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Recap and certification exam

You have completed the ten modules of the Python for AI — Complete course. Here is the synthesis to reread before the exam — each point condenses a module's closing box.

The synthesis in ten points​

  1. The useful syntax (module 1): dynamic types and their runtime errors, f-strings for all formatting, is None, slicing [start:stop] with stop excluded — the notation shared by the whole ecosystem.
  2. The structures (module 2): list, dictionary, tuple, set — each with its specialty; comprehensions transform in one line and train the vectorization reflex; variables are references, .copy() to copy.
  3. Functions and modules (module 3): inputs through parameters, output through return, a docstring; keyword arguments beyond two; stable logic lives in imported .py modules; if __name__ == "__main__".
  4. NumPy (module 4): the homogeneous ndarray, vectorization replacing loops, broadcasting to operate across shapes, boolean masks, axis = the axis that disappears.
  5. pandas (module 5): DataFrame = array + labels; head/info/describe/value_counts on every new file; loc by labels, iloc by positions; .copy() against the SettingWithCopyWarning.
  6. Cleaning (module 6): diagnose before treating; understand the absence mechanism before imputing; to_numeric/to_datetime with errors="coerce" then recount; identifiers as strings; every decision traced.
  7. Joins and grouping (module 7): merge with how="left" and the two checks (row count, unmatched rate); groupby split-apply-combine; pivot_table to cross-tabulate; chained composition.
  8. Visualization (module 8): plot to diagnose; fig, ax = plt.subplots(); four questions → four charts; hue to compare groups; bars from zero, finding-titles.
  9. Environments (module 9): one project = one virtual environment = one pinned requirements.txt; random seeds; untouchable raw data; the cloning-colleague test.
  10. Notebooks (module 10): perfect for exploring and narrating, dangerous through hidden state; Restart & Run All before any sharing; stabilized logic migrates to modules.

The certification exam​

  • 40 multiple-choice questions, covering the ten modules — code reading included
  • Passing threshold: 70%, i.e. at least 28 correct answers
  • Estimated duration: 45 minutes, with no enforced timer
  • Detailed correction after submission: each question shows the right answer and its explanation
  • Retakes allowed after a failure, with a daily quota

Preparation advice: the questions make you read short code snippets — slicing, boolean masks, merge, groupby — and predict their result or spot their flaw. Reread the code examples of modules 4, 5 and 7 in particular.

The certificate​

Upon success, a named certificate of completion is generated immediately:

  • as a downloadable PDF, with your name, the course, the date and the score;
  • carrying a unique, publicly verifiable number on the platform;
  • added to your profile, in the "My achievements" section.
Ready?

Open the exam from the My courses page, via this course's "Certification quiz" button, or from your dashboard. Good luck!

Going further in the track​

  • Mathematics for AI: put your vectorized-computation skills to work on the algebra and statistics behind the models;
  • Supervised Learning: apply your pandas directly — preparation, validation, evaluation — to your first scikit-learn models;
  • Feature Engineering: the craft of building, with pandas, the columns that separate an average model from a good one.

Final exam

Ready to validate this course?

40 questions drawn at random from the course bank · passing score 70% · verifiable PDF certificate issued immediately on success.

Start the exam

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