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