Overview
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.
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.
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
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
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