Recap and certification exam

You have completed the ten modules of the Introduction to AI — Complete course. This page gathers the essentials into a synthesis you can reread in ten minutes, then presents the exam that issues the certificate.

The synthesis in ten points

1. Operational definition (module 1): an AI system has behavior learned from data — not hand-written — for a task that would require judgment. AI ⊃ machine learning ⊃ deep learning.
2. History informs the present (module 2): rules hit the acquisition and maintenance walls; statistical learning won when data, compute and algorithms came together — the breakthrough dates to 2012.
3. All production AI is narrow (module 3): competence is bounded by the training distribution. LLMs offer a generality of interface, not general intelligence. Recut "general" requests into narrow tasks.
4. Learning = optimizing (module 4): parameters, a loss function, gradient descent. The model optimizes exactly what you measure — spurious regularities included.
5. Two typical failures (module 5): underfitting (bad everywhere) and overfitting (brilliant on training, disappointing elsewhere). Three watertight sets; the test set is used once.
6. Three regimes (module 6): supervised (labels — at what cost?), unsupervised (structure to interpret), reinforcement (simulator required). Self-supervision made foundation models possible.
7. Reading metrics (module 7): accuracy misleads on imbalanced classes; precision = trust in alerts, recall = coverage; the threshold is a business decision; always a trivial baseline.
8. The life cycle (module 8): modeling weighs 10–20% of the effort. Framing by the decision, baseline before ambition, gradual deployment, drift monitoring.
9. The field's patterns (module 9): successes absorb the error in the process; failures are design failures — learned bias, feedback loops, stand-in metrics, leaving the domain of validity.
10. The structural limits (module 10): data hunger, fragility, opacity, cost, reasoning — and the five research paths facing them. The method questions will outlive the tools.

The certification exam

The exam validates the whole course and conditions the certificate.

• 40 multiple-choice questions, covering the ten modules
• 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: reread the synthesis above, then the "Key takeaways" boxes of the modules you hesitate on. The questions test understanding — the distinctions, the decision criteria, the reading of numbers — not the memorization of details.

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 — a recruiter or employer can confirm its authenticity in seconds;
• 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 directly from your dashboard. Good luck!

Going further in the track

This introductory course naturally leads to three follow-ups, depending on your goal:

• Python for AI — Complete: the practical tooling to manipulate data and train your first models;
• Mathematics for AI: the algebra, optimization and statistics behind gradient descent, for those who want to open the hood;
• Supervised Learning: the direct deepening of modules 6 and 7 — algorithms, validation and rigorous evaluation.

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

You need to be signed in to your InSkillML account with an active subscription. You can also start the exam from My courses.