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CampusEduX
Free course

Generative AI

Learn Generative AI from the fundamentals to advanced concepts including LLMs, Transformers, Prompt Engineering, RAG, Fine-Tuning, AI Agents, Multimodal AI, Security, and Deployment.

Sections
17 sections
Lessons
207 lessons

What you'll learn

  • Unit 1: Introduction to Generative AI
  • Unit 2: Machine Learning & Deep Learning Basics
  • Unit 3: Transformers & Attention
  • Unit 4: Large Language Models (LLMs)
  • Unit 5: Prompt Engineering
  • Unit 6: Generative AI Models
  • Unit 7: Generative AI for Images
  • Unit 8: Generative AI for Audio & Video

+ 9 more sections in the curriculum below

Course content

17 sections · 207 lessons

1Unit 1: Introduction to Generative AI12 lessons
  1. 1What is Generative AI
  2. 2AI vs ML vs Deep Learning vs Generative AI
  3. 3Generative vs Discriminative Models
  4. 4Applications of Generative AI
  5. 5Text
  6. 6Image
  7. 7Audio
  8. 8Video
  9. 9Code
  10. 10Generative AI lifecycle
  11. 11Challenges and limitations
  12. 12Ethics, bias, privacy and responsible AI
2Unit 2: Machine Learning & Deep Learning Basics12 lessons
  1. 13Supervised, Unsupervised and Reinforcement Learning
  2. 14Neural Networks
  3. 15Perceptron
  4. 16Activation Functions
  5. 17Loss Functions
  6. 18Gradient Descent
  7. 19Backpropagation
  8. 20CNN
  9. 21RNN
  10. 22LSTM and GRU
  11. 23Training, Validation and Testing
  12. 24Overfitting and Underfitting
3Unit 3: Transformers & Attention11 lessons
  1. 25Sequence-to-Sequence Models
  2. 26Attention Mechanism
  3. 27Self-Attention
  4. 28Multi-Head Attention
  5. 29Positional Encoding
  6. 30Encoder and Decoder
  7. 31Transformer Architecture
  8. 32BERT vs GPT
  9. 33Encoder-only, Decoder-only and Encoder-Decoder models
  10. 34Tokenization
  11. 35Embeddings
4Unit 4: Large Language Models (LLMs)16 lessons
  1. 36What is an LLM
  2. 37LLM Architecture
  3. 38Pre-training
  4. 39Fine-tuning
  5. 40Instruction tuning
  6. 41RLHF
  7. 42Context Window
  8. 43Parameters
  9. 44Temperature
  10. 45Top-K and Top-P
  11. 46Hallucination
  12. 47GPT
  13. 48Gemini
  14. 49Claude
  15. 50Llama
  16. 51Mistral
5Unit 5: Prompt Engineering14 lessons
  1. 52What is Prompt Engineering
  2. 53Prompt structure
  3. 54Zero-shot prompting
  4. 55One-shot prompting
  5. 56Few-shot prompting
  6. 57Role prompting
  7. 58Chain-of-Thought
  8. 59ReAct prompting
  9. 60System, User and Assistant prompts
  10. 61Prompt templates
  11. 62Structured output
  12. 63JSON prompting
  13. 64Prompt optimization
  14. 65Common prompting mistakes
6Unit 6: Generative AI Models15 lessons
  1. 66Autoencoders
  2. 67Variational Autoencoders (VAE)
  3. 68GANs
  4. 69Generator
  5. 70Discriminator
  6. 71Training process
  7. 72Applications
  8. 73Diffusion Models
  9. 74Stable Diffusion
  10. 75Multimodal Models
  11. 76Text-to-Text
  12. 77Text-to-Image
  13. 78Text-to-Video
  14. 79Text-to-Audio
  15. 80Image-to-Text
7Unit 7: Generative AI for Images11 lessons
  1. 81Image generation concepts
  2. 82Diffusion process
  3. 83Text-to-image generation
  4. 84Image-to-image generation
  5. 85Inpainting
  6. 86Outpainting
  7. 87ControlNet
  8. 88Image prompting
  9. 89Negative prompts
  10. 90Image generation evaluation
  11. 91Applications of AI image generation
