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)
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Thank you man. I have an exam tomorrow and this video is really being helpful

eissa_rsh
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This is The Ultimate video about this topic! I will never tell any of my colleagues about it X)

unlearningcommunism
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This video is absolutely awesome! I love how it provides an overview of all the possible methods that can be used to tackle a dataset that is thrown at you, as well as how one should select one from among them. This kind of compare and contrast approach is missing in a majority of data science and bioinformatics videos, but is absolutely crucial.

tuskofgothos
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This is some very good material! Thanks for the effort of making such clear and perfectly paced explanation!

caiobustani
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this channel has some of the Best videos

farazyounus
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Wow! This video is treasure in explaining MV analysis in whole! <3

charlesSTATS