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Sentiment Analysis with Deep Learning using BERT
  • Level Intermediate
  • Duration 3 hours
  • Course by Coursera Project Network
  • Offered by

About

In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models. Finally, you will build a Sentiment Analysis model that leverages BERT's large-scale language knowledge. Note: This course works best for learners who are based in the North America region. We're currently working on providing the same experience in other regions.

Modules

Sentiment Analysis with Deep Learning using BERT

    • 1 Readings
    • 1 Plugin
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded Quiz: Test your Project understanding

1 Labs

  • Sentiment Analysis with Deep Learning using BERT

1 Readings

  • Project-based Course Overview

Auto Summary

Dive into sentiment analysis with deep learning using BERT in this 2-hour professional course by Coursera. Perfect for IT and computer science enthusiasts, you'll explore dataset analysis, fine-tune a PyTorch BERT model for multi-class classification, and optimize training performance. Ideal for North American learners, this engaging project offers a hands-on approach to mastering sentiment analysis with cutting-edge technology. Subscriptions start at the Starter level.

Ari Anastassiou
Instructor

Ari Anastassiou