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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.

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


4 weeks

Real-world Projects

Completion Certificate

Last Updated January 13, 2022

Skills you'll learn:
Machine translation • Attention mechanisms • Fasttext • Information extraction
Intermediate Python • Neural network basics • Deep learning framework proficiency

Course Lessons

Lesson 1

Introduction to Computing With Natural Language

An introduction of the course outline and prerequisite.

Lesson 2

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.

Lesson 3

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.

Lesson 4

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.

Lesson 5

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!

Lesson 6

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.

Lesson 7

RNN Keras Lab

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

Lesson 8 • Project

Project: Machine Translation

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

Taught By The Best

Photo of Luis Serrano

Luis Serrano


Luis was formerly a Machine Learning Engineer at Google. He holds a PhD in mathematics from the University of Michigan, and a Postdoctoral Fellowship at the University of Quebec at Montreal.

Photo of Jay Alammar

Jay Alammar


Jay is a software engineer, the founder of Qaym (an Arabic-language review site), and the Investment Principal at STV, a $500 million venture capital fund focused on high-technology startups.

Photo of Arpan Chakraborty

Arpan Chakraborty


Arpan is a computer scientist with a PhD from North Carolina State University. He teaches at Georgia Tech (within the Masters in Computer Science program), and is a coauthor of the book Practical Graph Mining with R.

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