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I learned in this lecture previous lecture summary all concepts are clear.
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I have done this video.
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AOA, In this lecture, we have learned which algorithm to use when, and the algorithms so far are as follows:.
1-LINEAR REGRESSION ( prediction of numeric variable )
(a)-Simple Linear Regression, which have one variable ( linear relation between X and Y)
(b)-Multilinear Regression ( which have more then one variable )
2-LOGISTIC REGRESSION
(a)-( classify the data )
(b)-( linear combination of feature )
3-SUPPORT VECTOR MACHINE ( S.V.M )
(a)-( apply for linear and nonlinear data )
(b)-(classification)
(c) (outlier detection)
4-K-Nearest Neighbors ( KNN )
(a)-Regression ( mean, median )
(b)-Classification (mode, which is repeated values )
(c) Use for non-parametric dataALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey,Ameen.
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v good ameen
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The overview of previously discussed algorithms (1) Linear Regression Algorithms (2) Logistic Regression Algorithms (3) Support Vector Machine Algorithms (SVM) (4) K-Nearest Neighbor Algorithms.
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