1. Course SyllabusModule 1: Introduction to Generative AI (Duration: 2 hours)

   This module defines Generative AI and reviews its history and key concepts. Learners see examples of AI-generated text and images (e.g. chatbots, art generators) and understand what makes a model “generative.” It covers core algorithms (GANs, VAEs, autoregressive transformers) and their roles in content creation. By the end, students appreciate the “transformative potential of AI” to automate creativity and workflows, setting the stage for hands-on work in later modules.

   Module 2: Machine Learning & Deep Learning Foundations (Duration: 3 hours)

   This module ensures all participants share a solid ML background. It reviews supervised and unsupervised learning, neural network basics (perceptrons, backpropagation, activation functions) and advanced architectures. Key topics include convolutional and recurrent networks, autoencoders and the basics of probabilistic models. Crucially, students learn the deep learning essentials needed for generative modeling. Practical exercises might include training a simple neural network in Python to reinforce coding skills before moving to complex models.

   Module 3: Text Generation and Language Models (Duration: 6 hours)

   Focusing on language, this module explores Natural Language Processing with generative models. Topics include transformers, attention mechanisms, and large language models (LLMs) such as GPT or BERT. Students learn how to use Hugging Face Transformers and OpenAI’s GPT API to generate text, summarize content, and build conversational agents. The instruction covers prompt engineering techniques – crafting inputs to guide LLM output effectively– as well as fine-tuning pre-trained models on custom text data. Hands-on labs reinforce programming skills: learners write Python code with TensorFlow or PyTorch to load models from the Hugging Face Hub and generate English text, reflecting Coursera’s emphasis on “programming skills to develop generative models using TensorFlow and PyTorch”.

   Module 4: Image and Creative Media Generation (Duration: 6 hours)

   This module covers generative models for images (and optionally audio/video). Students study classical approaches like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), and then focus on modern diffusion models (the technology behind Stable Diffusion and DALL·E). They learn the theory of diffusion-based generation and use Hugging Face’s Diffusers library to generate and manipulate images. Labs include training or fine-tuning a diffusion or GAN model on a small dataset. Learners practice tasks like style transfer or super-resolution, applying conditional generation techniques (adding text prompts or class labels). This hands-on experience mirrors the course aim of studying “how to generate images… with the popular Diffusers library”

   Module 5: Tools & Frameworks Workshop (Duration: 4 hours)

   This practical workshop familiarizes learners with the development environment and tools used throughout the course. Participants set up Python and use Jupyter/Colab notebooks. They get hands-on experience with libraries such as Hugging Face Transformers/Diffusers, OpenAI’s client libraries, and computer vision libraries (e.g. OpenCV). We also cover integration with cloud platforms (using AWS Sagemaker, Google Colab Pro, or Hugging Face Spaces) for training and hosting models. This module may include a mini-project where students implement a simple generative model from scratch in PyTorch or TensorFlow, ensuring comfort with the key tools (in line with the program’s hands-on focus on Python and deep learning frameworks).

   Module 6: Generative AI Use Cases in Industry (Duration: 4 hours)

   Learners explore real-world applications of Generative AI in various sectors. Through case studies, we examine how retail, healthcare, finance, entertainment, and other industries leverage GenAI for tasks like content creation, design prototyping, data augmentation, and customer insights. The module also introduces business-oriented GenAI tools (e.g. Google Vertex AI, Microsoft Copilot) to illustrate enterprise environments. Students discuss the value of GenAI (e.g. boosting productivity, enabling creativity) and learn to identify use-cases in their own field. This aligns with learning objectives such as “gain insights into real-world GenAI use cases across industries” and understanding “how generative AI enhances productivity, creativity, and empathy” in a business context.

   Module 7: Ethical and Social Implications (Duration: 3 hours)

   This module delves into the risks and responsibilities of Generative AI. Topics include algorithmic bias, privacy concerns, misinformation/deepfakes, and copyright/IP issues around AI-generated content. We review recent incidents (e.g. biased AI outputs) and discuss mitigation strategies. Regulatory aspects (e.g. data protection laws, content policies) are introduced, preparing learners to consider governance in AI projects. The content reflects the need to “recognize the potential risks associated with Generative AI” and understand “ethical and regulatory landscape”. Readings and discussions on Responsible AI guidelines (such as transparency and fairness principles) equip students to apply ethical best practices in their own work.

   Module 8: Deployment and MLOps (Duration: 3 hours)

   Here students learn how to deploy generative models into production. We cover model optimization (quantization, TensorRT/ONNX), containerization (Docker), and setting up inference services or APIs. The module introduces MLOps concepts for GenAI: version control for models, continuous integration workflows, and monitoring model performance post-deployment. Deployment strategies such as canary releases, blue-green deployments, and A/B testing are explained, echoing industry practice in scaling AI services. For example, learners see how to run a shadow deployment or conduct a rolling update on a chat model, aligning with best practices to ensure availability and safety of AI applications.

   Module 9: Regulatory Compliance and Governance (Duration: 2 hours)

   In this module, learners examine current AI regulations and compliance frameworks. We discuss international and regional laws (GDPR, the EU AI Act, etc.) that govern AI usage, as well as internal governance policies (ethics boards, audit trails). The focus is on understanding what rules apply to generative AI products and how organizations ensure compliance. By the end, participants can “navigate and implement strategies for AI compliance”. Case discussions may include how a company responds to an AI regulation or sets its own standards for quality and accountability in AI systems.

   Module 10: Capstone Project – Building a Generative AI Application (Duration: 7 hours)

   The program culminates in a hands-on capstone project. Working individually or in teams, learners apply the full pipeline: defining a project scope, preparing data, training a generative model (text or image), and deploying it via an API or simple web demo. Possible projects include a custom chatbot, an image synthesis app, or a text-autocompletion tool. Mentors guide students as they integrate Python, TensorFlow/PyTorch, and Hugging Face APIs to create and test their system. This project reinforces all prior learning, from modeling to ethics to deployment, and exemplifies the course’s emphasis on practical, portfolio-building outcomes

    

Key Features

•  Hands-on with Modern Tools: Every module includes practical labs using Python and popular frameworks. Learners work directly with TensorFlow and PyTorch to build models, and with specialized libraries like Hugging Face Transformers and Diffusers or APIs like OpenAI GPT and Stable Diffusion for text and image generation
•  Comprehensive Coverage: The curriculum spans from fundamental principles (GANs, VAEs, transformers, diffusion models) to advanced applications. It covers both text-based generators (LLMs, chatbots) and creative media (image/audio synthesis).
•  Real-World Use Cases: Students explore GenAI in practical settings (healthcare, finance, marketing, etc.) with case studies that show how AI can automate content creation, design products, and enhance decision-making. The program discusses industry tools (e.g. cloud AI services, enterprise platforms) and encourages identifying GenAI opportunities in one’s own field.
•  Ethics and Governance: Ethical risks and regulatory issues are integrated throughout. Learners examine bias, transparency, privacy and IP concerns in generative systems, and study existing guidelines and compliance strategies. For example, by course end they can “recognize the potential risks associated with Generative AI, understand the ethical and regulatory landscape, and develop internal standards for compliance”.
•  Accessible Yet Rigorous: Designed for a diverse audience (engineers, data scientists, business pros, students), the training is suitable for beginners but includes intermediate projects. It starts with basic AI literacy and progresses to building working generative applications, ensuring all learners gain both conceptual understanding and hands-on skills.
•  Capstone Project: The program culminates in a supervised project where participants apply what they’ve learned to design, train, and deploy a simple generative AI application, reinforcing practical integration of the course material.