Today, let's move to the next topic of Artificial Intelligence Roadmap:

AI Basics Part-2: AI vs Machine Learning vs Deep Learning

Artificial Intelligence (AI)
- The big umbrella
- Goal: Make machines act intelligently
- Includes rules, logic, learning systems
- Example: A chess program with fixed rules (no learning, still AI)

Machine Learning (ML)
- Subset of AI
- Systems learn from data, no hard-coded rules
- How it works:
- You give input and output data
- Model finds patterns
- Uses patterns for new data
- Examples:
- Predict house prices from past sales
- Fraud detection from transaction history

Deep Learning (DL)
- Subset of machine learning
- Uses neural networks with many layers
- Handles complex data
- Why it matters:
- Works well with images, audio, text
- Learns features automatically
- Examples:
- Face recognition
- Speech recognition
- Chatbots

Simple Comparison
- AI: The goal
- Machine Learning: How systems learn
- Deep Learning: Powerful learning using neural networks

Real Product Mapping
- Spam filter: AI system, machine learning model
- Face unlock: AI system, deep learning model

When Each is Used
- Rule-based AI: Small, fixed logic
- Machine Learning: Structured data, predictions
- Deep Learning: Images, voice, large-scale text

Takeaway
- AI is the field
- Machine learning is the engine
- Deep learning is the heavy machinery

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