AI Engineering
Practical AI projects, experiments, tutorials, and applied research.
Build an AI Signature Verification and Fraud Detection System
What if a signature matches perfectly because a fraudster copied the genuine signature?
That’s why signature verification needs more than one neural network.
This project combines:
YOLO detection → Siamese embeddings → structural analysis → document forensics → evidence…
AI Signature Extraction and Manual-Signing Indicator Analysis
Can AI tell whether a document contains a handwritten signature?
Finding the signature is only the beginning.
In this project, we combine YOLO, Segment Anything, OpenCV, feature extraction, quality gates, and human review to build an AI-assisted signature…
Build an AI Deepfake Image Detection System: A Beginner’s Guide to Multi-Evidence Digital Forensics
Learn how to build an AI-powered deepfake image detection system that combines computer vision, image forensics, metadata analysis, and explainable AI to detect manipulated images with confidence.
Build an AI-Powered Market Risk Control Cockpit with Python and Generative AI
Banks process enormous volumes of market data every day—but one stale or incorrect price can affect valuations, P&L, risk metrics, and regulatory reporting.
In this hands-on Python project, we build an AI-powered Market Risk Control Cockpit that…
Build a Face Recognition System with Python and InsightFace
Can AI tell whether two photos belong to the same person?
In this week’s hands-on AI project, you’ll build a complete face recognition system using Python and InsightFace. Learn how AI detects faces, generates facial embeddings, compares…
From Simulation to Prediction: Learning Fluid Dynamics with AI
Can an AI predict how fluid moves?
In this piece, I explore how a physics foundation model—WALRUS—learns to forecast the evolution of a heated fluid system. Instead of solving equations, the model observes patterns and generates its…
AI Engineering brings together practical AI projects, experiments, tutorials, and applied research notes on building real-world AI systems. The goal is to make AI engineering easier to understand through concrete examples, beginner-friendly explanations, and practical implementation notes.