This hands-on, project-driven bootcamp provides comprehensive coverage of Natural Language Processing fundamentals through advanced LLM applications. Designed for rapid skill acquisition, students will master everything from foundational text processing to building production-ready AI systems.
- 100% Practical: Every concept reinforced through live coding and labs
- Industry-Aligned: Projects mirror real-world AI applications
- Modern Stack: Latest tools (Transformers 4.x, spaCy 3.x, PyTorch 2.x)
- Career-Ready: Portfolio projects for interviews and resumes
- Zero Setup: All work in Google Colab with GPU acceleration
- Text Preprocessing & Feature Engineering: Tokenization, normalization, subword modeling (BPE, WordPiece, SentencePiece)
- Representation Learning: Word2Vec, GloVe, BERT, GPT embeddings — static vs. contextual
- Transformer Architecture: Attention mechanisms, encoder-decoder models, fine-tuning strategies
- Applied NLP Tasks: Classification, NER, summarization, QA, sentiment analysis
- LLM Integration: Prompt engineering, RAG systems, LangChain workflows
- Multimodal AI: Vision-language models (CLIP, BLIP), text-to-image understanding
- Conversational AI & Chatbots
- Document Intelligence & Summarization
- Sentiment & Opinion Mining
- Information Extraction (NER, RE)
- Content Generation & Augmentation
- Understand the NLP pipeline and its role in modern AI
- Master text cleaning, normalization, and tokenization
- Implement preprocessing using industry-standard libraries
- Introduction to NLP: Applications across search, translation, QA, generation
- The NLP Pipeline: Raw text → tokens → features → models → predictions
- Text Preprocessing Essentials:
- Cleaning (HTML tags, special characters, Unicode normalization)
- Tokenization strategies (word, sentence, subword)
- Stopword removal and its trade-offs
- Lemmatization vs. stemming with practical examples
Lab 1.1: Text cleaning pipeline with NLTK and spaCy
Lab 1.2: Comparative tokenization (whitespace, regex, spaCy, Transformers)
Lab 1.3: Building a custom preprocessing function for Twitter data
📓 Colab: Day 1 – Text Preprocessing Lab
Clean and tokenize a real dataset (movie reviews or tweets) and analyze token distributions
- spaCy Linguistic Features: https://spacy.io/usage/linguistic-features
- NLTK Tokenization Guide: https://www.nltk.org/api/nltk.tokenize.html
- Extract numeric features from text using TF-IDF and n-grams
- Understand subword tokenization and its advantages
- Implement BPE, WordPiece, and SentencePiece algorithms
- Classical Feature Extraction:
- Bag of Words (BoW) and its limitations
- Term Frequency-Inverse Document Frequency (TF-IDF)
- N-grams for capturing context (bigrams, trigrams)
- Subword Tokenization Deep Dive:
- The vocabulary problem in NLP
- Byte Pair Encoding (BPE) — used by GPT models
- WordPiece — used by BERT models
- SentencePiece — language-agnostic approach
- Handling out-of-vocabulary (OOV) words
Lab 2.1: Build TF-IDF features and train a simple classifier
Lab 2.2: Implement BPE from scratch (educational)
Lab 2.3: Use Hugging Face tokenizers (GPT-2, BERT, T5)
Lab 2.4: Visualize vocabulary coverage across tokenization methods
📓 Colab: Day 2 – Feature Engineering Lab
Compare tokenization strategies on multilingual text and analyze efficiency
