# Learning Tracks: Structured Paths to AI Mastery

Choose your path based on your goals and background.

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## Track 1: AI Engineer (Full Stack)

**Goal:** Build production AI systems end-to-end
**Duration:** 6-8 months
**Difficulty:** Beginner → Advanced

### Foundation (4 weeks)
1. Getting Started (Git, CLI, environments)
2. Python Fundamentals
3. Jupyter Notebooks & Google Colab

### Core ML (6 weeks)
4. AI Fundamentals
5. Maths & Statistics for AI
6. Scikit-Learn & Supervised Learning
7. Data Visualization

### Deep Learning (8 weeks)
8. Deep Learning Fundamentals
9. PyTorch & Neural Networks
10. Computer Vision
11. NLP & Transformers

### Generative AI (6 weeks)
12. Prompt Engineering
13. RAG Systems
14. AI Agents & Tool Use
15. Agentic AI Patterns

### Production (4 weeks)
16. MLOps & Deployment
17. AWS Fundamentals
18. Production Inference
19. **Capstone:** Build Your Own Private ChatGPT

**Portfolio Outcome:** Full-stack AI chatbot with RAG, local inference, and deployment

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## Track 2: Local LLM Engineer

**Goal:** Run and optimize LLMs locally
**Duration:** 3-4 months
**Difficulty:** Intermediate → Advanced

### Foundation (2 weeks)
1. Python Fundamentals
2. CLI & Environment Setup

### LLM Fundamentals (4 weeks)
3. Transformers & Attention
4. LLM Fine-Tuning
5. Quantization Engineering
6. Local LLM Architecture

### Optimization (4 weeks)
7. vLLM & Paged Attention
8. Production Inference
9. Apple Silicon Optimization
10. GPU Memory Management

### Deployment (2 weeks)
11. Docker & Containerization
12. Kubernetes for ML
13. **Capstone:** Deploy Quantized LLM Locally

**Portfolio Outcome:** Optimized local LLM running on your hardware with 10x cost savings

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## Track 3: AI Backend Engineer

**Goal:** Build AI-powered APIs and services
**Duration:** 4-5 months
**Difficulty:** Intermediate → Advanced

### Foundation (3 weeks)
1. Python Fundamentals
2. FastAPI & REST APIs
3. Databases & SQL

### AI Integration (5 weeks)
4. RAG Systems
5. Vector Databases
6. Embeddings & Semantic Search
7. Streaming & Real-time AI

### Orchestration (4 weeks)
8. AI Agents & Tool Use
9. Agentic AI Patterns
10. Multi-Agent Coordination
11. MCP Servers

### Production (3 weeks)
12. MLOps & Monitoring
13. AWS Bedrock & Cloud APIs
14. **Capstone:** Build AI-Powered SaaS Backend

**Portfolio Outcome:** Production API serving AI features to thousands of users

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## Track 4: Data Scientist → AI Engineer

**Goal:** Transition from data science to production AI
**Duration:** 5-6 months
**Difficulty:** Intermediate → Advanced

### Strengthen Foundations (2 weeks)
1. Python for Production (not just notebooks)
2. Git & Version Control
3. Testing & Debugging

### Modern ML (4 weeks)
4. Scikit-Learn Best Practices
5. Ensemble Learning
6. Model Evaluation & Metrics
7. Feature Engineering

### Deep Learning (6 weeks)
8. PyTorch (not just TensorFlow)
9. Transfer Learning
10. Fine-Tuning Pre-trained Models
11. Model Optimization

### Production Skills (4 weeks)
12. MLOps & Deployment
13. Model Monitoring & Observability
14. A/B Testing & Experimentation
15. **Capstone:** Deploy ML Model to Production

**Portfolio Outcome:** End-to-end ML system in production with monitoring

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## Track 5: Rapid Prototyper (4 Weeks)

**Goal:** Build AI projects quickly
**Duration:** 4 weeks
**Difficulty:** Beginner → Intermediate

### Week 1: Setup & Basics
1. Python Fundamentals
2. Google Colab (no setup needed)

### Week 2: AI Fundamentals
3. AI Fundamentals
4. Prompt Engineering

### Week 3: Build Something
5. RAG Systems
6. AI Agents

### Week 4: Deploy
7. **Capstone:** Build Your Own Private ChatGPT

**Portfolio Outcome:** Working AI chatbot you can show to anyone

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## Difficulty Levels Explained

