I'm Muhammad Huzaifa, a passionate software engineer and AI developer from Lahore, Pakistan. I specialize in Android development, machine learning, and artificial intelligence. With strong team leadership experience, I’ve led and contributed to multiple impactful, real-world projects across mobile, AI, and full-stack domains.
- 📧 Email: mianmhuzaifa@gmail.com
- 📱 Phone: +92 308 6142627
- 🔗 LinkedIn: linkedin.com/in/muhammad-huzaifa-5179b1195
Languages: Python, Java, C++, C, Dart, SQL Frameworks & Libraries: TensorFlow, Keras, PyTorch, Flask, Retrofit, OpenCV, EasyOCR Mobile Development: Android SDK, Flutter Databases: Firebase, Realtime DB, SQLite, MySQL, DynamoDB Tools & Platforms: Android Studio, VS Code, Jupyter, Google Colab, AWS (Lambda, EC2, S3), Git Key Domains: Machine Learning, Mobile Development, IoT, OCR, Image Processing
- ✅ AI Python for Beginners – DeepLearning.ai (May 2025)
- ✅ Python for Data Science – Udemy (June 2025)
- ✅ Build MakeMore (by Andrej Karpathy) – Self-study (June 2025)
Academic Android app for UCP students with past papers, GPA tools, and assessment tracking. Built with Firebase, MVVM, and SharedPreferences.
Android app that predicts handwritten digits via CNN model hosted on AWS using Flask. Real-time predictions with MNIST-trained model.
Match-3,4,5 puzzle game with combo logic, colorful terminal display, and dynamic scoring. Uses 2D arrays, LinkedLists, and Queues.
A reading and publishing app for public domain books with offline support, reviews, and educational content. It is built with a clean Android UI.
Animated circular clock as a live wallpaper and widget with rotating ticks and real-time digital time. Built using Canvas and custom views.
Image super-resolution tool using ESRGAN/Real-ESRGAN. Supports 2x–16x enhancement with DIV2K training, CLI-based workflow.
IoT-based mobile app that unlocks a Windows PC via Raspberry Pi and fingerprint-authenticated HTTPS requests.
Smart scanner with AI edge detection, auto enhancement, and OCR (Tesseract/EasyOCR). Exports to image/PDF.
Handwriting synthesis tool that mimics real handwriting using AI. Learns from samples and generates styled handwritten text.
AI-powered mobile app for early skin cancer detection using CNNs (VGG, ResNet, etc.) and Flutter+Flask deployment.
PySpark project for classifying product review sentiment using tokenized data, labeled by star ratings, and trained via Spark MLlib.
Always learning. Always building.