AI and Machine Learning

AI and machine learning, with the uncertainty left in

A model output is an estimate with an error bar, even when the dashboard shows it as a single confident number. That's the starting point for how we cover AI and machine learning.

This section looks at predictive analytics, forecasting, customer segmentation models, AI assistants inside BI tools, and the semantic layers they depend on. We cover how to evaluate a model before anyone acts on it, how to explain a forecast's range to non-technical readers, and what data governance an AI feature actually needs. We won't claim a model is reliable because it's new, and we'll always separate what the data observed from what the model inferred.

Coming up here: getting started with AI and machine learning in analytics, predictive analytics for beginners, and practical customer analytics models.