ML basics for AI engineers
Comes up in: AI engineering, Recruiter screen
This concept is being written. It gets the full five steps — explanation, predict, interview angle, practice with tests, and review cards. Here is exactly what it will cover, so you can study it elsewhere in the meantime.
What it covers
- supervised vs unsupervised learning
- train/validation/test splits
- overfitting and underfitting
- bias–variance in plain words
- classic metrics (accuracy, precision, recall, F1, confusion matrix, ROC-AUC)
Words you will know
What you will practise
- compute metrics for a small classifier by hand and in Python
Interview questions this answers
- Explain precision and recall with an example.