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About this interview
A technical interview on Classical Machine Learning Algorithms, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain linear and logistic regression including assumptions, optimization, and regularization from a practitioner angle
Compare SVMs and KNN on robustness to noise, scalability, and hyperparameter sensitivity
Articulate when naive Bayes is competitive despite its independence assumption
Choose appropriate evaluation metrics for classification and regression tasks in a business context
Topics covered
Linear RegressionLogistic RegressionLinear and Logistic Regression — RegularizationLinear Regression — DiagnosticsLinear Regression — AssumptionsSVMSVM — MarginsSVM — Kernel TrickSVM — Kernel SelectionSVM — ScalabilityKNNKNN — Hyperparameter SensitivityKNN — Computational PropertiesKNN — Preprocessing
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you walk me through what a linear regression model is actually doing under the hood — what relationship it's trying to capture and how it arrives at its coefficients?
“Can you explain what the kernel trick does in an SVM and why it's computationally clever — why do we not need to explicitly transform every data point into the higher-dimensional space?
“If you had a dataset that was small, high-dimensional, and needed a fast training time, which of these four — linear regression or logistic regression, SVM, KNN, or naive Bayes — would you lean toward for classification and why?