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# Artificial Intelligence #1: Linear & MultiLinear Regression

## Regression techniques for students and professionals. Learn Linear & Multilinear Regression and code them in python

3.95 (29 reviews) Students

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## Topics

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## What you will learn

Program Linear Regression from scratch in python.

Program Multilinear Regression from scratch in python.

Predict output of model easily and precisely.

Use Regression model to solve real world problems.

Create Regression Model to find global temperature in the next years.

Build good and accurate Regression Model to estimate advertising campaign sales.

## Description

In statistics, Linear Regression is a linear approach for modeling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression.

In Linear Regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models.

In this Course you learn Linear Regression & Multilinear Regression
You learn how to estimate and predict simple and single variable regression to find the possible future output Next you go further
You will learn how to estimate output of Multivariable model by using Multilinear 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 Number

• Diabetes

• Boston House Price

• Built in Dataset

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

• Global Temprature

• Total Sales of Advertising Campaign

• Built in Dataset

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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!

• 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!

Sobhan

## Content

Introduction

Introduction

Required Softwares and Libraries

Linear Regression

Linear Regression Theory

Linear Regression Random Numbers Part-1

Linear Regression Random Numbers Part-2

Linear Regression Random Numbers Source

Linear Regression Diabetes Dataset Part-1

Linear Regression Diabetes Dataset Part-2

Linear Regression Diabetes Dataset Source

Linear Regression Boston Houses Dataset Part-1

Linear Regression Boston Houses Dataset Part-2

Linear Regression Boston Houses Dataset Source

Linear Regression Built-in Dataset

Linear Regression Built-in Dataset Source

Multilinar Regression

Multilinear Regression Theory

Multilinear Regression Global Temperature Part-1

Multilinar Regression Global Temperature Part-2

Multilinear Regression Global Temperature Source

Multilinear Regression Built-in Dataset

Multilinear Regression Built-in Dataset Source

## Reviews

T
Tharindu13 July 2019

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!

Z
Zaied29 August 2018

Having a very good learning experience. Contents are concise and practical problem oriented. Instructor is experienced and knows what he's doing. Looking forward to learn more from similar courses.

S
Sina19 November 2017

very good course about regression that i found on udemy. This course is very practical and helpful about linear and multi linear regression.

F
Fazi18 November 2017

This course is really good instructor send content clearly and simply. I appreciate the pace of learning. content are easy to understand. I highly recommend it.

Udemy ID

## 11/16/2017

Course created date

## 11/21/2019

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