- 01. Introducing Cezanne & Dan
- 02. Interview Segment: What is SageMaker and Why Learn It?
- 03. Course Outline, Case Studies
- 04. Unsupervised v Supervised Learning
- 05. Model Design
- 06. Population Segmentation
- 07. K-means, Overview
- 08. Creating a Notebook Instance
- 09. Create a SageMaker Notebook Instance
- 10. Pre-Notebook: Population Segmentation
- 11. Exercise: Data Loading & Processing
- 12. Solution: Data Pre-Processing
- 13. Exercise: Normalization
- 14. Solution: Normalization
- 15. PCA, Overview
- 16. PCA Estimator & Training
- 17. Exercise: PCA Model Attributes & Variance
- 18. Solution: Variance
- 19. Component Makeup
- 20. Exercise: PCA Deployment & Data Transformation
- 21. Solution: Creating Transformed Data
- 22. Exercise: K-means Estimator & Selecting K
- 23. Exercise: K-means Predictions (clusters)
- 24. Solution: K-means Predictor
- 25. Exercise: Get the Model Attributes
- 26. Solution: Model Attributes
- 27. Clean Up: All Resources
- 28. AWS Workflow & Summary