Chevron Left
Back to Applied Text Mining in Python

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

4.2
1,985 ratings
382 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

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.

Filter by:

151 - 175 of 374 Reviews for Applied Text Mining in Python

By Roberto L L

Mar 01, 2019

This an excellent course to open a door for NLP, an exciting topic.

By Christian E

Mar 27, 2019

Very good content

By Kristin A

Jan 30, 2019

A good intro to NLTK and text mining in Python, though sometimes the effort put in to render an assignment acceptable to the autograder was a headache.

By Lalit S

Jan 29, 2019

Awesome

By Lucas S R

Feb 08, 2019

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.

By Darius T

Apr 02, 2019

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.

By Keary P

Apr 14, 2019

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.

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.