Applied Text Mining in Python

Overview

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.

Syllabus

Module 1: Working with Text in Python
Module 2: Basic Natural Language Processing
Module 3: Classification of Text
Module 4: Topic Modeling

Taught by

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

Reviews

2.0 rating, based on 2 Class Central reviews

4.2 rating at Coursera based on 3809 ratings

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Will Wheeler

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…

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!
Raivis Joksts

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.

Taught by

Tags

Applied Data Science with Python

Introduction to Data Science in Python

Applied Machine Learning in Python

Text Preprocessing

Applied Plotting, Charting & Data Representation in Python

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