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1. Introduction to Natural Language Processing
- Definition and scope of NLP
- History and evolution of NLP
- Applications of NLP: Machine translation, sentiment analysis, chatbots, text summarization
- Challenges in NLP: Ambiguity, context, and diversity in natural languages
2. Basics of Language and Linguistics
- Elements of natural language: Phonetics, morphology, syntax, semantics, and pragmatics
- Language models and grammars
- Parts of speech (POS) tagging
- Lemmatization and stemming
- Tokenization and word segmentation
3. Text Preprocessing
- Cleaning text data: Stopword removal, punctuation handling
- Normalization techniques: Lowercasing, stemming, lemmatization
- Handling special characters and emojis
- N-grams and bag-of-words (BoW) model
- Term frequency-inverse document frequency (TF-IDF)
4. Language Modeling
- Statistical language models: Unigram, bigram, trigram
- Word embeddings, Word2Vec: Skip-gram and CBOW
- GloVe (Global Vectors for Word Representation), FastText
- Contextual embeddings: ELMo, BERT, GPT
5. NLP Techniques and Algorithms
- Text classification
- Naive Bayes, Logistic Regression, SVM
- Neural network-based approaches
- Named Entity Recognition (NER)
- Sentiment analysis
- Topic modeling: Latent Dirichlet Allocation (LDA)
- Sequence labeling and tagging
6. Parsing and Syntax Analysis
- Dependency parsing
- Constituency parsing
- Chunking and shallow parsing
- Syntax trees and grammar rules
7. Advanced NLP Models
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Units (GRUs)
- Transformers
- Attention mechanism
- Transformer architecture: BERT, GPT, T5
- Sequence-to-sequence models
- Applications in machine translation and text summarization
8. NLP Applications
- Machine translation: Rule-based, statistical, and neural approaches
- Text summarization: Extractive and abstractive methods
- Question answering systems
- Chatbots and conversational agents
- Speech-to-text and text-to-speech systems
- Information retrieval and search engines
9. NLP Tools and Libraries
- Natural Language Toolkit (NLTK)
- spaCy
- Stanford CoreNLP
- Gensim for topic modeling
- Hugging Face Transformers
- TensorFlow and PyTorch for deep learning-based NLP
10. Ethical and Social Implications
- Bias in NLP models
- Privacy and data security concerns
- Ethical considerations in deploying NLP systems
- Handling offensive and harmful content
11. Case Studies and Projects
- Sentiment analysis on Twitter data
- Building a chatbot using Rasa or Dialogflow
- Text summarization of news articles
- Machine translation using Transformer models
- Named entity recognition on legal or medical documents
- Topic modeling on large datasets
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