Artificial Intelligence #2: Polynomial & Logistic Regression

Artificial Intelligence #2: Polynomial & Logistic Regression, available at $19.99, has an average rating of 3.85, with 20 lectures, based on 13 reviews, and has 1925 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to make the right choice when starting to learn Linear & Multi Linear Regression. or Learners who want to work in data science and big data field or students who want to learn machine learning or Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method. or Modelers, Statisticians, Analysts and Analytic Professional. It is particularly useful for Anyone who wants to make the right choice when starting to learn Linear & Multi Linear Regression. or Learners who want to work in data science and big data field or students who want to learn machine learning or Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method. or Modelers, Statisticians, Analysts and Analytic Professional.

Enroll now: Artificial Intelligence #2: Polynomial & Logistic Regression

Summary

Title: Artificial Intelligence #2: Polynomial & Logistic Regression

Price: $19.99

Average Rating: 3.85

Number of Lectures: 20

Number of Published Lectures: 20

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: £94.99

Quality Status: approved

Status: Live

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

Who Should Attend

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.

Target Audiences

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.

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

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Important information before you enroll:

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

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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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Required Softwares and Libraries

Chapter 2: Polynomial Regression

Lecture 1: Polynomial Regression Theory

Lecture 2: Polynomial Regression Sine Function Part-1

Lecture 3: Polynomial Regression Sine Function Part-2

Lecture 4: Polynomial Regression Sine Function Source Code

Lecture 5: Polynomial Regression Built-in Dataset Part-1

Lecture 6: Polynomial Regression Built-in Dataset Part-2

Lecture 7: Polynomial Regression Built-in Dataset Source

Lecture 8: Polynomial Regression CO2vsTemp part-1

Lecture 9: Polynomial Regression CO2vsTemp part-2

Lecture 10: Polynomial Regression CO2vsTemp Source

Chapter 3: Logistic Regression

Lecture 1: Logistic Regression Theory

Lecture 2: Logistic Regression for Blobs Datasets part-1

Lecture 3: Logistic Regression for Blobs Datasets part-2

Lecture 4: Logistic Regression for Blobs Datasets Source

Lecture 5: Logistic Regression for IRIS Flowers

Lecture 6: Logistic Regression for IRIS Flowers Source

Lecture 7: Logistic Regression Handwritten Digits

Lecture 8: Logistic Regression Handwritten Digits Source

Instructors

Sobhan N.
AI Developer|Electrical Engineer (PhD)|21,000+ Students

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 1 votes
4 stars: 3 votes
5 stars: 7 votes

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