Population Segmentation
Back to Home
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
Back to Home
04. Unsupervised v Supervised Learning
L1 031 Unsupervised Vs Supervised Learning V1 RENDER V2
Next Concept