Module 3 · Foundations

Module 3: guided construction and mastery

Use this after the foundation bridge and before attempting independent practice; return to the module run instructions afterward. Read, predict, run, change, and explain. The shared runner remains the complete reference; these small steps expose its ideas.

Worked concept

Cleaning is a policy. A row can be unusable for one task and still carry evidence about measurement failure. Reconcile input = accepted + rejected before discussing averages.

Predict and construct

Predict the printed values, shape or rejection before running. Use the documented environment. The notebook contains this same runnable construction cell.

from pathlib import Path
from practical_ml_ai.data.events import prepare_file
import tempfile
learning_root = next(p for p in [Path.cwd(), *Path.cwd().parents] if (p / "pyproject.toml").is_file())
with tempfile.TemporaryDirectory() as learning_tmp:
    learning_cleaned = prepare_file(learning_root / "data/samples/network-events/events.csv", Path(learning_tmp) / "clean")
    print(learning_cleaned.report)
    assert learning_cleaned.report["rows_accepted"] == 6

Complete with less support

Inspect a rejected record and explain its policy reason. On a copy, repair one legitimate error and reconcile the changed counts.

Debug a plausible misconception

Should a missing latency be replaced by zero? Write a prediction before opening feedback. Include a counterexample or calculation, not only a definition.

Independent transfer

Write a data contract for records in another discipline, including invalid and missing values.

Minimum mastery evidence

Keep the evidence listed in the study workflow and name the module outcome each item supports. The criteria describe developing, meeting and extending the outcome.