Machine Learning using R
Our Machine Learning using R course provides specialized training in statistical learning techniques using the R programming language. You'll learn to implement ML algorithms with R's extensive packages, focusing on model interpretation and statistical rigor.
Course Highlights
Skills You'll Gain
- R programming fundamentals
- Data cleaning and manipulation
- Statistical hypothesis testing
- Regression and classification models
- Time series forecasting
- Data visualization
- Real-world project implementation
Our Graduates Work At
Course Curriculum
- R Studio IDE
- Basic R Operations
- Variables and Data Types
- R Help Features
- Vectors and Matrices
- Data Frames
- cbind/rbind Functions
- Data Cleaning Techniques
- Reading CSV/Excel
- MySQL Integration
- R Data Formats
- For/While Loops
- Date Conversion
- Date Slicing
- Time Differences
- Box Plots
- Histograms
- Scatterplots
- Multivariate EDA
- Hypothesis Testing
- Linear/Logistic Regression
- Decision Trees
- SVM
- Time Series Forecasting
- 3 Real-time Projects
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ICLP Certification
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Why Learn Machine Learning using R?
- Strong statistical foundation
- Excellent for data exploration
- Good visualization capabilities
- Comprehensive collection of packages
- Strong in academia and research
- Reproducible research capabilities
- Good for statistical modeling
- Active community in data science
- Integration with other languages
- Good documentation
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Top companies where our graduates work
Reviews
Average rating: 4.8/5 (1,000+ learners)
Machine Learning using R FAQs
Find answers to common questions about this course
R excels at statistical analysis, has excellent visualization (ggplot2), specialized ML packages, and is widely used in academia/research.
caret (unified interface), randomForest, xgboost, e1071 (SVM), glmnet (regularized regression), and tidymodels (modern ML workflow).
Use recipes (from tidymodels) or preProcess in caret for scaling, imputation, dummy variables, and other transformations in a reproducible way.
caret (Classification And REgression Training) provides a unified interface for hundreds of ML algorithms with consistent preprocessing and tuning.
Use confusionMatrix() for classification, postResample() for regression, and resampling methods like cross-validation via trainControl() in caret.
Predicting diabetes, customer churn analysis, credit risk modeling, or image classification using built-in datasets or Kaggle competitions.
R has stronger statistical packages and visualization, while Python is more general-purpose with better deployment options. Many use both.
A modern collection of packages (recipes, parsnip, rsample) providing a tidyverse-like interface for ML, emphasizing reproducible workflows.
Use the ROSE or DMwR packages for sampling methods, or caret's class weights and performance metrics suitable for imbalance.
Options include Plumber for APIs, Shiny for interactive apps, PMML for cross-platform, or integrating with cloud services through APIs.
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