NLP - The Visual Way (Open)

Learn how to prepare and model natural language data with visual tools in Dataiku DSS. (Open versions of courses do not count towards the certificate program.)

NLP - The Visual Way (Open)


About this course

About this course

To track your progress and work towards completion of the certificate program, you should register for this course through the Learning Paths.
Any progress in this open version of the course will not count towards a certificate.


“NLP - The Visual Way” is a course dedicated to natural language processing using visual tools in Dataiku DSS. It assumes a basic familiarity with visual machine learning in Dataiku DSS.


In the hands-on lessons, we will work with a dataset of movie reviews. Our task will be to build models to classify reviews as positive or negative.

Before each hands-on section, you will have a chance to grasp each new concept by watching short videos. In the concept videos, we’ll look at a simple example of classifying SMS messages as spam or not-spam.



Learning Objectives

At the end of this course, you will be able to:

1 - Understand the goals and challenges of Natural Language Processing (NLP)

2 - Prepare a dataset for NLP tasks with transformations like normalization, stopwords removal, and stemming

3 - Apply different strategies for handling text features in machine learning tasks and understand the tradeoffs between them


Course Properties

Course Title NLP - The Visual Way

Target Audience

Anyone with a basic understanding of visual machine learning in Dataiku DSS and wants to work with natural language data

Access Level

Free / included with registration

Estimated Time for Completion

70 Minutes

Completion Criteria

Pass all section waypoints with 65% and the course checkpoint with 80% 

Supplemental Materials (Y/N)


Knowledge Prerequisite(s)

Machine Learning Basics and Scoring Basics suggested

Technical Prerequisite(s)

Dataiku DSS - Latest version (FREE EDITION is enough)


Start by watching the course overview video and by looking at some key concepts: The Goal of NLP, Preparing Text Data, and Handling Text Features for ML.