• Basics of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)

• Introduction to Neural Networks

• Deep Learning Architectures (CNNs, RNNs, Transformers)

• Backpropagation and Optimization Techniques

• Text Processing and Tokenization

• Word Embeddings (Word2Vec, GloVe)

• Attention Mechanisms and Transformers

• Large Language Models (LLMs) like GPT, BERT

• Introduction to GANs

• Generator and Discriminator Networks

• Variants of GANs (DCGAN, CycleGAN, StyleGAN)

• Image Generation and Synthesis with GANs

• Markov Chains and LSTMs

• Transformer Models (GPT, T5, BERT)

• Fine-Tuning Large Language Models

• Prompt Engineering and Model Optimization

• Deepfake Technology and Face Synthesis

• AI-Based Image Enhancement (Stable Diffusion, DALL·E)

• Video Generation with AI (Runway ML, Synthesia)

• Ethical Considerations and Bias in AI-Generated Media

• AI-Powered Code Generation (GitHub Copilot, OpenAI Codex)

• AI-Assisted Software Development

• Debugging and Code Optimization with AI

• AI in Low-Code/No-Code Platforms

• AI-Based Voice Cloning (ElevenLabs, VALL-E)

• Text-to-Speech (TTS) and Speech-to-Text (STT)

• Music and Sound Generation with AI

• Ethical Concerns in Audio Deepfakes

• AI for Content Creation (ChatGPT, Jasper AI)

• SAI in Marketing and Advertising

• AI-Powered Chatbots and Virtual Assistants

• AI in Healthcare, Finance, and E-Commerce

• Ethical Issues in Generative AI

• AI Regulations and Responsible AI Development

• The Future of AGI (Artificial General Intelligence)

• Hands-on Projects and Case Studies