Think You’re a Machine Learning Expert? Answer These 7 Questions to Find Out

Author(s): Joseph Robinson, Ph.D.

Originally published on Towards AI.

Review the fundamentals, sharpen your skills, and ace that interview with this machine-learning pop quiz!Header created by the author using Canva.

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Machine learning is a field that promises a lot of complexity wrapped in elegant, often abstract, principles. Building models and deploying them in real-world scenarios is one thing, but understanding the fundamentals behind your decisions, knowing when they break, and the theory that underpins everything is another. Here are seven questions that separate surface-level knowledge from actual expertise. If you can answer these correctly, don’t be too quick to claim the title of “expert.” Let’s break it down.

· I. What Is the Bias-Variance Tradeoff, and How Does It Impact Model Performance?· II. What Is the Difference Between Parametric and Non-Parametric Models?· III. Can You Describe the Intuition Behind Cross-Entropy Loss and Why It’s Commonly Used in Classification Problems?· IV. Why Is Feature Scaling Important, and When Should You Apply Standardization vs. Normalization?· V. What Is the Difference Between Bagging and Boosting, and When Should You Use Each?· VI. What Are Precision and Recall, and How Do They Relate to the F1 Score?· VII. What Is the Curse of Dimensionality, and How Does It Impact Model Selection?· Expert-Level Answers· Are You an Expert?

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Published via Towards AI