AI & Data Science

Natural Language Processing (NLP) & Transformers

NLP enables computers to understand, interpret, and generate human language. Master tokenization, BERT embeddings, decoder transformers, LoRA fine-tuning, and evaluation metrics.

Curated 2026 Curriculum GuideProject-Based Track

HuggingFace TransformersPyTorchspaCyLoRA / QLoRAvLLMPython

🇮🇳 Indian Market Benchmark

Expected CTC Range₹10.0L – ₹28.0L LPA

Estimated Timeline14 – 18 Weeks

Demand Scope6,000+ High-Pay Openings

Experience LevelAdvanced

Top Hubs:Bengaluru, Gurugram, Hyderabad, Pune

Explore Career Compass Match

Core Track Highlights

Core discipline driving modern Generative AI and voice assistants

Substantial research and product roles in Indian Indic-language AI labs

Commands highest tier package ceilings in tech

Structured Phase-by-Phase Syllabus

Focus on build-by-doing milestones rather than passive video consumption.

Weeks 1-4

Phase 1: Classical NLP & Embeddings

TF-IDF, word2vec, FastText, subword tokenization (BPE)
Named Entity Recognition (NER) and POS tagging with spaCy
Sentiment classification with logistic regression vs Bi-LSTM

🎯 Milestone Proof Project: Customer Review Aspect-Based Sentiment Classifier.

Weeks 5-9

Phase 2: Transformers & BERT Architectures

Self-attention mechanism and positional encodings
BERT, RoBERTa for classification and token tasks
HuggingFace Trainer API and evaluation pipelines

🎯 Milestone Proof Project: Indian Legal Document Named Entity & Clause Extractor.

Weeks 10-14

Phase 3: LLM Fine-Tuning & Quantization

Instruction tuning with LoRA and QLoRA
Quantization (GGUF, AWQ, GPTQ) for low-latency GPU serving
Serving models with vLLM and TensorRT-LLM

🎯 Milestone Proof Project: Fine-Tuning Llama-3 on Indic Medical Q&A Dataset.

Technical Interview Questions & Answers

Q1: How does Multi-Head Attention work in Transformer models?

Multi-Head Attention projects Queries, Keys, and Values into multiple lower-dimensional subspaces, allowing the model to attend to information from different representation positions and semantic contexts simultaneously.