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Classification methods for students & professionals. Learn Support Vector Machine & Bayes Classification & code in python
An excellent training about Data Science
Artificial Intelligence #4:SVM & Logistic Classifier methods
In this Course you learnSupport Vector Machine & Logistic Classification Methods. In machine learning, Support Vector Machines (SVM) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. 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 Regressionwas 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. Inthis course you learn how to classify datasets by bySupportVector Machinesto find the correct class for data and reduce error. Next you go furtherYou will learn how to classify output of model by usingLogistic RegressionIn the first section you learn how to use python to estimate output of your system. In this section you can estimate output of: Random datasetIRIS FlowersHandwritten DigitsIn 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: BlobsIRIS FlowersHandwritten 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, Sobhan
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