Machine Learning with R

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

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