“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

Course overview

First, you’ll explore the concepts and terms necessary for working with sequential models in TensorFlow. You’ll discover recurrent neural networks (RNN) and how they compare with convolutional neural networks (CNNs), as well as some of the most common RNN applications.

Then, you’ll learn how to build, train, evaluate, and improve a basic RNN to predict song popularity using regression. You’ll also learn to use Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) techniques to improve model performance. You’ll implement these to predict the sentiment of the review (good or bad) on a dataset of IMDB reviews.

Next, you’ll combine convolutional neural networks with sequential models to add a convolutional layer to your LSTM model and compare its predictive performance before and after on a dataset of IMDB reviews, predicting sentiment.

Finally, you’ll optimize the tools already used for time-series forecasting on a dataset of movie ticket sales to prepare you for the guided project.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll apply your new skills to a project to build a model to better forecast how the S&P 500 futures index will move based on its behavior over the past several years.

Key skills

  • Processing and exploring text data
  • Visualizing text data using a word cloud
  • Implementing tokenization and word embeddings
  • Building sequence models
  • Building a transformer-based text classification model

Course outline

Natural Language Processing for Deep Learning [6 lessons]

Introduction to Natural Language Processing 2h

Lesson Objectives
  • Differentiate between text and non-text data
  • Explore the built-in datasets in the TensorFlow Datasets library
  • Prepare text data for analysis
  • Create a word cloud on text corpus

Text Vectorizer and Word Embeddings 2h

Lesson Objectives
  • Use the TextVectorizationxa0layer in TensorFlow for text transformation
  • Use and vary parameters needed for building the vectorizer layer
  • Perform word embedding using an embedding layer
  • Build, train, and evaluate a shallow neural network text classification model

Building Multi-layer (dense) Text Classification Models with TensorFlow 2h

Lesson Objectives
  • Build a multilayer deep learning text classification model
  • Initialize a GlobalAveragePooling1D layer to train a text classification model
  • Tune hyperparameters and compare the performance of the model using different architectures
  • Implement regularization techniques to mitigate overfitting

Building Sequence Models with TensorFlow 2h

Lesson Objectives
  • Build a single-layer LSTM deep learning text model
  • Build a multilayer LSTM deep learning text model
  • Tune hyperparameters for LSTM models

Building Text Models with Transformers 2h

Lesson Objectives
  • Prepare data for a transformer model
  • Perform tokenization using AutoTokenizer
  • Build and train a transformer model
  • Evaluate the effectiveness of a transformer text classification model

Guided Project: Classifying Disaster-Related Tweets as Real or Fake 2h

Lesson Objectives
  • Explore and Process text data
  • Visualize text data using a word cloud
  • Build text classification model using tokenizer and embedding layer
  • Build sequence models
  • Build a transformer-based text classification model

Projects in this course

Guided Project: Classifying Disaster-Related Tweets as Real or Fake

Build a deep learning text classification model to predict whether a given tweet is about a real disaster or not.

The Dataquest guarantee


Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.


We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

Master skills faster with Dataquest

Go from zero to job-ready

Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

Build your project portfolio

Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

Challenge yourself with exercises

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Showcase your path certification

Impress employers by completing a capstone project and certifying it with an expert review.

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Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”


Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”


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Associate Data Scientist at Callisto Media

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