Machine Learning with R

Live Online (VILT) & Classroom Corporate Training Course

R is a language that is most suited for ML programming and achieving mastery in it is paramount for a career in ML. Master ML with R and become part of the technology revolution that will shape the future world.
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  • Lifetime Access
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This course will help you learn all the concepts of R and ML along with Supervised vs Unsupervised Learning, the ways in which Statistical Modeling relates to Machine Learning, and a comparison of each using R libraries.



At the end of Machine Learning with R training course, participants will

  • Understand the behavior of data as they build significant models
  • Learn about the various libraries offered by R to manipulate, preprocess and visualize data
  • Supervised, Unsupervised Machine Learning and relation of statistical modelling to machine learning
  • Learn to use optimization techniques to find the minimum error in your machine learning model
  • Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail
  • Implement algorithms and R libraries such as CRAN-R in real world scenarios
  • Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
  • Learn to use multiple learning algorithms to obtain better predictive performance


  • Elementary programming knowledge
  • Familiarity with statistics

Course Outline

  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores

  • Intro to R Programming
  • Installing and Loading Libraries
  • Data Structures in R
  • Control & Loop Statements in R
  • Functions in R
  • Loop Functions in R
  • String Manipulation & Regular Expression in R
  • Working with Data in R
  • Data Visualization in R
  • Case Study

  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting
  • How to treat Data in ML

  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • K-NN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM – Support Vector Machines
  • Case Study

  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study

  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study: PCA/FA

  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study