YouTube Videos, Meetups, Book, and Code: https://datascienceonaws.com

In this workshop, we build a natural language processing (NLP) model to classify sample Twitter comments and customer-support emails using the state-of-the-art BERT model for language representation.

To build our BERT-based NLP model, we use the Amazon Customer Reviews Dataset which contains 150+ million customer reviews from Amazon.com for the 20 year period between 1995 and 2015. In particular, we train a classifier to predict the star_rating (1 is bad, 5 is good) from the review_body (free-form review text).

This workshop is FREE, but would otherwise cost <25 USD.

Click Attach Policies.

Select AmazonS3FullAccess and click on Attach Policy.

Note: Reminder that you should allow access only to the resources that you need.

Open the AWS Management Console

In the AWS Console search bar, type SageMaker and select Amazon SageMaker to open the service console.

Click File > New > Terminal to launch a terminal in your Jupyter instance.

Within the Terminal, run the following:

cd ~ && git clone https://github.com/data-science-on-aws/workshop

If you see an error like the following, just re-run the command again until it works:

fatal: Unable to create '/home/sagemaker-user/workshop/.git/index.lock': File exists.

Another git process seems to be running in this repository, e.g.
an editor opened by 'git commit'. Please make sure all processes
are terminated then try again. If it still fails, a git process
may have crashed in this repository earlier:
remove the file manually to continue.

Note: This is not a fatal error ^^ above ^^. Just re-run the command again until it works.

Navigate to 00_quickstart/ or 01_setup/ in your Jupyter notebook and start the workshop!

You may need to refresh your browser if you don't see the new workshop/ directory.