Part 1: Foundations

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

Part 2: Core Concepts

1. Chapter 11: Regression with LightGBM
2. Chapter 12: Binary Classification
3. Chapter 13: Multi-Class Classification
4. Chapter 14: Objectives
5. Chapter 15: Metrics
6. Chapter 16: Learning Rate
7. Chapter 17: Number of Boosting Rounds
8. Chapter 18: Number of Leaves
9. Chapter 19: Maximum Depth
10. Chapter 20: Minimum Data in Leaf

Part 3: Modeling and Training

1. Chapter 21: Feature Fraction
2. Chapter 22: Bagging Fraction
3. Chapter 23: Bagging Frequency
4. Chapter 24: L1 Regularization
5. Chapter 25: L2 Regularization
6. Chapter 26: Categorical Features
7. Chapter 27: Missing Values
8. Chapter 28: Class Imbalance
9. Chapter 29: Sample Weights
10. Chapter 30: Custom Weights Concepts

Part 4: Evaluation and Diagnostics

1. Chapter 31: Early Stopping
2. Chapter 32: Validation Sets
3. Chapter 33: Best Iteration
4. Chapter 34: Callbacks
5. Chapter 35: Logging Evaluation
6. Chapter 36: Cross-Validation
7. Chapter 37: Stratified Cross-Validation Concepts
8. Chapter 38: Prediction
9. Chapter 39: Probability Predictions
10. Chapter 40: Thresholds

Part 5: Advanced Workflows

1. Chapter 41: Feature Importance
2. Chapter 42: Split Importance Concepts
3. Chapter 43: Gain Importance Concepts
4. Chapter 44: SHAP Concepts
5. Chapter 45: Model Dump Concepts
6. Chapter 46: Saving Models
7. Chapter 47: Loading Models
8. Chapter 48: Reproducibility
9. Chapter 49: Random Seeds
10. Chapter 50: Overfitting

Part 6: Projects and Production

1. Chapter 51: Underfitting
2. Chapter 52: Hyperparameter Tuning
3. Chapter 53: Grid Search Concepts
4. Chapter 54: Random Search Concepts
5. Chapter 55: Scikit-learn API
6. Chapter 56: Pipelines Concepts
7. Chapter 57: Mini Project: Regression
8. Chapter 58: Mini Project: Binary Classifier
9. Chapter 59: Mini Project: Feature Importance Review
10. Chapter 60: Capstone: Production LightGBM Workflow