Load textual data into R, and pre-process it
Convert textual data into a document feature matrix Run an LDA topic model on your data
By the end of this project, you will know how to load and pre-process a data set of text documents by converting the data set into a document feature matrix and reducing it’s dimensionality. You will also know how to run an unsupervised machine learning LDA topic model (Latent Dirichlet Allocation). You will know how to plot the change in topics over time as well as explore the distribution of topic probability in each document.
sampling
Topic Modelling
Unsupervised Learning
Data Visualization (DataViz)
Text Corpus
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Load textual data into R, and pre-process it to prepare it for topic modelling
Convert textual data into a document feature matrix and reduce its dimensionality before applying the model.
Run an LDA topic model on your data and explore the topics identified by the model as well as the most frequently used words associated with each topic.
Plot the change in topics over time in your data as well as to explore the distribution of topic probabilities in each of your textual documents.
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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