Certification in Generative AI Models and Tools

Take the next step in your AI journey! Whether you’re an aspiring AI engineer, a creative professional, a business leader, or an AI enthusiast, this course will help you master the key concepts and technologies behind Generative AI. Learn how cutting-edge AI models like GANs, VAEs, and Transformers are transforming industries, from content creation to automation and beyond.

With this course as your guide, you learn how to:

• Master the fundamental skills and concepts required for Generative AI, including deep learning, neural networks, and AI model training.
• Build and optimize Generative AI models using open-source libraries and frameworks, ensuring efficient AI-driven content generation.
• Access industry-standard tools such as ChatGPT, DALL·E, MidJourney, Stable Diffusion, and Synthesia for hands-on experimentation.
• Explore real-world applications of Generative AI in creative industries, automation, healthcare, and more.
• Invest in learning Generative AI today and gain the skills to create and manage AI-powered solutions that drive innovation.

The Frameworks of the Course

Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises— this course is designed to explore Generative AI, covering AI model architectures, practical applications, and real-world AI implementations.

The course includes multiple case studies, resources such as templates, worksheets, reading materials, quizzes, self-assessments, and hands-on labs to deepen your understanding of Generative AI.

• In the first part of the course, you’ll learn the foundations of AI, machine learning, and deep learning, along with the history and evolution of Generative AI.
• In the middle part of the course, you’ll develop a deep understanding of GANs, VAEs, and Transformers, gaining hands-on experience with AI-powered tools and models.
• In the final part of the course, you’ll explore the ethical considerations, real-world applications, and future trends in Generative AI, along with career opportunities in AI development and research.

Course Content:

Part 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. Introduction to Generative AI

1.1. Overview of Artificial Intelligence and Machine Learning

1.2. What is Generative AI?

1.3. History and Evolution of Generative AI

1.4. Applications of Generative AI in Various Fields

1.5. Activity: Group discussion on popular generative AI use cases (e.g., ChatGPT, DALL·E, MidJourney)

1.6. Conclusion

Module 2. Core Technologies Behind Generative AI

2.1. Neural Networks and Deep Learning Basics

2.2. Introduction to Generative Adversarial Networks (GANs)

2.3. Variational Autoencoders (VAEs)

2.4. Transformers and Language Models (e.g., GPT, BERT)

2.5. Activity: Hands-on experiment with a pre-trained model (e.g., GPT-3)

2.6. Conclusion

Module 3. Popular Generative AI Tools

3.1. Text Generation Tools (ChatGPT, Jasper AI, Writesonic)

3.2. Image Generation Tools (DALL E, MidJourney, Stable Diffusion)

3.3. Video and Audio Generation Tools (Synthesia, Runaway ML, Resemble AI)

3.4. Coding and Development Tools (GitHub Copilot, Tabnine)

3.5. Activity: Practical exercises with tools like DALL E or ChatGPT

3.6. Conclusion

Module 4. Building Generative AI Models

4.1. Data Preparation and Preprocessing

4.2. Training GANs and Transformers

4.3. Fine-tuning Pre-trained Models

4.4. Deployment of Generative Models

4.5. Activity: Build a simple text generator or image generator using Python and open-source libraries

4.6. Conclusion

Module 5. Use Cases of Generative AI

5.1. Creative Content Generation (e.g., art, writing, video)

5.2. Code Generation and Automation

5.3. Personalized Recommendations

5.4. Healthcare Applications (e.g., drug discovery, diagnosis aids)

5.5. Activity: Case study analysis: Real-world applications of generative AI.

5.6. Conclusion

Module 6. Ethical Considerations and Challenges

6.1. Bias in Generative Models

6.2. Intellectual Property Concerns

6.3. Security Risks (e.g., deepfakes)

6.4. Addressing Environmental Impact (e.g., energy consumption of training models)

6.5. Activity: Debate or panel discussion on ethical concerns in generative AI.

6.6. Conclusion

Module 7. Future of Generative AI

7.1. Emerging Trends in Generative AI

7.2. Potential Innovations in Tools and Applications

7.3. Career Opportunities in Generative AI

7.4. Activity: Research project: Predicting the future impact of generative AI on a specific industry.

7.5. Conclusion