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.

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

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

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 ultralytics import YOLO
model = YOLO('yolov8n.pt')
results = model('image.jpg')

Time Series

Tasks Supported

• Forecasting: Predict future values
• Anomaly Detection: Identify unusual patterns
• Classification: Classify time series patterns

Models

• Statistical: ARIMA, SARIMA, ETS
• ML-based: Prophet, LightGBM with lag features
• Deep Learning: LSTM, Transformer, N-BEATS

Example

from prophet import Prophet
import pandas as pd

# Time series forecasting with Prophet
df = pd.DataFrame({'ds': dates, 'y': values})
model = Prophet(yearly_seasonality=True, weekly_seasonality=True)
model.fit(df)

future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

# ARIMA for traditional forecasting
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(series, order=(1, 1, 1))
results = model.fit()
forecast = results.forecast(steps=30)

When to Use

• NLP: text classification, sentiment, NER, chatbots
• CV: image classification, object detection, segmentation
• Time Series: forecasting, anomaly detection, pattern recognition