Foundations of NLP: Introduction to NLP: Syntax, Semantics, Morphology, Word Representation: One-hot Encoding, Bag-of-Words (BoW), Term Frequency – Inverse Document Frequency (TF-IDF), Language Models: n-grams, Neural Network-based Word Embedding Algorithms, Advanced Text Embeddings: Word2Vec, GloVe, FastText, Contextual Embeddings (BERT, ELMo, GPT, XLNet, RoBERTa, Sequences and Sequential Data
Machine Learning and Deep Learning for NLP: Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Gated Recurrent Units (GRUs), Sequence to Sequence Modelling, Encoder-Decoder Architectures, Attention Mechanism, Transformer Networks, Topic Modelling: LSA, LDA, Dynamic Topic Models
Practical Tools and Applications: NLP Toolkits (Eg. NLTK, SpaCy, Stanford NLP, OpenNLP) and case studies, Applications: Part-of-Speech Tagging, Named Entity Recognition (NER), Dependency Parsing, Sentiment Analysis, Machine Translation, Text Summarization, Evaluation Metrics, Visualization of Text Data, Emerging Trends: Zero-shot and Few-shot Learning, Multilingual and Cross-lingual Models, Explainable AI, Ethical Considerations