# Week 4 - Vector Space Models For this week you should have gone through the lectures of week 3 of the first Coursera course on NLP and the assignment. In this week we dive into representing our words as words vectors. Remember any ML Algorithm requires its input in a mathematical forms i.e in digits. The last two weeks we assiociated to each tweet two digits - one for posive sentiment and one for negative sentiment - and fed that vector in our models. From now on we will try a different approach indepedent from sentiment analysis ahd thus more general. For that we encode each word as a vector. Thus we need a dictionary mapping each word to its corresponding vector. In general we do not know what is the best vector to represent a word. So we have also to learn that. Luckily there are a lot of pretrained word embeddings online and we can normally use one of those :slight\_smile: For the next week you should go through all the course videos, the assignment and the quiz of week 4 of course 1 in the NLP specialization. Take notes and notice if you have any questions about the material. In the next meeting we will discuss these. To quote the NLP tutor: "And remember to have fun". See you next week :slight\_smile: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/archive/gan/week-4.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.