I am reading the Wikipedia article on statistical models here, and I am somewhat perplexed as to the meaning of "non-parametric statistical models", specifically:
A statistical model is nonparametric if the parameter set $\Theta$ is infinite dimensional. A statistical model is semiparametric if it has both finite-dimensional and infinite-dimensional parameters. Formally, if $d$ is the dimension of $\Theta$ and $n$ is the number of samples, both semiparametric and nonparametric models have $d \rightarrow \infty$ as $n \rightarrow \infty$. If $d/n \rightarrow 0$ as $n \rightarrow \infty$, then the model is semiparametric; otherwise, the model is nonparametric.
I get that if the dimension, (I take that to literally mean, the number of parameters) of a model is finite, then this is a parametric model.
What does not make sense to me, is how we can have a statistical model that has an infinite number of parameters, such that we get to call it "non-parametric". Furthermore, even if that was the case, why the "non-", if in fact there are an infinite number of dimensions? Lastly, since I am coming at this from a machine-learning background, is there any difference between this "non-parametric statistical model" and say, "non-parametric machine learning models"? Finally, what might some concrete examples be of such "non-parametric infinite dimensional models" be?