Python Foundations for AI
Learn Python from first notebook cells through NumPy, pandas, debugging, files, and a reproducible AI-ready data capstone.
Account-free and browser-localProgress and notebook work stay in this browser. The course does not require enrolment and is not graded, certified, or a formal credential.
Progress overview
Course sequence
- Module 1: Running Python and Thinking in StepsRun notebooks reliably and remove hidden state.
- Module 2: Values, Variables, Types, and TextRepresent and transform simple information with intentional types.
- Module 3: Collections and Structured InformationChoose suitable built-in collections and avoid accidental mutation.
- Module 4: Conditions, Loops, and Problem DecompositionTurn requirements into branches, loops, and checked edge cases.
- Module 5: Functions, Modules, and Readable CodeRefactor repeated logic into clear functions and modules.
- Module 6: Errors, Debugging, Validation, and TestsInvestigate failures systematically and prove a repair with tests.
- Module 7: Files, Paths, CSV, and JSONConvert structured files without overwriting source evidence.
- Module 8: NumPy FoundationsReason about numeric shape, selection, axes, broadcasting, and copies.
- Module 9: pandas and Practical Data PreparationPrepare a small table with visible, auditable decisions.
- Module 10: Capstone: Reproducible AI-Ready Data NotebookPrepare and defend one bounded synthetic dataset pathway.
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