### Beginner
- No prerequisites
- Hands-on, practical focus
- Short modules (15-30 min)
- Lots of examples
- **Time:** 2-4 weeks per course

### Intermediate
- Requires Python basics
- Deeper concepts
- Longer modules (30-60 min)
- Mix of theory and practice
- **Time:** 4-6 weeks per course

### Advanced
- Requires ML fundamentals
- Production-focused
- Complex implementations
- Research papers referenced
- **Time:** 6-8 weeks per course

### Production
- Enterprise-grade patterns
- Deployment & scaling
- Observability & monitoring
- Real-world constraints
- **Time:** 4-6 weeks per course

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## How to Choose Your Track

### "I'm completely new to AI"
→ **AI Engineer Track** (full foundation)

### "I know Python, want to run LLMs locally"
→ **Local LLM Engineer Track**

### "I'm a backend engineer, want to add AI"
→ **AI Backend Engineer Track**

### "I'm a data scientist, need production skills"
→ **Data Scientist → AI Engineer Track**

### "I have 4 weeks, want to build something"
→ **Rapid Prototyper Track**

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## Milestone Checkpoints

Each track has built-in checkpoints:

### Checkpoint 1: Foundations
- Can write Python code
- Understand basic ML concepts
- Can use Git

### Checkpoint 2: Core Skills
- Can train a model
- Understand neural networks
- Can evaluate performance

### Checkpoint 3: Advanced
- Can optimize for production
- Understand deployment
- Can debug complex systems

### Checkpoint 4: Capstone
- Built a real project
- Have portfolio piece
- Ready for jobs/freelance

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## Recommended Pace

### Full-Time (40 hrs/week)
- **AI Engineer Track:** 6-8 months
- **Local LLM Track:** 3-4 months
- **Rapid Prototyper:** 4 weeks

### Part-Time (10 hrs/week)
- **AI Engineer Track:** 6-8 months → 24-32 months
- **Local LLM Track:** 3-4 months → 12-16 months
- **Rapid Prototyper:** 4 weeks → 16 weeks

### Flexible (5 hrs/week)
- **AI Engineer Track:** 48-64 months
- **Local LLM Track:** 24-32 months
- **Rapid Prototyper:** 32 weeks

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## Success Metrics

By the end of your track, you should be able to:

### AI Engineer
- [ ] Build a RAG system from scratch
- [ ] Train and deploy a neural network
- [ ] Optimize inference for production
- [ ] Debug ML systems
- [ ] Explain your architecture to others

### Local LLM Engineer
- [ ] Run a 70B model locally
- [ ] Quantize models to 4-bit
- [ ] Achieve 10x cost savings
- [ ] Optimize for your hardware
- [ ] Deploy with Docker

### AI Backend Engineer
- [ ] Build a production API
- [ ] Integrate AI features
- [ ] Handle 1000+ req/s
- [ ] Monitor and debug
- [ ] Scale horizontally

### Data Scientist → AI Engineer
- [ ] Deploy models to production
- [ ] Monitor model performance
- [ ] A/B test changes
- [ ] Collaborate with engineers
- [ ] Own full ML lifecycle

### Rapid Prototyper
- [ ] Build working AI project
- [ ] Deploy to production
- [ ] Share with others
- [ ] Iterate based on feedback
- [ ] Have portfolio piece

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## Next Steps

1. **Choose your track** based on your goals
2. **Start with the first course** in your track
3. **Complete modules in order** (they build on each other)
4. **Build the capstone project** (most important!)
5. **Share your work** (portfolio + GitHub)
6. **Join the community** (Discord, showcase projects)

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## Resources

- **[Discord Community](https://discord.gg/NwxDwP2Y)** - Ask questions, share projects
- **[GitHub](https://github.com/ailearningclub)** - Code examples, projects
- **[Blog](https://ailearningclub.com/blog)** - Latest AI trends
- **[Roadmap](https://ailearningclub.com/roadmap.html)** - Full course catalog