Computing With Natural Language

Learn advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures.

• 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.