What you will learn in Introduction to Generative AI Course
- Learn the fundamentals of generative AI and how it differs from traditional AI.
- Explore different generative AI models such as GANs, transformers, and diffusion models.
- Understand the role of large language models (LLMs) like ChatGPT and Bard.
- Discover how generative AI is used in text, image, video, and music creation.
- Gain insights into ethical concerns, biases, and responsible AI usage.
- Learn about real-world applications in industries like healthcare, marketing, and finance.
Program Overview
Introduction to Generative AI
⏱️ 2-3 weeks
- Learn what generative AI is and how it works.
- Explore different AI model architectures used for content generation.
- Understand the evolution of AI from rule-based systems to deep learning models.
Types of Generative AI Models
⏱️ 3-5 weeks
- Study major generative AI models, including GANs, transformers, and diffusion models.
- Learn how models like GPT, DALL·E, and Stable Diffusion generate text and images.
- Understand reinforcement learning and how it enhances AI capabilities.
Applications of Generative AI
⏱️4-6 weeks
- Explore how generative AI is used in marketing, healthcare, gaming, and other industries.
- Learn about AI-generated art, deepfake technology, and automated content creation.
- Examine AI’s impact on creative industries and business automation.
Ethical Considerations and AI Bias
⏱️5-7 weeks
- Understand the risks of AI-generated misinformation and bias.
- Learn responsible AI practices and guidelines for ethical AI development.
- Discuss privacy concerns and regulatory frameworks for AI governance.
Hands-on Project
⏱️ 6-8 weeks
- Apply generative AI tools to create text, images, or videos.
- Develop a small AI-powered application or analyze existing AI-generated content.
- Present findings and discuss AI’s impact on future innovations.
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Job Outlook
- High Demand for AI Professionals: The AI industry is projected to grow exponentially, creating opportunities in machine learning, data science, and AI development.
- Competitive Salaries: AI engineers and machine learning specialists earn $100,000 – $150,000 per year, with top AI professionals making over $200,000.
- Industry Applications: Generative AI is widely used in marketing, content creation, healthcare, software development, and finance.
- Entry into AI Careers: This course provides a strong foundation for roles such as AI Researcher, Machine Learning Engineer, and AI Product Manager.
- Gateway to Advanced AI Learning: Builds a foundation for further studies in deep learning, AI ethics, and advanced machine learning techniques.
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Generative AI: Introduction and Applications Course – Gain practical insights into generative AI tools, workflows, and real-world applications.
Related Reading
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Specification: Introduction to Generative AI Course
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FAQs
- Start with basic concepts: understand what Generative AI is and how it differs from traditional AI techniques.
- Learn about model types: GANs, transformers, diffusion models, and LLMs like ChatGPT and Bard.
- Explore real-world applications: AI-powered text, image, video, and music generation across various industries.
- Include ethics and bias discussions to grasp responsible AI use.
- Apply knowledge through a hands-on project, such as building a simple text- or image-generating application.
- Yes—there are beginner-friendly, low-cost options typically lasting 2–3 weeks, often available on subscription platforms.
- Many are non-technical, focusing on foundational knowledge rather than code.
- Some may offer free previews or audit options, though the course.careers page indicates a one-time purchase (“Lifetime” access).
- Generative AI fundamentals: learn how these models work and architectural evolution from rule-based to deep learning.
- Model overview: cover GANs, transformers, diffusion models, and how LLMs like ChatGPT generate content.
- Applications across formats: see how Generative AI creates text, imagery, video, and music.
- Ethical awareness: address bias, misinformation, privacy, and AI governance.
- Practical execution: use tools to create generative outputs and analyze their impact.
- Yes—the growing demand for AI professionals, especially in creative and tech roles, makes such courses valuable.
- Roles like AI Researcher, ML Engineer, and AI Product Manager become more accessible with foundational knowledge.
- Competitive salaries—ranging from $100k to $150k, with top professionals earning over $200k—highlight the economic upside.
- It’s a stepping stone—ideal for those considering deeper studies in AI ethics, deep learning, or advanced modeling.
- Typical timeline: 2–3 weeks for core modules, followed by 6–8 weeks for the hands-on project.
- Course structure:
Intro to Generative AI: 2–3 weeks—understanding fundamentals.
Model types: 3–5 weeks—covering architectures like GANs, transformers, diffusion.
Applications: 4–6 weeks—industry use cases and content creation.
Ethics: 5–7 weeks—AI bias, privacy, regulation.
Capstone project: 6–8 weeks—hands-on generative AI tool implementation.
