Machine Learning with R

Features Includes:
  • Self-paced with Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App

Course Preview Video

  • Categories

    All Development

  • Duration


  • 2 Students Enrolled


This is the bite size course to learn R Programming for Machine Learning and Statistical Learning. In CRISP DM data mining process, machine learning is at the modeling and evaluation stage. 

You will need to know some R programming, and you can learn R programming from my "Create Your Calculator: Learn R Programming Basics Fast" course. You will learn R Programming for machine learning and you will be able to train your own prediction models with naive bayes, decision tree, knn, neural network, linear regression, and evaluate your models very soon after learning the course.

You can take the course as follows, I may allow you to have the SVBook certificate in Data Mining using R in future after you passed a quiz and completed all the courses below: 

  • Create Your Calculator: Learn R Programming Basics Fast (R Basics)
  • Applied Statistics using R with Data Processing (Data Understanding and Data Preparation)
  • Advanced Data Visualizations using R with Data Processing (Data Understanding and Data Preparation, in future)
  • Machine Learning with R (Modeling and Evaluation)

Basic knowledge
  • Computer Knowledge
  • Basic coding knowledge

What will you learn


  • Getting Started
  • Getting Started 2
  • Getting Started 3
  • Data Mining Process
  • Download Data set
  • Read Data set
  • Some Explanations
  • Simple Linear Regression
  • Build Linear Regression Models
  • Predict Linear Regression Models
  • KMeans Clustering
  • KMeans Clustering in R
  • Agglomeration Clustering
  • Agglomeration Clustering in R
  • Decision Tree ID3 ALgorithm
  • Decision Tree in R: Split train and test set
  • Decision Tree in R: Train Decision Tree
  • Decision Tree in R: Predict Decision Tree
  • KNN Classification
  • Train KNN in R
  • Predict KNN in R
  • Naive Bayes Classification
  • Naive Bayes in R
  • Neural Network Classification
  • Neural Network in R
  • What Algorithm to Use?
  • Model Evaluation
  • Model Evaluation using R for Classification
  • Model Evaluation using R for Regression
Course Curriculum
Number of Lectures: 30 Total Duration: 01:48:43

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