done

I revised all the previous algorithms: 1: Linear Regression (Simple Linear Regression, Multi-Linear Regression) 2: Logistic Regression ( Used for Classification using a Sigmoid function) 3: SVM (Used for both linear/non-linear data, Used for Regression-SVR and Classification-SVC and outlier Detection) 4: KNN (Can work on Non-parametric Data, Used for Classification, Regression. Concept of distance measurement: Euclidean, Manhattan, Minkowski, Hamming )

Done

Done

I learned in this lecture previous lecture summary all concepts are clear.

I have done this video.

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.

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.