IBM Generative AI Engineering Professional Certificate Course Syllabus

Overview (80-120 words) describing structure and time commitment. The IBM Generative AI Engineering Professional Certificate is a comprehensive program designed for beginners to gain hands-on experience in Generative AI and Large Language Models (LLMs). This course covers theoretical foundations and practical applications using industry-standard tools like TensorFlow, PyTorch, and Hugging Face. Over approximately 12 weeks, learners will progress through six modules, starting with the basics of Generative AI and advancing to ethical deployment and a capstone project. With a total time commitment of roughly 60–80 hours, the course blends self-paced learning with real-world projects, culminating in a professional portfolio-ready application that demonstrates mastery of AI engineering concepts.

Module 1: Introduction to Generative AI

Estimated time: 10 hours

• Understand what Generative AI is and how it differs from traditional AI
• Explore key technologies including GANs (Generative Adversarial Networks) and Transformers
• Learn about real-world applications of AI-generated text, images, and videos
• Discover use cases across industries such as healthcare, finance, and entertainment

Module 2: Deep Learning & Neural Networks for AI

Estimated time: 16 hours

• Master the fundamentals of deep learning and neural networks
• Understand how models learn patterns from large datasets
• Work with TensorFlow and PyTorch to build basic AI models
• Implement simple neural networks for classification and generation tasks

Module 3: Generative AI Model Development

Estimated time: 24 hours

• Learn how to train and fine-tune large-scale generative models
• Work with LLMs like GPT, BERT, and DALL·E for content generation
• Build AI-powered tools for text generation, coding assistance, and automation
• Use Hugging Face to access and customize pre-trained models

Module 4: Prompt Engineering & AI Optimization

Estimated time: 20 hours

• Master prompt engineering techniques to control AI outputs
• Understand tokenization, embeddings, and context length management
• Optimize AI model efficiency and response accuracy
• Apply best practices for improving model performance in real-world scenarios

Module 5: Ethical AI & Responsible Deployment

Estimated time: 18 hours

• Explore AI bias, fairness, and ethical challenges in generative models
• Learn strategies to reduce misinformation and hallucinations in AI outputs
• Understand compliance standards and governance for AI applications
• Implement responsible AI deployment practices in production environments

Module 6: Final Project

Estimated time: 30 hours

• Design and build a custom generative AI model using real-world datasets
• Develop an AI-powered application such as a chatbot or content generator
• Showcase the project in a professional portfolio to demonstrate skills

Prerequisites

• Basic knowledge of Python programming
• Familiarity with fundamental AI concepts (helpful but not required)
• Access to a computer with internet for hands-on labs and tools

What You'll Be Able to Do After

• Explain the core principles of Generative AI and deep learning
• Build, fine-tune, and deploy generative AI models using TensorFlow and PyTorch
• Apply prompt engineering to optimize AI-generated content
• Develop AI-powered applications such as chatbots and text generators
• Deploy AI models responsibly while addressing ethical concerns