Latent Dirichlet Allocation you own this product

prerequisites
intermediate Python • linear algebra • probability • basics of machine learning
skills learned
implementing a simplified version of the LDA algorithm • preprocessing and converting a text corpus into a document-to-word matrix • generating topics using Gensim’s LDA algorithm • evaluating generated topics using Coherence and Diversity metrics • visualizing derived topics with a variety of techniques
Aneesha Bakharia
1 week · 6-8 hours per week · INTERMEDIATE
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In this liveProject, you’ll use the latent dirichlet allocation (LDA) algorithm from the Gensim library to model topics from a magazine’s article back catalog. Thanks to your work on topic modeling, the new Policy and Ethics editor will be better equipped to strategically commission new articles for under-represented topics. You’ll build your text preprocessing pipeline, use topic coherence to find the number of topics, and visualize and curate the algorithm’s output for your stakeholders to easily read.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Aneesha Bakharia
Aneesha Bakharia completed her PhD in interactive topic modeling at Queensland University of Technology in Australia. She is currently the Manager of Learning Analytics at The University of Queensland where she leads a team of programmers and data scientists. She has written 10 books on programming and web development for Cengage publishing. She also blogs about data science related topics, including topic modeling and she publishes academic peer reviewed publications on educational technologies and learning analytics.

prerequisites

This liveProject is for data scientists and programmers who are confident programming with Python and the Python data ecosystem. To begin this liveProject you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basics of Jupyter Notebook
TECHNIQUES
  • Linear algebra
  • Referencing Matrix cells by row and column index
  • Matrix subtraction, multiplication, and division
  • Basic probability
  • Basics of machine learning

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