Artificial Intelligence #4:SVM & Logistic Classifier methods

Description

In this Course you learn Support 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 variable—that 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.

In this course you learn how to classify datasets by by Support Vector Machines to find the correct class for data and reduce error. Next you go further  You will learn how to classify output of model by using Logistic Regression

In the first section you learn how to use python to estimate output of your system. In this section you can estimate output of:

• Random dataset

• IRIS Flowers

• Handwritten Digits

In the Second section you learn how to use python to classify output of your system with nonlinear structure .In this section you can estimate output of:

• Blobs

• IRIS Flowers

• Handwritten Digits

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Important information before you enroll:

• In case you find the course useless for your career, don't forget you are covered by a 30 day money back guarantee, full refund, no questions asked!

• Once enrolled, you have unlimited, lifetime access to the course!

• You will have instant and free access to any updates I'll add to the course.

• You will give you my full support regarding any issues or suggestions related to the course.

• Check out the curriculum and FREE PREVIEW lectures for a quick insight.

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Best Regrads,

Sobhan

What You Will Learn!

• Classify datasets by using Support Vector Machine method
• Understand main concept behind Support Vector Machine method.
• Use different Kernel function for Support Vector Machine method
• Classify Handwritten Images by Logistic classification method
• Classify IRIS Flowers by Logistic classification method
• Classify images by using Support Vector Machine method
• Plot outputs of classification methods in decision regions space

Who Should Attend!

• Anyone who wants to make the right choice when starting to learn SVM & Logistic Classifier methods.
• Learners who want to work in data science and big data field
• students who want to learn machine learning
• Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method.
• Modelers, Statisticians, Analysts and Analytic Professional.

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