Analyze Text Data with Yellowbrick

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Neste projeto guiado, você irá:

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
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

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.

Habilidades que você desenvolverá

Data ScienceNatural Language ProcessingMachine LearningPython ProgrammingData Visualization (DataViz)

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  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

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo



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