Generative AI Foundational Certificate Course

Discover how generative AI creates new content—from text and images to code and music—by learning patterns from massive data sets, unlike traditional AI.

Trace the evolution from rule-based and symbolic ai to learning models, deep learning, and transformers, culminating in foundational generative ai capabilities.

Compare generative adversarial networks and transformers by examining their architectures and training dynamics. GANs excel in image synthesis, while transformers enable broad, multi-domain language tasks.

Explore large language models and their training on vast data and parameters. See how GPT, Claude, and Mistral enable generative AI through self-attention and next-word prediction, not true understanding.

Explore tokens, parameters, and embeddings and see how they shape large language models, enabling cost-aware input, deeper model behavior, and meaningful connections in generative AI.

Prompt engineering crafts effective prompts to guide generative AI toward useful outputs. Use clear, context-rich prompts—questions, instructions, and example-based prompts, including role-based prompting—and apply zero-shot and few-shot techniques.

Explore zero-shot, few-shot, and chain-of-thought prompting to tailor language-model responses. Learn when to use direct instructions, templates with examples, and step-by-step reasoning for consistent results in complex, multi-step tasks.

Learn role-based prompting and instruction tuning to shape the model's tone and structure. Combine both to align outputs with your audience and goals for education, support, and creative ideation.

Learn to tailor prompts for writing, analysis, and research to guide AI behavior and generate structured, task-specific outputs. Practice defining tone, audience, length, and format to improve accuracy and usefulness.

Recognize the limitations of generative AI, including hallucinations, bias, and overreliance. Verify outputs, treat AI as a co-pilot, stay in the driver's seat, and use it responsibly.

Explore image generation with Dall-E, Midjourney, and Stable Diffusion, translating prompts into visuals and highlighting each tool's strengths: detailed control, stylized visuals, and open-source customization.

Explore three AI audio tools—11 Labs, Suno, and Open Voice—that showcase voice cloning, AI music, and multilingual voice transfer, while enabling tone, emotion, and pacing control for creators.

Explore two leading tools, GitHub Copilot and Amazon Codewhisperer, that auto-complete code, translate plain English into working code, explain unfamiliar code, and boost security and enterprise development.

Explore multimodal models like GPT-4o, Gemini, and Claude that combine text, vision, audio, and video to enable natural, context aware interactions across real time tasks, productivity, and long documents.

Explore how deepfakes, misinformation, and legal grey zones shape responsible use of generative AI, and learn to detect deception while upholding ethics and trust.

Explore how generative AI accelerates marketing, education, law, and healthcare by automating content creation, document analysis, personalizing learning, and supporting clinical tasks.

Explore how fairness, accountability, and explainability guide responsible generative AI use. Build inclusive data practices, audit trails, and clear ownership to ensure trustworthy, transparent AI outcomes.

Explore privacy, IP, and regulatory considerations in generative AI, including data protection, ownership of AI-generated content, privacy by design, and evolving regulations such as GDPR and HIPAA.

Assess generative AI by strategic value and ROI, emphasizing time savings, cost reduction, and growth opportunities aligned with core business goals.

Design for collaboration in the augmented workforce by using ai to augment creativity, writing, and analysis. Humans provide judgment and oversight to guide outputs and build trust.