MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 613 MB | Duration: 1h 22m
What you'll learn
Linear Regression
Visualising Data
Machine learning libraries
Requirements
Python Programming
Basic understanding of coding
Description
Hi , and welcome to the Everything about linear regression Course
Are you someone who is new to machine learning ?
Are you someone who wants to get started with machine learning ?
YES?
Then this course is for you -->
Machine learning which is a buzz word has been in the market for quite some time now , and to start with machine learning the basic algorithm which everyone focuses on is linear regression . In this course we will start with linear regression and all about it , then we will dive straight into the project whose dataset i have taken from kaggle . We will be using the dataset , visualising it and making predictions from it .
Let us look at what you'll be requiring throught the course -
Materials Required -
Mac or Windows
At least 4 gb ram
Good coding skills
Python language
Zeal to learn
In this course you'll learn -
What is Linear Regression
Types of linear regression
Visualising data
applying linear regression to real time datasets
What is gradient descent algorithm ?
What is cost function ?
How to calculate R2 Score
I believe in the concept of "Learn by doing " and this is emphasised in my class , I myself learn by doing things instead of listening to boring lectures !
You'll be able to use real time datasets after this class and learn all the necessary components required for getting started with machine learning
Will it be challenging ? YES
Will you get difficulty in understanding things ? YES (if you are a beginner)
But that is what my course is for , it will help you make an app in quick time and you will surely learn many things going forward !
Good luck !
Who this course is for:
Beginners who want to learn a machine learning algorithm
Homepage
https://www.udemy.com/course/everything-about-linear-regression/Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live
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