When does learning from data work (math starting from basic probability)

A ground-up technical exposition of the Fundamental Theorem of Statistical Learning, covering VC dimension, Markov's inequality, Hoeffding's lemma, and concentration inequalities to formally answer when generalization from training data is guaranteed. Useful for practitioners wanting rigorous ML theory foundations rather than intuition-only explanations. No new research, but a well-structured pedagogical resource.