Get started with custom lists to organize and share courses.

Sign up

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Applied Text Mining in Python

University of Michigan via Coursera

2 Reviews 139 students interested
  • Provider Coursera
  • Subject Data Science
  • Cost Free Online Course (Audit)
  • Session In progress
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 4 weeks long
  • Learn more about MOOCs

Taken this course? Share your experience with other students. Write review

Overview

Sign up to Coursera courses for free Learn how

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.

Taught by

Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero and V. G. Vinod Vydiswaran

Help Center

Most commonly asked questions about Coursera Coursera

Reviews for Coursera's Applied Text Mining in Python
2.0 Based on 2 reviews

  • 5 star 0%
  • 4 star 0%
  • 3 star 50%
  • 2 star 0%
  • 1 star 50%

Did you take this course? Share your experience with other students.

Write a review
  • 1
Will W
1.0 2 years ago
by Will is taking this course right now.
This class is rife with errors. The main problem is the very finicky autograder, which is frequently programmed incorrectly and often gives no useful feedback.

Other problems include readings in the first week that rely on modules from later weeks, incomplete instructions (e.g., how to break ties in a sorted list), and use of Python 2.7 in examples (although the class is in Python 3.5).

At the beginning of the course (but not in the advertised materials), they emphasize "self-learning," which really means going to the discussion forums and using Google to look up errors.

Because of the problems with the autograder and the emphasis on self-learning, estimated completion times are wildly inaccurate. The first week's assignment is beyond ridiculous, and people on the discussion forums report taking 20 or as much as 43 hours for a stated three-hour assignment!
Was this review helpful to you? Yes
Raivis J
3.0 6 months ago
Raivis completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
The topic is interesting, however as with the Machine Learning course from UM, this one suffers from too much theoretically focused graded assignments, and would benefit from more practical real life example tasks.
Was this review helpful to you? Yes
  • 1

Class Central

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free

Never stop learning Never Stop Learning!

Get personalized course recommendations, track subjects and courses with reminders, and more.