
Course 1: Natural Language Processing with Classification and Vector Spaces
In this course, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes; b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships; and, c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbor search.
Week 1: Logistic Regression for Sentiment Analysis of Tweets
Use a simple method to classify positive or negative sentiment in tweets
Week 2: Naïve Bayes for Sentiment Analysis of Tweets
Use a more advanced model for sentiment analysis
Week 3: Vector Space Models
Use vector space models to discover relationships between words and use principal component analysis (PCA) to reduce the dimensionality of the vector space and visualize those relationships
Week 4: Word Embeddings and Locality Sensitive Hashing for Machine Translation
Write a simple English-to-French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbors search