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Ideas worth exploring.
Data science and AI engineering, with an eye on what matters: understanding what we are building and what it is useful for.
Understanding the relational model: from tables to rules
Relations, tuples, attributes, domains, schema, degree and keys: an explanation from the ground up, using a small shop.
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Markov chains: where a fleet ends up
Three stations, a hundred bicycles and a question about their next move. Build a Markov chain from a concrete situation.
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PCA and SVD: a change of perspective
A geometric introduction to PCA: rotate the axes, project the observations and decide which differences you can afford to lose.
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Being right is not the same as understanding
A horse, a benchmark and six uncomfortable questions about what we can claim when an AI model answers correctly.
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Normalize or standardize: what are you measuring?
Revisiting my post about scaling: distances, ranges, outliers and a choice that deserves more than a quick rule.
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An AI can be accurate and still cause harm
Understanding AI ethics and how it connects with the AI Act, GDPR and governance standards. From principles to decisions in a real project.
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