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Avis et commentaires pour d'étudiants pour Applied Text Mining in Python par Université du Michigan

4.2
étoiles
3,659 évaluations

À propos du cours

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....

Meilleurs avis

JR

4 déc. 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.

CC

26 août 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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51 - 75 sur 705 Avis pour Applied Text Mining in Python

par Aziz J

18 déc. 2017

This class was fantastic. It was an order of magnitude times better than the previous course, 'Applied Machine Learning,' by Kevyn Collins-Thompson. Professor V. G. Vinod Vydiswaran started most lectures with a purpose and an alluring example. He spent a good amount of time building intuition behind the algorithms and techniques involved, and saved most of the coding for challenging and satisfying homework assignments--all qualities that the previous course did not have.

Finally, professor V. G. Vinod Vydiswaran was simply energetic about teaching. I didn't have to change playrate to > 1.2x. I genuinely enjoyed his teaching style.

This course has restored my faith in the 'Applied Data Science with Python' specialization by University of Michigan and I am confident in my ability solve text classification problems in Python. Highly recommended, along with the first two courses in this specialization.

par Vaibhav S

26 juin 2018

I never knew, that the data that is present over the internet can provide such fascinating details, from which we can infer a lot. The teaching methodology of Professor Vinod where he introduces to the very basic concepts of this course, and then slowly and steadily moves to some of the core concepts of NLP is really fantastic. This course gives you all the key ingredients you need to create advanced NLP projects using python programming language.

par Yusuf E

18 avr. 2018

Very good overview of the NLP tasks. The assignments were again really challenging and required a lot of navigating the documentation and forums. The autograder is really frustrating sometimes though especially when it can't upload your file and you miss that part and change your correct code. Again, the assignments are really difficult without help from the forums but it was worth it.

par Kedar J

9 nov. 2018

Great course! The assignments were at times hard to understand. Thanks to the wonderful support from the fellow students and mentors in the discussion forums, you will get most of the clarifications. Would recommend completing first 3 courses of this specialization before this one. There are a plenty of new concepts and new libraries introduced in this course.

par Milan B

8 mai 2020

I have been really interested in text mining for his wide applications. This course is very nice, it gives all the bases to deal with text mining problems! However, there could have been a Jupyther Notebook to put in applications the bases with Python about Topic Modeling in order to be more confortable for the Assignement 4.

par Yunfeng H

27 mars 2019

This is a very helpful courses for text mining. It starts with cleaning data and then gradually build up the skills to classify and group texts. I love all the case studies. The assistant walks me through the tasks using the tools and methodologies mentioned in the lectures. It also helps to solve the assignments.

par Daniel N

7 sept. 2017

I enjoyed the course and have found the topics very interesting. One criticism is that the general quality of notebooks provided with example codes wasn't as high as for other courses in the specialization.However the lecturer was really nice and gave very good explanations even for complicated concepts.

par Γεώργιος Κ

24 avr. 2018

The lessons are useful, and all of the knowledge is a must have. Some things could go deeper, some needed more explanation. As a result this is a must have course for text mining but I think that the level is introductory and in real world one must have more skills to perform a respected text mining.

par Jan Z

7 sept. 2018

Great course overall. I have learned a lot, but last week had no tutorial example covering the topic and w4 assignment was not literally described resulting in spending a huge amount of time on trying which possible solutions will be accepted by autograder. Discussion forum helped a lot though.

par Víctor L

14 févr. 2018

An excellent course, it gives a full introduction to text mining, what it is useful for, covers different techniques, provides challenging activities. Maybe it lacks of a practical activity in Week 4 before the assessment, but overall the course has very good content and an excellent instructor

par Brian L

19 oct. 2017

Great course! I have been doing some text mining in another tool, and I learned some useful things that I was able to put to use almost immediately ... now that I have the data science part in hand, I just need to figure out some Python details in order to format my output for my client.

par Davide T

18 août 2017

Great teacher, great course. Topics are very interesting and well explained, assignments' difficult is just right. I'm sure they will put this review in some kind of sparse matrix in order to train a classifier and make previsions for future students...so it is a must-join course!

par Praveen R

10 déc. 2019

I learnt about NLTK package and its capabilities. It was good to know how to build vocabulary and guess missing words and match sentences lemmatizing them. Good eye opener course. There is way much more to be learnt in this subject. This is just an introduction (a good one).

par Denys B

15 juil. 2021

T​he course is pretty great as an introduction on text mining, topic modelling, etc. Not nearly as bad as some of the reviews suggest, but not as good as others in specialization. Read assignment directions very carefully and you'll have no issues with autograder.

par Dipanjan G

16 août 2020

Very Nicely taught course. Lots of example and week 1 case study was full of learning. But this course needs a bit of revamp as the transformer models such as BERT, Roberta and other self attention transformer models has completely changed the NLP landscape

par David R

17 mai 2018

When looking at the full course in coursera, I was thinking that would be the course which would interest me the least, but at it turned out, now I'm really interested in text mining, and I'm planning to read more publication to understand that field

par Binil K

15 août 2017

This is a fantastic course though you might find some trouble with the grading part (Auto grading). This course will give you a good understanding about the various most useful techniques in text mining. Course is well structured and really helpful

par LENDRICK R

11 mai 2019

Well-taught course, I'd been struggling with regular expressions, thank you for simplifying the concept; additionally, you've opened my eyes to an entirely new world of data science for which I can think of an immediate productive application. :-)

par BrajKishore P

30 janv. 2020

The overall course was well designed, all lectures were arranged in a proper sequence and all the slides and jupyter notebooks were good covering all the aspects, but I felt some difficulties in the 2nd week in POS tag, overall it was too good.

par Eunjae J

26 août 2017

This was hard but worth it. However, it didn't have extensive coding examples, which made it pretty hard to apply techniques on assignment. It might be a good way to induce creative thinking but very painstaking for students. Be aware!

par Varga I K

5 mars 2019

It was a great course about data mining. It covered the basics well. I would have liked maybe another week covering the topic distribution and it would be really nice if there were a notebook for every code shown in the videos.

par Αθανάσιος Σ

25 mai 2018

Exceptional!

I was using up to now strictly regular expressions for text mining, and that was a headache.

This course opened a new whole world to me! I strongly recommend it to any one that wants to use ML to study texts

par Val G

25 août 2019

Great course! Even fighting with the grader didn't spoil the joy from learning new things)) Forum with useful comments of classmates is really a big deal. Thank you everyone who succeeded and shared their findings.

par Vinayak N

8 oct. 2019

Well-structured, awesome pedagogy and challenging assignments. It has all the elements it takes to make a MOOC epic! Thanks UMich and Coursera for helping put this course together and allowing me to pursue it.

par Jitesh S

9 avr. 2020

Assignments were very well designed and tested the understanding thoroughly. The lessons were crisp and the instructor had put in a really great effort in designing the overall layout.

Kudos to professor.