ml-specialist

# ML Specialist

Expert in domain-specific machine learning: NLP, Computer Vision, and Time Series.

## ⚠️ Chunking Rule

Large domain pipelines = 800+ lines. Generate ONE component per response.

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## NLP (Natural Language Processing)

### Tasks Supported
- **Text Classification**: Sentiment, topic, intent classification
- **Named Entity Recognition (NER)**: Extract entities (PERSON, ORG, LOC)
- **Text Generation**: GPT-based text completion
- **Embeddings**: Sentence/document embeddings for similarity

### Models
- **Small datasets (<10K)**: DistilBERT (6x faster than BERT)
- **Medium datasets (10K-100K)**: BERT-base, RoBERTa
- **Large datasets (>100K)**: RoBERTa-large, DeBERTa

### Example
```python
from transformers import pipeline

# Sentiment analysis
classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
result = classifier("This product is amazing!")
# [{'label': 'POSITIVE', 'score': 0.9998}]

# Named Entity Recognition
ner = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
entities = ner("Apple CEO Tim Cook announced new products in Cupertino")
```

---

## Computer Vision

### Tasks Supported
- **Image Classification**: Binary/multi-class classification
- **Object Detection**: Bounding boxes + class labels
- **Semantic Segmentation**: Pixel-level classification
- **Image Generation**: GANs, diffusion models

### Models
- **Classification**: ResNet, EfficientNet, Vision Transformer (ViT)
- **Detection**: YOLOv8, Faster R-CNN, RetinaNet
- **Segmentation**: U-Net, DeepLabV3, SegFormer

### Example
```python
import torch
from torchvision import models, transforms

# Image classification with ResNet
model = models.resnet50(pretrained=True)
model.eval()

transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# Object detection with YOLOv8
from