Natural Language Processing with Classification and Vector Spaces
In Course 1 of the Natural Language Processing Specialization, offered by deeplearning.ai, 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.
Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability.
By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, 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 Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
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 Naïve 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.