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Opiniones y comentarios de aprendices correspondientes a Applied Text Mining in Python por parte de Universidad de Míchigan

4.2
2,177 calificaciones
411 revisiones

Acerca del Curso

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....

Principales revisiones

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. :-)

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.

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376 - 400 de 403 revisiones para Applied Text Mining in Python

por Stanley C

May 15, 2019

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

por Lin Y

Jul 16, 2019

This course is probably the worst amongst all other courses in this specialization. The term 'applied' in the course title makes you think that this course helps you to build practical experiences in text mining. However, not true at all.

por 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.

por Ryan D

Aug 06, 2019

I have been working through the entire specialization, Applied Data Science with Python. The first two courses of this specialization had a lot of attention to detail, the assignments were well laid out and challenging, and the addition resources linked by the instructor were really helpful. Moreover, the lectures themselves were more engaging and segmented.

This course was less informative than the other courses I've taken in this specialization. You would be much better off purchasing the O'Reilly Text Analysis in Python book and reading through it in more detail prior to taking this course or in-between lectures.

por Yonatan S

Nov 15, 2019

A lot of exercises have unclear instructions (see discussion forums). The exercise on topic modeling especially was a waste of time, you're not really learning anything by running these small pre-frabricated scripts. In general the exercises were extremely shallow and did not require any creativity or actual problem-solving, in contrast to some of the earlier courses in this Specialization.

por carol a

Oct 23, 2019

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

por Steven G

Nov 06, 2019

Confusing explanations of NLP concepts. Inadequate explanations of how to use the Python packages to solve the assignment questions. I'm writing this review half-way through the Applied Social Networks Analysis course which is excellent and pitched just right. The contrast between the 2 courses couldn't be greater.

por Oliverio J S J

Feb 13, 2018

This course provides an interesting introduction to natural language processing in Python. The lessons are well thought, they are brief and to the point. It is very exciting to discover all the tools at our disposal to work in this field. The main problem of the course, as it seems to happen in the whole specialization, is resolving the assignments. Usually, they are poorly described, which forces the student to review the forums to understand what they are asked to do. In addition, the part of the tasks related to the course's topic is usually very simple, sometimes trivial. On the other hand, several hours may be required to generate the specific data structures required by the autograder an dealing with weird issues, that is, much more time is devoted to deal with autograder problems than learning about the subject. I do not understand why this problem keeps repeating one course after another.

por Raul M

Jun 02, 2018

I didn't like too much the structure of the lecture and the assignments, I don't think they were aligned that well. Also, I'm not sure how I'm going use this in real life.

The additional lectures were TOO MUCH theory which is not the purpose of the specialization.

por 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..

por Jose Á P L

Mar 23, 2019

Este curso no vale para nada, por favor no lo hagais!!!

por 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.

por 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)

por Mark R

Sep 21, 2017

Interesting topic, but a really poor course with barely any content.

Around an hour or less of lectures a week.

I've taken a lot of MOOC's on Coursera and other platforms and this one is poor

por Mahmoud

Apr 29, 2019

the worst ever I took here in Coursera

por Dario M

Jul 19, 2019

The difficulty of the assignments is in no way related to the simpleness of the lectures.

por Nicholas P

Jul 31, 2019

Unless the instructional staff updates the programming assignments to reflect updates in packages and ensures they can run without additions, do not take this course. It is a terrible reflection on the University of Michigan.

por 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!

por Vivek G

Dec 02, 2019

Only useful for coarse understanding of the topic.

por Feng Q

Oct 04, 2019

totally can't understand the Indian accent.

por Dongquan S

Oct 09, 2019

I have taken and passed all the first four courses in this specialization, and very much liked the first three courses. But the quality of this course on text mining is far below the average level of the first three. Go find some other courses if you want to learn text mining with Python.

There are too many areas of flaws in this course. I am only highlighting the top 5 below:

1. lacks good connection throughout the course content. This problem exists almost everywhere, both from slide to slide within a video and from video to video. Many times you would have questions in your head like “why is he talking about this?” or “what is this?”

2. use example just for the purpose of showing examples. Don’t really explain the point it is supposed to explain. In many times the examples do not provide clarity, but raise more confusion instead.

3. assignment tasks either too simple, or remotely related to what is introduced in the course. The worst case is assignment in week 4, where the assignment is so poorly constructed. You have to spent days to figure out the right answer. They call it “debug”, but there is nothing wrong with my code. I would say it is more of a process to “try to figure out what the instructor is asking for”.

4. talks too much about the theoretical things, not very good introduction of using python. Even when python code is demonstrated, it is almost always in a very abstract way. This is significantly different from the first three courses, and very annoying. You would need to spend about the same amount of time googling how the packages work as I have never took the course.

5. Repetition of content already introduced in previous courses, i.e., machine learning basics.

por Angertdev S

Nov 07, 2019

broken assignments

por Justin M

Sep 14, 2019

Videos are so high-level that they don't help at all understanding the necessary code. Assignments have spelling errors and ambiguity. Week 4 is missing the sample code notebook. I eventually found the sample code notebook in the forums, but this was a big cause of frustrations as I had zero context for how to do the assignment.

por 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.

por 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.