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Category:
Language:
English
Total Size:
1.7 GB
Info Hash:
4A466580BD3FEC567D9F00368C6DB488BDC8C14A
Added By:
Added:
June 3, 2026, 1:51 p.m.
Stats:
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(Last updated: June 3, 2026, 1:51 p.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 204 bytes |
| 1. Introduction.mp4 | 97.4 MB |
| 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 |
| 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 |
| 112. Conclusion.mp4 | 52.2 MB |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
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| Uploaded by freecoursewb | Size 3.7 GB | Health [ 10 /19 ] | Added 2023-06-01 |
NOTE
SOURCE: Udemy - Principal ML Engineer 2026 - Agentic and Sovereign Systems
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COVER
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MEDIAINFO
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.
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