Training on Introduction to Natural Language Processing (NLP)

Module 1: Text Processing and Representation

• Text preprocessing techniques: tokenization, stemming, lemmatization
• Stop words and punctuation removal
• N-grams and word frequency
• Bag-of-words and TF-IDF representation
• Word embeddings: Word2Vec, GloVe
• Case Study: Processing and representing text data

Module 2: Linguistic Features and Text Analysis

• Part-of-speech tagging
• Named entity recognition
• Dependency parsing
• Sentiment analysis and emotion detection
• Topic modeling: LDA, LSA
• Case Study: Analyzing sentiment in text data

Module 3: Text Classification and Categorization

• Text classification fundamentals
• Classical ML approaches: Naive Bayes, SVM, logistic regression
• Deep learning for text classification
• Multi-class and multi-label classification
• Evaluating classification models
• Case Study: Building a text classifier for a specific domain

Module 4: Modern NLP with Transformers

• Introduction to attention mechanisms
• Transformer architecture and BERT
• Fine-tuning pre-trained models for specific tasks
• Zero-shot and few-shot learning in NLP
• Key applications and use cases
• Case Study: Fine-tuning a transformer model for classification

Module 5: NLP System Design and Advanced Topics

• Building end-to-end NLP systems
• Handling multilingual and low-resource languages
• Data collection and annotation for NLP
• Model deployment and scaling
• Ethical considerations in NLP
• Case Study: Designing an NLP system for a business problem

Module 6: Advanced Text Preprocessing and Feature Engineering

• Advanced preprocessing techniques
• Handling noisy and unstructured text data
• Feature engineering for NLP models
• Building custom text features
• Automating feature extraction with libraries
• Case Study: Advanced text preprocessing for a complex dataset

Module 7: Sequence-to-Sequence Models and Applications

• Understanding sequence-to-sequence models
• Machine translation with seq2seq
• Text summarization and generation
• Question answering and conversational AI
• Evaluating seq2seq models
• Case Study: Building a machine translation model

Module 8: Named Entity Recognition (NER) and Information Extraction

• Introduction to NER and information extraction
• Rule-based and ML-based NER approaches
• Deep learning for NER with BiLSTM-CRF
• Extracting relations and events from text
• Applications of information extraction
• Case Study: Building a named entity recognition system

Module 9: Advanced NLP with Transformers and LLMs

• Transformer variants and architectures
• Fine-tuning transformers for various tasks
• Prompt engineering and few-shot learning
• Retrieval-augmented generation (RAG)
• Building applications with large language models
• Case Study: Building an application with a large language model

Module 10: NLP Deployment and Ethics

• Deploying NLP models to production
• Scaling NLP applications for real-time use
• Managing NLP model performance and drift
• Bias and fairness in NLP systems
• Ethical and responsible NLP practices
• Case Study: Deploying and monitoring an NLP application