- Hugging Face Tokenizers: https://huggingface.co/docs/tokenizers
- BPE Paper: Neural Machine Translation of Rare Words with Subword Units
- Understand embedding space geometry and semantic relationships
- Compare static (Word2Vec, GloVe) vs. contextual (BERT, GPT) embeddings
- Visualize and interpret embedding spaces using dimensionality reduction
- Why Embeddings?: From sparse one-hot to dense representations
- Static Embeddings:
- Word2Vec (CBOW vs. Skip-gram)
- GloVe (Global Vectors)
- FastText (subword-aware embeddings)
- Contextual Embeddings:
- ELMo (bi-directional LSTM)
- BERT (bidirectional Transformers)
- GPT (autoregressive Transformers)
- Embedding Properties:
- Semantic similarity and analogies (king - man + woman ≈ queen)
- Bias in embeddings and mitigation strategies
Lab 3.1: Train Word2Vec on custom corpus using Gensim
Lab 3.2: Load pre-trained GloVe and explore word analogies
Lab 3.3: Extract BERT embeddings using Transformers library
Lab 3.4: Visualize embeddings with t-SNE and PCA
Lab 3.5: Measure semantic similarity and solve analogy tasks
📓 Colab: Day 3 – Embeddings Lab
Build a semantic search engine using embeddings and cosine similarity
- Word2Vec Paper: Efficient Estimation of Word Representations
- BERT Paper: Pre-training of Deep Bidirectional Transformers
- Illustrated Word2Vec: https://jalammar.github.io/illustrated-word2vec/
- Master fundamental NLP tasks: POS tagging, NER, dependency parsing
- Understand Transformer architecture and self-attention mechanism
- Implement sequence labeling and classification tasks
- Linguistic Analysis:
- Part-of-Speech (POS) tagging
- Dependency parsing and constituency parsing
- Named Entity Recognition (NER)
- Transformer Architecture:
- Self-attention mechanism explained
- Multi-head attention and positional encoding
- Encoder-only (BERT), Decoder-only (GPT), Encoder-Decoder (T5)
- Fine-tuning Strategies:
- Feature extraction vs. full fine-tuning
- Layer freezing and learning rate scheduling
- Task-specific heads
Lab 4.1: POS tagging and dependency visualization with spaCy
Lab 4.2: NER with pre-trained models (BERT, RoBERTa)
Lab 4.3: Build a custom NER model using Transformers
Lab 4.4: Visualize attention patterns in BERT
Lab 4.5: Implement sequence classification from scratch
📓 Colab: Day 4 – Core NLP Tasks Lab
Build a domain-specific NER model (e.g., medical entities, job skills)
- Attention Is All You Need (Transformer paper)
- The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/
- Hugging Face Course: https://huggingface.co/course
- Fine-tune Transformer models for classification tasks
- Implement sentiment analysis and emotion detection
- Evaluate models using appropriate metrics
- Text Classification Fundamentals:
- Binary vs. multi-class vs. multi-label classification
- Class imbalance and sampling strategies
- Sentiment Analysis:
- Document-level vs. aspect-based sentiment
- Handling sarcasm and context
- Evaluation Metrics:
- Accuracy, Precision, Recall, F1-score
- Confusion matrix analysis
- ROC-AUC for binary classification
- Transfer Learning:
- Pre-training → fine-tuning paradigm
- Choosing the right base model
- Hyperparameter tuning strategies
Lab 5.1: Sentiment classification on IMDB reviews using BERT
Lab 5.2: Multi-class emotion detection (joy, anger, sadness, etc.)
