Machine Learning Essentials

• 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).