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.
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
Jonathan Dunn, Tom Coupe, Jeanette King and Girish Prayag