CIS 240 - Natural Language Processing

1. Introduction to NLP Foundations:

   • Understand the fundamental principles and significance of NLP in today’s technological landscape.
   • Explore the evolution of NLP and its impact on various industries.

2. Text Preprocessing and Analysis:

   • Learn to clean, preprocess, and transform raw text data into suitable formats for analysis.
   • Master techniques to analyze text data, extract features, and perform exploratory analysis.

3. Machine Learning Techniques in NLP:

   • Discover how machine learning algorithms are applied to solve NLP challenges, including classification, clustering, and regression.
   • Understand text representation methods such as Bag of Words, TF-IDF, and Word Embeddings.

4. NLP Models and Algorithms:

   • Gain proficiency in applying models like Naive Bayes, Support Vector Machines, and Recurrent Neural Networks (RNNs) for various NLP tasks.
   • Explore algorithms for sentiment analysis, part-of-speech tagging, and named entity recognition.

5. Language Understanding and Generation:

   • Acquire knowledge of language models, syntax, and semantics to comprehend and generate human-like text.
   • Learn about techniques for machine translation, summarization, and dialogue systems.

6. Practical Applications and Projects:

   • Apply NLP techniques to real-world applications, such as sentiment analysis in social media, topic modeling, and chatbot development.
   • Engage in hands-on projects to reinforce understanding and gain practical experience.

7. Ethical Considerations in NLP:

   • Understand the ethical implications of AI and NLP, emphasizing bias detection, privacy preservation, and responsible AI deployment.
   • Learn strategies to develop unbiased and inclusive NLP models.

8. Tools and Frameworks for NLP:

   • Gain hands-on experience with popular NLP tools and frameworks such as NLTK, spaCy, and TensorFlow.

1. Introduction to Natural Language Processing (NLP)
   1. Course Overview and Orientation
   2. Applications of NLP
   3. NLP Machine Learning Models
   4. Different Vectorization Techniques
2. Data Acquisition and Storage for NLP
   1. Different Dataset Types in NLP (Structured and Unstructured Data)
   2. Introduction to Cloud Storage
3. Data Curation in NLP: Tools and Sources
   1. The Importance of Data Curation
   2. Explaining the Data Curation Process
   3. Tools: NLTK, TextBlob, spaCy, Gensim
4. NLP Data Visualization
   1. Significance of Data Visualization in NLP
   2. Comparison of Different NLP Data Visualization Types
   3. NLP Data Visualization Techniques: N-grams, Word Clouds, Named-Entity Recognition
5. NLP Data Preprocessing
   1. Explanation of Text Vectorization
   2. Methods of Text Vectorization: Bag of Words (BoW), TF-IDF, Word2Vec
   3. Understanding Text Classification and Similarity Measuring Methods (Cosine Similarity, Jaccard Similarity, Euclidean Similarity)
6. Neural Language Models
   1. Comparison and Contrasting of Neural Language Models
   2. Basic Architecture and Working of Neural Network Models: N-gram, Sequential Models, Recurrent Neural Network
   3. Understanding Named Entity Recognition (NER) Models
7. NLP Model Deployment
   1. Introduction to Machine Learning Model Deployment
   2. Platforms for Model Deployment: Local and Cloud Deployment
   3. Tools Required for Deployment: Streamlit, Flask
8. Building a Chatbot
   1. Overview of Chatbots and Their Functionality
   2. Applications of Chatbots
   3. Tools for Chatbot Development: Chatteron and Heroku
   4. Services for Chatbot Deployment
9. LSTM, Transformers, and BERT
   1. Introduction to Neural Network Architectures: LSTM, RNN
   2. Introduction to Pretrained Models: BERT

Facilitated Discussion 20%-30%

Facilitated Group Work 40%-50%

 
English Prerequisite(s): None
Math Prerequisite(s): None
Course Corerequisite(s): CIS 210 
Course-Specific Placement Test: None
Course Aligned with IRW: NA
Consent to Enroll in Course: No Department Consent Required