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Kursteilnehmer-Bewertung und -Feedback für Applied Text Mining in Python von University of Michigan

3,002 Bewertungen
575 Bewertungen

Über den Kurs

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....



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!


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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526 - 550 von 570 Bewertungen für Applied Text Mining in Python

von Cong L

Mar 22, 2018

Lecture was long-winded and could not hit the main points. Assignment was difficult without many explanation. Tutors were more humiliating students rather than providing supports.

von Svitlana K

Jul 29, 2019

Worst course in the specialization so far. Tasks in the assignments are very poor written and are unclear. Just listening lectures don't help you to complete your assignments.

von Dan H

Mar 29, 2020

There were significant issues with the autograder and the instructions for the programming assignments. This course has been around for a while. Why aren't they fixed???

von Shikhar S

Jun 06, 2019

The content of the course was quite good. But the level of teaching was a way too less than the level of Assignments. Ist assignment was too difficult to perform..

von Tal Y

Feb 18, 2018

The course takes you through the important NLP topics, the instruction is decent, but the assignments are clunky and waisted many hours of my time unproductively.

von chris l

Jan 30, 2020

A lot of prior knowledge or independent learning is required to get the most out of this course. Needs more code walkthroughs.

von Stanley C

May 15, 2019

Assignment grading is way too rigid and not reflective of real world issues. It can be very frustrating.

von carol a

Oct 23, 2019

Instructions for assignments are vague and incorrect. Instructor was hard to follow during lecture.

von Sebastian

Apr 30, 2019

The video lectures are good, but there are many issues with the Jupyter notebook assignments.

von Alexandros B

Oct 04, 2017

poor organization of the lesson and many many mistakes during assignments

von Alex M

Aug 27, 2017

Instructors did a poor job of preparing students for the assignments.

von Ji S

Apr 15, 2018

Too coarse, quality worse than other courses in this specialization.

von naive666

Jun 29, 2019

Far from expectation, feel upset

von Elliot B

Mar 03, 2018

I found this course quite confusing and often unrelated between video lectures and assignments. The lectures maybe covered an assignment in broad strokes but to actually answer any of the questions needed extension research from the student. I felt like I was teaching myself the base content. At that point, what is the point of the lecture videos if they provide no value. I almost stopped my subscription and gave up on the data analysis specialization based on the quality of this specific course. Previous courses in the specialisation did provide useful information in lectures which was then extended upon in the assignments. This method of teaching something in the lectures then building on finessed usage in the assignments is a much better approached.

von Przemek P

Jul 22, 2020

For me it's the 7th Course completed on Courserea and by far the worst.

The topics covered by course are explained in an unclear way, with fast pace.

There are too many topics and too little time to explain them.

Lecturer constantly uses shortcuts and chaining when explaining new concepts.

Lecturer codes in a un-datascience way, ommiting dataframes and apply family functions, constantly looping trough lists instead.

And most annoying of all - you spend hours digging through forum to finish assignments, because each instruction is written in a very, very unclear way and autograder is set to accept only one anwer. Belive me, it's very frustrating - take a look at the forum of this course.

von Christopher I

Mar 14, 2018

The lectures for this course are terribly uninspired, giving very little useful information--the vast majority of it is the professor talking about obvious aspects of language at a very high and useless level. The autograder is frequently breaking for very minor things (such as returning numpy.float instead of float), the questions on the assignments are often misleading, poorly worded, vague, or just generally not very helpful. All in all, this was one of the worst MOOCs I have ever taken, though the Coursera bar is pretty low. It does make me wonder why I bother to pay at all--oh right, Coursera now makes not paying a major inconvenience to course progression.

von christopher h

Nov 18, 2017

Compared to other courses in the Applied Machine Learning focus, this is so far the worst. The content and quality are poor. The lecturer is too slow and fails to prepare the student for the assignments. First week is very basic and ends with an assignment in regex. There's plenty of regex resources out there. 2nd week moves forward but finalizes in an assignment that involves concepts not covered in the lecture (ngrams). Weeks 3 and 4 contain too many errors in the lecture and autograder (use of AUC, finding minimum of a sparse array). UofM should rebuild this course.

von Guo X W

Jun 21, 2020

This is my least favourite course in the specialisation. Natural language processing is an exciting field and I think there is a lot more potential to enthuse and engage students. The instructor scratches the surface of text mining by going through brief sets of codes on ppt slides. I thought it would be meaningful to use more real-world datasets (as in the previous courses in the specialisation) and have students follow through some examples on Jupyter Notebook. I also felt that the exposition by the instructor was not the most intuitive or lucid. It could be much clearer.

von Matt P

Apr 11, 2020

This course was much less helpful than others in the Specialization. The assignments are poorly conceived, and submissions are beset by finicky autograder issues. Certainly, data cleaning and code debugging are critical skills for text mining, but I find it difficult to believe that "try to understand what output a function should submit so as to satisfy the current autograder" is a useful way to teach text mining.

I hope this course will be re-done to bring it in line with the quality of the others in the Specialization.

von Prykhodko D

Aug 10, 2019

The course is a joke. Its outdated and not supported, you literally need to spend hours to try and figure and emulate versions used by autograder and even the file structure for files used by default is not accurate and you get file read errors on predefined by them functions on their own virtual environment and need to fix these for them!!! The virtual machine env provided is super slow so need to use your own. Very bad user experience and horrible use of time!

von Dr. D W

Aug 27, 2019

What a horrible course. Especially the assignments are such an unbelievable waste of time. Instead of focusing on important concepts and applications, one has to spend hours one "pleasing the autograder" by renaming columns and reading the discussion pages for the correct interpretation of all the ambiguously formulated questions. Very sad! Would be good for everyone if this was removed from the (otherwise great) series "Applied Data Science in Python".

von Maximilian W

Feb 14, 2020

Serious sub-par course in the specialisation. The lecturer is good, but sadly the assignments are terrible. Thus the reinforcement - and reliability to problem solving - of the content is poor.

Given the high standard of the first three modules in this specialisation, this is really a shame. I would urge learners to consider whether there is much point in doing this course (other than to get the specialisation completed).

von Eduardo C F

Feb 23, 2018

I was under the impression that the course is incomplete, especially week 4, which has no notebook examples of the theory presented. I needed to look at other sites for basic information. I could only complete the exercises because they are easy, otherwise, with the code presented during the course, I would not have been able to. I suggest strengthening the example code in python (see week 3, good code)

von Ruben G C

Apr 27, 2020

I have to say that the previous three courses were very well explained, with good examples and python code. However, this course is not well explained nor documented. It is a pity that the quality of the whole specialization program gets considerably reduced due to this course. The assignments do not allow you to learn and you may not pass them due to small differences in the coding.