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Analyze Text Data with Yellowbrick(으)로 돌아가기

Coursera Project Network의 Analyze Text Data with Yellowbrick 학습자 리뷰 및 피드백

82개의 평가

강좌 소개

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....

최상위 리뷰

필터링 기준:

Analyze Text Data with Yellowbrick의 9개 리뷰 중 1~9

교육 기관: Ali M H

2020년 4월 14일

교육 기관: Carlos A R Z

2020년 6월 19일

교육 기관: Ronny F

2020년 7월 25일

교육 기관: XAVIER S M

2020년 5월 31일

교육 기관: Vajinepalli s s

2020년 6월 18일

교육 기관: Kevin I L

2021년 4월 2일


2020년 6월 17일

교육 기관: Muhammad S A

2020년 6월 24일

교육 기관: Vipin

2020년 11월 4일