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Machine Learning for Cybersecurity: AI Threat Detection - charlie - 10-21-2026
![[Image: 716c2d13c4507be20f0abf30d500b747.jpg]](https://i129.fastpic.org/big/2026/1021/47/716c2d13c4507be20f0abf30d500b747.jpg)
Machine Learning for Cybersecurity: AI Threat Detection
Published 10/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 44m | Size: 1.47 GB
Build dual ML models (Isolation Forest & Random Forest), a SOC risk engine, FastAPI REST API, and Streamlit dashboard
What you'll learn
Machine Learning for Cybersecurity: Build AI Threat Detection
Build an end-to-end, production-grade defensive cybersecurity threat detection system in Python from scratch
Train an Isolation Forest exclusively on benign network telemetry to detect novel zero-day attacks without contamination leakage
Train and evaluate a Random Forest classifier on labeled attack patterns (brute force, crypto mining, data exfiltration)
Build an interactive Security Operations Center (SOC) monitoring dashboard in Streamlit featuring real-time KPI metrics and severity triage filters
Verify pipeline integrity with a rigorous 41-test master pytest suite running against real trained models and golden attack fixtures
Requirements
Basic Python Familiarity
Zero Prior Machine Learning or Cybersecurity Knowledge Required
A Standard Computer (Windows, macOS, or Linux)
Description
This course contains the use of artificial intelligence.
Traditional static firewall rules break the moment an attacker changes a single port or byte, flooding Security Operations Center (SOC) teams with thousands of false alarms. In this hands-on course, you will build, test, and deploy a complete, end-to-end Machine Learning Threat Detection and Intrusion Detection System from scratch in Python.
You will engineer a production-grade security pipeline using Python, scikit-learn, FastAPI, Pydantic, Streamlit, and pytest that runs 100% locally on any standard computer
- Zero-Leakage Telemetry Preprocessing: Generate reproducible enterprise host and network logs and scale 6 core security features (CPU, Memory, Network Bytes, Logins, File Writes, and Connections) using a StandardScaler fit strictly on training splits.
- Unsupervised Zero-Day Anomaly Detection: Train a 200-tree Isolation Forest exclusively on clean baseline traffic to isolate novel, unseen zero-day threats without contamination leakage.
- Supervised Threat Classification: Train a 300-tree Random Forest classifier on labeled attack patterns, including brute-force login floods, stealthy crypto-miners, covert data exfiltration, and mass file modification.
- The 3x3 Combined Risk Engine: Synthesize continuous anomaly scores and attack probabilities through a deterministic lookup matrix that assigns LOW, MEDIUM, HIGH, or CRITICAL severity tiers alongside plain-English explanations for SOC analysts.
- Asynchronous FastAPI Service & Live Streamlit SOC Dashboard: Expose real-time predictions via /predict and /health REST API endpoints, visualize live KPIs and severity distributions in Streamlit, and stream telemetry at 23 milliseconds per event.
- Cryptographic Model Security & 41-Test Master Suite: Protect serialized model files against tampering with SHA-256 hash verification and validate the entire pipeline with a 41-test pytest suite achieving 100% attack detection and 0% false alarms on golden attack benchmarks.
How This Course Is Taught
Every concept is illustrated with Pure Diagrammatic Animations-active hardware diagrams, decision trees, coordinate hyperplanes, and score tachometers with zero walls of bullet-point slides-followed by line-by-line full-screen Python code walkthroughs and live terminal execution.
Everything runs locally on a standard laptop CPU with zero paid cloud services or GPU required. Download the complete production codebase and enroll today to build your own AI-powered cybersecurity threat detection system!
Who this course is for
SOC Analysts & Cybersecurity Professionals** who want to understand how modern AI threat detection engines work under the hood and upgrade from static regex signatures to behavioral machine learning
Machine Learning Practitioners & Data Scientists** seeking real-world, practical applications of anomaly detection and classification on security telemetry, APIs, and real-time streaming pipelines
Python Developers & Software Engineers** looking to break into the high-demand cybersecurity and AI defense space by building an end-to-end security project
DevSecOps, Cloud Engineers & Security Operations Teams** aiming to automate threat triage, build transparent risk scoring engines, and eradicate alert fatigue
Homepage
Code:
https://www.udemy.com/course/machine-learning-for-cybersecurity-ai-threat-detection/Code:
https://rapidgator.net/file/aec6bceda7093dbbe329cf5b170ca7c2/Machine_Learning_for_Cybersecurity_AI_Threat_Detection.part2.rar.html
https://rapidgator.net/file/533865b2355c108851e35c8f616d808e/Machine_Learning_for_Cybersecurity_AI_Threat_Detection.part1.rar.html
https://nitroflare.com/view/E0B47B07C9BCF15/Machine_Learning_for_Cybersecurity_AI_Threat_Detection.part2.rar
https://nitroflare.com/view/97F14F2889B2832/Machine_Learning_for_Cybersecurity_AI_Threat_Detection.part1.rar