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Learner Reviews & Feedback for Applied Text Mining in Python by University of Michigan

4.2
2,050 ratings
389 reviews

About the Course

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....

Top reviews

CC

Aug 27, 2017

Quite challenging but also quite a sense of accomplishment when you finish the course. I learned a lot and think this was the course I preferred of the entire specialization. I highly recommend it!

GK

May 04, 2019

Lectures are very good with a perfect explanation. More than lectures I liked the assignment questions. They are worth doing. You will get to know the basic foundation of text mining. :-)

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251 - 275 of 382 Reviews for Applied Text Mining in Python

By Ben E

Nov 10, 2017

This course did cover some good topics (Naive Bayes model, similarity, part of speech tagging). However, I felt the homework was more about manipulating Python data structures than learning anything significant about text mining. Some of the theory behind the models was covered, but didn't make it to the homework.

It would be difficult since this is a short class, but I would have preferred more about tips on which model to use and feature engineering / selection, and examples of practical applications of text mining. (Or stories of failures in the instructors' experience!)

By Vijay C S

Oct 24, 2017

The course is pitched at a introductory level. I would have like to have more practical tutorials.

By Siddharth S

Jun 12, 2018

The fact that the strategy of a Jupyter Notebook Demonstration during explanation was not followed in week 3 and 4 was a disappointment.This specialisation had been wonderful with its use of demonstration in Lectures with the Notebook,If this had been followed in Week 3 and Week 4 then the course would definitely had shined.Please correct the same, the course deserves that, It has wonderful content.

By Joan P

Nov 07, 2017

A lot of issues with the auto graders

By Rasoul N

Dec 26, 2017

Course materials are amazing but there are not much support for assignments.

I did all the quizzes and assignments except the last one. It seems there was something wrong with auto-grader or the assignment was not clear. There were complains about this issue on the forum but no one from staff answered the questions.

By Georgios P

Oct 30, 2017

Week 4 was not sufficient

By Bernardo A

Aug 19, 2017

I liked the course, but it felt as a very raw overview, I think it could have been more challenging when it comes to the models explained.

By Juan C E

Oct 30, 2017

A bit lack of coherence in theory. Sometimes, the theory needed for the assignments was not given with enough detail, and you had to browse the forums for the information, and applying it to your assignment just to pass, sometimes without understanding why you were doing what you were doing.

More Python examples needed. For week 3, the tutorial about recommender systems was perfect for the assignment.

By Qian H

Oct 04, 2017

The homework is quite not related to the lecture. And it is so hard to finish.

By Silvia

Apr 24, 2018

Assignments were too difficult.

By Mile D

Nov 23, 2017

This course was quit ok. I have expected just more exercises and explanations because of the difficult topic.

By James M

Apr 18, 2018

Autograder bugs make for a frustrating time completing the assignments. Independent research and self-guided learning will come in handy for this course as the lectures (mostly) are uninformative.

By Siwei Y

Sep 14, 2017

autograder 经常 犯些低级错误, 导致很多人在对付 autograder 上 花了很多时间。 请授课方务必改正, 否则 有不负责任之嫌。另外 编程作业的 说明委实不清不楚, 模棱两可。除此之外内容还算中规中矩, 虽然我个人 认为太表浅了一些。

Autograder is so buggy, that people have to spend lots of time to figure out, what the solution is.

Additionally, the Instuction of python assignment is often ambiguous. Please fix them ASAP.

Personally I find that the content is somehow like an introduction. I had hoped something more about detail.

By Maxime R

Mar 13, 2018

I really think that the 3rd and 4th week of the course should have more practical presentation (especially the 4th week for which the assignment is quite 'new' in terms of programming). Having a notebook for the 4th week would be a good additional material.

By Kieran W

Feb 21, 2018

Overall good material. Not enough actual code examples at times, especially towards the end. The assignments weren't completely relevant and slightly buggy.

By Valeriya P

Aug 28, 2017

the course is ok, should be more technical though.

By Jim B

Aug 24, 2017

Of all of the Applied Data Science with Python classes I have taken, this was the worst. If it were not for the discussion groups I would not have been able to complete the course. And the discussions groups requested help from instructors and received little to none. Part of the problem is that the auto-graders were broken, the rest of the problem was that this class relied on the online documentation. And of the classes in Applied Data Science with Python, this one has the worst documentation. Hence the class needed more help.

By Ashwini B

Jun 03, 2018

Topics like LDA need better explanations.

By Muhammad H R

Feb 13, 2018

This course was just too theoretical. There were just too many lectures on the English language and nothing really practical. I learned nothing that I can actually use. There were hardly any useful text mining techniques that I learned.

By Teo S

Aug 24, 2017

potentially great course, but I will just say it was good.

The last week was especially poor as they lecturer did very minimal teaching in the coding portion and expect the students to deliver on their own in the assignments. Even after I finished the course, I still felt that there were portions that I did not understand clearly. Will appreciate if they can cover more content like the previous machine learning course.

By Sakina F

Apr 15, 2018

Very very long videos. Makes a person zone out. The videos need to be smaller in length as they become very hard to complete. However, the content is good and easy to understand.

By Panit A

Oct 22, 2017

Bad assignment. Grader not reliable. No control over the discussion board, many confusing comments mixed with good comments.

By Nitish K

Sep 16, 2017

While the course gives a good broad understanding of how any NLP task would work in theory, but the course is very unstructured. For example, if I had to be a given task on doing a sentiment analysis, I can broadly tell what is the conceptual theory behind it but I dont know how exactly to do it because the professor talked about so many tools which were repetitive in their use and were not clearly demarcated as to what tool should be used for what?

By Paula C R

Aug 04, 2017

I think the course was superficial and could be better explored. It's good start, though.

By Brian R v K

Oct 30, 2017

I enjoyed this course, but some aspects of it felt "light touch", particularly week 4. That week would be greatly improved with a jupyter notebook and an applied demonstration by the absolutely awesome Teaching Assistant, Filip Jankovic. Whenever he does a demonstration, it's clear, concise, practical, and always helpful. Let's see more of him!