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LightGBM

A comprehensive 60-chapter LightGBM course for modern tabular machine learning, covering datasets, boosted trees, regression, classification, ranking concepts, categorical features, imbalance, early stopping, cross-validation, tuning, feature importance, and production workflows.

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Beginner → practical: 60 chapters with clear explanations, technical terms in simple parentheses, practical Python examples, expected output, step-by-step explanations, common mistakes, exercises, and Q&A.

Complete 60-Chapter Course

  1. Chapter 1: What LightGBM Is
  2. Chapter 2: Installing and Importing LightGBM
  3. Chapter 3: Gradient Boosting Review
  4. Chapter 4: Decision Trees Review
  5. Chapter 5: Boosted Trees
  6. Chapter 6: Leaf-Wise Growth Concepts
  7. Chapter 7: Histogram-Based Learning Concepts
  8. Chapter 8: Dataset Objects
  9. Chapter 9: Features and Labels
  10. Chapter 10: Train and Validation Data
  11. Chapter 11: Regression with LightGBM
  12. Chapter 12: Binary Classification
  13. Chapter 13: Multi-Class Classification
  14. Chapter 14: Objectives
  15. Chapter 15: Metrics
  16. Chapter 16: Learning Rate
  17. Chapter 17: Number of Boosting Rounds
  18. Chapter 18: Number of Leaves
  19. Chapter 19: Maximum Depth
  20. Chapter 20: Minimum Data in Leaf
  21. Chapter 21: Feature Fraction
  22. Chapter 22: Bagging Fraction
  23. Chapter 23: Bagging Frequency
  24. Chapter 24: L1 Regularization
  25. Chapter 25: L2 Regularization
  26. Chapter 26: Categorical Features
  27. Chapter 27: Missing Values
  28. Chapter 28: Class Imbalance
  29. Chapter 29: Sample Weights
  30. Chapter 30: Custom Weights Concepts
  31. Chapter 31: Early Stopping
  32. Chapter 32: Validation Sets
  33. Chapter 33: Best Iteration
  34. Chapter 34: Callbacks
  35. Chapter 35: Logging Evaluation
  36. Chapter 36: Cross-Validation
  37. Chapter 37: Stratified Cross-Validation Concepts
  38. Chapter 38: Prediction
  39. Chapter 39: Probability Predictions
  40. Chapter 40: Thresholds
  41. Chapter 41: Feature Importance
  42. Chapter 42: Split Importance Concepts
  43. Chapter 43: Gain Importance Concepts
  44. Chapter 44: SHAP Concepts
  45. Chapter 45: Model Dump Concepts
  46. Chapter 46: Saving Models
  47. Chapter 47: Loading Models
  48. Chapter 48: Reproducibility
  49. Chapter 49: Random Seeds
  50. Chapter 50: Overfitting
  51. Chapter 51: Underfitting
  52. Chapter 52: Hyperparameter Tuning
  53. Chapter 53: Grid Search Concepts
  54. Chapter 54: Random Search Concepts
  55. Chapter 55: Scikit-learn API
  56. Chapter 56: Pipelines Concepts
  57. Chapter 57: Mini Project: Regression
  58. Chapter 58: Mini Project: Binary Classifier
  59. Chapter 59: Mini Project: Feature Importance Review
  60. Chapter 60: Capstone: Production LightGBM Workflow