Udemy - AI Agents for Beginners - Build Alex, your AI HR Assistant [TorrentX]

Adversarial ML: Attack & Defend Recommenders in E-commerce https://WebToolTip.com Published 9/2026 Created by Bayt Al Hikmah MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English | Duration: 110 Lectures ( 20h 47m ) | Size: 763.5 MB From shilling attacks that fool your ranking model to a governed, sovereign recommender that survives them. What you'll learn ⚡ Build a real recommender system from scratch — popularity baselines, implicit ALS matrix factorization and FAISS vector retrieval. ⚡ Launch controlled adversarial attacks against your own recommender — random and bandwagon shilling, nuke attacks, session injection, low-and-slow poisoning. ⚡ Detect manipulation using schema validation, bot-profile similarity features, Isolation Forest anomaly detection, time-window spike detection. ⚡ Defend with evidence: quarantine pipelines, rank-recovery comparison ⚡ Engineer the full MLOps stack — MLflow experiment tracking, DVC data versioning, Feast feature stores, Airflow/Kafka orchestration ⚡ Serve and harden the model via FastAPI — health/readiness endpoints, fallback circuit breakers, canary releases, shadow-mode scoring, and load testing ⚡ Deploy to Kubernetes with Helm, OPA policy-as-code, RBAC least privilege, Trivy container scanning, Syft SBOMs, and Cosign artifact signing. ⚡ Govern privacy and compliance — pseudonymization, retention windows, DPIA-style checklists, user-deletion propagation, and a GDPR/EU AI Act/DORA-aware ⚡ Architect for sovereignty — region-aware data residency configuration, federated evaluation patterns, and decentralized, reproducible research bundles. ⚡ Deliver a capstone-grade sovereign, adversarially robust recommendation engine — attacked, defended, monitored, deployed, and defensible in an architecture Requirements ❗ Knowledge: Basic Python (variables, functions, running scripts). No prior machine learning, recommender systems, or security experience required — Module 1 builds everything from a working baseline model up. Comfort with a terminal and copy-pasting commands. Basic SQL/pandas familiarity helps in the data-engineering modules but isn't required; every script is explained before you run it. Software (all free/open-source): Docker and Docker Compose, Git, Python 3.11+, uv (Python package manager, installed in Lab 1). Open-source ML/data stack used via uv add and containers: pandas, scikit-learn, PyTorch-adjacent libraries (implicit, FAISS), FastAPI, MLflow, DVC, Feast, DuckDB, PostgreSQL, MinIO, Kafka, Airflow. Security/MLOps tooling via containers: Trivy, Syft, Cosign, OPA, Locust — no license or account required. Optional for Modules 7–8: kind and kubectl for local Kubernetes labs — a lightweight local cluster, not a cloud account. Hardware: 20GB+ free disk space, 8GB+ RAM recommended (Kafka, PostgreSQL, MinIO, and Kubernetes running concurrently in later modules). No real e-commerce data, no real user