D24
Data & AIIntermediate10 weeks
Natural Language Processing
Learn to build language-understanding systems — from tokenization and classical NLP through transformer fine-tuning and retrieval-augmented LLM applications.
What you'll learn
Text preprocessing, tokenization, and core linguistics techniques like POS tagging and NER
Classical NLP methods including TF-IDF, word embeddings, and sequence models
Fine-tuning transformer models with Hugging Face for classification, NER, and QA
Building and deploying retrieval-augmented LLM applications with vector search
Course details
Duration
10 weeks
Best for
Developers with Python experience who want to specialize in building language-based AI systems
On completion
Verified certificate & placement assistance
Course Curriculum
A module-by-module breakdown of what you'll cover in Natural Language Processing.
01
Text Preprocessing & Linguistics Foundations
Weeks 1–2- String handling, regex, and Unicode normalization
- Word and subword tokenization
- POS tagging and named entity recognition
- Dependency and constituency parsing with spaCy
02
Classical NLP & Word Representations
Weeks 3–4- Bag-of-words, n-grams, and TF-IDF
- word2vec and GloVe embeddings
- Cosine similarity for document comparison
- Building and comparing document classifiers
03
Sequence Models & Evaluation
Weeks 5–6- RNN and LSTM fundamentals
- Sequence labeling and seq2seq architectures
- BLEU, ROUGE, and span-level evaluation metrics
- Confusion matrices and error analysis
04
Transformers & Fine-Tuning
Weeks 7–8- Self-attention and positional encoding
- BERT and GPT-family architectures
- Fine-tuning for classification, NER, and QA with Hugging Face
- WordPiece and BPE tokenizers
05
Building with LLMs
Weeks 9–10- Prompt engineering and few-shot prompting
- Embeddings and semantic search
- Retrieval-augmented generation and vector databases
- Serving NLP models with FastAPI and Docker
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
Python
spaspaCy / NLTK
HugHugging Face Transformers
PyTPyTorch
LanLangChain
VecVector databases (Pinecone / FAISS)
APIFastAPI
WeiWeights & Biases
Hands-on Projects
This is project-based learning — you'll build real, portfolio-ready work as you go, not just watch lectures.
Resume and invoice information extraction using rule-based and statistical NER to pull structured fields from documents
Support ticket triage and sentiment classifier comparing a TF-IDF baseline against a fine-tuned DistilBERT model
Abstractive text summarization service fine-tuned on a domain-specific corpus and evaluated with ROUGE
Semantic search and RAG assistant that answers questions over a custom document set with citations
Production NLP API serving a fine-tuned classification model with monitoring and drift detection
Every course includes a guided internship on top of your project work — so you graduate with real experience, not just a certificate.
Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Student Reviews
“Going from TF-IDF to fine-tuning BERT and finally building a RAG assistant gave me a real sense of when each approach actually earns its complexity.”
Divya Rajagopalan
NLP Engineer
“The linguistics fundamentals early on made the transformer material click much faster. The production NLP API project is now the centerpiece of my portfolio.”
Karthik Iyer
Machine Learning Engineer
