Computing With Natural Language

Introduction to Computing With Natural Language

An introduction of the course outline and prerequisite.

Feature extraction and embeddings

Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVE to extract features that you can use in machine learning models.

Topic Modeling

In this section, you'll learn to split a collection of documents into topics using Latent Dirichlet Analysis (LDA). In the lab, you'll be able to apply this model to a dataset of news articles.

Sentiment Analysis

Learn about using several machine learning classifiers, including Recurrent Neural Networks, to predict the sentiment in text. Apply this to a dataset of movie reviews.

Sequence to Sequence

Here you'll learn about a specific architecture of RNNs for generating one sequence from another sequence. These RNNs are useful for chatbots, machine translation, and more!

Deep Learning Attention

Attention is one of the most important recent innovations in deep learning. In this section, you'll learn attention, and you'll go over a basic implementation of it in the lab.

RNN Keras Lab

This section will prepare you for the Machine Translation project. Here you will get hands-on practice with RNNs in Keras.

Project: Machine Translation

Apply the skills you've learned in Natural Language Processing to the challenging and extremely rewarding task of Machine Translation.