Certification In Generative AI Models And Tools

Learn Generative AI and tools like DALLE, Jasper, ChatGPT, BERT, Synthesia, RunwayML with models and networks.

What you'll learn
You will learn about the Introduction of Generative AI, including its history, evolution, and key differences from traditional AI and machine learning
You will gain expertise in Core Generative AI Technologies, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs)
Learn Transformer-based models such as GPT and BERT. You will also explore their applications in text, image, and video generation.
Learn about Popular Generative AI Tools, covering text generation tools like ChatGPT and Jasper AI, image generation tools like DALL·E
Learn MidJourney, and video/audio generation tools such as Synthesia and Runway ML. Explore their capabilities and real-world applications
Develop hands-on skills in building Generative AI models, including data preparation, model training, and fine-tuning pre-trained models
Gain proficiency in applying Generative AI to various fields such as content creation, code generation, personalized recommendations.
Understand the Ethical Considerations and Challenges of Generative AI, including bias in AI models, intellectual property concerns, deepfake risks
Explore the Future of Generative AI, including emerging trends, potential innovations, and career opportunities in AI research, development, and applications.

Requirements
You should have an interest in the fundamentals of Generative AI and how AI models generate text, images, and other media
Be interested in gaining knowledge about popular Generative AI tools and their applications across industries

Description
DescriptionTake 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 CourseEngaging 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 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. Introduction to Generative AI1.1. Overview of Artificial Intelligence and Machine Learning1.2. What is Generative AI?1.3. History and Evolution of Generative AI1.4. Applications of Generative AI in Various Fields1.5. Activity: Group discussion on popular generative AI use cases (e.g., ChatGPT, DALL·E, MidJourney)1.6. ConclusionModule 2. Core Technologies Behind Generative AI2.1. Neural Networks and Deep Learning Basics2.2. 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. ConclusionModule 3. Popular Generative AI Tools3.1. 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 ChatGPT3.6. ConclusionModule 4. Building Generative AI Models4.1. Data Preparation and Preprocessing4.2. Training GANs and Transformers4.3. Fine-tuning Pre-trained Models4.4. Deployment of Generative Models4.5. Activity: Build a simple text generator or image generator using Python and open-source libraries4.6. ConclusionModule 5. Use Cases of Generative AI5.1. Creative Content Generation (e.g., art, writing, video)5.2. Code Generation and Automation5.3. Personalized Recommendations5.4. Healthcare Applications (e.g., drug discovery, diagnosis aids)5.5. Activity: Case study analysis: Real-world applications of generative AI.5.6. ConclusionModule 6. Ethical Considerations and Challenges6.1. Bias in Generative Models6.2. Intellectual Property Concerns6.3. 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. ConclusionModule 7. Future of Generative AI7.1. Emerging Trends in Generative AI7.2. Potential Innovations in Tools and Applications7.3. Career Opportunities in Generative AI7.4. Activity: Research project: Predicting the future impact of generative AI on a specific industry.7.5. Conclusion

AI enthusiasts looking to gain expertise in Generative AI, deep learning, and neural networks for text, image, and video generation.,Developers, data scientists, and engineers interested in learning how to build and fine-tune Generative AI models for various applications.,Content creators, marketers, and designers who want to leverage AI-powered tools for content generation.