Section 2
Machine Learning Workflow
8. Section Overview
05:12 (Preview)
9. Data Preparation, Representation and Model Selection
30:35
10. Loss Functions
23:12
11. Training, Optimisation & Hyper-parameter Tuning
17:37
12. Exploring If Models are Any Good - Training Curves
21:47
13. Is My Model any Good - Validation Plots
13:04
14. Conclusion, Certificate, and What Next?
6. K-Means Explainer
Introduction to Machine Learning

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7. Trying out Clustering with K-Means