Applied Text Mining in Python

Coursera Applied Text Mining in Python

Platform
Coursera
Provider
University of Michigan
Length
4 weeks
Language
English
Credentials
Paid Certificate Available
Part of
Course Link
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.

WHAT YOU WILL LEARN
  • Apply basic natural language processing methods
  • Describe the nltk framework for manipulating text
  • Understand how text is handled in Python
  • Write code that groups documents by topic
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
Author
Coursera
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