Retour à Analyze Text Data with Yellowbrick
Avis et commentaires pour d'étudiants pour Analyze Text Data with Yellowbrick par Coursera Project Network
80 évaluations
À propos du cours
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.
Meilleurs avis
Filtrer par :
1 - 9 sur 9 Avis pour Analyze Text Data with Yellowbrick
par Ali M H
•14 avr. 2020
It was an amazing test and this lecture i like same with my area teaching.
par Carlos A R Z
•19 juin 2020
Analyze Text Data with Yellowbrick is a perfect course :3
par Ronny F
•25 juil. 2020
thanks for your guidence
par XAVIER S M
•31 mai 2020
Thank You !
par Vajinepalli s s
•18 juin 2020
nice
par Kevin I L
•2 avr. 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.
par MOHAMMED B
•17 juin 2020
thanks
par Muhammad S A
•24 juin 2020
It was good but it would be nice to have more explanations on the topics.
par Vipin
•4 nov. 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 !
Coursera Footer
© 2021 Coursera Inc. Tous droits réservés.
