Artificial Intelligence & Machine Learning
Complete Overview, Use Cases & Mind Maps
1. The AI Landscape - Complete Hierarchy
2. AI vs ML vs DL vs Data Science
Definitions
Artificial Intelligence (AI)
- Broad field: Making machines intelligent
- Any technique that enables computers to mimic human behavior
- Includes rule-based systems, search algorithms, reasoning
Machine Learning (ML)
- Subset of AI
- Learning from data without explicit programming
- Statistical approaches to pattern recognition
Deep Learning (DL)
- Subset of ML
- Neural networks with multiple layers
- Automatic feature extraction
Data Science
- Interdisciplinary field
- Extract insights from data
- Includes ML + Statistics + Domain Expertise + Visualization
| Aspect | AI | ML | Deep Learning | Data Science |
|---|---|---|---|---|
| Scope | Broadest | Subset of AI | Subset of ML | Parallel field |
| Goal | Mimic intelligence | Learn patterns | Complex patterns | Extract insights |
| Data Needs | Varies | Medium | Large | Varies |
| Techniques | Rules, Search, ML | Algorithms | Neural Networks | Stats, ML, Viz |
| Example | Chess AI | Spam filter | Face recognition | Customer analysis |
3. Machine Learning - Detailed Breakdown
Supervised Learning Algorithms
Unsupervised Learning Algorithms
4. Deep Learning Architecture Map
5. AI Applications by Industry
6. Computer Vision Applications
Popular Architectures:
- Image Classification: ResNet, EfficientNet, Vision Transformer
- Object Detection: YOLO, Faster R-CNN, RetinaNet
- Segmentation: U-Net, Mask R-CNN, DeepLab
- Face Recognition: FaceNet, ArcFace
- Generation: StyleGAN, Stable Diffusion, DALL-E
7. Natural Language Processing (NLP)
Key Technologies:
- Traditional: Bag of Words, TF-IDF, Word2Vec, GloVe
- Modern: BERT, GPT, T5, LLaMA, Claude
- Applications: ChatGPT, Google Translate, Grammarly, Siri
8. Reinforcement Learning Use Cases
Key Algorithms:
- Q-Learning
- Deep Q-Network (DQN)
- Policy Gradient
- Actor-Critic
- Proximal Policy Optimization (PPO)
- AlphaZero
9. Generative AI Landscape
Technologies Behind:
- Transformers: GPT, BERT, T5
- Diffusion Models: Stable Diffusion, DALL-E
- GANs: StyleGAN, BigGAN
- VAEs: Variational Autoencoders
10. AI Tech Stack by Layer
11. Real-World Use Cases by Domain
Healthcare
Examples:
- IBM Watson Health: Cancer diagnosis
- Google DeepMind: Protein folding (AlphaFold)
- PathAI: Pathology analysis
- Babylon Health: Symptom checker
Finance
Examples:
- JPMorgan COIN: Contract analysis
- Stripe Radar: Fraud detection
- Betterment: Robo-advisory
- Affirm: Credit decisions
Retail & E-commerce
Examples:
- Amazon: Product recommendations
- Amazon Go: Checkout-free shopping
- Stitch Fix: Personal styling
- Shopify: E-commerce optimization
Transportation
Examples:
- Tesla Autopilot: Self-driving
- Waymo: Autonomous taxis
- Uber: Route optimization
- UPS ORION: Delivery optimization
12. AI Model Types & When to Use
13. AI Project Workflow
14. ML Algorithm Selection Guide
Problem Type Decision Tree
15. Popular AI Tools & Platforms
16. Emerging AI Trends & Future
Future Applications (2024-2030)
17. Data Requirements by AI Type
| AI Type | Minimum Data | Optimal Data | Data Quality | Computational Needs |
|---|---|---|---|---|
| Traditional ML | 100s-1000s samples | 10K+ samples | High quality needed | Low (CPU) |
| Deep Learning | 10K+ samples | 100K+ samples | Can handle noise | High (GPU) |
| Transfer Learning | 100s samples | 1K+ samples | High quality needed | Medium (GPU) |
| Few-Shot Learning | <10 samples | 10-100 samples | Very high quality | High (GPU) |
| Zero-Shot Learning | 0 samples | Training on related tasks | N/A | High (GPU) |
| Reinforcement Learning | Simulation or millions of steps | Unlimited | Generated data | Very High (GPU/TPU) |