8Unit 8: Generative AI for Audio & Video8 lessons
  1. 92Text-to-Speech (TTS)
  2. 93Speech-to-Text (STT)
  3. 94Voice cloning concepts
  4. 95Music generation
  5. 96Text-to-video
  6. 97Image-to-video
  7. 98Video editing using AI
  8. 99Applications and limitations
9Unit 9: Embeddings & Vector Databases13 lessons
  1. 100What are embeddings
  2. 101Text embeddings
  3. 102Semantic similarity
  4. 103Vector search
  5. 104Vector databases
  6. 105Similarity metrics
  7. 106Cosine similarity
  8. 107Euclidean distance
  9. 108FAISS
  10. 109Pinecone
  11. 110Chroma
  12. 111Weaviate
  13. 112Metadata filtering
10Unit 10: RAG – Retrieval Augmented Generation16 lessons
  1. 113What is RAG
  2. 114Why RAG is required
  3. 115RAG Architecture
  4. 116Document loading
  5. 117Text chunking
  6. 118Embedding generation
  7. 119Vector storage
  8. 120Retrieval
  9. 121Context injection
  10. 122Response generation
  11. 123Basic RAG
  12. 124Advanced RAG
  13. 125Hybrid Search
  14. 126Reranking
  15. 127RAG evaluation
  16. 128RAG vs Fine-tuning
11Unit 11: Fine-Tuning & Model Adaptation11 lessons
  1. 129Fine-tuning concepts
  2. 130Full fine-tuning
  3. 131Parameter-efficient fine-tuning
  4. 132LoRA
  5. 133QLoRA
  6. 134Transfer Learning
  7. 135Dataset preparation
  8. 136Training datasets
  9. 137Model evaluation
  10. 138Model quantization
  11. 139Model deployment
12Unit 12: AI Agents14 lessons
  1. 140What is an AI Agent
  2. 141Agent Architecture
  3. 142Tools and Function Calling
  4. 143Planning
  5. 144Memory
  6. 145Reasoning
  7. 146ReAct Agents
  8. 147Multi-Agent Systems
  9. 148Agent workflows
  10. 149Autonomous agents
  11. 150Agent evaluation
  12. 151LangChain
  13. 152LangGraph
  14. 153CrewAI concepts
13Unit 13: Generative AI Development12 lessons
  1. 154Python for Generative AI
  2. 155APIs
  3. 156OpenAI-compatible APIs
  4. 157Hugging Face
  5. 158LlamaIndex
  6. 159Model inference
  7. 160Streaming responses
  8. 161Function calling
  9. 162Structured outputs
  10. 163Authentication & API keys
  11. 164Error handling
  12. 165Cost management
14Unit 14: Generative AI Applications11 lessons
  1. 166AI Chatbots
  2. 167AI Coding Assistants
  3. 168AI Search
  4. 169Document Q&A
  5. 170Resume/Job AI
  6. 171Education AI
  7. 172Healthcare applications
  8. 173Marketing & content generation
  9. 174Recommendation systems
  10. 175Customer support
  11. 176AI-powered automation
15Unit 15: Security, Ethics & Responsible AI13 lessons
  1. 177AI Bias
  2. 178Fairness
  3. 179Privacy
  4. 180Data protection
  5. 181Copyright
  6. 182Deepfakes
  7. 183Prompt Injection
  8. 184Jailbreaking
  9. 185Data Leakage
  10. 186Model Security
  11. 187AI Governance
  12. 188Responsible AI
  13. 189Human-in-the-loop
16Unit 16: Deployment & MLOps17 lessons
  1. 190Cloud deployment
  2. 191APIs and backend integration
  3. 192Docker basics
  4. 193GPU vs CPU inference
  5. 194Model optimization
  6. 195Quantization
  7. 196Monitoring
  8. 197Logging
  9. 198Latency optimization
  10. 199Scalability
  11. 200Cost optimization
  12. 201Suggested architecture
  13. 202Suggested deliverables
  14. 203Short-answer strategy
  15. 204Long-answer strategy
  16. 205Viva strategy
  17. 206Project-defense questions
17Unit 17: Course Summary & Project Roadmap1 lesson
  1. 207Course Summary & Project Roadmap