Microcredential
Text Analytics with Python
University of Canterbury via edX Professional Certificate
Overview
Learn the core techniques of text analytics and natural language processing (NLP) while discovering the cognitive science that makes it possible in this certificate Text Analytics with Python. On the practical side, you’ll learn how to actually do an analysis in Python: creating pipelines for text classification and text similarity using machine learning. These pipelines are automated workflows that go all the way from data collection to visualization. On the scientific side, you’ll learn what it means to understand language computationally. Artificial intelligence and humans don’t view text documents in the same way. Sometimes deep learning sees patterns that are invisible to us. But often deep learning misses the obvious. We have to understand the limits of a computational approach to language together with the ethical requirements that guide how we choose what data to use and how we protect the privacy of individuals.
Along the way, you’ll explore real-world case studies using pandas, numpy, scikit-learn, tensorflow, matplotlib, seaborn, gensim, and spacy within jupyter notebooks to gain useful insights from unstructured data.
Courses
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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.
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Introduction to Text Analytics with Python is part one of the Text Analytics with Python professional certificate. This first course introduces the core techniques of natural language processing (NLP). But we introduce these techniques from data science alongside the cognitive science that makes them possible.
How can we make sense out of 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 actually do an analysis in Python: creating pipelines for text classification and text similarity that use machine learning. These pipelines are automated workflows that go all the way from data collection to visualization. You’ll learn to use Python packages like pandas, scikit-learn, and tensorflow.
On the scientific side, you’ll learn what it means to understand language computationally. Artificial intelligence and humans don’t view documents in the same way. Sometimes AI sees patterns that are invisible to us. Sometimes AI misses the obvious. We have to understand the limits of a computational approach to language and the ethical guidelines for applying it to real-world problems. For example, we can identify individuals from their tweets. But we could never predict future criminal behaviour using social media.
This course will cover topics you may have heard of, like text processing, text mining, sentiment analysis, and topic modeling.
Taught by
Girish Prayag, Jonathan Dunn, Tom Coupe and Jeanette King
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