Natural Language Processing (NLP)

Module 1: Introduction to NLP

• What is NLP and key application areas
• NLP pipeline overview: text input to structured output
• Challenges in NLP: ambiguity, context, and language diversity
• NLP tools and libraries: NLTK, spaCy, and Hugging Face overview

Module 2: Text Preprocessing and Representation

• Tokenisation: word, sentence, and subword tokenisation
• Stopword removal, stemming, and lemmatisation
• Bag-of-words, TF-IDF, and n-gram representations
• Text normalisation and cleaning best practices

Module 3: Classical NLP Techniques

• Part-of-speech tagging and dependency parsing
• Named entity recognition with spaCy
• Coreference resolution overview
• Rule-based NLP with regex and pattern matching

Module 4: Text Classification and Sentiment Analysis

• Supervised text classification with Naive Bayes and SVM
• Sentiment analysis: lexicon-based and ML approaches
• Multi-class and multi-label text classification
• Evaluation metrics: accuracy, precision, recall, and F1

Module 5: Word Embeddings and Semantic Representations

• Word2Vec: CBOW and Skip-gram models
• GloVe and FastText word embeddings
• Sentence embeddings with Sentence-BERT
• Visualising and analysing word embedding spaces

Module 6: Sequence Models and Attention

• RNNs, LSTMs, and GRUs for sequence modelling
• Sequence-to-sequence models for translation and summarisation
• Attention mechanism explained intuitively
• Introduction to transformer architecture for NLP

Module 7: Transformer-Based NLP with Hugging Face

• BERT for text classification and NER fine-tuning
• GPT-2 for text generation tasks
• T5 for summarisation, translation, and Q&A
• Practical fine-tuning and evaluation with Hugging Face Trainer

Module 8: Hands-on Projects and Assessment

• Building a sentiment analysis system for product reviews
• NER and information extraction pipeline project
• Text summarisation application with transformer models
• Final assessment and course certification

Experience in the Industry Learn from NLP researchers and engineers who have built text classification, sentiment analysis, information extraction, and conversational AI systems for enterprise applications.

Backgrounds at the Top Our NLP trainers have worked on language AI projects at leading technology companies, research institutions, and AI-focused consultancies across India and globally.

Clear & Effective Teaching NLP fundamentals including tokenisation, POS tagging, NER, text classification, and language modelling are taught clearly from traditional methods to modern transformer-based approaches.

Hands-On Learning Focus Students build practical NLP projects including sentiment analysis, text summarisation, and named entity recognition using NLTK, spaCy, and Hugging Face Transformers.

Up-to-Date Knowledge Trainers continuously update the curriculum with the latest NLP advances including large language models, multilingual NLP, and domain-specific fine-tuning techniques.