Natural Language Processing with Classification and Vector Spaces

In Course 1 of the specialization "Natural Language Processing" you will study: - a) Perform sentiment analysis on tweets using logistic regression followed by Naive Bayes; - b) Use vector space models to discover relationships between words, and use PCA to reduce the dimensionality of the vector space and visualize these relationships; - c) Write a simple English-French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words using approximate k-nearest neighbor search. By the end of this specialization, you will have developed NLP applications that perform question answering and sentiment analysis, built tools for language translation and text summarization, and even built a chatbot! This specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an AI instructor at Stanford University who also helped create the Deep Learning specialization. Lukasz Kaiser is a Staff Scientist at Google Brain and co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.