the mean and variance of that metric this is the key difference in non-parametric methodsi we don' have fixed number of parameters that are required to describe an unknown random variable instead the number of parameters are dependent on the amount of training data the module nonparametric in the statsmodels library will help us perform non-parametric analysis on our data it includes kernel density estimation for univariate and multivariate datakernel regressionand locally weighted scatterplot smoothing summary this introduced select group of packages that we will use routinely to processanalyzeand model our data you can consider these libraries and frameworks as the core tools of data scientist' toolbox the list of packages we covered is far from exhaustive but they certainly are the most important packages we strongly suggest you get more familiar with the packages by going through their documentation and relevant tutorials we will keep introducing and explaining other important features and aspects of these frameworks in future the examples in this along with the conceptual knowledge provided in the first should give you good grasp toward understanding machine learning and solving problems in simple and concise way we will observein the subsequent that often the process of learning models on our data is reiteration of these simple steps and concepts in the next you learn how to wield the set of tools to solve bigger and complex problems in the areas of data processingwranglingand visualization