• Duration: 6 months
• Rating: 4.5 / 5

The Advanced Generative AI Program is designed to build strong foundations in artificial intelligence, deep learning, and modern generative AI technologies.

The program combines theoretical concepts, hands-on labs, and real-world projects to help learners develop practical expertise in building AI systems. Students will gain experience working with neural networks, generative models, transformers, large language models, and diffusion models, along with deploying AI applications to production environments.

By the end of the program, learners will be able to build end-to-end generative AI solutions, fine-tune large language models, develop AI-powered applications, and deploy scalable AI systems while understanding ethical and responsible AI practices.

Course Outline

The Advanced Generative AI program follows a structured learning pathway covering the following areas:

1. AI & Machine Learning Foundations

• Understanding the evolution of Artificial Intelligence, Machine Learning, and Generative AI
• Learning the core mathematical foundations used in AI systems
• Working with Python and essential libraries for data processing and visualization
• Training and evaluating basic machine learning and deep learning models

2. Deep Learning Architectures

• Understanding neural networks and deep learning fundamentals
• Working with CNNs for image-based applications
• Using RNNs and LSTM networks for sequence and text data
• Learning feature representations and embeddings for AI models

3. Generative Model Development

• Understanding generative models and how they create new data
• Building and training Generative Adversarial Networks (GANs)
• Exploring modern generative architectures used in image synthesis
• Developing models capable of generating realistic visual outputs

4. Transformers & Large Language Models

• Understanding transformer architectures and attention mechanisms
• Working with GPT-style models and modern LLM ecosystems
• Fine-tuning language models for domain-specific applications
• Building conversational AI systems and intelligent assistants

5. Generative AI Applications

• Developing AI-powered content generation systems
• Building image generation and editing applications
• Exploring audio generation, voice synthesis, and speech models
• Creating multimedia AI solutions that combine multiple AI capabilities

6. AI Deployment & Optimization

• Optimizing AI models for performance and efficiency
• Deploying AI applications using modern backend frameworks
• Containerizing AI applications for scalable deployment
• Understanding ethical AI, responsible AI practices, and industry trends

7. End-to-End Generative AI Project Development

• Designing real-world Generative AI applications
• Building complete AI pipelines from data preparation to deployment
• Developing production-ready AI systems
• Presenting final projects with technical documentation and demonstrations

Highlights 

• Generative AI Foundations
• Deep Learning Architectures
• GAN & Image Generation
• Transformers & Large Language Models
• Diffusion Models
• Multimedia AI Systems
• Hands-on Labs & Mini Projects
• Industry-Style Capstone Project
• Model Optimization & Deployment
• Ethical & Responsible AI
• Recruiter-Ready Portfolio