based on the number of variablesthe probability distribution of output variables and form of relationship (linear versus nonlinear)we have different types of regression models the following are some of the major categories of regression models simple linear regressionit is the simplest of all the regression modelsbut it is very effective and widely used for practical purposes in this casewe only have single independent variable and single dependent variable the dependent variable is real value and assumed to follow normal distribution in linear regressionwhile developing the model we assume linear relationship between the independent and dependent variable multiple linear regressionit is the extension of the simple linear regression modelto include more than one independent variable the other assumptions remain the samei the dependent variable is still real value and follows normal distribution non linear regressiona regression modelin which the dependent variable is dependent on nonlinear transformation of the parameters\coefficientsis termed nonlinear regression model it is slightly different from models in which we use nonlinear transformation of the independent variables let' consider an example to make this point clear consider the modely in the previous model we have used the square of the independent variable but the parameters of the models (the betasor coefficientsare still linear hence this model is still an example of linear regression modelor to be more specifica polynomial regression model the model in which the coefficients are not linear is model that can be termed as nonlinear regression model consider an example that will fulfill this criterion and hence can be termed as nonlinear regression model (log ) these models are quite hard to learn and hence not as widely used in practice in most casesa linear model with nonlinear transformations applied to the input variables usually suffices regression models are very important part of both statistics and machine learning and we encourage you to refresh your memory by checking out the "regressionsection in as well as to read some standard literature on regression models to deep dive into further detailed concepts as necessary we will be looking at regression in future dealing with real-world case study lustering models we briefly talked about clustering in in case you might want to refresh your memory clustering is member of different class of machine learning methodsknown as unsupervised learning the simplest definition of clustering is the process of grouping similar data points together that do not have any prelabeled classes or categories the output of typical clustering process is segregated groups of data pointssuch that the data points in the same group are similar to each other but dissimilar from the members (data pointsof other groups the major difference between the two methods is thatunlike supervised learningwe don' have pre-labeled set of data that we can use to train and build our model the input set for unsupervised learning problems is generally the whole dataset itself another important hallmark of the unsupervised learning set of problems is that they are quite hard to evaluateas we will see in the later part of this