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DTSTART;TZID=America/Los_Angeles:20231016T140000
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UID:41634-1697464800-1697475600@grad.berkeley.edu
SUMMARY:Python Text Analysis: Topic Modeling
DESCRIPTION:Topic Modeling. How do we identify topics within a corpus of documents? In this part\, we study unsupervised learning of text data. Specifically\, we use topic models such as Latent Dirichlet Allocation and Non-negative Matrix Factorization to construct “topics” in text from the statistical regularities in the data. \nPrerequisites: Python Text Analysis Fundamentals: Parts 1-2 \nWorkshop Materials: https://github.com/dlab-berkeley/Python-Text-Analysis \nSoftware Requirements: Installation Instructions for Python Anaconda \nIs Python Not working on your laptop? Attend the workshop anyway\, we can provide you with a cloud-based solution until you figure out the problems with your local installation. \nonlFeedback: After completing the workshop\, please provide us feedback using this form
URL:https://grad.berkeley.edu/event/python-text-analysis-topic-modeling/
LOCATION:Online via Zoom
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