About this course

__ _ 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.

At a Glance:
Institution: UCx
Subject: Computer Science
Level: Introductory
Prerequisites:
None
Associated programs:
Professional Certificate in Text Analytics with Python
Language: English
Video Transcript: English
Associated skills:Text Mining, Choropleth Map, Python (Programming Language), Deep Learning, Matplotlib, Artificial Intelligence, Word Embedding, Topic Modeling, Computational Linguistics, Natural Language Processing, Pandas (Python Package), Jupyter, Machine Learning

What You’ll Learn:
About this course

__ _ 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.