Associate Professor
Applied Text Mining in Python
What You'll Learn
- 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
4 Modules
24 Hours
6 hrs per module (approx.)
Rating
About Applied Text Mining in Python
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.
Skills You'll Gain
- Python For Data Analysis
- Python (Programming Language)
- Text Classification
- Text Mining
- Text Processing
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Applied Text Mining in Python introduces learners to techniques for extracting meaning from text data. You will work with text representations, regular expressions, natural language processing methods, and text classification. The course emphasizes practical skills for cleaning, analyzing, and modeling text using Python libraries, preparing you to apply text mining techniques to real-world problems.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Working with Text in Python
- Video: Introduction to Text Mining
- Reading: Syllabus
- Reading: Help us learn more about you!!
- Video: Handling Text in Python
- Reading: Notice for Auditing Learners: Assignment Submission
- Ungraded Lab: Working with Text
- Video: Regular Expressions
- Ungraded Lab: Regex with Pandas and Named Groups
- Video: Demonstration: Regex with Pandas and Named Groups
- Graded Assignment: Practice Quiz
- Video: Internationalization and Issues with Non-ASCII Characters
- Discussion Prompt: Introduce Yourself
- Reading: Resources: Common issues with free text
- Ungraded Lab: Assignment 1
- Graded: Module 1 Quiz
- Graded: Assignment 1 Submission
Module 2: Basic Natural Language Processing
- Video: Basic Natural Language Processing
- Ungraded Lab: Module 2
- Video: Basic NLP tasks with NLTK
- Video: Advanced NLP tasks with NLTK
- Graded Assignment: Practice Quiz
- Discussion Prompt: Finding your own prepositional phrase attachment
- Ungraded Lab: Assignment 2
- Graded: Module 2 Quiz
- Graded: Assignment 2 Submission
Module 3: Classification of Text
- Video: Text Classification
- Video: Identifying Features from Text
- Video: Naive Bayes Classifiers
- Video: Naive Bayes Variations
- Video: Support Vector Machines
- Video: Learning Text Classifiers in Python
- Ungraded Lab: Case Study - Sentiment Analysis
- Video: Demonstration: Case Study - Sentiment Analysis
- Ungraded Lab: Assignment 3
- Graded: Module 3 Quiz
- Graded: Assignment 3 Submission
Module 4: Topic Modeling
- Video: Semantic Text Similarity
- Video: Topic Modeling
- Video: Generative Models and LDA
- Graded Assignment: Practice Quiz
- Video: Information Extraction
- Reading: Additional Resources & Readings
- Ungraded Lab: Assignment 4
- Graded: Module 4 Quiz
- Graded: Assignment 4 Submission
Grading Policy
There are assignments in each module that account for 25% of your final grade. There is one quiz (5%) and one programming assignment (20%).
Intermediate Level
Some related experience required
