• What is Generative AI?
  • Overview, history, and evolution from traditional AI to modern generative methods.
  • High-impact applications including text generation, image synthesis, video production, code assistance, music generation and more.
• Market Trends & Career Opportunities
  • Insights into the current industry landscape and emerging roles demanding generative AI expertise.
• Essential Tools & Platforms
  • Brief overview of major frameworks and platforms that drive innovation in generative AI.

• Python Essentials for AI
  • Fundamentals: variables, loops, functions, and object-oriented programming (OOP).
  • Advanced topics: asynchronous operations and file handling tailored for AI workflows.
• Introduction to Machine Learning & Deep Learning
  • Core concepts: supervised vs. unsupervised learning, regression, classification, and neural networks (CNNs, RNNs).
  • Overview of popular libraries (e.g., PyTorch, TensorFlow) to prepare for AI model development.

• Transformer Architecture Deep Dive
  • Detailed look into transformer components: self-attention, positional encoding, layers, and overall architecture.
  • Tokenization techniques and the role of embeddings (Word2Vec, GloVe, FastText, BERT).
• LLM Concepts & Terminology
  • Key ideas: parameters, attention mechanisms, pre-training, fine-tuning, zero-shot learning, and more.
• LLM Landscape & Leaderboards
  • Comparative overview of models: GPT series (from GPT‑1 to GPT‑4), Meta’s LLAMA, Llama 3, Mistral, DeepSeek, and other state-of-the-art systems.
  • Open Source vs. Proprietary: examining differences (e.g., Llama, Mistral’s Mixture of Experts, Phi‑3, Gemini, Claude, Grok vs. GPT‑4).
• Evolution & Comparative Analysis
  • In-depth discussion on the progression of GPT and LLAMA models, their strengths, limitations (such as biases and hallucinations), and unique features.
• Function Calling and External Integrations
  • Explore the emerging concept of function calling in LLM APIs, which enables models to execute code and interact dynamically with external systems.
  • Examine real-world use cases and best practices for integrating function calling into LLM-driven applications.

• Fundamentals of Prompt Engineering
  • Importance of prompt design in leveraging LLMs effectively.
  • Core principles to craft clear, effective prompts.
• Techniques & Best Practices
  • Methods including zero-shot, few-shot, chain-of-thought, and role prompting.
  • Advanced strategies for enhancing output quality and consistency.

Risk Mitigation & Ethical Prompting
• Techniques to reduce biases, mitigate hallucinations, and prevent prompt hacking/jailbreaking.

• Introduction to Fine-Tuning Techniques
  • Overview of parameter-efficient methods such as PEFT, LoRA, and instruct tuning.
• Optimization Strategies
  • Techniques for model compression and quantization to ensure efficient deployment.
• Quantization & Hosting
  • Exploring platforms like AWS Bedrock and Groq’s LPU for hosting and scaling optimized models.

• Understanding Embeddings
  • How dense numerical vectors represent words, sentences, or documents.
  • Exploration of different embedding types (Word2Vec, GloVe, BERT) and their role in semantic understanding.
• Building Retrieval Systems
  • Applications: semantic search, clustering, and recommendation systems using embeddings.
• Vector Databases & RAG Systems
  • Overview of vector databases (Pinecone, FAISS, ChromaDB, Qdrant) and nearest neighbor search.
  • Integrating retrieval pipelines with generation models for enhanced contextual responses.

• Langchain Framework
  • Architecture overview: chains, prompts, tools, and memory management for connecting LLMs with external APIs and data sources.
• LlamaIndex for Data Indexing
  • Techniques for efficient document indexing, real-time querying, and knowledge-based Q&A.
• Integrated Hands-on Projects
  • Step-by-step guide to building applications that combine Langchain and LlamaIndex for tasks like chatbot development and document retrieval.

• Generative Models for Images
  • Deep dive into diffusion models with Stable Diffusion, including architecture, style transfer, and in/outpainting techniques.
  • Overview of DALL-E 3’s creative capabilities, compositional generation, and artistic applications.
• Comparative Analysis
  • Key differences between Stable Diffusion and DALL-E 3, and guidance on selecting the appropriate model for specific creative tasks.
• Text-to-Video Generation
  • Emerging technologies for video creation: overview of platforms such as OpenAI Sora, Runway, and Pika.
  • Techniques for improving video quality, style management, and scene composition.

• Concepts of AI Agents
  • Definition, need, and basic operational frameworks for autonomous AI workflows.
• Frameworks & Use Cases
  • Exploration of platforms such as CrewAI, Autogen, and LangGraph.
  • Case studies on integrating AI agents for customer service, process automation, and other business applications.
• Practical Labs
  • Hands-on development of an AI agent to solve a specific business problem.

• Model Evaluation Metrics
  • Quantitative metrics: accuracy, perplexity, BLEU, ROUGE, F1-score, etc.
  • Qualitative methods: human evaluation, crowdsourcing feedback, and scenario testing.
• Bias Detection, Fairness & Explainability
  • Techniques and tools (SHAP, LIME) for bias detection, fairness audits, and enhancing model transparency.
• Ethical & Legal Considerations
  • Best practices in AI ethics: privacy, transparency, accountability, and GDPR compliance.
  • Strategies for building a responsible AI framework with continuous monitoring, auditing, and feedback loops.

• Deployment Strategies
  • Overview of containerization, cloud platforms, and API integration for seamless deployment.
• Quantization & Hosting
  • Techniques for model compression and deployment on platforms like AWS Bedrock and Groq’s LPU engine.
• Scaling Best Practices
  • Approaches to scale applications efficiently, ensuring reliability and performance in production environments.

• Project 1: Chatbot with RAG
  • Integrate LlamaIndex with Langchain to build an intelligent Q&A system.
• Project 2: AI Art Portfolio
  • Generate marketing assets and creative content using Stable Diffusion and DALL-E 3.
• Project 3: Video Generator
  • Develop a text-to-video ad campaign tool leveraging OpenAI Sora and other cutting-edge video generation technologies.
• And other mini projects