The page looks very good for a SKETCH.

There is a basic concept that I think is missing: a discussion of loss/utility functions. When we make predictions, it is so we can take actions, even if it just cancelling a picnic due to a forecast of rain. These have costs and benefits depending on what kind of errors are involved, and can change the optimum threshold for a classifier, for example. (You know this of course, I am suggesting some ideas on how to explain it.)

> It should also be pointed out that there's not a fundamental distinction between **machine learning** and **statistics**.

I think **statistics** should be **statistical inference**.

Of course, loss/utility functions are subjective, as exemplified by Half man Half Biscuit's A Shropshire Lad:
Second greatest time I had
Was when they asked me and my Dad
To organise a festival
Along the lines of Donington
We took Chirk Airfield as our site
Booked the bands we thought were right
Received the long-range from the Met
They said it could be very wet
With this in mind, we thought it wise
To call the whole caboodle off
The greatest time I ever had
Was when we didn’t tell the bands

Boom boom boom
Let me hear you say
Hosepipe ban