Cybersecurity is increasingly data-driven, and Python's rich ML ecosystem makes it the perfect language for threat detection. I've built three complementary models that tackle critical security challenges: 1. **Intrusion Detection** – Classifies 10 types of network attacks using the UNSW-NB15 dataset with Random Forest and XGBoost ensemble methods 2. **Phishing Detection** – Identifies malicious URLs and web content with high accuracy 3. **Vulnerability Scoring** – Predicts CVSS severity scores and vulnerability risk levels This talk covers the full lifecycle: dataset preprocessing, feature engineering, model selection, evaluation, and deployment on Hugging Face Hub. I'll also demonstrate the interactive demo space, showing how anyone can test these models in real-time. Whether you're building security tools or want to learn applied ML, this talk offers practical insights and code examples you can use immediately.