About the Course
This program offers a comprehensive introduction to generative AI, blending theoretical foundations with practical, hands-on applications. Learners will explore how AI models generate text, images, audio, and video, while gaining the skills to design, implement, and evaluate AI-driven solutions in real-world scenarios.
Course Overview
Designed for professionals, students, and enthusiasts, this course covers core generative AI concepts, including foundation models, large language models (LLMs), multi-modal AI, and prompt engineering. Participants will work with industry-standard tools and platforms to understand model capabilities, limitations, ethical considerations, and business applications. The curriculum balances conceptual understanding with applied skills, preparing learners to confidently leverage AI in creative, technical, and business contexts.
Course Outcomes
Upon completing this program, participants will be able to:
- Understand the fundamentals of generative AI, machine learning, and foundation models.
- Apply prompt engineering techniques to optimize model outputs.
- Work with text, image, audio, and multi-modal generative AI applications.
- Evaluate AI models for performance, fairness, and reliability.
- Understand ethical, legal, and practical considerations in deploying AI solutions.
- Integrate generative AI tools into real-world projects and business workflows.
Career Path
The skills gained in this program are highly relevant across industries where AI-driven solutions are in demand. Career opportunities include:
- AI/ML Specialist or Engineer
- Prompt Engineer
- Data Scientist focusing on generative AI applications
- Product Manager for AI-driven products
- Creative Technologist / AI Content Developer
- AI Consultant for business process automation
This program equips learners with practical and strategic skills to harness generative AI effectively, opening doors to innovative roles in technology, research, and creative industries.
Course Outline
Module 1: Introduction to Generative AI
- What is Generative AI and its evolution
- Overview of Machine Learning, Deep Learning, and Neural Networks
- Core concepts: Tokens, embeddings, vectors, and foundation models
- Types of generative AI models: LLMs, diffusion models, multi-modal models
- Ethical considerations: bias, hallucinations, and responsible AI
Module 2: Text Generation & Large Language Models
- Understanding large language models (LLMs)
- Prompt engineering: zero-shot, few-shot, chain-of-thought prompts
- Text generation use cases: chatbots, summaries, translation, code generation
- Evaluating model outputs: accuracy, relevance, safety
- Hands-on labs with popular LLM tools and platforms
Module 3: Image, Video & Multi-Modal AI
- Introduction to generative models for images and video
- Diffusion models and GANs
- Creating AI-generated images, videos, and creative content
- Combining text and images: multi-modal AI applications
- Practical exercises with industry-standard platforms
Module 4: AI Application Design & Integration
- Designing AI solutions for real-world business problems
- Integrating generative AI into applications and workflows
- Retrieval-Augmented Generation (RAG) techniques
- Fine-tuning models for specific use cases
- Hands-on project: build a small AI-powered application
Module 5: Responsible AI & Evaluation
- Principles of responsible AI: fairness, transparency, and explainability
- Evaluating AI models using qualitative and quantitative metrics
- Human-in-the-loop systems for AI oversight
- Risk mitigation strategies: prompt injection, hallucinations, ethical challenges
Module 6: Capstone Project & Industry Applications
- End-to-end generative AI project implementation
- Applying skills in domains like customer support, marketing, content creation, and automation
- Presentation and peer review of projects
- Preparing for careers or further specialization in generative AI
