Course Outline
Module 1: Introduction to Natural Language Processing (NLP)
- What is NLP?
- The history of NLP
- Applications of NLP
Module 2: Natural Language Processing Fundamentals
- Language representation: Tokens, n-grams, and vectors
- Language modeling: Unigrams, bigrams, and trigrams
- Part-of-speech tagging (POS)
- Named entity recognition (NER)
Module 3: Text Classification
- Naïve Bayes classification
- Support vector machines (SVMs)
- Neural networks for text classification
Module 4: Text Preprocessing
- Tokenization
- Normalization
- Stemming and lemmatization
- Stop word removal
Module 5: Text Generation
- N-gram-based language models
- Recurrent neural networks (RNNs)
- Generative adversarial networks (GANs)
Module 6: Natural Language Understanding (NLU)
- Question answering
- Machine translation
- Sentiment analysis
Module 7: Natural Language Applications
- Chatbots
- Virtual assistants
- Text summarization
- Machine translation
