Foundations of Generative AI Models

Übersicht

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.

Lehrplan

• Foundations of Generative AI
Build a strong foundation in Generative AI with this module covering its importance, real-world impact, and core concepts. Understand why GenAI matters through relatable analogies and explore key model types, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformer-based models. Ideal for beginners starting their GenAI journey.
• Training, Evaluation, and Future of Generative AI
Explore how Generative AI models are trained, evaluated, and enhanced using Retrieval Augmented Generation (RAG). Learn the key steps in model training, techniques to assess model quality, and understand how RAG improves output accuracy by combining retrieval and generation. Discover emerging trends shaping the future of GenAI and gain insights into evolving industry applications.

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Unterrichtet von

Priyanka Mehta

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Fachgebiete

Computer Science