EP 19 | Duke ML for PMs: Linear Models
from AI Bites: The Academic Series Hosted by Jack Lakkapragada
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.
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