Artificial Intelligence #4:SVM & Logistic Classifier methods
What you'll 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
Requirements
- You should know about basic statistics
- You must know basic python programming
- Install Sublime and required library for python
- You should have a great desire to learn programming and do it in a hands-on fashion, without having to watch countless lectures filled with slides and theory.
- All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.
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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Sobhan
Who this course is for:
- 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.
Instructor
- 3.6 Instructor Rating
- 433 Reviews
- 24,092 Students
- 14 Courses
My passion is teaching people through online courses. I love learning new skills, and since 2015 have been teaching people like you everything. I create courses that teach you how to become the better version of yourself with all kinds of skills.
What would you like to learn?
Would you like to learn Artificial Intelligence in python?
Would you like to make money creating landing pages?
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If you want to do any of these things, just enroll in the course. You have a 30-day money back guarantee if you don't like it. And I'm always improving my courses so that they stay up to date and the best that they can be. Check them out, and enroll today!
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About Sobhan N:
I have PhD degree in Electrical Engineering and like to learn anything about Electronics, Programming and Artificial Intelligence. I like electronic stuff like Arduino, Raspberry Pi and microcontrollers.
My passion is helping people reveal hidden talents and guide them into the world of startups and programming. I currently am teaching Artificial Intelligence, App Deign, Web Design and MATLAB programming languages. I’m also teaching Electrical Engineering and Computer Science Courses in Universities.