course | Machine Learning for Business Decision-Making

Supervised Learning for Business 

• Linear and logistic regression review 
• Decision trees and ensemble methods (Random Forest, XGBoost) 
• Model training, validation, and hyperparameter tuning 
• Metrics for evaluating predictive models 
• Understanding business problem framing for ML applications 

Advanced Supervised Learning 

• Gradient boosting and AdaBoost 
• Support Vector Machines (SVM) 
• Handling imbalanced datasets 
• Feature engineering and selection best practices 
• Model selection strategies for different business contexts 

Unsupervised Learning and Clustering 

• Clustering techniques: K-Means, Hierarchical, DBSCAN 
• Principal Component Analysis (PCA) for dimensionality reduction 
• Segmenting customers, products, or operational units 
• Evaluating clustering performance and visualizing clusters 
• Identifying actionable patterns from unsupervised learning 

Model Interpretation and Explainable AI 

• Understanding feature importance and SHAP values 
• Visualizing model predictions and uncertainty 
• Translating model outputs into actionable decisions 
• Bias, fairness, and ethical considerations in ML 
• Communicating insights to non-technical stakeholders 

Advanced Applications and Decision Support 

• Combining predictive models for ensemble learning 
• Scenario planning and decision simulation 
• Integrating machine learning outputs with business intelligence tools 
• Predictive maintenance, churn analysis, and demand forecasting case studies 
• Strategic application of ML for enterprise decision-making