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Module 11

Predictive Analytics and Machine Learning

Build, evaluate and explain predictive models responsibly — and know when not to.

Difficulty:All beginner intermediate advanced expert

ML Foundations

Prediction vs explanation, splits, validation, over/underfitting, leakage.

How models are evaluated

✓Machine learning foundations — prediction vs explanation, validation, overfitting and leakageThe discipline that separates useful models from impressive-looking wrong ones — correct splits, validation, metrics, over/underfitting, leakage, and knowing when ML is unnecessary.Advanced80 min

Models

Regression, gradient boosting (XGBoost) and comparison.

Regression and boosting

✓Regression, gradient boosting (XGBoost), model comparison and time-series forecastingFit and compare linear and gradient-boosted models, forecast with backtesting against seasonal baselines, interpret results for the business and prepare models for deployment.Expert90 min