Section checkpoint

AI Foundations (No Math Fear)

8 questions drawn from the 4 chapters in this section. If most land on the first try, you are ready for the next section. If not, the explanation after each answer says which idea to revisit.

Checkpoint

8 questions · first-try answers earn the most XP

1. Your team wants AI to approve loan applications with no human review. What does the chapter's framing suggest?

2. What does the question "what data would change my mind?" help you decide?

3. Training accuracy keeps improving while validation accuracy drops. What does that indicate?

4. Why do we bother training on features at all rather than asking a human to write the spam rule?

5. Why did deep learning become practical in roughly the 2010s despite the underlying maths being older?

6. A working professional is building AI applications. Why does the chapter suggest using APIs and embeddings before training a network?

7. Why do you stratify the split when your classification data is heavily imbalanced?

8. What does preprocessing fitted on the full dataset, before splitting, leak?

What this checkpoint covers

Next section: Python for AI