Computer Vision / AI
Muhammed Midlaj — AI Specialist & Full Stack Developer
Portfolio of Muhammed Midlaj, an AI/ML engineer specializing in Computer Vision, Explainable AI, NLP, and full-stack web development with React, Next.js, PyTorch, and TensorFlow. Projects include plant disease detection, AI-powered plagiarism detection, credit scoring with SHAP explanations, sign language recognition, and multilingual sentiment analysis.
Architecting intelligent systems with deep learning & high-performance engineering.
Teaching machines to "see", "understand"
and explain decisions.
0%
Initializing Experience_
// Deciphering Complexity
I build apps that aren't just functional, but
intelligent.
My background in ML research allows me to integrate cutting-edge models into seamless architectures. I am a developer who believes that AI should be more than just a black box—it should be a transparent and powerful tool for solving real-world challenges.
AI & ML Architecture
Designing scalable deep learning models, computer vision pipelines, and explainable AI systems for complex data structures.
Full-Stack Engineering
Building highly responsive, interactive web applications using React, Next.js, and modern, resilient structural patterns.
Intelligent Systems
Automating massive-scale data workflows, robust validation processes, and generating unique insights using LLMs.
The Journey
AI Specialist
Freelance / Research
Focusing on Explainable AI (XAI) and Computer Vision applications. Built robust ML pipelines for credit risk and real-time detection.
Full Stack Engineer
Various Projects
Developed scalable web architectures using React, Node.js, and Java. Built 'Locus', a comprehensive microservices suite.
Software Developer
Early Contributions
Focused on core programming principles and building functional web applications for small businesses.
Computer Science Specialist
Academic Roots
Dedicated to mastering the fundamentals of computing while specializing in modern AI/ML frameworks and high-performance system designs.
Technical Arsenal
PyTorch
TensorFlow
Scikit-learn
Computer Vision
NLP
React
Next.js
Tailwind CSS
TypeScript
FastAPI
Node.js
PostgreSQL
MongoDB
Docker
AWS
Framer Motion
GSAP
LangChain
OpenAI
PyTorch
TensorFlow
Scikit-learn
Computer Vision
NLP
React
Next.js
Tailwind CSS
TypeScript
FastAPI
Node.js
PostgreSQL
MongoDB
Docker
AWS
Framer Motion
GSAP
LangChain
OpenAI
PyTorch
TensorFlow
Scikit-learn
Computer Vision
NLP
React
Next.js
Tailwind CSS
TypeScript
FastAPI
Node.js
PostgreSQL
MongoDB
Docker
AWS
Framer Motion
GSAP
LangChain
OpenAI
Intelligence
Showcase
Insights & Research
Thoughts on AI, machine learning, and building intelligent systems.
Explainable AI 8 min
Building Explainable AI: Why SHAP Matters in Credit Scoring
How SHAP (SHapley Additive exPlanations) brings transparency to black-box ML models in financial decision-making, and why regulators are demanding it.
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Computer Vision 12 min
Computer Vision for Agriculture: Detecting Plant Diseases with Deep Learning
A practical walkthrough of building a plant disease detection system using PyTorch, transfer learning, and image classification techniques.
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Deep Learning 10 min
Real-Time Sign Language Recognition with MediaPipe and LSTMs
Combining Google MediaPipe hand tracking with LSTM networks to build a real-time ASL gesture recognition pipeline from webcam video.
Read more
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"The best way to predict the future is to build it."