- 01. Welcome to SVM
- 02. Separating Line
- 03. Choosing Between Separating Lines
- 04. What Makes A Good Separating Line
- 05. Practice with Margins
- 06. SVMs and Tricky Data Distributions
- 07. SVM Response to Outliers
- 08. SVM Outlier Practice
- 09. Handoff to Katie
- 10. SVM in SKlearn
- 11. SVM Decision Boundary
- 12. Coding Up the SVM
- 13. Nonlinear SVMs
- 14. Nonlinear Data
- 15. A New Feature
- 16. Visualizing the New Feature
- 17. Separating with the New Feature
- 18. Practice Making a New Feature
- 19. Kernel Trick
- 20. Playing Around with Kernel Choices
- 21. Kernel and Gamma
- 22. SVM C Parameter
- 23. SVM Gamma Parameter
- 24. Overfitting
- 25. SVM Strengths and Weaknesses
- 26. SVM Mini-Project Video
- 27. SVM Mini-Project
- 28. SVM Author ID Accuracy
- 29. SVM Author ID Timing
- 30. A Smaller Training Set
- 31. Speed-Accuracy Tradeoff
- 32. Deploy an RBF Kernel
- 33. Optimize C Parameter
- 34. Accuracy after Optimizing C
- 35. Optimized RBF vs. Linear SVM: Accuracy
- 36. Extracting Predictions from an SVM
- 37. How Many Chris Emails Predicted?
- 38. Final Thoughts on Deploying SVMs