Applied Text Mining in Python Course Syllabus

A hands-on, intermediate-level course that equips you with practical text mining and NLP skills using Python and NLTK. This course spans four core modules and a final project, designed to be completed in approximately 4 weeks with a time commitment of 3-5 hours per week. You'll engage in real-world text processing tasks, from cleaning raw text to building classification models and discovering latent topics in document collections.

Module 1: Working with Text in Python

Estimated time: 4 hours

• Reading text files and handling file paths
• Interpreting UTF-8 encoding and character representation
• Tokenization into words and sentences using Python
• Writing regular expressions for pattern matching
• Cleaning sample text files and extracting dates

Module 2: Basic Natural Language Processing

Estimated time: 4 hours

• Introduction to the NLTK toolkit and its core functions
• Tokenization, stemming, and lemmatization techniques
• Part-of-speech tagging and syntactic analysis
• Stop-word removal and text normalization
• Feature derivation from processed text

Module 3: Text Classification and Supervised Learning

Estimated time: 4 hours

• Converting text to numerical representations (e.g., bag-of-words)
• Training and evaluating Naive Bayes classifiers
• Building document classification pipelines
• Performing sentiment analysis on real datasets
• Handling imbalanced datasets in text classification

Module 4: Topic Modeling and Document Similarity

Estimated time: 4 hours

• Understanding probabilistic topic models with LDA
• Vector space representations of documents
• Measuring document similarity using cosine similarity
• Clustering documents by thematic content
• Interpreting latent topics from real corpora

Module 5: Final Project

Estimated time: 6 hours

• Apply text preprocessing and cleaning techniques to a new dataset
• Build and evaluate a supervised text classification model
• Perform topic modeling using LDA and interpret results

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• Experience with data manipulation using Python libraries (e.g., pandas)

What You'll Be Able to Do After

• Clean and preprocess raw text using regular expressions and normalization techniques
• Represent and manipulate text data in Python, including tokenization and encoding
• Use the NLTK framework for part-of-speech tagging and feature extraction
• Build and evaluate supervised text classification pipelines for tasks like sentiment analysis
• Apply topic modeling and document similarity methods to uncover themes in text corpora