Analyze Text Data with Yellowbrick
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
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 desarrollarás
- Data Science
- Natural Language Processing
- Machine Learning
- Python Programming
- Data Visualization (DataViz)
Aprende paso a paso
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Introduction and Loading the Corpus
Vectorizing the Documents
Clustering Similar Documents with Squared Euclidean Distance And Euclidean Distance
Manhattan (aka “Taxicab” or “City Block”) Distance
Bray Curtis Dissimilarity and Canberra Distance
Cosine Distance
What Metrics Not to Use
Omitting Class Labels - Using KMeans Clustering
Cómo funcionan los proyectos guiados
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
Reseñas
Principales reseñas sobre ANALYZE TEXT DATA WITH YELLOWBRICK
Could have run through the theory behind the library functions a bit more as a refresher but for brevity's sake it is alright the instructor did not.
It was an amazing test and this lecture i like same with my area teaching.
Preguntas Frecuentes
Preguntas Frecuentes
¿Qué obtendré si compro un proyecto guiado?
¿Hay proyectos guiados disponibles en computadoras de escritorio y dispositivos móviles?
¿Quiénes son los instructores para proyectos guiados?
¿Puedo descargar el trabajo de mi proyecto guiado después de completarlo?
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
¿Puedo participar como oyente de un proyecto guiado y mirar el video de forma gratuita?
¿Cuánta experiencia necesito para hacer este proyecto guiado?
¿Puedo completar este proyecto guiado directamente a través de mi navegador web, en lugar de instalar un software especial?
¿Cómo es la experiencia de aprendizaje con los proyectos guiados?
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