AI & ML Interview Tutorial with Examples.
AI and ML interview tutorial: fundamentals, leakage, evaluation, deep learning, LLMs and RAG, and ML system design questions.
Beginner
Start here — no prior experience needed
The AI and ML Interview
Which of the four ML roles you are actually interviewing for, what each loop tests, and a warm-up question answered two ways.
ML Fundamentals Questions
Bias-variance, regularisation and the bias-variance-free lunch — each demonstrated with measured curves rather than recited definitions.
Feature Engineering and Data Leakage
The four kinds of leakage, each shown as the AUC it inflates — plus the scaling mistake almost everyone makes and the time-series split that hides it.
Model Evaluation Questions
Choosing a metric that matches the decision — precision/recall tradeoffs, why ROC AUC misleads on rare events, and calibration.
The ML Coding Round
Vectorised numpy, pandas without loops, and the algorithms you're asked to write from scratch — logistic regression, k-means, timed.
Intermediate
For developers with core concepts down
Classical ML Depth
Trees, bagging and boosting — what each does to bias and variance, and why gradient boosting still beats deep learning on tabular data.
Deep Learning Questions
Backpropagation, vanishing gradients, normalisation and optimisers — the mechanisms interviewers ask about, each shown as a measurement in PyTorch.
LLM and RAG Questions
Attention costs, chunking, retrieval evaluated with recall@k, and RAG failure modes — measured, so 'it hallucinated' becomes a diagnosis.
Advanced
Production-grade patterns for experienced engineers
ML System Design
The 45-minute round where the model is the smallest part — framing the problem, sizing it, and choosing a metric that tracks the business one.
MLOps and Production Questions
Drift detected with PSI, reproducibility that actually holds, rollback that works, and the retraining trigger that is not a calendar.