Not to detract from your point that math is important, but in that example proper methodology (e.g., cross-validation), proper feature engineering, and especially domain knowledge are probably even more important. You can be aware of the strengths &amp; weaknesses of different machine learning algorithms without being intimately familiar with the math. Ideally, ML methods are not treated as a &quot;black boxes&quot;, but some aspects are inherently black box, even if you do know the math (e.g., parameter tuning).