Lab 5.3: Aspect-Based Sentiment Analysis (ABSA)
Lab 5.4: Model evaluation and error analysis
Lab 5.5: Hyperparameter tuning with Weights & Biases
📓 Colab: Day 5 – Text Classification Lab
Build a fake news detector or clickbait classifier with >85% accuracy
- Hugging Face Fine-tuning Tutorial
- Scikit-learn Metrics: https://scikit-learn.org/stable/modules/model_evaluation.html
- Implement text summarization (extractive and abstractive)
- Build conversational AI systems using LLMs
- Explore question answering and text generation
- Text Summarization:
- Extractive methods (TextRank, LexRank)
- Abstractive methods (T5, BART, Pegasus)
- Evaluation metrics (ROUGE, BERTScore)
- Conversational AI:
- Rule-based vs. retrieval-based vs. generative chatbots
- Dialogue state tracking
- Context management in conversations
- Question Answering:
- Extractive QA (SQuAD-style)
- Open-domain QA and retrieval-augmented generation (RAG)
- Text Generation:
- Decoding strategies (greedy, beam search, sampling)
- Controlling generation (temperature, top-k, top-p)
Lab 6.1: Build an extractive summarizer using TextRank
Lab 6.2: Fine-tune T5 for abstractive summarization
Lab 6.3: Create a FAQ chatbot using sentence transformers
Lab 6.4: Implement extractive QA with BERT
Lab 6.5: Build a RAG system using LangChain and vector databases
📓 Colab: Day 6 – Advanced Applications Lab
Create a domain-specific document summarizer or customer service chatbot
- BART Paper: Denoising Sequence-to-Sequence Pre-training
- LangChain Documentation: https://python.langchain.com/
- Apply learned concepts to solve a real-world problem
- Present technical work effectively
- Evaluate and critique NLP systems
- Morning Session: Project implementation with instructor support
- Afternoon Session: Testing, evaluation, and documentation
Each student/team presents:
- Problem statement and motivation
- Approach and methodology
- Results and evaluation metrics
- Challenges and learnings
- Future improvements
- Dataset Selection: Finding quality datasets (Kaggle, HuggingFace Datasets, Papers with Code)
- Experiment Tracking: Using Weights & Biases or MLflow
- Model Deployment: Gradio demos and Streamlit apps
- Ethics & Limitations:
- Bias in language models
- Privacy concerns with text data
- Environmental impact of large models
- Responsible AI practices
📓 Colab: Day 7 – Project Template
Complete and present a production-ready NLP application
| Project | Description | Key Techniques | Datasets |
|---|---|---|---|
| Sentiment Analysis Dashboard | Analyze customer reviews with visualizations | BERT fine-tuning, Gradio UI | Amazon Reviews, Yelp |
| Email Spam Classifier | Detect spam vs. legitimate emails | TF-IDF, Naive Bayes, BERT | SpamAssassin, Enron |
| Text Autocomplete System | Build a smart text suggestion system | GPT-2, n-gram models | WikiText, OpenWebText |
| Project | Description | Key Techniques | Datasets |
|---|---|---|---|
| News Article Summarizer | Abstractive summarization of news | T5, BART, ROUGE evaluation | CNN/DailyMail, XSum |
| Resume Parser & Skill Extractor | Extract structured info from resumes | Custom NER, spaCy, regex | Kaggle Resume Dataset |
| Multi-lingual Sentiment Analyzer | Sentiment across different languages | XLM-RoBERTa, mBERT | Multilingual Amazon Reviews |
| Fake News Detection System | Classify misinformation | BERT, ensemble methods, LIME | LIAR, FakeNewsNet |
| Project | Description | Key Techniques | Datasets |
|---|---|---|---|
| Domain-Specific Chatbot | Healthcare/Legal/Finance Q&A bot | RAG, LangChain, vector DB | PubMedQA, FiQA |
| Aspect-Based Sentiment Analysis | Fine-grained opinion mining | ABSA models, attention viz | SemEval ABSA |
| Document QA System | Answer questions from documents | Extractive QA, retrieval, T5 | SQuAD, Natural Questions |
| Code Documentation Generator | Auto-generate docstrings | CodeBERT, CodeT5 | CodeSearchNet |
| Hate Speech & Toxicity Detector | Identify harmful content | RoBERTa, Perspective API | HateXplain, Jigsaw |
| Category | Tools | Version | Purpose |
|---|---|---|---|
| NLP Frameworks | Hugging Face Transformers | 4.35+ | Pre-trained models & fine-tuning |
| spaCy | 3.7+ | Industrial-strength NLP | |
| NLTK | 3.8+ | Classic NLP algorithms | |
| Deep Learning | PyTorch | 2.0+ | Model training & inference |
| TensorFlow/Keras | 2.14+ | Alternative framework | |
