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Practice machine learning concepts tested in ML, data science, and MLE interviews: bias-variance, regularization, evaluation metrics, overfitting, feature engineering, and common algorithms.
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A demand-forecasting model deployed on Amazon SageMaker AI performed well at launch, but eight months later its forecasts are noticeably worse. Investigation shows customer buying patterns have shifted since the training data was collected. The team wants to catch this kind of degradation automatically in the future. Which approach addresses the root cause and follows MLOps practice?
Practice in a session
10 questions from Machine Learning, one after another, then your score and streak.
The bias-variance tradeoff, overfitting/underfitting and how to detect them, regularization (L1/L2), train/validation/test splits and cross-validation, evaluation metrics (precision, recall, F1, ROC-AUC), handling class imbalance, feature engineering, and the intuition behind core algorithms (linear/logistic regression, trees, random forests, gradient boosting, k-means, SVMs).
Product-facing data science and many ML engineer roles emphasize conceptual understanding and applied judgment - why a model overfits, which metric fits the business problem, how to debug a model. Research and ML scientist roles add gradient derivations, optimization, and probabilistic modeling.
It depends on the problem: accuracy misleads on imbalanced data; precision/recall and F1 matter when false positives vs false negatives have different costs; ROC-AUC measures ranking quality across thresholds; PR-AUC is better for rare positives; RMSE/MAE for regression. Choosing the right metric for the business cost is a frequent interview question.
High bias (underfitting) means the model is too simple and misses signal; high variance (overfitting) means it memorizes noise and fails to generalize. Total error decomposes into bias, variance, and irreducible error. You reduce variance with more data, regularization, or simpler models, and reduce bias with more expressive models or better features.
Data science leans on statistics, experimentation, metrics, and model intuition; ML engineering adds coding, system design for ML serving/pipelines, and production concerns (latency, monitoring, data drift). Both share the core ML-concepts screen.