Hi, I'm Ares👋
Software Developer & AI Engineer with a strong focus on Machine Learning, Computer Vision, and Cybersecurity-related systems.
I enjoy building end-to-end ML pipelines — from data preprocessing and
feature engineering to model evaluation and real-time prediction.
Currently working on projects involving image processing, CNN-based models, and ML-driven applications, and creating structured learning guides for students and self-learners.
🧠 Core Expertise
- Machine Learning & Deep Learning
- Computer Vision & Image Processing
- Model Training & Evaluation (CNN, SVM, Hybrid CNN–SVM)
- Web-based deployment of ML systems
- ML-based Security Systems (SQL Injection Detection, Cybersecurity Foundations)
🧭 Guides & Learning Roadmaps
I also create beginner-to-intermediate guides and roadmaps to help students
and self-learners understand AI, Machine Learning, and Computer Vision
through structured learning paths and real projects.
- 🛡️ Cybersecurity Roadmap (2026)
Structured beginner-to-advanced cybersecurity roadmap with curated resources, tools, and hands-on labs.
https://github.com/ares-coding/cybersecurity-roadmap-2026
⚙️ Tech Stack
💻 Core Languages
🌐 Web & Backend Frameworks
🤖 Machine Learning & AI
📊 Data Science & Visualization
🔐 Databases & Security
🌐 Deployment & Platforms
🛠️ Developer Tools & Design
🚀 Featured Projects & Live Demos
🍺 Detecting Alcohol Intoxication Using Image Processing
An AI-based facial image analysis system that compares CNN, SVM, and a Hybrid CNN–SVM model to classify intoxicated vs. normal facial states using facial features.
Tech Stack:
Python · OpenCV · CNN · SVM · Hybrid CNN–SVM · Gradio · Hugging Face Spaces
Live Demo:
👉 https://huggingface.co/spaces/ares-coding/alcohol-intoxication-demo
Source Code:
👉 https://github.com/ares-coding/detecting-alcohol-intoxication-using-image-processing
Deployed as an interactive Gradio demo showcasing the end-to-end ML workflow: image input → inference → prediction visualization.
🔐 Malicious URL Detection System
A machine learning–based system designed to detect and classify malicious URLs by analyzing extracted URL features and patterns, helping prevent phishing and web-based attacks.
Tech Stack:
Python · Machine Learning · Scikit-learn · XGBoost · Feature Engineering · Streamlit
Source Code:
👉 https://github.com/ares-coding/malicious-url-detection-using-ml
Includes a Streamlit-based interface demonstrating real-time URL analysis and classification.
🛡️ SQL Injection Attack Detection
A security-focused project demonstrating real-world SQL injection vulnerabilities and detection using pattern analysis. Highlights how improper query handling can bypass authentication and shows mitigation through secure query practices.
Tech Stack:
Python · MySQL · SQL · Pattern Analysis · Cybersecurity
Source Code:
👉 https://github.com/ares-coding/sql-injection-attack-detection
Demonstrates both vulnerable and secured query implementations using real SQL queries.
📩 Spam Message Detection App
A machine learning–powered application that identifies spam messages using Natural Language Processing (NLP). Analyzes message content and classifies text as spam or legitimate in real time.
Tech Stack:
Python · NLP · Machine Learning · Scikit-learn · Text Classification
Source Code:
👉 https://github.com/ares-coding/spam-message-detection
Focused on text preprocessing, feature extraction, and classification accuracy for message filtering.
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