Module 1 · Foundations
Module 1: implement, explain and transfer
Read concepts, then attempt the starter notebook. In a source checkout open notebooks/practice/module01-practice.ipynb. Use the core environment and kernel; the practice checks need NumPy even where the module script does not. Three unfinished functions report not_attempted, not a pass. If a download link does not open, use the offline access route.
Construct route
Return a bool: priority >= threshold. Predict the result on route(2), implement it and call m01_route through the notebook check. Then add a changed case and explain which assumption it tests.
Hint for route
Use an inclusive boundary; do not return the priority itself.
Construct partition_role
This task previews the split roles that Module 5 develops with real data; it supports reasoning about evidence rather than a separate Module 1 outcome. Return fit, choose or evaluate for train, validation or test. Predict the result on partition_role('train'), implement it and call m01_partition_role through the notebook check. Then add a changed case and explain which assumption it tests.
Hint for partition_role
The final test must not choose a model.
Construct error_counts
Return [false_alerts, misses] for equally sized boolean lists. Predict the result on error_counts([False, True, True], [True, False, True]), implement it and call m01_error_counts through the notebook check. Then add a changed case and explain which assumption it tests.
Hint for error_counts
Count errors by their consequences, not only total accuracy.
Work with the complete study's results
Run the first-run script with --storage-percent values 80, 81, 100 and 100.5, each to its own output file. Predict each decision first. Which inputs give Review storage, which give Within the chosen threshold, and which is rejected? Relate each result to storage_percent > 80 and to the script's input check. Compare with the construction feedback afterwards.
Reason beyond the visible checks
Construct two prediction lists for the actual labels [True, False, False] that both have accuracy 2/3 but different error counts. Use error_counts to show the difference, then say which list you would prefer for a storage alert and why. The checks cannot mark this; compare your reasoning with the construction feedback.
Transfer to an IT decision
Frame an escalation aid with a human fallback. Explain one field unavailable at ticket arrival. Run the complete module study and verify one claim against its actual artifacts; keep the evidence the study workflow lists.
Evidence and feedback
- M01.1: Distinguish AI, ML, deep learning, and generative AI using an IT example.
- M01.2: Identify a problem's stakeholder, input, output, and reason for choosing a rule or learned model.
- M01.3: Execute and save a supplied Python example; in the notebook route, launch the interface, run a cell, and save the notebook. Explain the installation route.
- M01.4: Change an input, predict the result, and explain what happened.
The script route supplies the same programming evidence when a notebook interface is a barrier. It does not demonstrate notebook-interface operation. An instructor assessing that particular skill should arrange an accessible interface and support with the learner.
Use construction feedback after an attempt and recorded experiment feedback for the full workflow. Follow the shared study workflow for evidence, recovery and optional AI disclosure. Official assessment weights remain with your instructor.
Before opening the notebook
Follow the notebook setup in QUICKSTART. The standard-library-only claim applies to the first script; notebook practice requires the core environment and a Jupyter kernel. The practice rule intentionally uses >=, unlike the first storage example’s strict >. At the threshold, these rules make different decisions.
Evidence activity: Trace one decision
Use your first-run output and its input records. Recompute one rule decision, including the exact threshold boundary. Identify what a wrong decision would mean and explain why agreement with the rule is not measured predictive accuracy.
Follow the four steps in reproduce a result and justify its use. Submit your artifact path, independent calculation, discrepancy or agreement, and bounded use decision in the evidence worksheet. Use your complete reference run; this activity complements the small function checks.