AI Campus • Practical resources
Python for AI & Numerical Computing
Build a small, testable data pipeline using files, collections and explicit error handling.
Before you start
- A local Python installation and basic programming concepts. The official tutorial assumes some programming experience.
The reading and planning exercises need no paid course or API. Optional inference, training, IDE features and hosted services may require hardware, accounts or paid access. Use the local or manual alternative before spending money.
Start with the official learning material
Free-to-read references checked 3 November 2026. Reading documentation does not activate a provider or create a subscription.
Practice with a concrete exercise
1. Practice collections and functions
Read the control-flow, data-structures and functions sections. Write a function that accepts a list of records and returns counts by category, keeping missing categories in an explicit unknown group.
2. Read and validate input
Use the input/output and exception sections to load a local JSON dataset. Reject malformed JSON with a helpful error. Normalize whitespace without overwriting the original file.
3. Make a repeatable command
Add a command-line input path and write a JSON summary to a separate file. Test an empty dataset, duplicate rows and a missing file. Only add NumPy or Pandas after the standard-library version works and explain why the dependency helps.
Check what you learned
- The same input yields the same counts.
- Missing/invalid input reports an error instead of a success report.
- Original data remains unchanged and functions can be tested independently.
Keep your artifact, observed checks and remaining limitations in a short README. Mark any exercise you only designed rather than executed.