IIRF Online > Development > Data Science > Natural Language Processing > Applied Text Mining in Python
Applied Text Mining in Python by Coursera
Course Highlights
- Understand how text is handled in Python.
- Apply basic natural language processing methods.
- Write code that groups documents by topic.
- Describe the nltk framework for manipulating text.
Skills you will learn!
Curriculum
15 Topics
Introduction to Text Mining
Handling Text in Python
Regular Expressions
Demonstration: Regex with Pandas and Named Groups
Internationalization and Issues with Non-ASCII Characters
Syllabus
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Notice for Auditing Learners: Assignment Submission
Resources: Common issues with free text
Module 1 Quiz
Practice Quiz
Assignment 1
Introduce Yourself
Working with Text
Regex with Pandas and Named Groups
9 Topics
Basic Natural Language Processing
Basic NLP tasks with NLTK
Advanced NLP tasks with NLTK
Application: Spell Checker
Module 2 Quiz
Practice Quiz
Assignment 2
Finding your own prepositional phrase attachment
Module 2
10 Topics
Text Classification
Identifying Features from Text
Naive Bayes Classifiers
Naive Bayes Variations
Support Vector Machines
Learning Text Classifiers in Python
Demonstration: Case Study - Sentiment Analysis
Module 3 Quiz
Assignment 3
Case Study - Sentiment Analysis
11 Topics
Semantic Text Similarity
Topic Modeling
Generative Models and LDA
Information Extraction
Additional Resources & Readings
Post-Course Survey
Keep Learning with Michigan Online
Course 4 complete! ✔️ Time to celebrate
Module 4 Quiz
Practice Quiz
Assignment 4
Applied Text Mining in Python
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