Text Analytics 2: Visualizing Natural Language Processing
Visualizing Natural Language Processing is the second course in the Text Analytics with Python professional certificate (or you can study it as a stand-alone course). 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 word clouds, 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 models relate to human cognition? Artificial intelligence and humans don’t view language in the same way. You’ll see how both deep learning and human beings interact with the meaning that is encoded in language.
Upcoming start dates
- Self-paced Online
Who should attend?
Learn how to use machine learning to find out which words and documents have similar meanings.
Visualizing Text Analytics:
Learn how to explain a model using visualization and significance testing.
Applying Text Analytics to New Fields:
Learn how to apply computational linguistics to new problems and new data sets.
Course delivery details
This course is offered through University of Canterbury, a partner institute of EdX.
3-6 hours per week
- Verified Track -$249
- Audit Track - Free
Certification / Credits
What you'll learn
- Practice using document similarity and topic models to work with large data sets.
- Visualize and interpret text analytics, including statistical significance testing.
- Assess the scientific and ethical foundations of new applications for text analysis
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