This lesson covers the basis of linear regression in R. It includes a discussion of basic linear regression, polynomial regression and multiple linear regression as well as some assumptions and potential sources of problems when making linear regression models.

This lesson covers the basis of linear regression in R. It includes a discussion of basic linear regression, polynomial regression and multiple linear regression as well as some assumptions and potential sources of problems when making linear regression models.

This guide does not assume any prior exposure to R, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn R.

Subscribe: https://www.youtube.com/channel/UCwuvoN0QKjrXUi48G_Hv7kQ

Learn the fundamentals of R markdown in this in-depth tutorial, or simply use it as a quick reference guide and cheatsheet for R markdown formatting.In this blog post, we’ll look at how to use R Markdown. By the end, you’ll have the skills you need to produce a document or presentation using R Mardown, from scratch!

Are you a programmer that wants to learn R? Learn how to create web apps, REST APIs, machine learning models, and data visualization with the R - the most popular statistical language. Today you’ll learn how to: Load datasets; Scrape Webpages; Build REST APIs; Analyze Data and Show Statistical Summaries; Visualize Data; Train a Machine Learning Model; Develop Simple Web Applications. 6 Essential R Packages for Programmers:

We are going to learn the introduction of machine learning and linear regression in R 4.0 programming. We will start with the introduction of machine learning then we will discuss the introduction of linear regression. I will also discuss types of linear regression and use cases of linear regression. there are two types of linear regression; simple linear regression and multiple linear regression. Use cases of linear regression are in house price prediction, stock price prediction, Twitter popularity prediction. I will thereafter show you how to analyze the Boston housing dataset. We will analyze dataset variables to understand the variable dependency for the linear regression model. I will show you the linear and non-linear regression models. Thereafter, I will show how you can improve the accuracy of a linear regression model.

R for Machine Learning || How to Install R 4.0 in Anaconda on Windows 10 | R Programming. I will show you how to install R 4.+ on Windows 10 in Anaconda.

This video on Data Manipulation in R will help you learn how to transform and summarize your data using different packages and functions. You will use the dplyr package to select, filter, arrange, and mutate data. You will use the tidyr library to create tidy data. You will look at functions such as gather, spread, separate, and unite. Let's begin!