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• Unit 1: Natural Language Processing Fundamentals and Text Preprocessing
• Unit 2: Tokenization, Embeddings, Semantic Similarity and Text Representation
• Unit 3: Introduction to Neural Networks, RNN, LSTM and Sequence Models
• Unit 4: Attention Mechanism and Transformer Architecture
• Unit 5: Encoder, Decoder, BERT, GPT and Modern Language Model Concepts

Tools used in this Paper: Python, NLTK, Hugging Face, Transformers, BERT, GPT

• Unit 1: Large Language Models, Foundation Models and LLM Capabilities
• Unit 2: Tokens, Context Windows, Parameters and Model Inference
• Unit 3: Zero-Shot, One-Shot, Few-Shot and Role-Based Prompting
• Unit 4: Structured Prompts, Prompt Optimization and Output Evaluation
• Unit 5: Advanced Prompt Engineering for Business and AI Applications

Tools used in this Paper: ChatGPT, Gemini, Claude, OpenAI API, Prompt Engineering Tools

• Unit 1: Text Generation, Summarization, Question Answering and Content Creation
• Unit 2: AI Image Generation, Image Understanding and Visual Content Workflows
• Unit 3: Speech-to-Text, Text-to-Speech and Conversational Voice Applications
• Unit 4: Multimodal AI and Applications Combining Text, Image and Audio
• Unit 5: Building Practical Generative AI Applications with Multiple AI Models

Tools used in this Paper: ChatGPT, Gemini, Hugging Face, OpenAI API, Generative AI Platforms

• Unit 1: Retrieval-Augmented Generation and Grounded AI Applications
• Unit 2: Document Loading, Text Chunking and Knowledge Base Preparation
• Unit 3: Embeddings, Similarity Search and Semantic Retrieval
• Unit 4: Vector Databases, Metadata Filtering and Retrieval Pipelines
• Unit 5: Building and Evaluating RAG Applications for Enterprise Use Cases

Tools used in this Paper: LangChain, ChromaDB, Pinecone, OpenAI Embeddings, Vector Search

• Unit 1: LangChain Fundamentals, Prompt Templates and LLM Workflows
• Unit 2: Chains, Tools, Function Calling and External Data Connections
• Unit 3: AI Agents, Agentic Workflows and Task-Based Automation
• Unit 4: Building AI Assistants with Memory, Retrieval and Tool Integration
• Unit 5: Multi-Step AI Workflows, Agent Evaluation and Performance Optimization

Tools used in this Paper: LangChain, Python, OpenAI API, AI Agents, Function Calling

• Unit 1: Introduction to LLM Fine-Tuning and Instruction Tuning
• Unit 2: Preparing Datasets for Domain-Specific Language Models
• Unit 3: Parameter-Efficient Fine-Tuning and Model Adaptation
• Unit 4: LoRA, QLoRA and PEFT Techniques for Efficient Customization
• Unit 5: Hugging Face Models, Tokenizers, Evaluation and Model Optimization

Tools used in this Paper: Hugging Face, Transformers, PEFT, LoRA, QLoRA, Python

• Unit 1: Generative AI APIs, Authentication and Model Integration
• Unit 2: OpenAI, Gemini and Other LLM API Application Workflows
• Unit 3: Connecting Generative AI with Web and Business Applications
• Unit 4: Docker, Git, GitHub and AI Application Deployment Fundamentals
• Unit 5: Cloud-Based AI Deployment, Monitoring, Scaling and Production Practices

Tools used in this Paper: OpenAI API, Gemini API, Python, Docker, Git, GitHub, AWS, Azure, Google Cloud

• Unit 1: Advanced LLM Applications, Multimodal AI and Agentic Systems
• Unit 2: Enterprise RAG, AI Agents and Generative AI Solution Architecture
• Unit 3: LLM Evaluation, Testing and Response Quality
• Unit 4: Building and Deploying End-to-End Generative AI Capstone Projects
• Unit 5: AI Portfolio Development, Project Presentation, and Interview Readiness

Tools used in this Paper: Python, LangChain, OpenAI API, Hugging Face, RAG, AI Agents, GitHub, Cloud AI Platforms