# Specializations

## 🎯 Overview

Choose your path and build focused depth in one specialized AI domain at a time.

**Prerequisites:**
- ✅ Foundation (Phases 0-6)
- ✅ Vector Databases (Phase 7)
- ✅ RAG Systems (Phase 8)
- ✅ MLOps (Phase 9)

**Time:** 2-3 months per specialization  
**Outcome:** A solid specialization foundation plus at least one portfolio-quality project

This phase is best treated as a specialization hub, not as the deepest material in the entire repo. If you want research-heavy depth after this phase, continue into the later advanced modules such as `24-advanced-deep-learning/` or `28-practical-data-science/`.

---

## 🛤️ Choose Your Path

You've built a strong foundation. Now specialize in areas that interest you most:

### 1. [Computer Vision](computer-vision/) 🖼️
Work with images, videos, and multimodal AI

**You'll learn:**
- Image classification and object detection
- Image embeddings (CLIP, DINO)
- Generative models (Stable Diffusion, DALL-E)
- Multimodal RAG (text + images)
- Video understanding

**Best for:**
- Building visual search engines
- Content moderation systems
- Medical imaging applications
- Autonomous systems
- Creative AI tools

---

### 2. [Advanced NLP](nlp/) 📝
Master natural language processing beyond transformers

**You'll learn:**
- Named Entity Recognition (NER)
- Machine Translation
- Summarization (extractive & abstractive)
- Sentiment analysis at scale
- Question answering systems
- Information extraction

**Best for:**
- Building chatbots and assistants
- Document processing automation
- Content generation systems
- Language-specific applications
- Text analytics platforms

---

### 3. [AI Agents](../15-ai-agents/) 🤖
Create autonomous systems that can use tools and take actions. The curriculum now lives in the main Phase 15 AI Agents hub instead of a separate specialization track.

**You'll learn:**
- Agent frameworks (AutoGen, LangGraph)
- Tool use and function calling
- Planning and reasoning
- Multi-agent collaboration
- Memory and state management
- Human-in-the-loop systems

**Best for:**
- Task automation systems
- Research assistants
- Customer service bots
- Coding assistants
- Workflow automation

---

## 📂 Repository Structure

```
9-specializations/
├── computer-vision/
│   ├── README.md
│   ├── 00_START_HERE.ipynb
│   ├── 01_image_classification.ipynb
│   ├── 02_object_detection.ipynb
│   ├── 03_clip_embeddings.ipynb
│   ├── 04_stable_diffusion.ipynb
│   └── projects/
│
├── nlp/
│   ├── README.md
│   ├── 00_START_HERE.ipynb
│   ├── 01_ner.ipynb
│   ├── 02_translation.ipynb
│   ├── 03_summarization.ipynb
│   └── projects/
│
├── ai-agents/                         # legacy notebooks now merged into ../15-ai-agents/
│   ├── README.md
│   ├── 00_START_HERE.ipynb
│   ├── 01_function_calling.ipynb
│   ├── 02_autogen_agents.ipynb
│   ├── 03_langgraph.ipynb
│   └── projects/
│
└── README.md  # This file
```

---

## 🎯 How to Choose

### Choose **Computer Vision** if you want to:
- Work with visual data (images, videos)
- Build image search or recommendation systems
- Create generative art or design tools
- Develop medical imaging solutions
- Combine text and vision (multimodal)

**Example projects:**
- Visual search engine for e-commerce
- Medical image diagnosis assistant
- Content moderation for social media
- AI art generation tool
- Document OCR and understanding

---

### Choose **Advanced NLP** if you want to:
- Process and analyze text at scale
- Build translation or summarization systems
- Extract structured data from documents
- Create content generation tools
- Work with multiple languages

**Example projects:**
- Automatic meeting summarizer
- Multi-language customer support
- Contract analysis system
- News aggregation and summarization
- Research paper extraction pipeline

---

### Choose **AI Agents** if you want to:
- Build autonomous decision-making systems
- Create tools that can use other tools
- Develop complex workflows
- Build coding or research assistants
- Automate multi-step tasks

**Example projects:**
- Personal research assistant
- Automated customer service agent
- Code review and refactoring bot
- Data analysis assistant
- Multi-agent collaboration system

---

## 🚀 Getting Started

### 1. Start with ONE specialization
Don't try to do all three at once. Pick the one that:
- Aligns with your career goals
- Solves problems you care about
- Excites you the most

If you want maximum depth, pick one path here and then follow it with a later advanced module rather than trying to complete all three immediately.

### 2. Follow the learning path
Each specialization has:
- `00_START_HERE.ipynb` - Overview and quick wins
- Progressive notebooks building skills
- Hands-on projects
- Production-ready examples

### 3. Build projects
Theory is important, but projects teach you more:
- Start with guided projects
- Then build your own
- Share your work (GitHub, blog)
- Get feedback from community

### 4. Combine specializations
After mastering one, you can:
- Add a second specialization
- Combine them (e.g., multimodal agents)
- Create unique solutions

## What Comes Next

- If you want broader production depth, continue to [../15-ai-agents/README.md](../15-ai-agents/README.md), [../16-model-evaluation/README.md](../16-model-evaluation/README.md), or [../19-ai-safety-redteaming/README.md](../19-ai-safety-redteaming/README.md).
- If you want deeper theory and research, continue to [../24-advanced-deep-learning/README.md](../24-advanced-deep-learning/README.md).
- If you want applied interview and portfolio work, continue to [../28-practical-data-science/README.md](../28-practical-data-science/README.md).

