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 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:
In this liveProject, you’ll master topic modeling—an amazing skill for quickly analyzing textual datasets. You’ll learn to apply the LDA topic modeling algorithm in a real-world setting:
geekle is based on a wordle clone.