Advanced NLP Specialization

📝 Overview

Go beyond transformers to master specialized NLP tasks!

Time: 2-3 months | 150-200 hours
Prerequisites: Phases 1-8 complete
Outcome: Build production NLP applications

Use this track when you want classic and applied NLP depth beyond general prompting or LLM chat interfaces. It fits especially well if you care about extraction, summarization, multilingual systems, or document workflows.

📚 What You’ll Learn

• Named Entity Recognition (NER)
• Machine Translation
• Text Summarization (extractive & abstractive)
• Sentiment Analysis at scale
• Information Extraction
• Question Answering systems
• Text Generation fine-tuning
• Multi-language NLP

🗂️ Module Structure

nlp/
├── 00_START_HERE.ipynb
├── 01_ner.ipynb
├── 02_translation.ipynb
├── 03_summarization.ipynb
├── 04_sentiment_analysis.ipynb
├── 05_information_extraction.ipynb
├── projects/
│   ├── meeting_summarizer/
│   ├── contract_analyzer/
│   └── multilingual_support/
└── README.md

🎯 Key Projects

1. Meeting Summarizer - Auto-summarize transcripts
2. Contract Analyzer - Extract key terms from legal docs
3. Multilingual Support - Translation + sentiment across languages
4. Research Assistant - Extract insights from papers

How To Use This Track Well

• Start with extraction, summarization, and QA tasks before trying to cover every NLP problem family.
• Compare task-specific pipelines against general LLM baselines so you can see where specialized NLP still matters.
• Pair this track with evaluation work from the main curriculum to avoid relying on subjective quality checks.

What Comes Next

• Continue to ../../12-llm-finetuning/README.md if you want to adapt models for your NLP tasks.
• Continue to ../../15-ai-agents/README.md if you want tool-using language systems on top of NLP workflows.
• Continue to ../../28-practical-data-science/README.md if you want applied project depth and portfolio work.

Start here: 00_START_HERE.ipynb