4. Simple Linear Regression

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5. Multiple Linear Regression

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6. Polynomial Regression

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7. Support Vector Regression (SVR)

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8. Decision Tree Regression

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9. Random Forest Regression

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10. Evaluating Regression Models Performance

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11. Part 3 Classification

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12. Logistic Regression

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13. K-Nearest Neighbors (K-NN)

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14. Support Vector Machine (SVM)

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15. Kernel SVM

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16. Naive Bayes

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17. Decision Tree Classification

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18. Random Forest Classification

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19. Evaluating Classification Models Performance

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20. Part 4 Clustering

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21. K-Means Clustering

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22. Hierarchical Clustering

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23. Part 5 Association Rule Learning

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24. Apriori

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