• Regression
  • Linear Regressionn.md)ogularization]]
  • Ridge Regression
  • StepwiElastic-Net: Regularization
  • Polynomial Regression
  • Ordinary Least Squares Regression (OLSR)
  • Multivariate Adaptive Regression Splines (MARS)
  • Locally Estimated Scatterplot Smoothing (LOESS)
• Support Vector Machine (SVM)
• SVM Regression
• Ensemble Model
  • Random Forest

Algorithms for creating meaningful groups or collections from a set of unlabeled data.

• K-Means
• Gaussian mixture model (GMM)
• Hierarchical Clustering
• DBSCAN
• OPTICS
• Mean-Shift
• BIRCH
• k-Medians
• Fuzzy c-Means
• Fuzzy k-Modes
• Fuzzy clustering
• Expectation Maximization
• Minimum spanning tree

• Bayesian Models
  • Naive Bayes
• Tree-based Models
  • Decision Trees
• Ensemble Model
  • Random Forest
  • eXtreme Gradient Boosting (XGBoost)
• k-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)

• Outlier Detection
  • Support Vector Machine (SVM)
  • k-Nearest Neighbors (KNN)
  • Apriori Algorithm
  • Robust Regression (RANSAC)
  • Local outlier factor
  • Eclat algorithm
  • FP-growth
  • Isolation forest
• Rule-Based Machine Learning (RBML): The rule-based machine learning applies some form of learning algorithm to automatically identify useful rules, rather than a human needing to apply prior domain knowledge to manually construct rules and curate a rule set.
  • Ripper Algorithm
  • Repeated Incremental Pruning to Produce
  • Cubist
  • OneR
  • ZeroR
• Dimensionality Reduction
  • Principal Components Analysis (PCA)
  • Principal Component Regression (PCR)
  • Independent Component Analysis (ICA)
  • Linear Discriminant Analysis (LDA)
  • Partial Least Squares Regression (PLSR)
  • Sammon Mapping
  • Multidimensional Scaling (MDS)
  • Projection Pursuit
  • Non-negative matrix factorization (NMF)
  • Regularized Discriminant Analysis (RDA)
  • Mixture Discriminant Analysis (MDA)
  • Partial Least Squares Dimension Analysis (PLSDA)
  • Quadratic Discriminant Analysis (QDA)
  • Canonical correlation analysis (CCA)
  • Flexible Discriminant Analysis (FDA)
  • Diffusion map
• Regularization
• Ridge Regression
  • Lasso Regression
  • Elastic-Net
  • Stepwise
  • Least-Angle Regression (LARS)

• Artificial Neural Networks (ANN): are modeled after the way that neurons interact in the human brain to interpret information and solve problems.
  • Perceptron
  • Multilayer perceptron (MLP)
  • Recurrent Neural Networks (RNN)
  • Feed-Forward Neural Networks (FFNN)
  • Convolutional Neural Networks (CNN)
  • Generative Adversarial Networks (GAN)
  • Long-Short Term Memory (LSTM)
  • Hopfield Networks (HN)
  • Boltzmann Machines (BM)
  • Restricted Boltzmann Machines (RBM)
  • Autoencoders (AE)
    • Variational Autoencoders (VAE)
    • Stacked Autoencoders(SAEs)
  • Deep Belief Networks (DBN)
• Tree-based Models: use a series of “if-then” rules to generate predictions from one or more decision trees.
  • Decision Trees
  • Conditional Decision Trees
  • Decision Stump
  • C4.5 and C5.0
  • Chi-squared Automatic Interaction Detection (CHAID)
  • Classification and Regression Tree (CART)
  • Iterative Dichotomiser 3 (ID3)
  • M5
• Bayesian Models
  • Naive Bayes
  • Bayesian Network
  • Averaged One-Dependence Estimators (AODE)
  • Gaussian Naive Bayes
  • Bernoulli Naive Bayes
  • Multinomial Naive Bayes
  • Bayesian Belief Network (BBN)
• Reinforcement Learning Algorithms
  • State Action Reward State Action (SARSA)
  • Q-learning
  • Deep Q Network (DQN)
  • Learning Automata
  • Deep Deterministic Policy Gradient (DDPG)
  • Normalized Advantage Function (NAF)
  • Asynchronous Advantage Actor Critic (A3C)
  • Trust Region Policy Optimization (TRPO)
  • Proximal Policy Optimization (PPO)
  • Constructing skill trees
• Instance-Based Algorithms: This supervised machine learning algorithm performs operations after comparing current instances with previously trained instances that are stored in memory. This algorithm is called instance based because it is using instances created using training data.
  • k-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
  • Self-Organizing Map (SOM)
  • Locally Weighted Learning (LWL)
• Transformers
• Other Algorithms
  • Logic learning machine
  • Markov Chain Monte-Carlo (MCMC)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)