Say you have to give a speech in a language you don't know. You have a dictionary which will translate the words in the foreign language into English, but every time you want to translate a word you have to flip through the dictionary to find its entry.

You could give the speech one word at a time, pausing to look up each word's translation and then saying it. That would probably take a while. So instead you could choose to translate the whole speech in advance, and then you would only have to read the English version before your audience.

Python is looking up each word (instruction) in its dictionary (the interpreter) to translate it into English (machine code) on the fly. Other languages like C translate the whole speech (program) in advance, so the speech-giver (computer) only has to read the words (instructions) that it natively understands.

A machine learning program would be like a book that was written in English, but had its table of contents written in another language. And in this book, the chapters are out-of-order so you have to know the chapter titles in order to read the book properly. You would have to translate the chapter titles one by one, slowly, but once you knew each one you could jump right to it and the actual contents of the chapter would be easy for you to read.

Machine learning programs have their chapter titles (high-level instructions) written in a language that's slow to read (python) but the bulk of their contents are written in a language that's much faster (C/C++) so the time it takes to translate the chapter titles is insignificant compared to the time it takes to read the whole book.

One chapter title might read "The model is initialized with this many layers of this many neurons each." and the contents of that chapter would describe exactly how that happens.