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

4.2
2,169 ratings
410 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

CC

Aug 27, 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!

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. :-)

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176 - 200 of 402 Reviews for Applied Text Mining in Python

By Muhammad S J

Mar 08, 2019

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.

By Ayush A

Jul 14, 2018

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.

By Archit A

Jul 11, 2018

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!

By Christopher H

Aug 11, 2018

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.

By Tarrade F

Aug 17, 2018

Nice course, good introduction

By YOGESH K M

Sep 01, 2018

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.

By Pushpendra S

Oct 15, 2018

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

By Kai H

Nov 05, 2018

Some of the codes are not shown in Jupyter notebook. The assignment statements are not so clear, need to resort to Discussion board for additional information.

By bictor

Nov 06, 2018

Very interesting

By Vo C C

Nov 09, 2018

The assignments are a little hard and have some errors, but the overall explanation is awesome.

By Rahila T

Nov 15, 2018

Good

By Ankit G

Nov 18, 2018

basic and nice course

By Liran Y

Apr 07, 2018

Great Content.

By Aditya h

Jul 09, 2018

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

By Beda K

Aug 27, 2017

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.

By Srinivas K R

Sep 16, 2017

A good course which introduces you to the basics of text processing and text mining in python and exposes you to tools such as regex, nltk and gensim. While the lectures and assignments do promote this learning, a lot of the criticism that is directed at the course is due to the auto-grader issues. You can easily side-step a lot of these problems by going through the forums. However, I do think that the course could have been better planned and executed, even IF the only purpose is applied text mining for e.g., better context and some exposure to theory or at least pointers to where more material could be found for self-study would have been helpful. However, I did learn some things from the class giving me a push towards learning more on the subject on my own.

By Pankaj K

Jan 07, 2018

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!

By Mauro G

Oct 03, 2017

The lectures are in my opinion too concise. The programming assignments are very interesting. Perhaps the week 1 programming assignment is too complex.

By Charles F

Sep 20, 2017

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.

By Oscar J O R

Sep 02, 2017

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.

By Han C

Sep 01, 2017

Learned lots of new stuff, but some details are not established well including autograder issue at the last assignment. Hope this gets cleared out soon.

By Leo C

Feb 17, 2018

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.

By Carl W S

Sep 01, 2017

Overall, a solid course, though it felt a bit like a face-to-face lecture course recorded to video. The material was helpful and well-explained, but I feel it could benefit from taking advantage of the MOOC medium more effectively, such as by providing code sample notebooks for the students to run and modify, which have been very helpful to me in understanding the material in other courses in the same specialization.

By Traci L J

Dec 07, 2017

I learned a lot about regular expressions, how to use NLTK to parse words and parts of speech, and to apply machine learning techniques from the third course to text.

The homework assignments were finicky with the autograder and often there was a lot of frustration regarding the exact data types of the output. I spent a lot of type debugging over simple things that could have been clarified in the assignment description. However, the discussion forums are active and people are willing to give feedback!

By João R W S

Aug 23, 2017

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).