Engineer AI Models: Explain, Tune & Experiment prepares program and project managers to guide AI projects beyond “just working” toward being trusted, explainable, and reproducible. You’ll learn how feature engineering and hyperparameter tuning improve model performance, how explainability methods like SHAP and LIME build stakeholder confidence, and how structured experimentation ensures reliable results. Through real-world scenarios — from boosting fraud detection F1 scores, to presenting credit approval models to risk committees, to planning experiments in Jupyter — you’ll gain the skills to ask the right questions, guide technical teams, and translate complex model outputs into business impact. By the end, you’ll know how to move AI projects from black box to business-ready.