List of Top Machine Learning Algorithms

Machine learning is a wide topic in the sense of learning. it has a lot of algorithms that can be used for a specific task to complete. so in this post am going to share with you a complete list of machine learning algorithms.

Machine learning algorithms list

Regression algorithms

1. Linear regression
2. Logistic regression
3. Ordinary least squares regression (OLSR)
4. Stepwise regression
5. Multivariate adaptive regression splines (MARS)
6. Locally estimated scatterplot smoothing (LOESS)
7. jackknife regression

Regularization algorithms

1. Ridge regression
2. Least absolute shrinkage and selection operator ( LASSO)
3. Elastic net
4. Least angle regression (LAR)

Dimensionality reduction algorithms

1. Principal component analysis (PCA)
2. Principal component regression (PCR)
3. Partial Least squares regression (PLSR)
4. Sammon mapping
5. Multidimensional scaling (MDS)
6. Projection pursuit

Discriminant analysis algorithm

1. Linear discriminant analysis
2. Mean decrease inaccuracy
3. Quadratic discriminant analysis

Instance-based algorithms

1. k-Nearest Neighbor (KNN)
2. Learning vector quantization (LVQ)
3. Self-organizing Map (SOM)
4. Locally weighted learning (LWL)

Associated rule algorithms

1. Apriori
2. Eclat
3. FP-Growth

Ensemble algorithms

1. Logit boost
2. Bootstrapped aggregation
3. Adaboost
4. Stacked Generalization
5. Gradient boosting machines
6. Gradient-boosted regression trees
7. Random forest

Bayesian algorithms

1. Naive Bayes
2. Gaussian naive Bayes
3. Multinomial naive Bayes
4. Averaged one-dependence estimators
5. Bayesian belief network
6. Bayesian network
7. Hidden Markov models
8. Conditional random fields

Decision tree algorithms

1. Classification and regression tree (CART)
2. Iterative Dichotomiser 3 (ID3)
3. Chi-squared automatic interaction detection (CHAID)
4. Decision stump
5. M5
6. Random forests
7. Conditional decision trees

Clustering algorithms

1. Single linkage clustering
2. K-Means 
3. K-Medians
4. Expectation maximization (EM)
5. Hierarchical clustering
6. Fuzzy clustering
7. DBSCAN
8. Optics algorithm
9. Non-negative matrix factorization
10. Latent Dirichlet allocation (LDA)

Neural Networks algorithms

1. Self-organization map
2. Perceptron
3. Backpropagation
4. Hopfield network
5. Radial basis function network (RBFN)
6. Backpropagation
7. Autoencoders
8. Hopfield networks
9. Boltzmann machines
10. Restricted Boltzmann machines
11. Spiking neural networks
12. Learning vector quantization (LVQ)

Deep learning algorithms

1. Deep Boltzmann machine (DBM)
2. Deep belief networks (DBN)
3. Convolutional Neural network (CNN)
4. Stacked auto-encoders

Reinforcement learning algorithms

1. Q learning
2. Temporal difference
3. State action reward state action (SARSA)

Other algorithms

1. Support Vector Machine (SVM)
2. Evolutionary algorithms
3. Inductive logic programming (ILP)
4. ANOVA
5. Information fuzzy network (IFN)
6. Page rank
7. Conditional random fields (CRF)