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學生對 密歇根大学 提供的 Applied Text Mining in Python 的評價和反饋

3,457 個評分
663 條評論


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....



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!


Excellent course to get started with text mining and NLP with Python. The course goes over the most essential elements involved with dealing with free text. Definitely worth the time I spent on it.


551 - Applied Text Mining in Python 的 575 個評論(共 654 個)



Topic Modelling should be explained in more detail.

創建者 Vasilis S


Poor ability from lecturer to explain key concepts.

創建者 Valeriya P


the course is ok, should be more technical though.

創建者 Alonso G L


Very exciting topic, but not so exciting course.



Its ok but was the worst of the specialization

創建者 陆徐超


Good contents, but not very clearly explained.

創建者 Lavanya


programming assignments are too technical

創建者 Ashwini B


Topics like LDA need better explanations.

創建者 Navjyot W


The assignments were a little complex

創建者 Joan P


A lot of issues with the auto graders

創建者 Dhanush P


Last week is not properly thought

創建者 Imran A G


Good for basic understanding only

創建者 Abhijit K


More Hands on is required on it.

創建者 Silvia


Assignments were too difficult.

創建者 Georgios P


Week 4 was not sufficient

創建者 Yeifer R C


Is difficult, but good.

創建者 Sara C


I like the lecturer.

創建者 Xuening H


Bad autograder

創建者 pavan b


good training

創建者 Aditya M



創建者 Alperen B O



創建者 Peter B


I have major qualms with this course. So far in the specialization, this course is certainly the worst. *The autograder is terrible, having had serious, known issues for 8+ months at the time of this review.*The course content is incorrect, teaching learners the incorrect way to calculate roc_auc_score. *The course blows through certain topics, like Part-of-Speech tagging & Parsing sentence structure, leaving learners like myself without a good overview. I don't even have a good set of links to learn more. I can run a few commands and understand why it might be important, but I have no idea how to use it in practice. *Unlike other courses in the specialization, this one doesn't have good links to interesting academic papers or real world applications.*Unlike other courses, every week does NOT include a weekly Juptyer notebook.Here's a simple solution - give Uwe, an excellent and active Mentor, the permissions to fix this broken course. On the plus side: the instructor is ok, the topic is interesting, and this course really only feels terrible relative to the excellent courses in this specialization. I can still hardily recommend the specialization...

創建者 Anna K


Unfortunately, this is one of the worst courses I have ever taken. The later lectures did not have much of a content, and assignments were very badly described and evaluated. The latter is in general one of the weaknesses of this specialisation, but this course made me particularly frustrated. There did not seem to be any moderator answering students' questions which at least in one case led to a big confusion as one of the students wrote that his wrongly (as I got it later) written code worked ok which led to a long and misleading discussion between students how to interpret and tweak the assignment to pass the grader, which made me waste a lot of time. Would be great if wrong interpretations and statements written by students are timely deleted, corrected or flagged.

In summary, the assignments' descriptions and grading system do need to be improved (for example, one can introduce some hints such as 'the grader expected this output for this input0, but the student solution returned this' as it is done in a few other courses on Coursera).

創建者 Oliverio J S J


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.

創建者 Jonathan B


Text mining and NLP were areas of this specialization that I was particularly interested in learning more about and I was mostly disappointed by the course. The staff's refusal to update to the latest versions of software is frustrating because being successful in this industry means staying up with the latest trends. I recall at least one lesson that required Python 2.x, which as of 2020 is no longer supported.

While it is completely understandable that assignments include some concepts that were not taught in the lectures; this course had way too many self-learning concepts in the assignments many of which were covered in the very next lesson.

On a good note, the instructor is very passionate about the topic and covers a lot of material. The course mentors are very knowledgeable and helpful and there is no way I would have been able to pass the course if it wasn't for them.