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Models and Platforms for Generative AI

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Understanding the core concepts and foundational models of generative AI gives you the footing that you need in the rapidly developing field of generative AI, which is transforming our world. Along with deep learning and LLMs, you'll explore GANs, VAEs, transformers, and diffusion models, which are the building blocks of generative AI. This course is for all enthusiasts and practitioners with an interest in learning what generative AI is and how it works.

Language

  • English

Topic

  • Artificial Intelligence

Enrollment Count

  • 3.14K

Skills You Will Learn

  • Artificial Intelligence, Application Development, Deep Learning, Transformers

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 6 hours

Platform

  • edX

Last Update

  • June 24, 2025
About this Course
This course is designed for enthusiasts and practitioners who share an interest in the rapidly advancing field of generative AI.

This course centers around the core concepts and generative AI models that form the building blocks of generative AI. You will delve into the concepts of deep learning and large language models (LLMs). You will learn about GANs, VAEs, transformers, and diffusion models – the fundamental components of generative AI.

You will learn about the concept of foundation models. You will gain insights into the capabilities of pre-trained models and platforms for AI application development. The course will also cover how foundation models utilize these platforms to generate text, images, and code. Additionally, participants will explore various generative AI platforms such as IBM watsonX and Hugging Face.

The course includes practical hands-on labs, offering participants the chance to delve into the applications of generative AI using the IBM Generative AI Classroom and platforms like IBM watsonX. Throughout the course, you'll have the opportunity to explore various models, including IBM Granite, OpenAI GPT, Google Flan, and Meta Llama. Additionally, expert practitioners will share insights into the capabilities, applications, and tools of generative AI.

What you'll learn
  • Describe the fundamental concepts of generative AI.
  • Explore the building blocks of generative AI, including GANs, VAEs, transformers, and diffusion models.
  • Explain the concept of foundation models in generative AI.
  • Explore the ability of foundation models to generate text, images, and code using pre-trained models.
  • Describe the features, capabilities, and applications of different generative AI platforms, including IBM Watson and Hugging Face.

Course Syllabus

Module 1: Models for Generative AI
  • Video: Course Introduction
  • Reading: Course Overview
  • Reading: Program Overview
  • Reading: Helpful Tips for Course Completion
  • Video: Deep Learning and Large Language Models
  • Video: Generative AI Models
  • Video: Foundation Models
  • Labs: Generative AI Foundation Models
  • Expert Viewpoints Video: Advancing Generative AI Capabilities
  • Expert Viewpoints Video: Exploring Generative AI Capabilities of IBM Watsonx
  • Reading: Module Summary
  • Practice Quiz: Core Concepts and Models of Generative AI
  • Discussion Prompt: Working with Foundation Models
  • Reading: IBM Granite Foundation Models
  • Graded Quiz: Models for Generative AI
Module 2: Platforms for Generative AI
  • Video: Pre-trained Models: Text-to-Text Generation
  • Lab: Develop AI Applications with the Foundation Models
  • Video: Pre-trained Models: Text-to-Image Generation
  • Video: Pre-trained Models: Text-to-Code Generation
  • Lab: Develop AI Applications for Code Generation
  • Video: IBM Watsonx.ai
  • Reading: IBM Watsonx. data and Watsonx. governance
  • Video: Hugging Face
  • Expert Viewpoints Video: Fostering Innovation through Hugging Face
  • Reading: Module Summary
  • Practice Quiz: Pre-trained Models and Platforms for AI Applications Development
  • Graded Quiz: Platforms for Generative AI
Module 3: Course Quiz and Final Project
  • Reading: Module Introduction and Learning Objectives
  • Glossary - Generative AI: Foundation Models and Platforms
  • Final Project: Working with IBM Granite Foundation Models
  • Final Exam: Graded Quiz: Generative AI: Foundation Models and Platforms
Course Wrap-Up
  • Reading: Congratulations and Next Steps
  • Reading: Thanks from the Course Team

Recommended Skills Prior to Taking this Course

This course is for anyone wanting to learn and get familiar with the concepts of generative AI. No prior knowledge is required for this course. Note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.

Instructors

Leon Katsnelson

Director & CTO, IBM Developer Skills Network

I've had a very productive career in tech. I've touched many areas from mainframe, to manufacturing automation (IoT), to databases, big data, data science and AI, blockchain, and of course full stack and cloud-native development and DevOps. I started my career in test and QA, did quite a bit of development, product management, team leadership, and people management before becoming an executive. I had some great wins including bringing to market a billion $ product. And had some failures along the way. But throughout my career, one thing has always remained constant. I learned everything I could and used every chance I had to get a new skill. My goal in life is to help those who have an appetite for learning to acquire knowledge and skill to build their career or simply become better users of the latest tech.

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Rav Ahuja

Global Program Director, IBM Skills Network

Rav Ahuja is a Global Program Director at IBM. He leads growth strategy, curriculum creation, and partner programs for the IBM Skills Network. Rav co-founded Cognitive Class, an IBM led initiative to democratize skills for in demand technologies. He is based out of the IBM Canada Lab in Toronto and specializes in instructional solutions for AI, Data, Software Engineering and Cloud. Rav presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in managing and analyzing data. Rav holds B. Eng. from McGill University and MBA from University of Western Ontario.

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