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How to select a multivariate analysis or machine learning method
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This video is an overview of multivariate methods and machine learning methods that are used in AI.
1. Get familiar with the Iris data set
2. How to standardize the data (02:25)
3. How to plot multivariate data (03:23)
4. Identify outliers in a multivariate space (05:00)
5. Correlation matrix (06:43)
6. Canonical correlation analysis (07:10)
7. The scatter plot matrix (07:48)
8. PCA (10:00)
9. Hierarchical clustering (11:47)
10. Heatmap (13:45)
11. k-means clustering (15:52)
12. Unsupervised vs supervised machine learning (17:15)
13. How to select a classification method: LR, LDA, SVM, DT, NB, KNN, ANN (19:15)
14. Multivariate tests: Hotelling's T-square & MANOVA (27:00)
15. Partial least squares and principal component regression (28:15)
16. LASSO regression (29:40)
1. Get familiar with the Iris data set
2. How to standardize the data (02:25)
3. How to plot multivariate data (03:23)
4. Identify outliers in a multivariate space (05:00)
5. Correlation matrix (06:43)
6. Canonical correlation analysis (07:10)
7. The scatter plot matrix (07:48)
8. PCA (10:00)
9. Hierarchical clustering (11:47)
10. Heatmap (13:45)
11. k-means clustering (15:52)
12. Unsupervised vs supervised machine learning (17:15)
13. How to select a classification method: LR, LDA, SVM, DT, NB, KNN, ANN (19:15)
14. Multivariate tests: Hotelling's T-square & MANOVA (27:00)
15. Partial least squares and principal component regression (28:15)
16. LASSO regression (29:40)
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