Week 2: Data, ML, and How Models Learn
Data handling, feature thinking, evaluation, and classical ML before the LLM layer.
What the machine expects from you.
This lesson is about data shaping as engineering work, not notebook theater. You are learning how raw tables become trustworthy model inputs.
Bad data silently poisons everything downstream. If your features are inconsistent, mislabeled, or leaky, your model quality and your product decisions become fiction.
Think of the dataset as an interface contract between the real world and the model. Every column carries assumptions about meaning, freshness, allowable values, and transformation history.
This lesson teaches the decision boundary between common supervised learning tasks and the discipline of choosing the simplest model that fits the job.
Three dense lessons, one enforced deliverable.
What survives the week.
ML Pipeline Audit
A structured audit describing data prep, evaluation design, leakage risks, and privacy boundaries.
A simple ML pipeline with evaluation and a leakage audit.
Each week leaves behind portfolio evidence that compounds into the final SaaS and its operating narrative.