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Analyze Text Data with Yellowbrick に戻る

Coursera Project Network による Analyze Text Data with Yellowbrick の受講者のレビューおよびフィードバック



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....



Analyze Text Data with Yellowbrick: 1 - 6 / 6 レビュー

by Ali M H

Apr 14, 2020

It was an amazing test and this lecture i like same with my area teaching.

by Carlos A R Z

Jun 19, 2020

Analyze Text Data with Yellowbrick is a perfect course :3


Jun 01, 2020

Thank You !

by Vajinepalli s s

Jun 18, 2020



Jun 17, 2020


by Muhammad S A

Jun 25, 2020

It was good but it would be nice to have more explanations on the topics.