AIM 232 - Natural Language Processing

1. Introduction to Natural Language Processing (NLP)
2. Text preprocessing and tokenization
3. Linguistic foundations and text representation
4. Language modeling and sequence processing
5. Feature extraction and sentiment analysis
6. Named entity recognition and part-of-speech tagging
7. Machine translation and summarization
8. Ethics, bias, and applications in NLP

1. Explain key techniques in Natural Language Processing (NLP) and their real-world applications across industries. (1, 3, 4, 5, 6, 7, 8)
2. Apply appropriate data acquisition and storage methods to prepare datasets for NLP tasks.(2, 3)
3. Demonstrate the use of NLP libraries to perform core language processing tasks. (2, 3, 5)
4. Apply data preprocessing techniques and machine learning methods to classify and analyze text (2, 3, 4, 5)
5. Compare neural language model architectures and their capabilities. (4, 7, 8)
6. Implement NLP tasks such as language detection, transliteration, translation, and sentiment analysis using Python-based tools. (4, 5, 6, 7)
7. Develop simple NLP projects that integrate preprocessing, modeling, and evaluation techniques to address real-world language problems. (2, 3, 4, 5, 6, 7, 8)
8. Design and deploy basic chatbots using tools like Chatteron and platforms such as Heroku. (5, 6, 7, 8)
9. Discuss advanced NLP models and their influence on human€"computer interaction. (4, 7, 8)