation and SHAP feature importances properly;
  • spot the outliers in several dimensions at once with Mahalanobis distance;
  • combat skewness with QuantileTransformer/PowerTransformer;
  • calibrate;
  • avoid the PCA trap in classification;
  • engineer features with symbolic regression.

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