Common Pitfalls (Red Flags)

In this section, we’re cautioned against common mistakes that even capable candidates make during ML interviews. By knowing what to avoid, we can better showcase our understanding with clarity, relevance, and maturity.

• We avoid math without context—using complex terms without explaining their real-world application often hurts more than it helps.
• We steer clear of overcomplicating explanations, especially when simpler, intuitive answers are more effective.
• We make sure not to misapply ML techniques, choosing the right tool for the problem rather than defaulting to complexity.
• We are careful not to confuse correlation with causation, especially when interpreting features or results without proper experimentation.
• We stay grounded by not overstating model performance and always considering data quality as a core factor in success.

By being clear, humble, and practically minded, we avoid these pitfalls and come across as thoughtful and well-prepared candidates.

If you want to learn even more from Yayun:

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