Classification model accuracy with ensemble methods
This comes from the Binomial distribution, where you have $n=1000$ independent trials (models), $p=0.51$ of each model being right and since you care about the majority vote you want to have at least $k=500$ successful trials. That leads to:
$$\text{Pr}(k\geq500 \text{ models are right}) = \sum^{1000}_{k=500}\binom{1000}{k}0.51^{k}(1-0.51)^{1000-k}=0.74675\approx0.75$$
Here is how I calculated it:
    import numpy as np
    from scipy.stats import binom
    np.sum([binom.pmf(k,1000,0.51) for k in range(500,1001)])