Udemy - Principal ML Engineer 2026 - Agentic and Sovereign Systems

Files:

• 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