Machine Learning with R Last Updated : 28 Oct, 2021 Improve Improve Like Article Like Save Share Report Machine Learning as the name suggests is the field of study that allows computers to learn and take decisions on their own i.e. without being explicitly programmed. These decisions are based on the available data that is available through experiences or instructions. It gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect. This Machine Learning with R Programming tutorial aims to help learn both supervised and unsupervised machine learning algorithms with the help of well-explained and good examples. Introduction An Introduction to Machine Learning What is Machine Learning ? Getting Started with Machine Learning ML – Applications Setting up Environment for Machine Learning with R Programming Introduction to Machine Learning in R Supervised and Unsupervised Learning in R Programming Data Processing Introduction to Data in Machine Learning Understanding Data Processing Data Cleansing Feature Scaling Supervised Learning Regression Analysis in R Programming Linear Regression Analysis in R Programming – lm() Function How to Extract the Intercept from a Linear Regression Model in R Polynomial Regression in R Programming Logistic Regression in R Programming Regularization in R Programming Lasso Regression in R Programming Ridge Regression in R Programming Elastic Net Regression in R Programming Quantile Regression in R Programming Naive Bayes Classifier in R Programming Decision Tree for Regression in R Programming Decision Tree Classifiers in R Programming Conditional Inference Trees in R Programming Random Forest Approach in R Programming Random Forest Approach for Regression in R Programming Random Forest Approach for Classification in R Programming Random Forest with Parallel Computing in R Programming Regression using k-Nearest Neighbors in R Programming K-NN Classifier in R Programming Testing Trained Models Cross-Validation in R programming K-fold Cross Validation in R Programming Repeated K-fold Cross Validation in R Programming LOOCV (Leave One Out Cross-Validation) in R Programming The Validation Set Approach in R Programming Unsupervised Learning K-Means Clustering in R Programming Hierarchical Clustering in R Programming How to Perform Hierarchical Cluster Analysis using R Programming? DBScan Clustering in R Programming Linear Discriminant Analysis in R Programming Association Rule Mining in R Programming Apriori Algorithm in R Programming Time Series Analysis Time Series Analysis using ARIMA model in R Programming Exponential Smoothing in R Programming Time Series Analysis using Facebook Prophet in R Programming Misc Kolmogorov-Smirnov Test in R Programming Moore – Penrose Pseudoinverse in R Programming Spearman Correlation Testing in R Programming Poisson Functions in R Programming Feature Engineering in R Programming Adjusted Coefficient of Determination in R Programming Mann Whitney U Test in R Programming Bootstrap Confidence Interval with R Programming Applications and Projects Predictive Analysis in R Programming Like Article Suggest improvement Next Introduction to Machine Learning in R Share your thoughts in the comments Add Your Comment Please Login to comment...