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

4.2
1,700 calificaciones
325 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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1 - 25 de 326 revisiones para Applied Text Mining in Python

por David M

Nov 06, 2018

It is no exaggeration to say it took me longer to complete this course than the first 3 courses in the specialty and the time was utterly wasted. I wouldn't object if I felt like I was learning new skills but it is mostly battling a poorly constructed course, with terrible assignments, a broken autograder and a Professor who is utterly disinterested in the education of his students.

When considering this course we need to separate the subject (which is fascinating) and the tools (which seem quite powerful) from the course itself. I had really high hopes during the lectures in week 1, where the videos are stronger and close to a well taught university lecture than others in the specialisation. However the assignments and the autograder issues are too great to ignore! Assignments are poorly worded (in one question it is literally trial and error) and the autograder often breaks. There are cases of people spending 10+ hours on work getting incredibly frustrated by the lack of feedback to find out the solutions were correct and the autograder was playing up.

por lcy9086

Feb 22, 2019

I would see autograder and unspecific instructions ruin this course.....Sometimes you know how to get the answer and the answer looks just right! but you still cannot get passed! I would not be taking this course if it was not part of this Specialization........ Improvements need to be made!

por Li Q

Aug 10, 2018

Very painful going through this course although i have quite well coped with course 1-3.

But this course seems lack of systematic structure of building the knowledge, it just walked through the topics quickly and extensively. I had to spend a few hours to learn about the whole structure of text mining to build in-depth knowledge, more than 20 hours to watch the online nltk & genism tutorials cause i m new to text mining & nlp.

just hope the course can simplify the complicated topics such as where we are in the whole process, what's it, why we need it, working theory, coding, how we use these parameters, etc. to make life easier.

por Aryan P

May 15, 2018

Instructor does not explain concepts, just superficially goes through subjects.

Some lectures lack coherence between subjects. you wouldn't know what is the relation between topics.

But it introduces some basic stuff which worth knowing anyway.

por Jian G

Mar 07, 2019

This is almost a waste of my time. The structure can be clearer and the connection to Python is outdated. The assignments are poorly designed. The instruction is not effective.

por Alejandro C M

Feb 10, 2019

The instructor provided very low quality material.

por Michael T B

Jan 29, 2019

Instructor was poor. Inadequate coverage of the material in the lectures, some questions not clear as to what was expected. You can do better reading a book on this subject on your own.

por Niccolo A H

Oct 23, 2018

Curriculum is valuable but the course quality isn't on par with the other Applied Data Science using Python courses by University of Michigan. Week 4 assignment doesn't do enough to bring all the previous topics together in a realistic application. Week 3 lectures and notebook have teach the use of a scoring function wrongly - an issue addressed in forum threads for months but no edits to the video lectures and notebook have been made as of yet.

por Eklavya s

Aug 05, 2018

This course makes you give up on data science and MOOCs.

Seriously, the content is poorly presented he keeps on speaking , telling 2-3 lines about a function and so on.

I highly recommend stay away from this pathetic specialization.

por Мирзабекян А В

Jul 19, 2018

The most discouraging course in specialization.

por David W

Apr 05, 2018

Unclear assignment instructions, buggy autograder, and no instructor help.

por David C

Aug 08, 2017

I really wanted to like this course, and there were some redeeming features, but overall I'm unable to recommend it in its current state. IMO, the lectures were at much too high a level while the programming assignments were very detailed with vague instructions and little guidance. There was no link between what was discussed in class and how the fine details of the assignments were to be understood. In addition, the course was published with errors in the auto-grader and no resources in the Resources link (not even slide decks from the lectures, so to review material you were forced to re-visit all the recorded lectures which was very inefficient). My recommendation to Coursera and the Univ of Michigan is to completely re-do the course, doubling the number of lectures to provide not just the broad overview of the topics, but also some detailed descriptions of recommended ways to implement what was discussed. I would also recommend using Professor Andrew Ngs Machine Learning course as a guide for how to create great programming assignments, with detailed PDFs (typically 5-6 pages) describing what is to be done AND WHY (linking back to the lectures) and "telling a story" that is cohesive and leads the student to create something end-to-end (in small steps) that does something amazing by the end. The programming assignments in this course seemed, in contrast, to be a shotgun blast of "do this", "create this", "make this happen" with little context of how the small pieces fit together or what the overall goal of the assignment is to accomplish -- and at the end, a feeling of "I passed the autograder's expections, but have no idea what I've really done or why". There were so many great things that could have been done with the Text Mining topic, and this course touched on just a few in a very haphazard way that simply left me confused and wondering why I spent so much time to learn so little.

por Harshith S

Jun 19, 2019

What an improvement from the previous few courses. The instructor teaches much better. I could mine text in my sleep now

por Pengcheng Z

Jun 19, 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.

por Thúllio D M Z

Jun 17, 2019

The module 4 could have more hours. The key concepts are passed too fast and doesn't have a notebook with the classes content.

por DEBAYAN M

Jun 12, 2019

Excellent! Too Good!

por Anurag B

Jun 12, 2019

Great Content!! Productive Session!!

por Juan M

Jun 11, 2019

a bit abstract at times.

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 Ron B

May 28, 2019

I am a Data Engineer with a degree in Computer Science who wanted to learn more about Natural Language Processing for a small project I wanted to build. I had no prior knowledge of NLP other than some regular expression work from college and a basic knowledge of what tokenizing, tagging and classification were at a high level. This course was a great introduction into the field and has given me a solid applicable foundation to continue my education. I wanted something that was light in theory and heavier in application and this course hit a great balance. Contrary to many of the other reviews, I didn't have a problem with the autograder, most of the time I got an answer incorrect was due to not reading the question carefully enough. The assignments were great in my opinion and actually helped drive home the points made in the lectures. I recommend this class to anyone who wants to get their feet wet in the subject.

por John H E O

May 28, 2019

This course for me was demanding! I truly enjoyed all the concepts I learned.

por kaushal

May 25, 2019

it's great course to learn text mining in python , you will find many good examples which are related to real worlds problems

por Shadi A

May 24, 2019

Best instructor and teaching method

por Sarah H H

May 21, 2019

Loved this course. the pacing, the instruction. it flowed and I felt i could execute what I learned without too much head scratching due 'missing leaps of info'.

por MANCINI L

May 19, 2019

In general, the course is good, lesson explanations are excellent but it lacks of pratical lessons. Assignments are quite difficult in comparison with the material of the course lesson. It took me a lot of time to do them and understand where my mistakes were.