Back to AI/ML & Deep Learning
About this interview
A technical interview on Classic ML Algorithms, pitched at the easy 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 the mechanics of linear and logistic regression including cost functions and decision boundaries
Describe how SVMs work, including the kernel trick and soft/hard margin tradeoffs
Compare and contrast the assumptions and failure modes of each algorithm
Identify which algorithm to choose given data size, dimensionality, and label availability
Topics covered
Supervised Learning BasicsAlgorithm CategoriesLinear RegressionLogistic RegressionOptimizationSupport Vector MachinesAlgorithm SelectionAlgorithm ComparisonRegularizationAlgorithm Failure Modes
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“Can you explain the difference between classification and regression, and give me an example of each from a real product?
“What is a support vector? How many support vectors does a typical SVM solution have relative to the size of the training set?
“Why does L1 regularization encourage sparse solutions while L2 does not? Can you give the geometric or algebraic intuition for this?