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- What is Artificial Intelligence (AI)?
- Practical examples from daily life (Google Maps, Siri, ChatGPT)
- Difference between Narrow AI and General AI
- AI vs Machine Learning (ML)
- Simple analogy (AI as the goal, ML as the method)
- Infographic comparing AI, ML, and Deep Learning
- Statistical ML vs Deep Learning
- Why statistical methods still matter
- Where deep learning excels
- Comparison table with real-world examples
- Linear & Logistic Regression
- Concepts in simple terms
- Real-life applications (predicting house prices, spam detection)
- Decision Trees
- How they “ask questions” to make decisions
- Use case: loan approval system
- K-Means Clustering
- What “grouping data” really means
- Example: segmenting customers for marketing
- What is a Neural Network?
- Structure: input, hidden, output layers
- Visual diagram + human brain analogy
- Different Architectures of Neural Networks
- Feedforward NN
- Convolutional Neural Networks (CNNs) – for images
- Recurrent Neural Networks (RNNs) – for sequences
- Transformers – for large-scale language & multimodal tasks
- Why Transformers Changed AI
- No recurrence → faster training
- Attention mechanism → context understanding
- Parallelisation & scalability
- Architecture of a Transformer Model
- Encoder, decoder, and self-attention explained with a visual flow
- Applications of Transformers
- Text summarisation, translation, image generation, speech recognition
- Popular Transformer Models
- BERT – for understanding text
- T5 – for text-to-text tasks
- DALL·E – for image generation
- Whisper – for speech-to-text
- Trivia box: fun facts about each model
- AI Agents & Agentic AI
- What are AI agents?
- Examples: AutoGPT, Devin, ChatGPT’s advanced agents
- Physical AI
- AI in robotics and real-world automation
- Examples: Boston Dynamics robots, AI-driven warehouse bots
- What’s Next? The Future of AI
- Realistic trends in the next 5–10 years
- Potential career paths in AI
