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Data Science 2 Months Intermediate Live on Zoom

NLP & Text Analytics

Complete NLP course from text preprocessing to Transformers and LLMs. Sentiment analysis, NER, text classification, question answering and more.

4.6 (20 reviews) 130+ enrolled Certificate included

Natural Language Processing is at the heart of the AI revolution. This 2-month course covers classical NLP, modern transformer-based approaches, and practical text analytics applications.

Over 32 live Zoom sessions, you will build real NLP applications: sentiment analyzers, chatbots, document classifiers, question-answering systems, and more. Includes working with Hindi and multilingual text.

  • Tokenization, stemming & lemmatization
  • Stop words & text normalization
  • Bag of Words & TF-IDF
  • Word embeddings: Word2Vec, GloVe, FastText
  • Text classification with ML
  • Regex & pattern matching

  • BERT architecture & fine-tuning
  • Sentiment analysis with transformers
  • Named Entity Recognition (NER)
  • Zero-shot & few-shot classification
  • Question answering systems
  • Text summarization

  • Using LLMs for NLP tasks
  • Text generation & completion
  • Information extraction with LLMs
  • Multilingual NLP (Hindi & regional)
  • Document Q&A with RAG
  • Building NLP APIs

  • Fine-tuning LLMs (LoRA, QLoRA)
  • Topic modeling & clustering
  • Text analytics dashboards
  • NLP pipeline optimization
  • Production deployment patterns
  • Capstone: end-to-end NLP application

What You'll Learn

  • Build NLP applications end-to-end
  • Fine-tune BERT and LLMs for custom tasks
  • Implement sentiment analysis, NER, and classification
  • Create RAG-based document Q&A systems
  • Handle Hindi and multilingual text processing
  • Deploy NLP pipelines to production

Tools & Technologies You'll Master

Python
spaCy
Hugging Face Transformers
NLTK
OpenAI API
FastAPI

Python knowledge is required. Basic ML understanding is helpful but not mandatory.

Live Zoom classes, Monday to Thursday, 7:00 PM to 8:30 PM IST. 4 days per week, 90 minutes each.

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