Overview of Machine Learning Algorithms
In this post we are going to see most useful machine learning algorithms.
When you are learning machine learning then we'll come across supervised machine learning problems are strong bias towards algorithms used for classification and regression.
Regression Algorithms
Regression algorithms find the patter between the output values based on input features from the data. After that algorithm builds a model on the features of training data and using the model to predict value for new data.
The most popular regression algorithms are:
Ordinary Least Squares Regression (OLSR)
Linear Regression
Logistic Regression
Stepwise Regression
Multivariate Adaptive Regression Splines (MARS)
Locally Estimated Scatterplot Smoothing (LOESS)
Instance-based Algorithms
k-Nearest Neighbor (kNN)
Learning Vector Quantization (LVQ)
Self-Organizing Map (SOM)
Locally Weighted Learning (LWL)
Support Vector Machines (SVM)
Regularization Algorithms
Ridge Regression
Least Absolute Shrinkage and Selection Operator (LASSO)
Elastic Net
Least-Angle Regression (LARS)
Decision Tree Algorithms
Classification and Regression Tree (CART)
Iterative Dichotomiser 3 (ID3)C4.5 and C5.0 (different versions of a powerful approach)
Chi-squared Automatic Interaction Detection (CHAID)
Decision Stump
M5
Conditional Decision Trees
Bayesian Algorithms
Naive Bayes
Gaussian Naive Bayes
Multinomial Naive Bayes
Averaged One-Dependence Estimators (AODE)
Bayesian Belief Network (BBN)
Bayesian Network (BN)
Clustering Algorithms
k-Means
k-Medians
Expectation Maximisation (EM)
Hierarchical Clustering
Association Rule Learning Algorithms
Apriori algorithm
Eclat algorithm
Artificial Neural Network Algorithms
Perceptron
Multilayer Perceptrons (MLP)
Back-Propagation
Stochastic Gradient Descent
Hopfield NetworkRadial Basis Function Network (RBFN)
Deep Learning Algorithms
Convolutional Neural Network (CNN)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Stacked Auto-Encoders
Deep Boltzmann Machine (DBM)
Deep Belief Networks (DBN)
Dimensionality Reduction Algorithms
Principal Component Analysis (PCA)
Principal Component Regression (PCR)
Partial Least Squares Regression (PLSR)
Sammon MappingMultidimensional Scaling (MDS)
Projection Pursuit
Linear Discriminant Analysis (LDA)
Mixture Discriminant Analysis (MDA)
Quadratic Discriminant Analysis (QDA)
Flexible Discriminant Analysis (FDA)
Ensemble Algorithms
Boosting
Bootstrapped Aggregation (Bagging)
AdaBoost
Weighted Average (Blending)
Stacked Generalization (Stacking)
Gradient Boosting Machines (GBM)
Gradient Boosted Regression Trees (GBRT)
Random Forest
Other Machine Learning Algorithms
Feature selection algorithms
Algorithm accuracy evaluation
Performance measures
Optimization algorithms
Other specialty subfields of machine learning, such as:
Computational intelligence (evolutionary algorithms, etc.)
Computer Vision (CV)
Natural Language Processing (NLP)
Recommender Systems
Reinforcement Learning
Graphical Models
And more…
if you know of an algorithms which is not listed, put it in the comments and share it with us.