Natural Language Processing with Classification and Vector Spaces

DeepLearning.AI via Coursera

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Overview

In Course 1 of the Natural Language Processing Specialization, you will: a) Perform sentiment analysis of tweets using logistic regression and then nave 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. By the end of this Specialization,...

Syllabus

Sentiment Analysis with Logistic Regression

Learn to extract features from text into numerical vectors, then build a binary classifier for tweets using a logistic regression!

Sentiment Analysis with Nave Bayes

Learn the theory behind Bayes' rule for conditional probabilities, then apply it toward building a Naive Bayes tweet classifier of your own!

Vector Space Models

Vector space models capture semantic meaning and relationships between words. You'll learn how to create word vectors that capture dependencies between words, then visualize their relationships in two dimensions using PCA.

Machine Translation and Document Search

Learn to transform word vectors and assign them to subsets using locality sensitive hashing, in order to perform machine translation and document search.