Machine Learning on R 2021

A clear understanding about the machine learning theory, techniques and its application in R Studio platform

What you’ll learn:
The program builds a solid foundation by covering the most popular and widely used machine learning technologies and its applications.
Students will have a good working knowledge on Machine Learning on R and can use it for their Educational as well as Business projects and assignments.
Most professionals opting for only this module generally are looking at automation techniques and applications for some daily work they are doing.
The Course Includes Naive Bayes theory, K Nearest Neighbors (KNN) theory and application, Random forest theory and application, Gradient Boosting Theory.

Requirements
Prior knowledge of R programming and Data Science on R is recommended

Description
There are people who are eager to move to Analytics careers but do not have the requisite skill sets. As we move into our 12th year in the Analytics Industry, OrangeTree Global has designed specific courses for freshers and working professionals who are looking at moving to Data Science, Machine Learning and Big Data Careers.

Since 2009, OrangeTree Global has embarked on an ambitious vision of providing affordable and effective Analytics Training and Education across the country.

OrangeTree Global has over a decade’s experience in upskilling professionals and helping them move to analytics jobs and careers within and outside India. If you are reading this, we hope to be a part of your journey too.The program builds a solid foundation by covering the most popular and widely used machine learning technologies and its applications, including Naive Bayes theory and application, K Nearest Neighbors (KNN) theory and application, Random forest theory and application, Gradient Boosting Theory and Application and also Support Vector Machine Theory and Application–laying the building blocks for truly expanded analytical abilities.

The program builds a solid foundation by covering the most popular and widely used machine learning technologies and its applications, including Naive Bayes theory and application, K Nearest Neighbors (KNN) theory and application, Random forest theory and application, Gradient Boosting Theory and Application and also Support Vector Machine Theory and Application–laying the building blocks for truly expanded analytical abilities.

Who this course is for
For Students and Business Professionals