How I Teach my Second Grade Kid what AI is and How this Relates to BI that Impact...
summary
Uses simple kid-play analogies to map concrete ML techniques to business problems: supervised/unsupervised/reinforcement correspond to labeled examples, clustering, and reward signals; regression is estimating numeric answers; decision trees show feature-based narrowing and overfitting risk. Recommends embedding AI into BI at the modeling layer, following the CRISP-DM process, with a technology-agnostic architecture, APIs, self-service analytics and workforce development. Publishes a companion YouTube demo and argues the practical outcome is clearer case selection for AI investments and better BI-driven decisions.
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chapters (6)
- 0:00Start
- 2:16Strata
- 11:57Supervised Learning Example - Detecting Cancer
- 15:06Unsupervised Learning Example - Customer Segmentation
- 27:10Al can help me to do homework and the teacher will not find out I didn't do that?
- 30:06Process: Cross-industry standard process for data mining
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