Fundamentals of NLP: Word Embeddings to Capture Relationships in Text

“Before trAIning any text-based machine learning model it is necessary to encode that text into a machine-readable numeric form. Embeddings are the preferred way to encode text as they capture data about the meaning of text and are performant even with large vocabularies.

You will start this course by working with Word2Vec embeddings which represent words and terms in feature vector space capturing the meaning and context of a word in a sentence. You will generate Word2Vec embeddings on your data corpus set up a Gaussian Na∩ve-Bayes classification model and trAIn it on Word2Vec embeddings.

Next you will move on to GloVe embeddings. You will use the pre-trAIned GloVe word vector embeddings and explore how to view similar words and identify the odd one out in a set.

Finally you will perform classification using many different models including NAIve-Bayes and Random Forest models.”