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Artificial Intelligence #2: Polynomial & Logistic Regression (Udemy.com)

Regression techniques for students and professionals. Learn Polynomial & Logistic Regression and code them in python

4.8
( 8 Reviews )

Created by: Sobhan N.

Produced in 2017

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What you will learn

  • Program Polynomial Regression from scratch in python.
  • Program Logistic Regression from scratch in python.
  • Predict output of model easily and precisely.
  • Use Regression model to solve real world problems.
  • Use Polynomial Regression to Model Non Linear Datasets.
  • Build Model to Predict CO2 and Global Temperature by Polynomial Regression.
  • Classify Handwritten Images by Logistic Regression
  • Classify IRIS Flowers by Logistic Regression

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Quality Score

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Overall Score : 96 / 100

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Course Description

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 Regression You learn how to estimate output of nonlinear system by Polynomial Regressions to find the possible future output 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:
  • Nonlinear Sine Function
  • Python Dataset
  • Temperature and CO2



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:
  • Classify Blobs
  • Classify IRIS Flowers
  • Classify 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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It's time to take Action!
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 you in the course!
Best Regrads,
Sobhan




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Who this course is for:
  • Anyone who wants to make the right choice when starting to learn Linear & Multi Linear Regression.
  • 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.
Read More product length iconTime: 2h

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Instructor Details

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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?
Would you like to build your own AI programs & do something awesome for you?
Would you like to learn Xamarin to make both iOS/Android apps?
Would you like to learn how to write codes in HTML5 and CSS3?
Would you like to learn MATLAB the scientific language for researchers?

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

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Reviews

4.8

8 total reviews

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By Mohd Jibly

Great course to learn Polynomial and Logistic Regression for Artificial Intelligence .

By Zaied Zaman

Contents are real world oriented and compact. Explanations are good, more clear about the needed tools. instructor is experienced and himself, clear about what he is going to teach. Great experience.

By Fazi

Good course to understand how to use polynomial regression with python. Instructor define codes comprehensively. He also was very clear and easy to follow. Highly recommend it.

By Sina

video and audio are perfect and helpful for me to understand information about logistic regression. All of example in this course are practical and very clear.

By Mircea Sorogaru

Subject is very important but the author is not captivating and he is bored some way. Also, he does not explain what he is doing and why he is doing many things, instead he reads the lines of codes he writes as we cannot see what he is typing.

By Stephen Doroff

So far it looks like there is going to be great material but the course moves a bit too slowly for me.

By Richard Alan Robey

The math has in the back of my mind and has been hunting me ever since I graduated college (1984). I think I am being to see the light.

By Tharindu Buddhika Adhikari

This course is amazing and above my expectations! Very good exercises, good speed, well communicated. The instructor made me feel very comfortable and was able to take many things away. Excellent content and very knowledgeable instructor!

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Artificial Intelligence #2: Polynomial & Logistic Regression
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  • Video Duration: icon 2h
  • Price: 11.99 Track Price 11.99 Track Price
  • Provider: icon Udemy
  • Year: icon 2017
  • Language: icon English
  • Certificate: icon Yes
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