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.
Overview
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
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Reviews
2.0 rating, based on 2 Class Central reviews
4.2 rating at Coursera based on 3809 ratings
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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…
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Raivis JokstsThe 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.
