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Learning Paths

Machine Learning Engineer

A Learning Path Editor roadmap document.

Stage 1 · Programming & Engineering Foundations Stage 2 · Mathematics and Statistics for ML Stage 3 · Data Engineering Fundamentals Stage 4 · Classical Machine Learning Stage 5 · Deep Learning Stage 6 · Machine Learning Systems Stage 7 · MLOps Stage 8 · Cloud and Distributed ML Stage 9 · Advanced ML Engineering Machine Learning Engineer Programming & Engineering Python & OOP Git & CLI Tools NumPy & Pandas Testing & Logging Mathematics & Statistics Linear Algebra Probability & Stats Optimization &Gradients Data Engineering Data Cleaning &Validation SQL, ETL & ELT Data Warehouses &Storage Classical MachineLearning Supervised Learning Unsupervised & PCA Feature Engineering Evaluation & Tuning Deep Learning Neural Networks Basics CNNs & RNNs Attention &Transformers PyTorch / TensorFlow Machine Learning Systems Model Serving &Inference Model Registries &Stores Data & Concept Drift Experiment Tracking MLOps CI/CD for ML Docker & Kubernetes Pipeline Orchestration Monitoring &Observability Cloud & Distributed ML Cloud ML Platforms Distributed Training &GPUs Advanced ML Engineering RecSys & Retrieval GenAI & LLM Inference Responsible AI &Privacy Final Capstone: Production MLPlatform Curated ResourcesMade With MLFull Stack Deep LearningML System DesignMLOps Community Start Your Journey
  1. Machine Learning Engineer
  2. Programming & Engineering
  3. Python & OOP
  4. Git & CLI Tools
  5. NumPy & Pandas
  6. Testing & Logging
  7. Mathematics & Statistics
  8. Linear Algebra
  9. Probability & Stats
  10. Optimization & Gradients
  11. Data Engineering
  12. Data Cleaning & Validation
  13. SQL, ETL & ELT
  14. Data Warehouses & Storage
  15. Classical Machine Learning
  16. Supervised Learning
  17. Unsupervised & PCA
  18. Feature Engineering
  19. Evaluation & Tuning
  20. Deep Learning
  21. Neural Networks Basics
  22. CNNs & RNNs
  23. Attention & Transformers
  24. PyTorch / TensorFlow
  25. Machine Learning Systems
  26. Model Serving & Inference
  27. Model Registries & Stores
  28. Data & Concept Drift
  29. Experiment Tracking
  30. MLOps
  31. CI/CD for ML
  32. Docker & Kubernetes
  33. Pipeline Orchestration
  34. Monitoring & Observability
  35. Cloud & Distributed ML
  36. Cloud ML Platforms
  37. Distributed Training & GPUs
  38. Advanced ML Engineering
  39. RecSys & Retrieval
  40. GenAI & LLM Inference
  41. Responsible AI & Privacy
  42. Final Capstone: Production ML Platform
  43. Curated Resources
  44. Start Your Journey