How To Properly Learn

In this section, we focus on the kind of preparation that truly sets us apart in machine learning interviews. Rather than relying on surface-level memorization, we learn how to build a deep, applied understanding that shines under follow-up questions and real-world discussion.

• We aim to go beyond surface knowledge, learning not just definitions but also how and why concepts work.
• We must understand implementation details, like the loss function in logistic regression or how feature importance is calculated in random forests.
• We should avoid vague “buzzword” answers and instead be honest if unsure—showing how we would reason through the problem is often more impressive than guessing.
• We connect theory to practice, drawing clear links between ML concepts and our own project experience to demonstrate applied understanding.
• We focus on breadth with clarity, going deep only where relevant, and always aim to explain ideas in a concise and structured manner to reflect true expertise.

If you want to learn even more from Yayun:

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