Mastering Generative AI From Neural Networks to Multi-Agents

Deepen your expertise in Generative AI with this comprehensive masterclass, designed to take you from fundamental machine learning concepts to building powerful, production-ready AI applications. Whether you're a software engineer, data scientist, or tech professional seeking to lead in the AI revolution, this course equips you with the skills to become a Generative AI expert.

Core Foundations: Building a Strong AI Base

• Machine Learning Fundamentals: Gain a solid understanding of core Machine Learning principles, including supervised, unsupervised, and reinforcement learning.
• Neural Networks & Deep Learning: Explore the inner workings of Neural Networks and Deep Learning, mastering architectures crucial for modern AI.
• Embeddings, Transformers & Diffusion Models: Understand the power of embeddings, transformers, and diffusion models – the engines behind today's most advanced AI.
• Foundation Models: Learn how foundation models like GPT, Claude, and Stable Diffusion function and how to leverage their capabilities.

Natural Language Processing (NLP) Mastery

• Large Language Models (LLMs): Build and fine-tune Large Language Models (LLMs) for diverse applications like conversational AI and text generation.
• Text Processing Techniques: Master essential NLP techniques including tokenization, text classification, topic modeling, and named entity recognition.
• Evaluation & Benchmarking: Understand the key evaluation metrics and industry benchmarks used to assess NLP model performance.
• Supervised Fine-Tuning: Implement supervised fine-tuning for specialized AI applications, tailoring models to specific tasks.

Image Generation & Computer Vision

• Text-to-Image & Image-to-Image Models: Create stunning images using cutting-edge text-to-image and image-to-image models.
• Image Editing & Manipulation: Master techniques like image editing, inpainting, and style transfer to refine and enhance visuals.
• Fine-Tuning Image Models: Fine-tune image generation models for custom use cases, creating unique and personalized visuals.

Advanced Model Customization

• Prompt Engineering: Harness the power of prompt engineering to guide AI models towards desired outputs.
• In-Context Learning: Explore in-context learning strategies for adapting models with minimal training data.
• Retrieval Augmented Generation (RAG): Build Retrieval Augmented Generation (RAG) systems to ground AI in your own data, improving accuracy and relevance.
• GraphRAG & StructRAG: Implement advanced GraphRAG and StructRAG architectures for enhanced knowledge retrieval.
• Parameter-Efficient Fine-Tuning (PEFT) & LoRA: Apply Parameter-Efficient Fine-Tuning (PEFT) and LoRA techniques for efficient model adaptation.
• Reinforcement Learning from Human Feedback (RLHF) & Direct Preference Optimization (DPO): Train models using Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) for improved alignment with human preferences.
• Knowledge Distillation: Optimize models through knowledge distillation to achieve smaller, faster, and more efficient AI solutions.

Agentic AI & Advanced Orchestration

• AI Agents Fundamentals: Understand the core concepts of AI agents and agentic reasoning.
• ReAct Framework: Master the ReAct (Reasoning and Acting) framework for building tool-using agents.
• Multi-Agent Systems: Design and implement multi-agent systems with specialized roles and collaborative capabilities.
• Agent Topologies: Implement various agent topologies including sequential, hierarchical, and collaborative patterns.
• Agent Coordination: Implement automatic handoffs and agent coordination strategies for seamless task execution.
• Complex Task Execution: Create agents capable of planning, reasoning, and executing complex multi-step tasks.

Hands-On Learning Experience

This course emphasizes practical application with 8 comprehensive labs that progressively build your skills:

• Lab 1: Neural Network Fundamentals & Transfer Learning – Build an image classifier from scratch using ResNet and leverage transfer learning for state-of-the-art performance.
• Lab 2: AWS & Generative Image Creation – Set up your AWS environment, utilize Amazon Bedrock, and create and edit images with Amazon Nova models.
• Lab 3: Embeddings & Vector Search – Master embedding models with HuggingFace, build a production-ready RAG system, and implement efficient **