You need to start from high school, the AP classes need to be revamped. Currently they are focused on purely frequentist statistics. Frequentist statistics is great for most empirical sciences like biology. The formulas are mostly plug and play and even pure life science people with no mathematical talent can wield them without trouble. The problem is that they are very far from statistical learning.
Here's the current AP stats curriculum, it is meant to be equivalent to Stat 1.
https://library.fiveable.me/ap-stats
If you want to develop a strong foundation for ML, Unit 6, 7, and 8 ought to be thrown out entirely. The level they are taught at doesn't really teach anything more than plugging formulas. Unit 4.5 (Conditional Probability) and Unit 5 (Sampling) need to be further developed to cover the Bayesian theories, perhaps a segue into graphical models and Markov chains. Generative ML for example interprets likelihood as an information generator (since in the Bayesian formula, it is roughly the "inverse" of conditional probability), unfortunately most stats classes outside of physics and high level ML theory will never mention this. Heck most classically trained statisticians won't ever encounter this idea. But it is the bread and butter of generative AI. Having a vague idea of KL-divergence and what Metropolis-Hastings is coming out of high school is infinitely more useful for their career in ML than knowing how to fiddle with a p-value. You can teach most of these concept without calculus if you simplify some things and replace integrals with their discrete summation versions. Rejection sampling for example is very easy to teach. The Common Core needs a revamp, and perhaps it's time to shift away from the historical focus on calculus/pre-calc as the central tenet of pre-college mathematical teaching.