Algorithm Coding Quality Tips

In this wrap-up of the ML algorithm coding interview section, we focus on what truly elevates our performance beyond just getting a working implementation. Code quality, structure, and composure matter just as much as correctness.

• We should structure our code clearly, breaking it into logical methods like fit, predict, and update_centroids instead of writing one long script.
• Clean abstractions not only make our solution easier to understand, but also easier to debug and maintain—key traits of strong engineering.
• While full optimization isn’t expected, we must show awareness of time and space complexity, and briefly discuss trade-offs where relevant.
• If time allows, we should include test cases using toy data, showing that we think like engineers who validate their code.
• If our code breaks, we demonstrate a calm, methodical debugging mindset, which interviewers value as highly as technical correctness.

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