Somewhere in almost every machine learning (ML) design round, right after you have talked through features (which Feature engineering covers), the interviewer asks: "What model would you start with, and how would you train it?"

Model training is the answer to that question. It means fitting a model's parameters to labeled historical examples by making a loss, a number that measures how wrong the predictions are, as small as possible, and then packaging that fit so you can repeat it next week and get the same model back.

Interviewers spend about 5-10 minutes here. A mid-level answer starts with a baseline, justifies the model family and loss, and explains how to prevent the model from memorizing its training data. A senior-level answer also weighs each increase in model complexity against its cost and describes a pipeline that can rebuild, compare, and roll back a shipped model.

We'll work through one running example: a ride-hailing app predicting whether a driver accepts a trip offer within 15s. You'll leave able to pick a baseline, choose a loss, decide when to stop training, and set up retraining you can trust. How metrics are computed and how data is split belong to Offline evaluation, and retraining triggered by drift belongs to production monitoring and retraining, so this tutorial takes a validation set and an offline metric as given.