BBS-AIIM - Natural Language Processing and AI Course Materials

This repository contains comprehensive materials for a Natural Language Processing and AI course, organized into progressive modules covering classical NLP techniques through modern AI applications. The content spans from basic text processing to advanced multi-agent systems and fine-tuning techniques.

Repository Structure

Module 1: Text Preprocessing and Fundamentals

• 01_Text_Preprocessing_Pipeline.ipynb - Complete preprocessing pipeline using spaCy and NLTK
• 02_Modern_Tokenization_Comparison.ipynb - Modern tokenization techniques
• 03_Advanced_spaCy_Features.ipynb - Advanced spaCy functionalities

Module 2: Traditional NLP and Embeddings

• 01-N-grams.ipynb - N-gram language models
• 02-Bag-of-Words.ipynb - BoW implementation with sklearn
• 03-Text-Classification-Project.ipynb - Practical text classification
• 04_Feature_Engineering_Text_Data.ipynb - Feature engineering techniques
• 05_Word-Vectors.ipynb - Word2Vec embeddings
• 06_Word-Vectors-GloVe.ipynb - GloVe embeddings
• 07_Sentence-Transformers-Embeddings.ipynb - Modern transformer embeddings

Module 3: Deep Learning and Transformers

• 01-LSTM_for_Classification.ipynb - LSTM text classification
• 02-Classification with Transformers.ipynb - Transformer-based classification
• 03-Huggingface_intro.ipynb - Hugging Face ecosystem introduction
• 04-Q&A with finetuned BERT.ipynb - BERT fine-tuning for question answering

Module 4: Language Models and Generation

• 01_Text_Generation.ipynb - GPT-based text generation with sampling strategies
• 02_Exploring_Modern_LLMs_with_Gemini.ipynb - Google Gemini integration
• 03_RAG_Pipeline.ipynb - Retrieval-Augmented Generation implementation

Module 5: Agentic AI and Miscellanea

• 01-multi-agent_system/ - Production-ready travel assistant with vector search
• 02_LoRA_Fine_Tuning.ipynb - Low-Rank Adaptation fine-tuning techniques
• 03_Quantization_Comparison.ipynb - Model quantization and optimization methods

Key Learning Outcomes

• Traditional NLP: Text preprocessing, feature engineering, n-grams, bag-of-words, TF-IDF
• Modern Embeddings: Word2Vec, GloVe, sentence transformers, semantic search
• Deep Learning: LSTM networks, attention mechanisms, transformer architectures
• Language Models: GPT text generation, BERT classification, fine-tuning strategies
• Agentic AI: Multi-agent systems, production deployment, retrieval-augmented generation, model optimization

Technologies Used

• Core Libraries: spaCy, NLTK, scikit-learn, transformers, sentence-transformers
• Deep Learning: torch, tensorflow, huggingface ecosystem
• Advanced Tools: langchain, langgraph, peft (LoRA), datasets
• Production Systems: chromadb, vector search, multi-agent deployment
• Visualization: matplotlib, seaborn, plotly
• Data: pandas, numpy

Prerequisites

• Python 3.8+
• Basic Python programming knowledge
• Understanding of machine learning concepts
• Familiarity with neural networks (for advanced modules)

Setup Instructions

1. Clone repository:

git clone <repository-url>
cd BBS-AIIM

2. Install base dependencies:

pip install torch transformers datasets pandas numpy matplotlib seaborn

3. Install specialized packages per module:

# Module 1-2: Traditional NLP
pip install spacy nltk scikit-learn sentence-transformers

# Module 3-4: Deep Learning
pip install accelerate evaluate

# Module 5: Advanced Systems  
pip install langchain langgraph peft openai chromadb

4. Download language models:

python -m spacy download en_core_web_sm
python -m spacy download it_core_news_sm

Resources

• Hugging Face Documentation
• LangChain Documentation
• spaCy Documentation
• PyTorch Documentation

Contributing

Educational repository for learning purposes. Issues and improvements welcome.

License

Educational materials for academic and learning purposes.