Skip to content

A series of articles to get started into the field of Machine Learning with R language

Notifications You must be signed in to change notification settings

Shawcker/A-guide-to-Machine-Learning-in-R

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

24 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

A Guide to Machine Learning in R for Beginners.

While choosing the best programming language for data science, two of the most popular languages around, R and Python come to mind but choosing between them is always a dilemma for a data scientist.But the main point is deep understanding of the algorithms and their application can be in any language of choice.

In this series of articles on Machine Learning in R we delve into fundamentals of Machine Learning and the various algorithms needed.

  1. A Guide to Machine Learning in R for Beginners- Part 1
    In the first part, we learn about the building blocks of Machine Learning: Statistics.

  2. A Guide to Machine Learning in R for Beginners- Part 2
    In the second part, we learn about downloading and installing the R statistical language

  3. A Guide to Machine Learning in R for Beginners- Part 3
    In the third part, we discuss basic operations in R.We also delve into Exploratory Data Analysis in R

  4. A Guide to Machine Learning in R for Beginners- Part 4
    In this part , we get to know about functions , models and hypothesis. We also study in detail about Linear Regression with code in R

  5. A Guide to Machine Learning in R for Beginners- Part 5
    In this part, we discuss in detail about Logistic Regression in R.

  6. A Guide to Machine Learning in R for Beginners- Part 6
    In this part, we discuss in detail about Decision Trees in R with an example


About

A series of articles to get started into the field of Machine Learning with R language

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • R 100.0%