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Applied Text Mining in Python, 密歇根大学

4.2
1,460 個評分
275 個審閱

課程信息

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python....

熱門審閱

創建者 BK

Jun 26, 2018

Would love to see these courses have more practice questions in each weeks lesson. Would be helpful for repetition sake, and learning vs only doing each question once in the assignments.

創建者 SC

Jul 05, 2018

Great course, very well balanced pace of learning. Adds good amount of working knowledge with NLP tools; definitely not covers everything but more than what I expected.

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269 個審閱

創建者 Daissy Daniela Miranda Restrepo

Feb 19, 2019

Good topics and well explanations. A Notebook to support content of week 4 is definitely needed. More explanations in assignment for week 4 is needed. In general, week 4 lacks of organization and good content. that is why I give 3 stars instead of 5

創建者 CaitlinYao

Feb 17, 2019

The assignments are much harder than the slides, which means much more self-learning is required.

創建者 Abdoulaye Diallo

Feb 11, 2019

I'm auditing this course, as I don't have enough to get the certificate. I hugely recommend it. It's well explained.

創建者 CMC

Feb 11, 2019

I will not say that I did not learn anything. I just wish the autograder was a little better. Basically, quite frustrating to fight a black-box grader. An example of a better autograder is the one implemented by the Princeton people for their algorithm courses.

創建者 Alejandro Cruz Marcelo

Feb 10, 2019

The instructor provided very low quality material.

創建者 Lucas Serrer Richter

Feb 08, 2019

The course presented a good content for beginners in NLP and I feel confident to start using what I learned in my work. But, the grader for the assignments is too slow and buggy, this should be fixed so new learners don't feel frustrated. In addition, for assignment 4, the lda trainning parameters are not viable for trainning in coursera's environment, it should be reviewed.

創建者 Mateusz Mitula

Feb 06, 2019

Some of the topics where elaborated very briefly. There was not enough practical examples and instructor was no clear in what he was saying.

創建者 AHMED BEN KHALIFA

Feb 05, 2019

Excellent course

創建者 charles lenfest

Feb 04, 2019

This course was outstanding - excellent lectures, notes and examples!

創建者 Adolfo Garza Salazar

Jan 30, 2019

Excellent course, you must to take it and work by yourself in the Assignments