Generative AI is a subset of deep learning.  It uses AI neural networks and can process both labelled and unlabelled data using supervised, unsupervised, and semi-supervised methods.

It refers to a class of artificial intelligence models and algorithms designed to create new content. These models can generate text, images, music, and other forms of data that mimic human-created content.

Generative AI applications are built on top of large language models (LLMs) and foundation models. LLMs are deep learning models.

LLMs are a subset of deep learning. LLMs are AI models that power chatbots, such as ChatGPT, Copilot, Google Gemini, etc. LLMs refer to large, general-purpose language models that can be pre-trained and then fine-tuned for specific purposes.

**Course Lessons**

Section A: AI – Introduction

1. Artificial Intelligence vs Data Science vs Machine Learning vs Deep Learning
2. Deep Learning Types

Section B: Generative AI and its techniques

3. What is Generative AI
4. Techniques for implementing Generative AI

Section C: What are Transformer Models

5. Generative AI – Transformers

Section D: Large Language Models

6. Large Language Models (LLMs) and their use case

Section E: More about Generative AI

7. Generative AI – Applications & Challenges
8. Generative AI – Chatbots (Model Types)
9. Generative AI – Features & Examples

Section F: Prompts and AI Chatbots

10. What are Prompts
11. Popular AI Chatbots

After completing this course, you will be ready to learn from the following courses on Eduonix:

• Google Gemini Course: https://www.eduonix.com/google-gemini-masterclass 
• ChatGPT Course: https://www.eduonix.com/chatgpt-masterclass
• Microsoft Copilot: https://www.eduonix.com/microsoft-copilot-masterclass