Coursera – Generative AI Fundamentals Specialization (2026)

Building and deploying advanced Generative AI systems.. Mastering Generative AI Models and Applications

What you’ll learn
+ Master the theory and tech of GenAI. Learn to build, deploy, and apply generative models for audio and images from foundation to production.

Skills you’ll gain
• Machine Learning
• Image Analysis
• OpenAI API
• Retrieval-Augmented Generation
• Generative Adversarial Networks (GANs)
• Embeddings
• Python Programming
• Large Language Modeling
• Open Source Technology
• LangChain
• Model Deployment
• ChatGPT
• Vision Transformer (ViT)
• Prompt Engineering
• Google Cloud Platform
• Artificial Intelligence and Machine Learning (AI/ML)
• Generative AI
• Probability & Statistics
• Generative Model Architectures
• Generative AI Agents
This specialization is intended for learners with foundational knowledge in Python and Machine Learning who seek to develop a deep, hands-on understanding of Generative AI and its real-world applications. Across three courses, you’ll explore the full spectrum of GenAI. from understanding what it is and how it works, to building and deploying advanced systems responsibly. You’ll begin with the foundations of Large Language Models, learning about tokenization, embeddings, and the Transformer architecture that powers modern AI tools. Then, you’ll dive into multimodal generation, discovering how models like VAEs, GANs, Transformers, and Diffusion networks create and manipulate audio, image, and video content. In the final course, you’ll move from theory to implementation: building a Transformer from scratch in Python, extending its capabilities with Retrieval-Augmented Generation (RAG), and developing agentic AI systems using Google’s Agent Development Kit (ADK) on GCP. Throughout, you’ll engage with ethical considerations around bias, transparency, copyright, and responsible deployment.

Applied Learning Project
Throughout the specialization, you’ll complete progressive hands-on projects that evolve in complexity: from lightweight inference labs in the first two courses to full implementation in the final one. In Course 3, you’ll code a Transformer from scratch in Google Colab, build a RAG pipeline, and deploy a functional AI Agent using Google’s ADK on GCP.