Artificial Intelligence #2: Polynomial & Logistic Regression
- 4.1
-
![]()
![]()
![]()
![]()
Brief Introduction
Regression techniques for students and professionals. Learn Polynomial & Logistic Regression and code them in pythonDescription
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.
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
___________________________________________________________________________
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.
___________________________________________________________________________
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
Music from Jukedeck - create your own at http: // jukedeck .com
Requirements
- 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.
Sobhan N.
4.1 Raiting
Machine Learning A-Z™: Hands-On Python & R In Data Science
- 4.5
-
![]()
![]()
![]()
![]()
![]()
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. Interested in the field of Machine Learning?...
Artificial Intelligence #2: Polynomial & Logistic Regression
- 4.1
-
![]()
![]()
![]()
![]()
![]()
Regression techniques for students and professionals. Learn Polynomial & Logistic Regression and code them in python Logistic Regression was developed by statistician David Cox in 1958....
Logistic Regression in Python
- 4.1
-
![]()
![]()
![]()
![]()
![]()
Logistic regression in Python tutorial for beginners. You can do Predictive modeling using Python after this course. You've found the right Classification modeling course!...
Linear Regression and Logistic Regression using R Studio
- 4
-
![]()
![]()
![]()
![]()
![]()
Linear Regression and Logistic Regression for beginners. Understand the difference between Regression & Classification You've found the right Linear Regression course!...
