EP 19 | Duke ML for PMs: Linear Models

Sometimes the best solution is the simplest one. In this episode, we unpack Linear Models—the most interpretable and transparent tools in a Product Manager's AI toolkit. We break down how the math works in plain English so you can explain your model's decisions to any stakeholder.Key Topics:Linear vs. Logistic Regression: The difference between predicting a continuous number (like price) and predicting a category (like churn vs. retain).Weights and Bias: How models assign importance to different features.The Power of Interpretability: Why "simple" models are often favored in highly regulated industries like finance and healthcare over complex neural networks.Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.