Great material with practical applications! I utilized a lot from this course in my work! I think the assignments should be made a little bit more clearer, specially the first one. Took a lot of time to do the first one, due to some exceptions that were not mentioned in the exercise, at least one should mention that there might be cases other than specified here.

Overall a great course! Thanks!

Good introduction into the field of text mining, but very brief. I think the structure could do with some fine tuning as for example the extraction of features from text is left mostly untouched or is covered by the home work only. All in all I found it slightly less well structured than the previous parts in the series, but it was still very useful and helpful as a starting point.

I am a Self Driving Car Engineer, I have worked with deep learning but i wanted to know about Machine Learning So i was exploring here. I am new to Text mining and not interested much, but it was worth exploring and to to know potential of Test Mining. Course was very well summed up for me as a this is new for me. Content was good enough to start and hit some practical questions.

The course presented a good content for beginners in NLP and I feel confident to start using what I learned in my work. But, the grader for the assignments is too slow and buggy, this should be fixed so new learners don't feel frustrated. In addition, for assignment 4, the lda trainning parameters are not viable for trainning in coursera's environment, it should be reviewed.

The course content is very interesting and high quality; however, the video slides include code that is not available in e.g. jupyter notebooks. Also, the assignment markers do not give any useful feedback - more than half of the time spent was usually when 99% of the task was complete but some very minor detail threw the marker off.

It is interesting, cognitive and very useful. But, there were very few answers from the teaching staff in the discussions at the forum. In previous courses of this and other specializations, the teaching staff took an active part in the forum and this greatly helped in understanding and fulfilling the tasks of the course.

A good brief introduction to test mining with python. The professor attempts to explain the topics well. Good rigor of the assignments. How ever for the last module , absence of explaination with a notebook is strongly felt as the concepts get deeper in understanding and woud have helped with the last assignment.

Good intro into NLP and NLTK. Assignments provided great hands on practice with NLTK, SciKit Learn and regular expressions. Could use additional materials for key concepts such as sentiment analysis and ngrams. Could also use a more real world case study for the final project.

Great course! very much handy if you are looking for a 'Text processing in Python' primer. The good thing about the course is that it explains the libraries. For example - NLTK vs SciPy for applying ML on text. What's missing, is the Deep Learning aspects of text processing

Course content has to be modified, the instructor has to more in depth in some of the topics especially the final week topics. Rest apart, I enjoyed the course, the assignments and quizzes are of optimal length and difficulty. Thanks for making this course!

Lecture materials are not comprehensive enough to solve the assignments. Course is dependent on precursor courses in the specialization. Assignments often require reference from upcoming weeks. lectures are inadequate. The course is average at its best

Overall course is very usefull for me. But there is lot of detail is missing in week 4. Wordnet and Gensim usage, Detail about the LDA and semantic similarity. I hope next time there is separate video lecture for detailed about Semantic simalarity.

Nice introduction to the topic and interesting tools. The evaluation system could be improved adding more resources focused on the use of the nltk functions or giving some advice about the critical points in the Python demonstrations.

The course material is good. The main issue with this course are some of the assignments, which are pretty complicated, are not explained well enough and sometimes don't even test the knowledge of understanding text mining.

Not well organized. Some of the assignments took way too much time. Instructor's code could have been written out better and could have explained the topics in detail before expecting students to sort through the mess

This course offers a good package of skills with great notebooks (except week 4) and assignments. The videos could be a lot better but mostly understandable. You can always search Youtube for better explained videos.

The course was good, but as I progressed in the course, the approach for code began slackening off, as it felt to me. Topics are discussed well, but the implementation in code was something that took a star away.

Course is great except for the auto grader issues. Please look into the issue. I would like to take this opportunity and thank Prof V. G. Vinod Vydiswaran and all those who helped me to complete it.

Very good course with very good material and teachers. I just missed some more practical examples to follow along the classes, and more further readings (specially for information extraction).

Love the focus on conceptual text processing and practical guides to implementation in python, but the assignment grader was extremely specific for no reason, especially the Week3 assignment.

It would help to connect the teaching better to the assignments. Especially in Week 4 there is to little connection. There is not even a course notebook to practice some of the material.

The course itself is good, but the assigment system is not robust and some sentences are also ambiguous to users. Seeing from the forums, many users get confused in the assigments.

Excellent course for someone like me who is ambitious and aspires to gain knowledge on new things. The videos can be made bit more elaborate, seems to be rushing towards the end.

Passionate instructor and a great primer on how software can infer useful data from text. Gives a preliminary understanding on the algorithms used in scikit learn and nltk.

A little bit stretched my python skill, but learned a lot. Forum is a good place, and maybe next I will join some study group online or offline to have more discussions.