Machine Learning Algorithms in Python / R

The complete Machine Learning Library in R and Python with data examples.

Table of Contents

• Pre-processing
  • Missing Data
  • Feature Scaling
  • Encoding categorical data (OneHotEncoder)
• Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Decision TreeRegression
  • Random Forest Regression
  • Evaluating Regression Model
• Classification
  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification
  • Evaluating Classification Models Performance
• Clustering
  • K-Means Clustering
  • Hierarchical Clustering
• Association Rule Learning
  • Apriori
  • Eclat
• Reinforcement Learning
  • Upper Confidence Bound (UCB)
  • Thompson Sampling
• Natural Language Processing
• Deep Learning
  • Artificial Neural Networks (ANN)
  • Convolutional Neural Networks (CNN)
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Kernel PCA
• Model Selection & Boosting
  • Model Selection
  • XGBoost