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学生对 Coursera Project Network 提供的 Analyze Text Data with Yellowbrick 的评价和反馈

81 个评分
9 条评论


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....



1 - Analyze Text Data with Yellowbrick 的 9 个评论(共 9 个)

创建者 Ali M H

Apr 14, 2020

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

创建者 Carlos A R Z

Jun 19, 2020

Analyze Text Data with Yellowbrick is a perfect course :3

创建者 Ronny F

Jul 25, 2020

thanks for your guidence


May 31, 2020

Thank You !

创建者 Vajinepalli s s

Jun 18, 2020


创建者 Kevin I L

Apr 2, 2021

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.


Jun 17, 2020


创建者 Muhammad S A

Jun 24, 2020

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

创建者 Vipin

Nov 4, 2020

I'd expect at the end using K-means clustering will check with actual labels instead of saying "wow it did a great job". Free youtube videos often do a better job than this !