Generative AI – From Basics to Advanced

Description

✅ What You Will Learn

📚 Foundations of Generative AI

• Introduction to Generative AI & Python Basics

• Introduction to Machine Learning & NLP Concepts

• Traditional NLP, Sentence Embeddings

• Machine Translation with GRU & LSTM Models

🧠 Deep Learning & Transformers

• Encoder-Decoder Architectures

• Hugging Face & Transformers Deep Dive

• Fine-tuning Transformer Models for Custom Tasks

• Understanding Generative AI Models & Hallucination Issues

💬 Prompt Engineering & LLMs

• ChatGPT and GPT Models Overview

• Prompt Engineering Fundamentals & Advanced Techniques

• Instruction Fine-tuning & RAG with SLM

• Evaluation & Hallucination Mitigation

🔗 LangChain, LangGraph & RAG

• Implementing RAG in LangChain

• AI Agents in LangGraph & React Agent

• Memory, Output Parser, and QLoRA Fine-tuning

🚀 AI Deployment & Scalability

• Quantization Methods for model efficiency

• Deploying LLMs using Docker

• AWS Deployment & Streamlit App Integration

• LLM Operations, Scalability & Ethics in AI Systems

🎨 Generative AI Tools & Applications

• Copilot for AI-powered coding

• Stable Diffusion & Autoencoder for image generation

👤 Who Is This Course For?

• Aspiring AI Engineers who want to build and deploy AI models

• ML & Data Professionals looking to transition into Generative AI

• Developers & Researchers interested in LLM fine-tuning & prompt engineering

• Tech enthusiasts wanting hands-on AI project experience