Natural Language Processing, that is, processing large amounts of natural lenguage data using computers, is both challenging and relevant for practical applications. The topic of the seminar is lenguage modeling. In this task the goal is "fill in the blank" in a sentence - in other words, to predict the probability distribution of words in a given context. Lenguage modeling is important not only because immediate applications (e.g. in optical character recognition or in predctive keyboards), but also because such models are good starting points for other lenguage processing tasks. We will cover both statistical (count-based) and neural lenguage models. Specifically, I will focus on how discrete objects (words and sentences) can be represented to facilitate natural lenguage processing tasks. I will also overview the challenges of developing commercial lenguage models in the industry setting.