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

3,474 ratings
668 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

Dec 4, 2020

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.

Aug 26, 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!

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451 - 475 of 659 Reviews for Applied Text Mining in Python

By José G G

Feb 15, 2021

In my opinion this course lacks of clear goals, it is not easy to understand where the instructor is going and also the autograder was really confusing. I spent hours struggling with it and from the opinions in the forum it is a common issue. On top of that, several homeworks were 'recipe' oriented, I mean you have to follow a procedure without actually understanding the concepts behind it. From the positive side, this was my first sight to this topic and in a general manner I got a sense of what it is about, but it was very superficial.

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 Max P

Jan 6, 2018

Although the topic of Text Mining is very interesting, I find that the AP did not dive deep enough into the various topics. The matter that he did explain was interesting, but at some parts not really clear. I missed a clear line of thought.

Concerning the assignments: very interesting topics, but the guidelines could be clarified to nip any possible confusion in the bud. Also, some exercises could be split up into multiple ones so that debugging becomes easy. Many students in the Discussion Forum mentioned difficulties.

By Izabella J

Jul 28, 2019

It's the worst course in this specialization. Still it's OK, but... You get a lot of things during the lecture which are not connected to the Notebooks. Notebooks are the poorest I saw here. Assignments have some errors in code (you need to add download part for example to run the Notebook, or change folder where data are). But the content is interesting, I learn quite a lot doing assignments. But I still feel disappointed, as other courses were much better and I was expecting more.

By Stanislav S

Jun 9, 2020

I really enjoyed the content and I learned a lot from the assignments. I wish they emphasized on the machine learning more. Week 4 could be at least 2 or 3 weeks. What was very unpleasant for me was the assignment grading. The autograder uses outdated versions of the packages and has 1000 requirements that are not specified anywhere. If you take this course, be ready to spend 50% of your time dealing with the autograder. Furthermore, this course is not actively maintained anymore.

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 Jonathan B

Aug 10, 2017

While the video of the course were OK, the assignments were of really bad quality. So many problems with the auto-grader, and some questions were absolutely not clear. I still put 3 stars because the subject is interesting and I got things I can work with out of it, but don't expect too much from it, you'll spend most of your time trying to deal with the weird assignments questions. For the time spent, they could have added 1 or 2 weeks of videos.

By Pengcheng Z

Jun 18, 2019

Overall the course structure and assignments are very good. But need too much extra effort to finish homework. The course video itself may only covered 20% of content so a lot of extra times is required for me to finish homework. Some of the effort from my perspective is not necessary. From my perspective If the course could cover 70% of the content while push student to explore the remaining 30% it would be more efficient and encouraging.

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 Siyang

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 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 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 Nicolas G

Feb 22, 2021

Lack of more practice, on the first two weeks, there so much programming (witch are not difficult) things on strings and re that when you see the jupyter notebook, there is no one single example, and the assigment become on a look for answer on web, which is good, however the intentions to see the videos is to learn and practice, but if you focus only on a self learning, the course lose importance

By Vinamra B

Jun 11, 2020

It was okay. Not as good as rest of the courses. I expected to learn more but was little disappointed. It would have been good if little more explaination was given for the functions that were used to do the tasks. But was Satisfactory. I would recommend you to take the course, but you might need to learn from different sources also to develop a knack in the topics of this course.

By Anand N A

Nov 16, 2019

This course contents was good, but the assessment was really bad. You guys need to fix the autograder issues ASAP and I feel the instructor was not taking as much care as others to set the autograder propertly. Lot of time was unnecessarily wasted I would say as the instructions could have been better. Very disappointed with the assessment. Course is very useful and valuable.

By yiding y

Aug 24, 2018

The assignments in every week are valued practice to get familiar with the knowledge in text mining. For example, regular expression, sentiment analysis, semantic similarity, LDA topic modeling and so on.

However, the videos are sometimes confused and less organized. It could be better if having more details or at least sharing more reading related materials.

By Matthew O

Dec 6, 2019

I found this course the least valuable of the courses in the specialisation so far. The video content wasn't quite as slick/informative, the assignments not quite as useful or well worded, making them ambiguous in a few places and generally it just wasn't quite as good. Not terrible, but just not quite up to the high standards of the other courses so far.

By Yulo L

Jan 15, 2018

The course Assignments could be more clear and consistent with what is actually taught in the class. A good example is when n-grams were required to calculate the similarities, but have actually not been introduced in the video yet.

Also, an expected answers would be nice for the assignments.

Other than that, it was a nice introduction to NLP in Python.

By Brian R v K

Oct 29, 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!

By Cathryn S

Jul 1, 2020

A light introduction to a big topic. The exercises are what make it worthwhile, and take most of the time. I spent 10-15 hours on each 'week' about 2/3 on the exercises.

Be prepared to do a lot of your own research and reading - there are very few readings provided and you'll need to use blogs and other resources to fill out your knowledge.

By Joshua B

Aug 3, 2019

Professor was great and gave engaging and interesting lectures but the course materials were lacking both in maintenance and definitley could have been more in depth. However, one of the mentors (Uwe) was very helpful in his forum posts which made some of the deficiencies in the assignments less of an issue for me. Thanks Uwe!

By Avi A

Jan 16, 2019

Great instructor, but the assignments are a big jump from the course notebooks in terms of difficulty. I also faced numerous issues with the autograder. In the last module, there were wrong pieces of code in the notebook and module (like ROC score being calculated from model.predict() instead of model.predict_proba()).

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 Kyle S

Mar 16, 2021

The exercises at the end of each week were great, but the lectures seemed only "mildly" related to what you had to come up with in the exercises. Pro Tip: Skip quickly through the lectures, because they do not provide much value and you will need to scour the forums and stack exchange for any answers anyway.

By Greg S

Apr 13, 2019

I found this course to be a good introduction to NLP. The lectures where fine as such, but lacked in technical focus making it difficult to tie them to the homework. I expect this is the style of the professor. The homework problems where good, but you do need to work to put it together with the lectures.