SYED_ALI_FARAZ.PORTFOLIO

MACHINE LEARNING ENGINEER

I train models. Then I make them work for a living.

Six deployed projects spanning computer vision, NLP, and classic ML. Each card below is a spec sheet for a live, working system — launch it directly or read the source.

6 SYSTEMS IN PRODUCTION

// PROJECT_INDEX

DEEPFAKE_DETECTOR

LIVE

Classifies uploaded face images and video as real or AI-generated, with a confidence score and frame-sampling for video.

Task
Binary Classification
Architecture
EfficientNet-B0
Dataset
100k+ labeled faces
Stack
PyTorch · FastAPI · Next.js

PICASSIFY

LIVE

Transforms any photo into stylized artwork by blending it with a chosen painting's style, using neural style transfer.

Task
Neural Style Transfer
Architecture
VGG19 (pretrained)
Dataset
User content + style pairs
Stack
PyTorch · Streamlit

NEURAL_LENS

LIVE

Real-time image recognition across 1,000+ object categories, built on a MobileNetV2 backbone.

Task
Image Classification
Architecture
MobileNetV2
Dataset
ImageNet-pretrained
Stack
TensorFlow · FastAPI · Next.js

DIABETES_DETECTOR

LIVE

Predicts diabetes risk from clinical metrics, with real-time probability scoring and downloadable reports.

Task
Risk Classification
Architecture
Random Forest
Dataset
Clinical health metrics
Stack
scikit-learn · Streamlit

SENTIMENT_SENSE

LIVE

Aspect-based sentiment analysis dashboard detecting business topics (food, service, price) and emotional context in real time.

Task
Aspect-Based Sentiment Analysis
Architecture
DistilBERT + BART (zero-shot)
Dataset
Business feedback text
Stack
Transformers · Streamlit

AETHERQUANT

LIVE

Crypto market trend classifier analyzing 24 technical indicators (RSI, MACD, VWAP) via live market data.

Task
Market Trend Classification
Architecture
XGBoost
Dataset
Yahoo Finance (24 indicators)
Stack
Pandas · Plotly · Streamlit