Machine Learning Fundamentals
Core ML concepts interviewers probe for: model families, evaluation, and the training practices behind them.
Subject: Machine Learning · Roles: MLOps Engineer, Machine Learning Engineer
Concepts
Supervised vs. Unsupervised Learning
Learning from labeled examples versus finding structure in unlabeled data — and where semi-supervised and reinforcement learning fit.
Bias-Variance Tradeoff
Why model error splits into bias and variance, and why reducing one often increases the other.
Feature Engineering & Selection
Turning raw data into model-ready inputs, and choosing which ones actually help.
Model Evaluation Metrics
Picking the right metric — accuracy, precision/recall, F1, ROC-AUC — for the problem and its class balance.
Regularization (L1/L2, Dropout)
Penalizing model complexity to fight overfitting — L1/L2 weight penalties and dropout.
Neural Network Fundamentals
Forward pass, backpropagation, and the activation functions that make deep networks work.