From ML Foundations → Real-Time Computer Vision → AI Security Dashboard

I am building production-oriented, security-aware AI systems from the ground up.
This roadmap documents my transition from ML foundations → real-time CV → multi-module AI Security Dashboard.

Category	Tools
Programming
ML / DL	,
CV / Vision	,
Backend / Deployment	,
Database
Version Control

• Advanced Python (OOP mindset)
• NumPy / Pandas
• Feature Engineering
• Logistic Regression (from scratch)
• Gradient Descent intuition
• Evaluation Metrics (Precision / Recall / F1)

• Structured data pipeline
• Manual train/test split
• Logistic Regression implementation
• Performance analysis report

• Spam Email Classifier
• House Price Prediction
• Logistic Regression (From Scratch)

• Pipeline thinking
• Mathematical ML understanding
• Metric debugging

• Cross Validation
• Hyperparameter tuning
• Regularization
• ANN (NumPy implementation)
• Introduction to ML Security: Adversarial attacks, Model robustness

• Model comparison experiments
• ANN from scratch
• Confidence threshold analysis
• Mini adversarial demo

• Fraud Detection System
• NLP Sentiment Analysis
• ANN MNIST Classifier
• Adversarial Example Demo

• Overfitting detection
• Model tuning mastery
• ML attack surface awareness

Framework: PyTorch

• Tensor mechanics
• CNN architecture
• Backpropagation
• Transfer Learning
• Face embeddings

• CNN image classifier
• Real-time webcam face detection
• Face embedding + SQLite storage
• Confidence filtering logic

🎥 Real-Time Face Recognition System

• Real-time inference
• CNN optimization
• Product-level system thinking

• YOLOv8
• Bounding boxes & IoU
• Confidence scoring
• Real-time optimization
• SORT / DeepSORT tracking

• Live camera detection
• Object tracking integration
• Alert system
• Logging module

🎯 Real-Time Detection & Tracking System

• Industrial CV pipeline
• Multi-object tracking
• Performance optimization mindset

• OCR (Tesseract)
• ROI Cropping
• FastAPI
• REST APIs
• Docker containerization

• License plate detection
• OCR extraction
• Entry/exit time logging
• Dockerized API deployment

🚗 Parking License Plate Recognition System

• Model + Backend integration
• Deployment architecture
• Production-level thinking

• Advanced FastAPI
• Docker Compose
• Model deployment strategies
• Suspicious input logging
• Confidence filtering
• Basic anomaly detection

• Face recognition module
• Object detection module
• License plate module
• Central logging system
• Admin dashboard interface

Camera Input │ ├── Face Recognition ├── Object Detection ├── License Plate OCR │ └── Logging & Monitoring │ Security Filtering │ Admin Dashboard

Modern AI systems are vulnerable to:

• Adversarial examples
• Model stealing
• Data poisoning
• Confidence manipulation

This roadmap integrates security awareness from Month 2 onward.

Accuracy is not enough.
Resilience is essential.

• Weekly GitHub commits
• Monthly project release
• Clean documentation
• Metrics reporting
• Versioned milestones

ai-security-roadmap/ │ ├── month1-ml-foundations/ ├── month2-ml-robustness/ ├── month3-deep-learning/ ├── month4-object-detection/ ├── month5-system-integration/ ├── month6-ai-security-dashboard/ │ └── docs/

After completing this roadmap:

• 🧠 Strong ML foundation
• 👁 Advanced Computer Vision capability
• ⚡ Real-time inference systems
• 🐳 Deployment experience
• 🛡 Security-aware ML mindset
• 🏗 Multi-module AI architecture

This is not:

❌ “I finished an AI course.”

This is:

🚀 “I design and deploy secure AI systems.”

This repository demonstrates:

• Mathematical ML understanding
• Real-time system engineering
• Backend integration capability
• Deployment knowledge
• Security-aware AI development

Transition from:

“AI learner”

To:

🛡 AI Systems Engineer (Security-Aware)