Machine Learning with R

Machine Learning with R

The Introduction to Machine Learning with R course can develop your understanding of the complete course of machine learning.

Course Overview

Introduction– Its algorithms, like linear regression, logistic regression, decision tree, random forest, SVM, and hierarchic clustering techniques. and its varied applications. you'll additionally learn R programming well along side statistic analysis in R.

About python data science certification– You will receive a Course Completion Certificate from XcelitPto upon finishing the f Machine Learning with R program. . As shortly because the certificate is unbarred, you'll receive a mail with a link to your mail address. Click the link to look at and transfer your certificate. you'll even add the certificate to your resume and share it on social media platforms. – 

Key features

  • Statistical Learning
  • R for Machine Learning
  • Fundamentals of Machine Learning
  • optimization Techniques
  • Machine Learning Algorithms
  • Ensemble Learning
  • spatiality Reduction


  • There are not any prerequisites to learn Machine Learning with R. However, it's advised that learners have a basic understanding of mathematics, statistics and programming.

Course Syllabus

Machine Learning with R Course Syllabus

  1. What is supervised learning
  2. Algorithms in Supervised learning
  3. Steps in Supervised learning

Regression & Classification

  1. Regressionvs classification
  2. Computation of co-relation coefficient and Analysis
  3. Performance and accuracy measurement of a Model
  4. NaiveBaye’s classifier, Model Training, Validation and Testing
  5. Ordinary Least squares, Variable selection
  6. R-Square coefficient and RMSE as a strength of model, Prediction and confidence interval determination and application
  7. Proviso of Regression, Dummy variables, Types of Regression: Linear and Logistic( Simple and multiple)
  8. Sum of least squares, ROC and AUC curves, Homoscedasticity and Heteroscedasticity, Multicollinearity and vif, Confusion matrix
  9. Techniques to improve accuracy and performance of regression models
  10. Assignment

Decision Trees and Random Forest Test

  1. Introduction to Decision tree Algorithms and it’s applications
  2. Classification and regression trees-CART models,ID3,C4.5, CHAID analysis
  3. Building Decision Trees using R, Decision nodes and leaf nodes
  4. Variable Selection, Parent and child nodes branching
  5. Stopping Criterion, Tree pruning, Depth of a tree, Overfitting
  6. Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
  7. Regression using decision tree
  8. Interpretation of a decision tree using If-else
  9. Pros and cons of a decision tree
  10. Introduction to Random forest test and it’s applications, Why Random forest test?
  11. Tree bagging, Models and algorithms in Random Forest test
  12. Training Data set, Tree grouping and decision making on majority voting
  13. Boosting algorithms-Gradient Boosting, Adaptive boosting-Adaboost , Xgboost ( Advanced)
  14. Accuracy estimation using cross validation