Summary
Minneapolis one-day Python session on ML/AI essentials: supervised/unsupervised learning, model evaluation, and neural-network basics.
Why it matters
A strong foundation in evaluation and data is what separates useful AI projects from demos that break the moment the input distribution shifts.
Tokenmaxxing angle
Treat every exercise like an eval loop: define success metrics, run small experiments, and keep notes on what actually moves performance per unit cost.
Source takeaway
The event page describes a one-day Python workshop that covers supervised and unsupervised learning, evaluation, and introductory neural networks.