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Visualizing Text Analytics and Natural Language Processing with Python

Extend your knowledge of the core techniques of text analytics by looking at how to make sense of the output of models.

There is one session available:

After a course session ends, it will be archived.
Estimated 6 weeks
3–6 hours per week
Progress at your own speed
Optional upgrade available

About this course

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Visualizing Text Analytics with Python is the second course in the Text Analytics with Python professional certificate. Natural language processing (NLP) is only useful when its results are meaningful to humans. This second course continues by looking at how to make sense of our results using real-world visualizations.

How can we understand the incredible amount of knowledge that has been stored as text data? This course is a practical and scientific introduction to text analytics. That means you’ll learn how it works and why it works at the same time.

On the practical side, you’ll learn how to visualize and interpret the output of text analytics. You’ll learn how to create visualizations ranging from wordclouds, heatmaps, and line plots to distribution plots, choropleth maps, and facet grids. You’ll work through real case-studies using jupyter notebooks and to visualize the results of machine learning in Python using packages like pandas, matplotlib, and seaborn.

On the scientific side, you’ll learn what it means to understand language computationally. How do word embeddings and topic modeling relate to human cognition? Artificial intelligence and humans don’t view text documents in the same way. You’ll see how both deep learning and human beings interact with the meaning that is encoded in language.

At a glance

What you'll learn

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  1. Practice using document similarity and topic models to work with large data sets.
  2. Visualize and interpret text analytics, including statistical significance testing.
  3. Assess the scientific and ethical foundations of new applications for text analysis.

Module 1. Text Similarity

Learn how to use machine learning to find out which words and documents have similar meanings

Module 2. Visualizing Text Analytics

Learn how to explain a model using visualization and significance testing

Module 3. Applying Text Analytics to New Fields

Learn how to apply computational linguistics to new problems and new data sets

About the instructors

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