AI Literacy bridges the gap between Python skills and real-world AI applications, enabling you to assess AI feasibility and optimize AI-generated content. This course explores AI, machine learning, deep learning, and key breakthroughs shaping industries. While addressing AI security and ethics, you’ll dive into transformers, NLP, generative AI, and open-source tools like Hugging Face. Hands-on learning with GPT models will help you apply AI to business and creative contexts.

This course deepens your understanding of modern AI systems by exploring generative models and transformer-based architectures. This course covers VAEs, GANs, LLMs, and attention mechanisms, equipping you to design, fine-tune, and optimize AI models. With hands-on learning in LangChain and AI workflows, you'll gain the skills needed to develop scalable GenAI applications and assess their future impact across industries.

This course focuses on the hands-on implementation of LLM-driven solutions. This course covers advanced prompt engineering, LangChain frameworks, and fine-tuning techniques to create high-performance AI applications. You’ll build and deploy retrieval-augmented generation (RAG) systems, customize LLMs for domain-specific tasks, and evaluate model performance, equipping you with the skills to develop optimized, real-world AI solutions.

This course explores agentic AI and multi-agent systems, equipping you to build intelligent agents that perceive, reason, and act. Using frameworks like LangGraph, AutoGen, and CrewAI, you'll design collaborative agents, orchestrate workflows, and apply them to real-world enterprise scenarios through hands-on demos and practical exercises.

This course takes AI automation to the next level, enabling agents to generate and refine high-quality images. Leverage stable diffusion, denoising, and autoencoders to create visuals from text. Explore shared embedding spaces to enhance multimodal AI and develop custom AI-generated images for diverse applications. Expand your AI’s creative potential with practical techniques and real-world use cases.

As AI-generated content advances, responsible governance is essential for ethical, secure, and transparent AI systems. This course explores governance challenges, regulatory considerations, and best practices for deploying generative AI. Learn to mitigate risks, address bias and privacy concerns, and navigate legal compliance. Develop frameworks to ensure fairness, accountability, and trust in AI-driven industries.

In this final step, apply your skills to design, train, and deploy a functional generative AI solution. Address ethical concerns, bias mitigation, and regulatory compliance while ensuring technical excellence. This project reinforces real-world implementation, preparing you to create ethical, secure, and impactful AI solutions for business and society.