Course Structure & Learning Objectives

This course will cover theoretical foundations, practical implementation, and advanced applications of Generative AI models. By the end of the course, participants will:
✅ Develop and deploy Generative AI models for text, image, audio, and video generation.
✅ Gain hands-on experience with GANs, Transformers (GPT-4, LLaMA), and Diffusion Models (Stable Diffusion, DALL·E).
✅ Learn Model Optimization, Fine-tuning LLMs, and Ethical Considerations in Generative AI.
✅ Work on Live Projects & Real-World Case Studies in various domains.

📖 Course Syllabus

📌 Module 1: Fundamentals of AI & Machine Learning (Weeks 1-4)

1.1 Introduction to AI & Generative AI

• Evolution of AI: Machine Learning, Deep Learning & Generative AI
• Applications of Generative AI in Text, Images, Audio & Video
• Ethical Considerations & AI Safety

1.2 Mathematical & Statistical Foundations

• Linear Algebra (Vectors, Matrices, Eigenvalues)
• Probability & Statistics for AI (Bayes Theorem, Random Variables)
• Optimization Algorithms (Gradient Descent, Backpropagation)

1.3 Introduction to AI Development Tools

• Python for AI: NumPy, Pandas, Matplotlib
• Deep Learning Frameworks: TensorFlow & PyTorch
• Working with Jupyter Notebook & Google Colab

🔹 Hands-on Lab: Train a simple Deep Learning model using TensorFlow.
🔹 Mini Project: Build a Sentiment Analysis Model using NLP.

📌 Module 2: Deep Learning & Autoencoders (Weeks 5-8)

2.1 Deep Learning Foundations

• Neural Networks: Perceptrons, Activation Functions
• Convolutional Neural Networks (CNNs) for Image Processing
• Recurrent Neural Networks (RNNs) & Long Short-Term Memory (LSTM)

2.2 Autoencoders & Feature Learning

• Introduction to Autoencoders & Variational Autoencoders (VAEs)
• Sparse, Denoising & Contractive Autoencoders
• Feature Representation & Embeddings

🔹 Hands-on Lab: Implement CNNs & LSTMs for Image and Text Data.
🔹 Mini Project: Build an AI model to remove noise from images.

📌 Module 3: Generative Adversarial Networks (GANs) (Weeks 9-12)

3.1 Introduction to GANs

• Generator vs Discriminator Architecture
• Training GANs & Challenges (Mode Collapse, Vanishing Gradients)

3.2 Advanced GAN Architectures

• Deep Convolutional GANs (DCGANs)
• Conditional GANs (cGANs) for Controlled Image Generation
• StyleGAN, CycleGAN & Pix2Pix for Image Manipulation

🔹 Hands-on Lab: Train a DCGAN for Image Generation.
🔹 Mini Project: Develop AI-generated human faces using StyleGAN.

📌 Module 4: Transformer-Based Models & LLMs (Weeks 13-16)

4.1 Understanding Transformers

• Self-Attention & Multi-Head Attention Mechanism
• Encoder-Decoder Models & Language Modeling

4.2 Large Language Models (LLMs) & Fine-Tuning

• Pretrained Models: GPT-3, GPT-4, LLaMA, Falcon
• Fine-Tuning LLMs for Custom NLP Applications

4.3 Text-to-Image & AI Creativity

• Introduction to Diffusion Models (Stable Diffusion, DALL·E, Midjourney)
• Training Custom AI Models for Text-to-Image Generation

🔹 Hands-on Lab: Fine-tune GPT models for text generation.
🔹 Mini Project: Build a custom AI-powered chatbot using OpenAI API.

📌 Module 5: Generative AI for Real-World Applications (Weeks 17-20)

5.1 Generative AI for Text & Content Creation

• AI-Powered Content Writing (ChatGPT, Bard, Claude)
• AI Summarization & Sentiment Analysis

5.2 Generative AI for Images & Videos

• Deepfake Technology & Ethical Concerns
• AI-Powered Image Editing (FaceSwap, Super-Resolution)

5.3 Generative AI for Audio & Music

• AI Voice Synthesis & Speech Cloning
• AI-Powered Music Generation

🔹 Hands-on Lab: Create AI-generated music & voice models.
🔹 Mini Project: AI-based video editing using Stable Diffusion.

📌 Module 6: Advanced Topics, Model Deployment & Capstone Project (Weeks 21-24)

6.1 Model Optimization & Deployment

• Model Pruning, Quantization & Acceleration Techniques
• Deploying AI Models using Flask, FastAPI & Docker

6.2 AI Ethics, Bias & Future Trends

• Ethical AI & Regulatory Challenges
• AI in Business, Healthcare, and Finance

6.3 Capstone Project (Final 3 Weeks)

• Develop a real-world Generative AI application
• Live project presentation & peer review

🔹 Final Assessment & Certification: Live project evaluation by industry experts.

🛠 Tools & Technologies Covered

✔ Python, TensorFlow, PyTorch, OpenAI API, Google Colab
✔ Hugging Face Transformers, Stable Diffusion, DALL·E
✔ Flask, FastAPI, Docker for AI Model Deployment

📜 Certification & Career Support

✅ Certificate of Completion from [Your Institute Name]
✅ Industry-Recognized Project Portfolio
✅ Placement Assistance & Career Mentorship
✅ Internship Opportunities with AI Companies

Why Enroll in This Course?

🔹 Industry-Oriented Curriculum: Covers the latest AI trends & technologies.
🔹 Hands-On Learning: Live coding sessions, labs, and projects.
🔹 Expert Mentorship: Taught by AI professionals & industry experts.
🔹 Career Support: Resume building, mock interviews, and job assistance.