Applied Text Mining in Python at Coursera Overview
Duration | 29 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Student Reviews
Applied Text Mining in Python at Coursera Highlights
- 29% started a new career after completing these courses.
- 32% got a tangible career benefit from this course.
- Earn a shareable certificate upon completion.
Applied Text Mining in Python at Coursera Course details
- 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.
Applied Text Mining in Python at Coursera Curriculum
Module 1: Working with Text in Python
Introduction to Text Mining
Handling Text in Python
Regular Expressions
Demonstration: Regex with Pandas and Named Groups
Internationalization and Issues with Non-ASCII Characters
Course Syllabus
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Notice for Auditing Learners: Assignment Submission
Resources: Common issues with free text
Practice Quiz
Module 1 Quiz
Module 2: Basic Natural Language Processing
Basic Natural Language Processing
Basic NLP tasks with NLTK
Advanced NLP tasks with NLTK
Practice Quiz
Module 2 Quiz
Module 3: Classification of Text
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
Module 4: Topic Modeling
Semantic Text Similarity
Topic Modeling
Generative Models and LDA
Information Extraction
Additional Resources & Readings
Post-Course Survey
Keep Learning with Michigan Online
Practice Quiz
Module 4 Quiz
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Applied Text Mining in Python at Coursera Students Ratings & Reviews
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