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Generative AI The Future of AI-Powered Creativity Pro TM
Understanding Generative AI How It Works, Applications, and Future Trends
Last updated 4/2025
English
What you'll learn
- Students will learn what Generative AI is, its types, and how it's transforming industries.
- Students will understand the complete workflow of AI systems from basic AI to advanced deep learning.
- Students will explore how large language models generate human-like text for various real-world applications.
- Students will identify the limitations of current GenAI models including bias, hallucination, and data constraints.
- Students will discover how RAG enhances model accuracy by integrating external knowledge sources.
- Students will master the architecture and real-world implementation of RAG with LLMs.
- Students will learn how GenAI creates stunning images using tools like DALL·E and Stable Diffusion.
- Students will understand how GenAI generates music and visual art through creative AI algorithms.
- Students will explore how artists can collaborate with AI tools to enhance creativity and productivity.
- Students will learn the role of transformer models and attention mechanisms in GenAI systems.
- Students will explore techniques for handling long sequences and dependencies in generated content.
- Students will learn how to adapt pre-trained models to specific tasks using various fine-tuning methods.
- Students will gain skills to fine-tune GenAI models for industries like healthcare, finance, and legal.
- Students will explore how GenAI models combine text, image, audio, and video for richer experiences.
- Students will understand how to implement RAG in multimodal systems for intelligent information retrieval.
- Students will examine the ethical implications of GenAI, including fairness, privacy, and misinformation.
- Students will learn strategies for building responsible, transparent, and accountable AI models.
- Students will build a fully functional AI chatbot using LangChain, OpenAI APIs, and retrieval techniques.
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Course content
19 sections • 19 lectures • 3h 29m total length
Requirements
- This masterclass is designed for everyone—no prior experience is required, as the concepts are explained in a simple and accessible manner.
Description
Unlock the power of Generative AI, the most transformative technology of the 21st century. From text and image generation to multimodal learning and Retrieval-Augmented Generation (RAG), this course offers a step-by-step journey through the core components, real-world applications, and ethical considerations of Generative AI.
Learn how Large Language Models (LLMs) like GPT, transformer architectures, and domain-specific fine-tuningare reshaping content creation, automation, and AI innovation across industries.
Whether you're a developer, data scientist, AI enthusiast, or a digital creative, this course equips you with hands-on skills and strategic insights to build cutting-edge AI solutions — including chatbots using LangChain + OpenAI.
1. Introduction to Generative AI
What is Generative AI?
Types: Text, Image, Audio, Code
Use cases across industries (marketing, healthcare, education)
2. How Generative AI Works End-to-End | AI → ML → DL
Overview of AI, Machine Learning, Deep Learning
Key concepts in Neural Networks
Training vs Inference vs Deployment
3. Text Generation Using Generative AI
Understanding LLMs (GPT, BERT, T5)
Applications in text summarization, translation, storytelling
4. Challenges and Limitations of Current Text Generation AI
Bias, hallucination, prompt limitations
Computational cost, token limits
5. Retrieval-Augmented Generation (RAG) in AI – Enhancing Model Knowledge
What is RAG and why it matters
Augmenting LLMs with external knowledge
6. How RAG Works with LLMs – Mastering Retrieval-Augmented Generation
Architecture of RAG
Building pipelines with vector databases
Practical examples (e.g., Question Answering Systems)
7. Introduction to Image Generation Using Generative AI
Tools: DALL·E, Midjourney, Stable Diffusion
Generating realistic, abstract, and branded visuals
8. Music and Art Creation Using Generative AI
AI for creative expression: from background scores to generative art
Tools and platforms used in the industry
9. Enhancing Artists' Workflow with Iterative Gen AI
Co-creation with AI
Style transfers, iterative refinement, ideation acceleration
10. Transformer Architecture in Generative AI
Self-attention, encoder-decoder design
Evolution of Transformers: BERT → GPT → PaLM
11. Modeling Long-Range Dependencies in Text Generation AI
Challenges with long documents
Solutions: attention masks, hierarchical models
12. Fine-Tuning Pre-Trained Models for Generative AI
Transfer learning basics
Low-Rank Adaptation (LoRA), PEFT, and prompt tuning
13. Techniques for Domain-Specific Fine-Tuning in Generative AI
Medical, legal, finance — customizing GenAI
Dataset preparation, fine-tuning pipelines
14. Multimodal Generative Models Explained
Combining text, image, audio, and video
CLIP, Flamingo, and Gemini models
15. Multimodal Retrieval-Augmented Generation (RAG)
Bridging modalities using RAG
Visual QA, document understanding with RAG + LLMs
16. Ethical Considerations in Generative AI
Misinformation, deepfakes, consent
Regulatory frameworks: EU AI Act, Responsible AI
17. Enforcing Accountability & Responsibility in AI Model Development
Auditing AI systems
Bias detection, mitigation strategies, transparency in model design
18. Building a Chatbot with LangChain + OpenAI
Introduction to LangChain
Integrating OpenAI APIs
End-to-end project: RAG-based Q&A Chatbot
Who this course is for:
- This course is ideal for anyone aspiring to learn future-ready skills and pursue careers such as Deep Learning Engineer, Data Scientist, Senior Data Scientist, AI Scientist, AI Engineer, AI Researcher, or AI Expert.