---

## 💡 Recommended Learning Order

### If you're interested in ALL three:

**Option 1: NLP-first path**
```
1. Advanced NLP (2-3 months)
   ↓
2. AI Agents (2 months) - Leverage NLP skills
   ↓
3. Computer Vision (2-3 months) - Multimodal agents
```
**Best for:** Text-heavy applications, chatbots, content platforms

**Option 2: Vision-first path**
```
1. Computer Vision (2-3 months)
   ↓
2. Advanced NLP (2 months) - Multimodal understanding
   ↓
3. AI Agents (2-3 months) - Multimodal agents
```
**Best for:** Visual applications, e-commerce, creative tools

**Option 3: Agents-first path**
```
1. AI Agents (2-3 months)
   ↓
2. Advanced NLP (2 months) - Better language agents
   ↓
3. Computer Vision (2-3 months) - Visual agents
```
**Best for:** Automation, productivity tools, assistants

---

## 🛠️ Common Technologies

**All Specializations Use:**
- PyTorch / TensorFlow
- HuggingFace Transformers
- Your RAG knowledge (Phase 8)
- Production skills (Phase 9)
- Vector databases (Phase 7)

**Specialization-Specific:**

**Computer Vision:**
- torchvision, timm
- CLIP, DINO, SAM
- Stable Diffusion, DALL-E
- OpenCV, PIL

**Advanced NLP:**
- spaCy, NLTK
- HuggingFace datasets
- Translation models (NLLB, MarianMT)
- Summarization models (BART, T5)

**AI Agents:**
- LangGraph, AutoGen
- OpenAI Assistants API
- CrewAI, AgentOps
- Tool libraries (APIs, databases)

---

## 📈 Career Paths

### Computer Vision Specialist
**Roles:**
- Computer Vision Engineer
- Multimodal AI Engineer
- AI Artist / Creative Technologist
- Medical Imaging ML Engineer

**Industries:**
- Healthcare, Retail, Entertainment
- Autonomous vehicles, Robotics
- Social media, Security

---

### NLP Specialist
**Roles:**
- NLP Engineer
- Conversational AI Engineer
- Content AI Engineer
- Localization ML Engineer

**Industries:**
- Tech companies, Finance
- Legal tech, Healthcare
- Translation services, Media

---

### AI Agent Specialist
**Roles:**
- AI Agent Developer
- Applied AI Engineer
- AI Automation Engineer
- Coding Assistant Developer

**Industries:**
- Productivity tools, DevTools
- Customer service platforms
- Research tools, Enterprise automation

---

## ✅ Success Criteria

After completing a specialization, you should:

- [ ] Built 3+ projects in that domain
- [ ] Understand state-of-the-art models
- [ ] Know when to use which approach
- [ ] Can evaluate model performance
- [ ] Deployed at least one project
- [ ] Contributing to open source (optional)
- [ ] Building a portfolio

---

## 🔗 Resources

### Communities
- [Hugging Face Forum](https://discuss.huggingface.co/)
- [r/MachineLearning](https://reddit.com/r/MachineLearning)
- [Papers with Code](https://paperswithcode.com/)
- [GitHub Discussions](https://github.com/zero-to-ai/discussions)

### Staying Updated
- [ArXiv ML](https://arxiv.org/list/cs.LG/recent) - Research papers
- [Hugging Face Daily Papers](https://huggingface.co/papers)
- [AI newsletters](https://www.deeplearning.ai/the-batch/)
- Twitter/X AI community

### Project Ideas
- [HuggingFace Spaces](https://huggingface.co/spaces) - Browse projects
- [GitHub Trending](https://github.com/trending/jupyter-notebook) - Popular repos
- [Kaggle Competitions](https://www.kaggle.com/competitions)

---

## 🎓 What's Next?

After specializations, consider:

### 1. **Research & Papers**
- Read cutting-edge research
- Implement papers from scratch
- Contribute to open source

### 2. **Advanced Topics**
- Reinforcement Learning
- Graph Neural Networks
- Federated Learning
- Edge AI / TinyML

### 3. **Build & Ship**
- Create a startup
- Freelance / consulting
- Open source projects
- Technical content creation

### 4. **Leadership**
- ML team lead
- AI product manager
- ML architect
- Technical educator

---

## 📊 Your Journey So Far

```
✅ Phase 0: Course Setup
✅ Phase 1: Python Fundamentals
✅ Phase 2: Data Science Foundations
✅ Phase 3: Mathematics for ML
✅ Phase 4: Tokenization
✅ Phase 5: Embeddings
✅ Phase 6: Neural Networks
✅ Phase 7: Vector Databases
✅ Phase 8: RAG Systems
✅ Phase 9: MLOps
🎯 Phase 10: Specializations ← YOU ARE HERE
```

**You've come incredibly far!** 🎉

From foundational math to production ML systems, you now have the skills to:
- Build intelligent applications
- Deploy ML at scale
- Choose and apply the right tools
- Create real-world AI solutions

**Now specialize and become an expert in your chosen domain!**

---

**Ready to specialize?** → Pick your path and open that folder!

- [Computer Vision →](computer-vision/)
- [Advanced NLP →](nlp/)
- [AI Agents →](../15-ai-agents/)

**🚀 The future of AI is yours to build!**
