What are ngram counts and how to implement using nltk?
I don't think there is a specific method in nltk to help with this. This isn't tough though. If you have a sentence of n words (assuming you're using word level), get all ngrams of length 1-n, iterate through each of those ngrams and make them keys in an associative array, with the value being the count. Shouldn't be more than 30 lines of code, you could build your own package for this and import it where needed.
I've read a paper that uses ngram counts as feature for a classifier, and I was wondering what this exactly means.
Example text: "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam"
I can create unigrams, bigrams, trigrams, etc. out of this text, where I have to define on which "level" to create these unigrams. The "level" can be character, syllable, word, ...
So creating unigrams out of the sentence above would simply create a list of all words? 
Creating bigrams would result in word pairs bringing together words that follow each other? 
So if the paper talks about ngram counts, it simply creates unigrams, bigrams, trigrams, etc. out of the text, and counts how often which ngram occurs? 
Is there an existing method in python's nltk package? Or do I have to implement a version of my own?