Machine Learning Algorithms - A complete list | HackThatCORE

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Machine learning techniques have several Algorithms to work with datasets. Every dataset is unique and to work with it efficiently you have to choose a suitable Machine Learning Algorithm for it. Here I am telling you a list of several Machine Learning Algorithms, the more information about them can be found in the later posts of this blog...

Machine Learning Algorithms

Bayesian Algorithms

• Naive Bayes
• Averaged One-Dependence Estimators (AODE)
• Bayesian Belief Network (BBN)
• Gaussian Naive Bayes
• Multinomial Naive Bayes
• Bayesian Network (BN)



Decision Tree

• Classification and Regression Tree (CART)
• Iterative Dichotomiser 3 (ID3)
• C4.5
• C5.0
• Chi-squared Automatic Interaction Detection (CHAID)
• Decision Stump
• Conditional Decision Trees
• MS



Dimensionality Reduction

• Principal Component Analysis (PCA)
• Partial Least Squares Regression(PLSR)
• Sammon Mapping
• Multidimensional Scaling (MDS)
• Projection Pursuit
• Principal Component Regression (PCR)
• Partial Least Squares Discriminant Analysis
• Mixture Discriminant Analysis (MDA)
• Quadratic Discriminant Analysis (QDA)
• Regularized Discriminant Analysis (RDA)
• Flexible Discriminant Analysis (FDA)
• Linear Discriminant Analysis (LDA)



Instance Based

• k-Nearest Neighbour (kNN)
• Learning Vector Quantization (LVQ)
• Self-Organizing Map (SOM)
• Locally Weighted Learning (LWL)



Clustering

• k-Means
• k-Medians
• Expectation Maximization
• Hierarchical Clustering



Deep Learning

• Deep Boltzmann Machine (DBM)
• Deep Belief Networks (DBN)
• Convolutional Neural Network (CNN)
• Stacked Auto-Encoders



Ensemble

• Random Forest
• Gradient Boosting Machines (GBM)
• Boosting
• Bootstrapped Aggregation (Bagging)
• AdaBoost
• Stacked Generalization (Blending)
• Gradient Boosted Regression Trees (GBRT)



Neural Networks

• Radial Basis Function Network (RBFN)
• Perception
• Back Propagation
• Hopfield Network



Regularization

• Ridge Regression
• Least Absolute Shrinkage and Selection Operator (LASSO)
• Elastic Net
• Least Angle Regression (LARS)



Rule System

• Cubist
• One Rule (OneR)
• Zero Rule (ZeroR)
• Repeated Incremental Pruning to Produce Error Reduction (RIPPER)



Regression

• Linear Regression
• Ordinary Least Squares Regression (OLSR)
• Stepwise Regression
• Multivariate Adaptive Regression Splines (MARS)
• Locally Estimated Scatterplot Smoothing (LOESS)
• Logistic Regression

That's all. These all are the most popular Machine Learning Algorithms. Their details will be published on the blog very soon. Have fun with them...