| LLM Tools | LangChain | 0.1+ | LLM application framework |
| OpenAI API | - | GPT-3.5/4 integration | |
| Embeddings | Sentence Transformers | 2.2+ | Semantic similarity |
| Gensim | 4.3+ | Word2Vec, FastText | |
| Visualization | Matplotlib, Seaborn | - | Data visualization |
| Plotly | - | Interactive plots | |
| Utilities | Pandas, NumPy | - | Data manipulation |
| scikit-learn | 1.3+ | ML utilities & metrics |
- Gradio: Quick ML demos and interfaces
- Streamlit: Data apps and dashboards
- FastAPI: REST API development
- Docker: Containerization for deployment
- Weights & Biases: Experiment management
- TensorBoard: Training visualization
- MLflow: Model versioning
Upon completing this bootcamp, students will be able to:
✅ Preprocessing: Clean, normalize, and tokenize text data for ML pipelines
✅ Feature Engineering: Extract TF-IDF, n-grams, and embeddings from text
✅ Model Development: Fine-tune Transformers for classification, NER, QA, and generation
✅ Evaluation: Apply appropriate metrics and perform error analysis
✅ Deployment: Create interactive demos and APIs for NLP models
✅ Transformer Mastery: Understand attention mechanisms and model architectures
✅ LLM Integration: Build RAG systems and prompt engineering workflows
✅ Real-world Applications: Develop chatbots, summarizers, and classifiers
✅ Best Practices: Implement efficient data pipelines and model optimization
✅ Project Portfolio: 7+ hands-on projects showcasing NLP expertise
✅ Industry Readiness: Practical experience with production NLP tools
✅ Problem-solving: Ability to design and implement NLP solutions end-to-end
✅ Communication: Present technical work clearly to stakeholders
- Python Programming: Comfortable with functions, classes, and libraries
- Basic ML Knowledge: Understanding of supervised learning concepts
- Mathematics: Linear algebra basics (vectors, matrices)
- Familiarity with Jupyter notebooks
- Basic understanding of neural networks
- Git/GitHub for version control
- Google account for Colab (no local setup needed!)
- Stable internet connection
- Optionally: GitHub account for project hosting
- Daily lab completion (30%)
- Mini challenges (20%)
- Capstone project (40%)
- Presentation quality (10%)
- Attend all 7 sessions
- Complete at least 5/7 daily labs
- Submit and present final project
- Achieve 70%+ overall score
- 09:00-10:30: Theory session with live demonstrations
- 10:30-10:45: Break
- 10:45-12:45: Hands-on lab with instructor support
- 12:45-13:00: Q&A and mini challenge briefing
- Flipped classroom: Pre-reading materials shared 1 day prior
- Live coding: All concepts demonstrated in real-time
- Pair programming: Collaborative problem-solving
- Code reviews: Instructor feedback on implementations
- Office hours: Additional support via Discord/Slack
- ✨ Fast-track learning: Comprehensive NLP in just 1 week
- 💼 Career boost: Portfolio projects for job applications
- 🚀 Latest tech: Work with state-of-the-art models (GPT, BERT, T5)
- 🎓 Hands-on focus: 70% practical labs, 30% theory
- 📈 Upskill teams rapidly: Transform beginners into NLP practitioners
- 💡 Immediate ROI: Students build real applications
- 🔧 Industry-relevant: Tools and projects mirror production systems
- 🤝 Collaborative learning: Team projects foster knowledge sharing
- Discord Community: Real-time Q&A and peer support
- Code Repository: All notebooks, datasets, and solutions
- Reading List: Curated papers and blog posts
- Office Hours: 1-on-1 support sessions
- Alumni Network: Connect with past students
- Advanced Track: Recommendations for continued learning
- Career Support: Resume reviews and interview prep
- Lifetime Access: All course materials remain available
Ideal for: Computer Science, AI/ML, and Data Science students at NIELIT Chandigarh seeking intensive, practical NLP training for academic projects, research, or industry careers.
Course Format: 7 consecutive days, 4 hours/day (28 total hours)
Difficulty: Beginner to Intermediate
Class Size: Maximum 30 students for personalized attention
Prerequisites: Python programming and basic ML concepts
For questions or registration inquiries, please contact:
NIELIT Chandigarh Training Department
Transform from NLP novice to practitioner with hands-on projects, modern tools, and expert guidance. Join us for an intensive learning experience that will accelerate your AI career!
Last Updated: November 2025
Version: 1.0
Instructor: Lovnish Verma