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Regression techniques for students and professionals. Learn Polynomial & Logistic Regression and code them in python
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Artificial Intelligence #2: Polynomial & Logistic Regression
In statistics, Logistic Regression, or logit regression, or logit model is a regression model where the dependent variable (DV) is categorical. This article covers the case of a binary dependent variablethat is, where the output can take only two values, “0” and “1”, which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. Cases where the dependent variable has more than two outcome categories may be analysed in multinomial logistic regression, or, if the multiple categories are ordered, in ordinal logistic regression. In the terminology of economics, logistic regression is an example of a qualitative response/discrete choice model. Logistic Regression was developed by statistician David Cox in 1958.The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features). It allows one to say that the presence of a risk factor increases the odds of a given outcome by a specific factor. Polynomial Regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in X. Polynomial regression fits a nonlinear relationship between the value of X and the corresponding conditional mean of Y. denoted E(y x), and has been used to describe nonlinear phenomena such as the growth rate of tissues, the distribution of carbon isotopes in lake sediments, and the progression of disease epidemics. Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y x) is linear in the unknown parameters that are estimated from the data. For this reason, Polynomial Regression is considered to be a special case of multiple linear regression. The predictors resulting from the polynomial expansion of the “baseline” predictors are known as interaction features. Such predictors/features are also used in classification settings. In this Course you learn Polynomial Regression & Logistic RegressionYou learn how to estimate output of nonlinear system by Polynomial Regressions to find the possible future outputNext you go furtherYou will learn how to classify output of model by using Logistic RegressionIn the first section you learn how to use python to estimate output of your system. In this section you can estimate output of: Nonlinear Sine FunctionPython DatasetTemperature and CO2In the Second section you learn how to use python to classifyoutput of your system with nonlinear structure. In this section you can estimate output of: Classify BlobsClassify IRIS FlowersClassify Handwritten Digits Important information before you enroll: In case you find the course useless for your career, don’t forget you are covered by a30 day money back guarantee, full refund, no questions asked! Once enrolled, you haveunlimited, lifetime access to the course! You will haveinstant and free access to any updatesI’ll add to the course. You will give youmy full supportregarding any issues or suggestions related to the course. Check out the curriculum andFREE PREVIEWlecturesfor a quick insight. It’s time to takeAction! Click the “Take This Course” button at the top right now. Don’t waste time! Every second of every day is valuable.I can’t wait to see youin the course! Best Regrads, SobhanMusic from Jukedeck – create your own at http: // jukedeck.com
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