Coding Round Grades

In this segment, we break down what interviewers really look for in a machine learning coding round. It’s not just about solving the problem—it's about how we think, communicate, and write code that reflects real engineering practices.

• We understand that weak performance stems from messy code, poor use of data structures, or inability to explain complexity.
• Mediocre performance may reach the right answer but relies heavily on interviewer help, signaling a lack of confidence or strategy.
• A great performance involves writing clean, efficient code and clearly explaining trade-offs and scalability.
• To be exceptional, we should offer multiple solutions, consider edge cases, and write production-quality code.
• We improve by following a systematic approach, thinking out loud, handling edge cases, and prioritizing clean, readable code—just like real ML engineers do in production.

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