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!
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. :-)
By Joan P•
A lot of issues with the auto graders
By Dhanush P•
Last week is not properly thought
By Imran A G•
Good for basic understanding only
By Abhijit K•
More Hands on is required on it.
Assignments were too difficult.
By Georgios P•
Week 4 was not sufficient
By Yeifer R C•
Is difficult, but good.
By Sara C•
I like the lecturer.
By Xuening H•
By pavan b•
By Aditya M•
By 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...
By 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).
By 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.
By 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.
By Ryan D•
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.
By Warnakulasuriya S A F•
Really was some painful experience. I did manage to get over the first two weeks battling myself in those overrated assignments. But after the 3rd week it was really like a typical lecture in the university where professor reads out his slides and give the assignment to check whether the student was able to stay awake the whole time of his presentation. It was really hard to catch up the gap between those couple of weeks. Not recommended for a beginner or a person who has just entered the field of DS. Believe me, you're in for a really grumpy ride with this course.
By Mandeep G•
After first 2 courses in this specialization this was a real disappointment. The course felt rushed and mostly dealt with how. It was real short on content related to application and why things are being done. Even the assignments didn't provide clarity on how the results are to be interpreted and what could be ther real world implications.
Material needs to be expanded to ensure that this course is not just to show how python can be used for text mining but also to include examples of where and how this is useful.
By Deleted A•
The overall material was good. That being said, this is the first time I have taken a MOOC course and felt like 90% of the time I spent was fighting with the auto grader. The instructions in many instances were unclear, so when you are dealing with a grading system that grades items as 100% correct, vs 100% incorrect with really no feedback as to what you did wrong it can be very frustrating. Without the Discussion forums there is no way I would have ever figured out what to do for some parts of assignments.
By Stephen S•
Out of the 5 courses in the specialization, this course was not up to the level of the other courses. Full of theory not much practical explanations and there wasn't much practice modules in each week just like other modules.
For Assignments, i was not even able to refer any module in this course to check for syntaxes. It was very tough for me to solve assignments as there was no reference in the videos or practice modules.
Need huge improvements in the course.
By Massimo A•
Course packed of information and topics in four weeks so it feels sometimes rushed.
Especially the forth week (topic modelling, information extraction, semantic similarity and generative models all in one week) feels disconnected from the rest .
The exercises do not help too much, with several mistakes and ambiguity.
Nevertheless, the theme is really interesting. Possibly the errors can be corrected in the next runs.
Plus for using Python and NLTK.
By Anand M•
The course is very boring & way of explaining concepts is not great at all.I have wasted lot of my time in understanding the assignment questions & sorting the autograder issues .I guess its high time that teachers should revamp the course considering the quality vs the price a student is paying for the course .
There are better courses on udemy which I could have taken & they explain concepts in a very simple manner rather than boring methods.
By Chris M•
Content in the course is interesting and given the amount of data stored in text very valuable. However, I would encourage the staff to provide more coding examples. I would also suggest moving away from assignments and towards projects - (a) projects would likely force more comprehension instead of code shopping and (b) the autograder is terrible: I can't believe the amount of time I wasted because the autograder was not set up properly.
By Mark H•
I was disappointed by the lectures in this course. My impression is that extremely complex concepts are mentioned in passsing and poorly explained, while a large amount of time is spent on trivial examples. The programming assignments are more interesting and appropriately challenging (compared to other courses in the specialization), but leave me without any confidence that I could accomplish a text mining task in python independently.
By Dan B•
It's really unacceptable that there should be errors with the autograder (which were left unfixed) and I wasted a lot of time trying to debug code which was actually working. As well this course did a good job with the introduction to the concepts in the first two weeks and then dropped the ball with content that appears rushed and disorganized. The LDA and other concepts need to be presented better.