使用 Python 中的 AutoGluon 库进行自动化机器学习
- 1. Course Overview and Introduction
- 2. Course Welcome
- 3. Course Curriculum Overview
- 4. AutoGluon Overview
- 2. Tabular Data - Classification and Regression
- 1. Introduction to Tabular Data Section
- 2. OPTIONAL Supervised Learning Overview
- 3. AutoGluon Classification Part One Data and Split
- 4. AutoGluon Classification Part Two Training the Model
- 5. OPTIONAL Train Test Splits and Cross-Validation
- 6. AutoGluon Classification Part Three Validation
- 7. AutoGluon Classification Part Four Interpretability
- 8. OPTIONAL Classification Metrics
- 9. AutoGluon Regression Data, Split, Training, and Validation
- 10. OPTIONAL Regression Metrics
- 11. AutoGluon Fit Parameters Inference Constraints and Manual Hyperparameters
- 12. Advanced AutoGluon Presets and Deployment
- 13. Advanced AutoGluon Custom Feature Engineering Pipeline
- 3. Multi-Modal Datasets
- 1. Introduction to Multi-Modal Data Problems
- 3. Natural Language - MultiClass Problem - Part One
- 4. Natural Language - MultiClass Problem - Part Two
- 6. MultiModalPredictor on Binary Class with Natural Language Text
- 4. Time Series Forecasting
- 1. Introduction to Time Series
- 2. Overview of Time Series in AutoGluon
- 3. Single Variate Time Series Forecasting in AutoGluon - Part One
- 4. Single Variate Time Series Forecasting in AutoGluon - Part Two
- 5. Single Variate Time Series Forecasting in AutoGluon - Part Three
- 6. Known Covariate Time Series Forecasting in AutoGluon - Part One
- 7. Known Covariate Time Series Forecasting in AutoGluon - Part Two
- 8. Past Covariate Time Series Forecasting