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

4.2
stars
3,800 ratings

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

BK

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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.

CB

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Excellent course! Video lectures are high quality, with realistic problems and applications. Exercises are reasonably challenging, and all quite fun to do! Strongly recommend this course

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76 - 100 of 740 Reviews for Applied Text Mining in Python

By Jean-Michel P

Jun 2, 2021

After 4 courses in the stack, this one is the worst. My tip would be to look on youtube for a tutorial of the topic at hand and skip the UoM lecture.

By Sean M

Apr 17, 2021

lectures didnt cover material in assignments specifically week 4. lead to a lot of supplementary research and headache.

By Luis d l O

Nov 20, 2017

Too simple. Few information and content, and extremely simple (though with a lot of problems) assignments.

By Dario M

Jul 19, 2019

The difficulty of the assignments is in no way related to the simpleness of the lectures.

By Akshat S

Mar 26, 2020

The NLTK library was not explained properly. No code explanation was provided.

By Zhongtian Y

Aug 2, 2020

Teacher does not explain well and lectures are not detailed.

By PS

Nov 26, 2020

Horrible assignment and horribl week 3 and 4. Please avoid!

By Jose A P L

Mar 23, 2019

Este curso no vale para nada, por favor no lo hagais!!!

By Vivek G

Dec 2, 2019

Only useful for coarse understanding of the topic.

By Yu C

Oct 14, 2021

poorly prepared lectures, assignment instructions

By kumar s

Apr 26, 2020

tired of auto grader,...and prof not interested

By Feng Q

Oct 4, 2019

totally can't understand the Indian accent.

By Mahmoud

Apr 29, 2019

the worst ever I took here in Coursera

By Sandy F

Apr 25, 2020

worst course in the series by far.

By Christopher S

Apr 22, 2021

Horrible, I lost motivation

By Sourav P

Dec 9, 2018

I was not satisfied

By Angertdev S

Nov 7, 2019

broken assignments

By David P

Feb 7, 2022

Obsolete course

By Lawrence T

Jan 23, 2021

Horrible!

By Aziz J

Dec 18, 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.

By Vaibhav S

Jun 26, 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.

By Yusuf E

Apr 18, 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.

By Kedar J

Nov 9, 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.

By Milan B

May 8, 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.

By Yunfeng H

Mar 27, 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.