Foundations of Generative AI Models

This comprehensive Generative AI Training, Evaluation, and Trends course equips you with the skills to build, optimize, and future-proof GenAI systems. Begin by learning how generative models are trained and evaluated using real-world metrics. Explore Retrieval Augmented Generation (RAG) to improve model accuracy by combining external data with LLMs. Progress into key trends shaping GenAI—like scalable architectures, real-time applications, and model transparency—while examining how these advancements apply across industries like healthcare, finance, and education. To be successful in this course, you should have a foundational understanding of machine learning, language models, and basic Python programming. By the end of this course, you will be able to: - Train and Evaluate GenAI Models: Build and assess model quality using proven techniques - Enhance Outputs with RAG: Apply retrieval-augmented generation for more accurate responses - Track Emerging Trends: Understand scalable architectures and real-time GenAI innovations - Prepare for Industry Use: Translate GenAI advancements into real-world business applications Ideal for AI practitioners, data scientists, and ML engineers advancing their generative AI expertise

• Train and evaluate generative AI models using real-world techniques
• Apply Retrieval Augmented Generation (RAG) to improve output accuracy
• Understand emerging trends in GenAI architecture and deployment
• Translate GenAI advancements into practical, industry-ready solutions

• No deep prior experience is required
• Basic computer and internet skills
• Ability to read course instructions in English and complete short practice activities