• Classification:

Logistic Regression

Perceptron

Naive Bayes

Linear Discriminant Analysis

Quadratic Discriminant Analysis

• Regression:

Linear Regression

Polynomial Regression

Ridge Regression

Kernel Ridge Regression

Lasso

ElasticNet

Least Angle Regression

LARS Lasso

Orthogonal Matching Pursuit

Bayesian Regression

Robust Regression

• Classification & Regression:

SVM

Nearest Neighbors

Decision Trees

Stochastic Gradient Descent

Online Passive Aggressive

Gaussian Processes

Neural Network(supervised)

• Ensemble methods:

Bagging

Random Forests

AdaBoost

Gradient Tree Boosting

Voting Classifier

GBDT

XgBoost

• Unsupervised Learning:

Gaussian mixture models

Manifold learning

Neural network models (unsupervised)

• Clustering:

K-means

Affinity Propagation

Mean Shift

Spectral clustering

Hierarchical clustering

• Dimensionality reduction:

PCA

Feature Selection

• Preprocessing data

Standardization, mean removal, variance scaling

Non-linear transformation

Normalization

Binarization

Imputation of missing values

Generating polynomial features

• Others:

Cross decomposition

Multiclass & Multilabel

Semi-Supervised Learning