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      <title>Every ML Model You Must Know, and When to Actually Use It</title>
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      <description>A field guide to picking ML models: why gradient boosting is still what you ship on production tables, where TabPFN changed the small-data story, how trees, forests, boosting, kNN and k-means actually work inside, when deep learning is genuinely the answer, and the data problems that sink more projects than model choice ever does.</description>
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