NLP

Natural Language Processing

Coming SoonIntermediate to Advanced

Natural Language Processing

Dive deep into language models, transformers, and modern NLP techniques. Build applications that understand and generate human language using state-of-the-art methods and frameworks.

What You'll Master

Core NLP Techniques

  • • Text preprocessing and tokenization
  • • Named Entity Recognition (NER)
  • • Part-of-speech tagging and parsing
  • • Sentiment analysis and classification
  • • Topic modeling and clustering

Advanced Models

  • • Transformer architectures and attention
  • • BERT, GPT, and T5 model families
  • • Large language model fine-tuning
  • • Building conversational AI systems
  • • Multilingual and cross-lingual NLP

Comprehensive Curriculum

Weeks 1-3: NLP Fundamentals

Text processing, linguistic foundations, and classical NLP techniques

Weeks 4-6: Machine Learning for NLP

Feature engineering, traditional ML models, and evaluation metrics

Weeks 7-9: Deep Learning & Transformers

Neural networks for NLP, attention mechanisms, and transformer architecture

Weeks 10-12: Modern Language Models

BERT, GPT models, fine-tuning strategies, and transfer learning

Weeks 13-14: Applications & Projects

Building end-to-end NLP applications and capstone project development

Hands-On Projects

Core Projects

  • • Sentiment analysis system
  • • News classification pipeline
  • • Named entity extraction tool
  • • Text summarization engine

Advanced Projects

  • • Chatbot with intent recognition
  • • Question-answering system
  • • Language translation model
  • • Document search and retrieval

Tools & Technologies

Python Libraries

  • • NLTK & spaCy
  • • Hugging Face Transformers
  • • scikit-learn
  • • Gensim

Deep Learning

  • • PyTorch
  • • TensorFlow
  • • Keras
  • • FastAPI

Cloud & Deployment

  • • Docker containers
  • • AWS/GCP services
  • • Model deployment
  • • API development

Prerequisites

!

Strong Python Programming

Proficiency in Python, data structures, and object-oriented programming

+

Machine Learning Basics

Understanding of ML concepts, supervised learning, and basic statistics

~

Deep Learning Knowledge

Familiarity with neural networks and deep learning frameworks (helpful but not required)

Ready to Master Natural Language Processing?

Duration: 14 weeks • Projects: 8+ Hands-on Applications

Build powerful NLP applications that understand and generate human language.

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