Download Udemy - Principal ML Engineer 2026 - Agentic and Sovereign Systems torrent

Udemy - Principal ML Engineer 2026 - Agentic and Sovereign Systems

Torrent:	Udemy - Principal ML Engineer 2026 - Agentic and Sovereign Systems
Description:	Principal ML Engineer 2026: Agentic & Sovereign Systems https://WebToolTip.com Published 5/2026 Created by Dar Al Taqniya MP4 \| Video: h264, 1920x1080 \| Audio: AAC, 44.1 KHz, 2 Ch Level: Expert \| Genre: eLearning \| Language: English \| Duration: 112 Lectures ( 6h 34m ) \| Size: 1.7 GB Master agentic systems, GPU orchestration, and EU AI Act compliance in 100 labs. What you'll learn ⚡ Software Engineers transitioning to AI who want to move beyond "Prompt Engineering" into core system architecture and autonomous agent development. ⚡ Data Scientists who need to master MLOps, distributed training, and the deployment of sovereign models within regulated environments. ⚡ IT Architects and Tech Leads responsible for implementing enterprise-wide AI governance and navigating the August 2026 EU AI Act enforcement. ⚡ Senior Developers aiming for Staff or Principal Machine Learning roles where total compensation regularly exceeds $400,000. Requirements ❗ Fundamental Machine Learning Knowledge: A working understanding of supervised learning, neural networks, and model evaluation metrics. ❗ System Design Basics: Familiarity with Docker, gRPC/REST APIs, and standard cloud infrastructure (AWS, Azure, or GCP). ❗ Proficiency in Python: Experience with NumPy, Pandas, and asynchronous programming (Asyncio) is essential for handling agentic workflows.
Downloads:	47
Category:	Other /Tutorials
Size:	1.7 GB
Show Files »	[ WebToolTip.com ] Udemy - Principal ML Engineer 2026 - Agentic and Sovereign Systems Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Introduction 1. Introduction.mp4 (97.4 MB) 10 - Advanced AI Systems & Autonomy 100. Lab 10 — Autonomous ML Pipelines.html (13.4 KB) 90. Advanced AI Systems & Autonomy.mp4 (152.4 MB) 91. Lab 1 — Reinforcement Learning Fundamentals.html (12.7 KB) 92. Lab 2 — Deep Reinforcement Learning Systems.html (14.6 KB) 93. Lab 3 — Generative Models (GANs, VAEs).html (15.3 KB) 94. Lab 4 — Diffusion Models Architecture.html (12.6 KB) 95. Lab 5 — LLM Agent Systems.html (13.0 KB) 96. Lab 6 — Multi-Agent Coordination Protocols.html (13.0 KB) 97. Lab 7 — Distributed Training Systems.html (13.3 KB) 98. Lab 8 — GPU Cluster Optimization.html (12.9 KB) 99. Lab 9 — Model Compression & Quantization.html (13.3 KB) 11 - Sovereign AI & PhD-Level Capstone 101. Sovereign AI & PhD-Level Capstone.mp4 (161.3 MB) 102. Lab 1 — AI Security & Adversarial Robustness.html (13.8 KB) 103. Lab 2 — Data Sovereignty Architecture.html (13.2 KB) 104. Lab 3 — Compliance-Aware ML Systems.html (13.8 KB) 105. Lab 4 — Federated Learning Systems.html (13.0 KB) 106. Lab 5 — On-Device ML Deployment.html (13.8 KB) 107. Lab 6 — Cross-Border Data Pipeline Design.html (14.2 KB) 108. Lab 7 — Enterprise AI Governance Systems.html (13.4 KB) 109. Lab 8 — Self-Healing ML Infrastructure.html (14.9 KB) 110. Lab 9 — Autonomous AI Operating System Design.html (12.9 KB) 111. Lab 10 — PhD-Level Global ML Capstone System.html (30.1 KB) 12 - Conclusion 112. Conclusion.mp4 (52.2 MB) 2 - ML Foundations & Environment Mastery 10. Lab 08 — Statistics for Model Evaluation.html (12.7 KB) 11. Lab 09 — First Linear Regression Model from Scratch.html (12.4 KB) 12. Lab 10 — First End-to-End ML Pipeline Execution.html (11.7 KB) 2. ML Foundations & Environment Mastery.mp4 (169.8 MB) 3. Lab 01 — Production-Grade ML Environment Setup.html (12.9 KB) 4. Lab 02 — Python for High-Performance ML Engineering.html (12.7 KB) 5. Lab 03 — NumPy Vectorized Computation Deep Dive.html (13.0 KB) 6. Lab 04 — Pandas for Large-Scale Data Handling.html (12.6 KB) 7. Lab 05 — Data Visualization for Model Insight.html (12.2 KB) 8. Lab 06 — Linear Algebra for ML Systems.html (13.0 KB) 9. Lab 07 — Probability Foundations for Engineers.html (12.2 KB) 3 - Data Engineering & Feature Systems 13. Data Engineering & Feature Systems.mp4 (137.0 MB) 14. Lab 1 — Data Cleaning at Scale.html (11.3 KB) 15. Lab 2 — Missing Data Imputation Strategies.html (13.4 KB) 16. Lab 3 — Feature Encoding Architectures.html (13.3 KB) 17. Lab 4 — Feature Scaling and Normalization Systems.html (13.8 KB) 18. Lab 5 — Outlier Detection Pipelines.html (14.0 KB) 19. Lab 6 — Data Leakage Prevention Techniques.html (13.4 KB) 20. Lab 7 — Feature Engineering for Tabular Intelligence.html (13.0 KB) 21. Lab 8 — Building Reusable Feature Pipelines.html (13.5 KB) 22. Lab 9 — Introduction to Feature Stores.html (14.1 KB) 23. Lab 10 — Production Data Validation Systems.html (13.6 KB) 4 - Classical Machine Learning Algorithms 24. Classical Machine Learning Algorithms.mp4 (144.1 MB) 25. Lab 1 — Logistic Regression in Production Context.html (13.3 KB) 26. Lab 2 — Decision Trees Architecture Deep Dive.html (12.7 KB) 27. Lab 3 — Random Forest Optimization.html (13.7 KB) 28. Lab 4 — Gradient Boosting Systems (XGBoost LightGBM).html (12.7 KB) 29. Lab 5 — Support Vector Machines at Scale.html (13.0 KB) 30. Lab 6 — KNN Optimization Strategies.html (13.8 KB) 31. Lab 7 — Naive Bayes in Real Applications.html (12.8 KB) 32. Lab 8 — Clustering Algorithms (K-Means, DBSCAN).html (13.9 KB) 33. Lab 9 — Dimensionality Reduction (PCA, t-SNE).html (12.6 KB) 34. Lab 10 — Model Selection Frameworks.html (13.4 KB) 5 - Model Evaluation & Reliability 35. Model Evaluation & Reliability.mp4 (158.8 MB) 36. Lab 1 — Train Test Validation Architecture Design.html (13.4 KB) 37. Lab 2 — Cross Validation at Scale.html (13.6 KB) 38. Lab 3 — Precision-Recall Engineering.html (13.8 KB) 39. Lab 4 — ROC-AUC System Design.html (13.9 KB) 40. Lab 5 — Bias-Variance Diagnostics.html (13.1 KB) 41. Lab 6 — Overfitting Control Systems.html (14.0 KB) 42. Lab 7 — Model Drift Detection.html (12.8 KB) 43. Lab 8 — Explainability with SHAP LIME.html (13.7 KB) 44. Lab 9 — Model Monitoring Pipelines.html (14.4 KB) 45. Lab 10 — Production Model Validation Gates.html (12.6 KB) 6 - Deep Learning Foundations 46. Deep Learning Foundations.mp4 (207.6 MB) 47. Lab 1 — Neural Network Architecture Fundamentals.html (12.3 KB) 48. Lab 2 — Backpropagation Engineering Deep Dive.html (13.4 KB) 49. Lab 3 — PyTorch Production Setup.html (13.0 KB) 50. Lab 4 — TensorFlow vs PyTorch Systems Comparison.html (13.0 KB) 51. Lab 5 — Activation Functions Optimization.html (12.6 KB) 52. Lab 6 — Loss Functions Engineering.html (12.9 KB) 53. Lab 7 — Optimizers (Adam, SGD, RMSProp).html (13.3 KB) 54. Lab 8 — Batch Normalization Systems.html (14.3 KB) 55. Lab 9 — Regularization Techniques.html (13.6 KB) 56. Lab 10 — Training First Deep Neural Network.html (13.3 KB) 7 - Computer Vision Systems 57. Computer Vision Systems.mp4 (128.8 MB) 58. Lab 1 — CNN Architecture Fundamentals.html (13.3 KB) 59. Lab 2 — Image Preprocessing Pipelines.html (14.1 KB) 60. Lab 3 — Transfer Learning Systems.html (13.1 KB) 61. Lab 4 — Object Detection Architectures.html (13.8 KB) 62. Lab 5 — Image Segmentation Models.html (14.3 KB) 63. Lab 6 — Lab #56 — YOLO-Based Real-Time Detection (Production-Grade Edge AI Pipel.html (13.4 KB) 64. Lab 7 — Vision Transformer files
Added:	2026-05-15 01:43:44
Language:	English
Peers:	Seeders : 1 , Leechers : 22
Tags:	IT & Software Udemy
Release name:	Udemy - Principal ML Engineer 2026 - Agentic and Sovereign Systems
Trackers:	udp://tracker.torrent.eu.org:451/announce udp://tracker.tiny-vps.com:6969/announce http://tracker.foreverpirates.co:80/announce udp://tracker.cyberia.is:6969/announce udp://exodus.desync.com:6969/announce udp://explodie.org:6969/announce udp://tracker.opentrackr.org:1337/announce udp://9.rarbg.to:2780/announce udp://tracker.internetwarriors.net:1337/announce udp://ipv4.tracker.harry.lu:80/announce udp://open.stealth.si:80/announce udp://9.rarbg.to:2900/announce udp://9.rarbg.me:2720/announce udp://opentor.org:2710/announce