Advanced Machine Learning Machine Learning Lifecycle Data Cleaning EDA Outlier Detection & Treatment Data Pre-processing Categorical Encoding Model Training Project Skeleton Scikit-learn Pipeline and ColumnTransformer Hyperparameter Tuning Model Evaluation Explainability - SHAP Model Monitoring Drift in Machine Learning Model Model Type Bagging Method (Sampling with Replacement) Random Forest Gradient Boosting Method Gradient Boosting XGBoost Light GBM Cat Boost Classification Classification Regression Application Marketing Science Customer Churn Model (Non-Contractual) Statistics Hypothesis Testing Appendix 1. Projects 2. Resources Setup Create conda env: conda create -n ml_env --file requirements.txt Activate env: conda activate ml_env