Analyze Text Data with Yellowbrick

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In this Guided Project, you will:

Use visual diagnostic tools from Yellowbrick to steer your machine learning workflow

Vectorize text data using TF-IDF

Cluster documents using embedding techniques and appropriate metrics

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

Welcome to this project-based course on Analyzing Text Data with Yellowbrick. Tasks such as assessing document similarity, topic modelling and other text mining endeavors are predicated on the notion of "closeness" or "similarity" between documents. In this course, we define various distance metrics (e.g. Euclidean, Hamming, Cosine, Manhattan, etc) and understand their merits and shortcomings as they relate to document similarity. We will apply these metrics on documents within a specific corpus and visualize our results. By the end of this course, you will be able to confidently use visual diagnostic tools from Yellowbrick to steer your machine learning workflow, vectorize text data using TF-IDF, and cluster documents using embedding techniques and appropriate metrics. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Data ScienceNatural Language ProcessingMachine LearningPython ProgrammingData Visualization (DataViz)

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction and Loading the Corpus

  2. Vectorizing the Documents

  3. Clustering Similar Documents with Squared Euclidean Distance And Euclidean Distance

  4. Manhattan (aka “Taxicab” or “City Block”) Distance

  5. Bray Curtis Dissimilarity and Canberra Distance

  6. Cosine Distance

  7. What Metrics Not to Use

  8. Omitting Class Labels - Using KMeans Clustering

How Guided Projects work

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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Frequently Asked Questions

More questions? Visit the Learner Help Center.