Module 1: Introduction to Generative AI and LLMs

●  Overview of Generative AI

●  History and Evolution of AI

●  Introduction to Large Language Models (LLMs)

●  Applications of Generative AI

Module 2: Fundamentals of Machine Learning and Deep Learning

●  Basics of Machine Learning

●  Supervised vs. Unsupervised Learning

●  Introduction to Neural Networks

●  Deep Learning Architectures

Module 3: Natural Language Processing (NLP)

●  Introduction to NLP

●  Text Preprocessing Techniques

●  Tokenization, Lemmatization, and Stemming

●  Named Entity Recognition (NER)

●  Sentiment Analysis

Module 4: Transformer Architecture

●  Understanding Transformers

●  Attention Mechanism

●  Encoder-Decoder Models

●  BERT, GPT, and other Transformer-based Models

Module 5: Training and Fine-Tuning LLMs

●  Data Collection and Preparation

●  Training Large Language Models

●  Fine-Tuning Techniques

●  Transfer Learning

Module 6: Generative AI Techniques

●  Text Generation

●  Image Generation with GANs

●  Music and Art Generation

●  Ethical Considerations in Generative AI

Module 7: Advanced Topics in LLMs

●  Zero-shot and Few-shot Learning

●  Multimodal Models

●  Reinforcement Learning with LLMs

●  Scaling Laws and Model Optimization

Module 8: Practical Applications and Tools

●  Using LLMs for Code Generation

●  Building Conversational AI with ChatGPT

●  Image Processing with OpenCV

●  Tools like Hugging Face, LangChain

Module 9: Deployment and Performance Evaluation

●  Model Deployment Strategies

●  Performance Metrics and Evaluation

●  Real-world Case Studies

●  Challenges and Future Directions

Module 10: Capstone Project

●  Designing and Implementing a Generative AI Solution

●  Development of RAG Solution

●  Deployment Of RAG

●  Generating Summary

●  Building and deploying chatbot