Part 2 · 25 minutes

Revision should produce answers, not highlighted notes.

Someone who already knows ML does not need to relearn every course. The goal is to retrieve ideas without support, connect them to scenarios, and explain trade-offs under questioning.

Retrieval check

Without notes, explain why accuracy can be misleading on imbalanced data. Give one business example and one better evaluation approach.

The four-step revision cycle

Recall

Explain the topic from memory before opening a resource.

Repair

Study only the missing or incorrect part.

Apply

Use it on an example, comparison or small calculation.

Speak

Give a concise answer aloud and handle one follow-up.

Why it works: interviews require retrieval and explanation. Re-reading creates familiarity, but familiarity can feel like knowledge even when the idea cannot be produced independently.

Name the uncomfortable stage

Which part do you avoid most: recalling without notes, speaking an answer aloud, discovering that an answer is incomplete, or repeating the topic after getting it wrong?

What the grind actually looks like

Preparation rarely feels like steady improvement. The difficult middle is where passive familiarity is converted into interview-ready recall.

1 · MotivationThe syllabus looks manageable and the plan feels clear.

2 · FrictionFamiliar concepts become difficult without notes.

3 · ExposureMocks reveal weak reasoning and unclear language.

4 · RepairSpecific gaps are studied, practised and tested again.

5 · RecognitionQuestions begin to look like recurring patterns.

6 · ComposureStructure remains available even under pressure.

Forgetting is expected

Needing another retrieval cycle does not mean the first one failed.

First answers are messy

An unclear spoken attempt provides better feedback than silent rereading.

Progress can be invisible

Readiness often appears as calmer recovery, better questions and fewer repeated mistakes.

From the interview room: confidence was usually the memory of preparation. I felt calmer when I had already struggled with similar questions, repaired the gaps and answered them again.

Probable ML topic map

Area	Concepts	Questions to practise
Problem framing	Target, prediction unit, baseline, constraints, leakage	Should this problem use ML? What decision changes?
Statistics	Probability, Bayes, distributions, sampling, confidence intervals, testing	What uncertainty exists? Is an observed change credible?
Supervised learning	Linear/logistic regression, trees, forests, boosting, SVM, KNN, Naive Bayes	What does the model learn? What assumptions and failure modes follow?
Unsupervised learning	Clustering, PCA, anomaly detection	How will success be evaluated without ordinary labels?
Model development	Splits, CV, bias-variance, regularization, tuning, feature engineering	How do you know the improvement generalizes?
Evaluation	Regression/classification metrics, thresholds, calibration, slices	Which error matters to the business?
Data problems	Missingness, imbalance, noisy labels, drift, skew	Where can the pipeline silently become invalid?
Production ML	Batch/online serving, monitoring, retraining, reliability, cost	What happens after the notebook?
Role-specific	Recommendations, NLP, vision, time series, LLMs/RAG	Which domain concepts repeat in target JDs?

Build a revision matrix

Topic	JD frequency	Current confidence	Evidence	Next action
Class imbalance	High	Medium	Can explain metrics; weak on calibration	Answer aloud + one experiment
Online serving	High	Low	No production example	Study one architecture + design drill
PCA	Low	High	Can derive and demonstrate	Maintenance only

Priority rule: preparation priority rises when a topic appears frequently in suitable jobs and current evidence is weak. Do not spend equal time on every square.