- tackling data types often found in real-world datasets (missing values, categorical variables),
- designing pipelines to improve the quality of your machine learning code,
- using advanced techniques for model validation (cross-validation),
- building state-of-the-art models that are widely used to win Kaggle competitions (XGBoost),
- avoiding common and important data science mistakes (leakage).
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