A comprehensive guide to Machine Learning in R covering data preprocessing, supervised learning (regression and classification), evaluation and tuning, unsupervised learning, and advanced topics including natural language processing (NLP), deep learning, reinforcement learning, and dimensionality reduction.

Topics Covered

• Data Preprocessing: Importing data, missing values, categorical data, feature scaling, splitting datasets
• Regression: Simple/Multiple/Polynomial Linear Regression, SVR, Decision Tree, Random Forest Regression
• Classification: Logistic Regression, KNN, SVM, Kernel SVM, Naive Bayes, Decision Tree, Random Forest Classification
• Model Selection & Tuning: K-Fold Cross Validation, Grid Search, Boosting (XGBoost)
• Metrics: Confusion Matrix, Accuracy, Precision, Recall
• Clustering: K-Means Clustering, Hierarchical Clustering
• Association Rule Learning: Apriori, Eclat
• Natural Language Processing (NLP): Text processing with Artificial Neural Networks
• Deep Learning: Upper Confidence Bound, Thompson Sampling, Convolutional Neural Networks (CNN)
• Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Dimensionality Reduction: PCA, LDA, Kernel PCA

GitHub Repository

View all machine learning code examples on GitHub