AAI 302 - Natural Language Processing

Credits: 3
Lecture Hours: 2
Lab Hours: 2
Practicum Hours: 0
Work Experience: 0
Course Type: Voc/Tech

Fundamental concepts in Natural Language Processing (NLP) and text processing. Focus on knowledge and skills necessary to create a language recognition application.
Prerequisite: A grade of C- or better in CIS 189 and AAI 201
Competencies

Outline the data acquisition process in natural language processing (NLP)
1. Contrast how the process varies depending on the datasets being used
2. Compare different storage methods used for NLP datasets
Explore common NLP-focused libraries such as NLTK, TextBlob, spaCy, PyTorch, and Gensim
Apply data visualization techniques used in NLP
Identify different machine learning models (like Naïve Bayes Classifier, Decision Tree Classifier, Random Forest Classifier)
Compare different neural language models
Implement language detection, transliteration, translation, and sentiment analysis for different language scenarios
Develop Python-based use cases and AI projects using best practices

Competencies Revised Date: AY2026