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
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.