Intro alghorithms

Giving the computer system to "learn" using statistical techniques

A Tour of ML Alghorithms ​Machine Learning Mindmap​

​Machine Learning Alghorithms​

Deep Learning:
Deep Belief Network (DBN)
Deep Boltzmann Machine (DBM)
Stacked Auto Encoder
Convolutional Neural Network (CNN)

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Decision Tree:
Classification & Regression Tree (CART)
M5
C4.5
C5.0
Decision Stump
Conditional Decision Trees
Iterative Dichotomiser 3 (ID3)
Chi-squared Automation interaction Detection (CHAID)

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Bayesian
Gaussian Naive Bayes
Bayesian Network (BN)
Bayesian Belief Network (BBN)
Naive Bayes
Multinomial Naive Bayes
Average One Dependence Estimators (AODE)

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Clustering:
K-Means
K-Medians
Expectation Maximization
Hierarchical Clustering

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

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Neural Networks
Perceptron
Hopfield Network
Back-Propagation

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

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Instance Based
K-Nearest Neighbour (kNN)
Learning Vector Quantization (LVQ)
Self Organizing Map (SOM)
Locally Weighted Learning (LWL)

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

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

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Ensemble
Random Forest
Gradient Boosting Machines (GBM)
Boosting
Bootstrapped Aggregation (Bagging)
AdaBoost
Stacked Generalization (Blending)
Gradient Boosted Regression Trees (GBRT)
Radial Basis Function Network (RBFN)

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