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Merge pull request #2793 from devarajphukan/master 

machine learning algorithms
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Zaahir Moolla 2016-04-15 22:28:49 -04:00
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{
"id" : "machine_learning_cheat_sheet",
"name" : "Machine Learning Cheat Sheet",
"description" : "Machine Learning Techniques and Algorithms",
"metadata" : {
"sourceName" : "Data Science Central",
"sourceUrl" : "http://www.datasciencecentral.com/profiles/blogs/a-tour-of-machine-learning-algorithms-1"
},
"aliases": [
"machine learning algorithms", "types of machine learning algorithms"
],
"template_type": "links",
"section_order" : [
"Regression Algorithms",
"Instance-based Algorithms",
"Regularization Algorithms",
"Decision Tree Algorithms",
"Bayesian Algorithms",
"Clustering Algorithms",
"Association Rule Learning Algorithms",
"Artificial Neural Network Algorithms",
"Deep Learning Algorithms",
"Probabilistic Graphical Models",
"Ensemble Techniques"
],
"description" : "Types of Machine Learning Algorithms",
"sections" : {
"Regression Algorithms" : [
{
"key" : "Ordinary Least Squares Regression (OLSR)",
"link" : "http://setosa.io/ev/ordinary-least-squares-regression/"
},
{
"key" : "Linear Regression",
"link" : "http://www.statisticssolutions.com/what-is-linear-regression/"
},
{
"key" : "Logistic Regression",
"link" : "https://en.wikipedia.org/wiki/Logistic_regression"
},
{
"key" : "Stepwise Regression",
"link" : "http://support.minitab.com/en-us/minitab/17/topic-library/modeling-statistics/regression-and-correlation/basics/basics-of-stepwise-regression/"
},
{
"key" : "Multivariate Adaptive Regression Splines (MARS)",
"link" : "https://en.wikipedia.org/wiki/Multivariate_adaptive_regression_splines"
},
{
"key" : "Locally Estimated Scatterplot Smoothing (LOESS)",
"link" : "http://www.statsdirect.com/help/content/nonparametric_methods/loess.htm"
}
],
"Instance-based Algorithms" : [
{
"key" : "k-Nearest Neighbour (kNN)",
"link" : "http://www.statsoft.com/textbook/k-nearest-neighbors"
},
{
"key" : "Learning Vector Quantization (LVQ)",
"link" : "https://en.wikipedia.org/wiki/Learning_vector_quantization"
},
{
"key" : "Self-Organizing Map (SOM)",
"link" : "https://en.wikipedia.org/wiki/Self-organizing_map"
},
{
"key" : "Locally Weighted Learning (LWL)",
"link" : "https://www.cs.cmu.edu/~schneide/tut5/node29.html"
}
],
"Regularization Algorithms" : [
{
"key" : "Ridge Regression",
"link" : "https://onlinecourses.science.psu.edu/stat857/node/155"
},
{
"key" : "Least Absolute Shrinkage and Selection Operator (LASSO)",
"link" : "https://en.wikipedia.org/wiki/Lasso_(statistics)"
},
{
"key" : "Elastic Net",
"link" : "https://en.wikipedia.org/wiki/Elastic_net_regularization"
},
{
"key" : "Least-Angle Regression (LARS)",
"link" : "https://en.wikipedia.org/wiki/Least-angle_regression"
}
],
"Decision Tree Algorithms" : [
{
"key" : "Classification and Regression Tree (CART)",
"link" : "http://documents.software.dell.com/Statistics/Textbook/Classification-and-Regression-Trees"
},
{
"key" : "Iterative Dichotomiser 3 (ID3)",
"link" : "https://en.wikipedia.org/wiki/ID3_algorithm"
},
{
"key" : "C4.5 and C5.0 (different versions of a powerful approach)",
"link" : "http://www.cs.bris.ac.uk/Research/MachineLearning/Kepler/en/tool-c45.html"
},
{
"key" : "Chi-squared Automatic Interaction Detection (CHAID)",
"link" : "http://documents.software.dell.com/Statistics/Textbook/CHAID-Analysis"
},
{
"key" : "Decision Stump",
"link" : "https://en.wikipedia.org/wiki/Decision_stump"
},
{
"key" : "Conditional Decision Trees",
"link" : "http://www.slideshare.net/christophmolnar/conditional-trees"
}
],
"Bayesian Algorithms" : [
{
"key" : "Naive Bayes",
"link" : "http://scikit-learn.org/stable/modules/naive_bayes.html"
},
{
"key" : "Gaussian Naive Bayes",
"link" : "https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Gaussian_naive_Bayes"
},
{
"key" : "Multinomial Naive Bayes",
"link" : "https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes"
},
{
"key" : "Averaged One-Dependence Estimators (AODE)",
"link" : "https://en.wikipedia.org/wiki/Averaged_one-dependence_estimators"
},
{
"key" : "Bayesian Network (BN)",
"link" : "https://www.norsys.com/tutorials/netica/secA/tut_A1.htm"
}
],
"Clustering Algorithms" : [
{
"key" : "k-Means",
"link" : "http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html"
},
{
"key" : "k-Medians",
"link" : "https://en.wikipedia.org/wiki/K-medians_clustering"
},
{
"key" : "Expectation Maximisation (EM)",
"link" : "https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm"
},
{
"key" : "Hierarchical Clustering",
"link" : "http://www.analytictech.com/networks/hiclus.htm"
}
],
"Association Rule Learning Algorithms" : [
{
"key" : "Apriori algorithm",
"link" : "https://en.wikipedia.org/wiki/Apriori_algorithm"
},
{
"key" : "Eclat algorithm",
"link" : "https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_Eclat_Algorithm"
}
],
"Artificial Neural Network Algorithms" : [
{
"key" : "Perceptron",
"link" : "https://en.wikipedia.org/wiki/Perceptron"
},
{
"key" : "Back-Propagation",
"link" : "http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/"
},
{
"key" : "Hopfield Network",
"link" : "https://en.wikipedia.org/wiki/Hopfield_network"
},
{
"key" : "Radial Basis Function Network (RBFN)",
"link" : "https://en.wikipedia.org/wiki/Radial_basis_function_network"
},
{
"key" : "Recurrent neural network (RNN)",
"link" : "https://en.wikipedia.org/wiki/Recurrent_neural_network"
}
],
"Deep Learning Algorithms" : [
{
"key" : "Deep Boltzmann Machine (DBM)",
"link" : "https://en.wikipedia.org/wiki/Boltzmann_machine"
},
{
"key" : "Deep Belief Networks (DBN)",
"link" : "http://deeplearning.net/tutorial/DBN.html"
},
{
"key" : "Convolutional Neural Network (CNN)",
"link" : "https://en.wikipedia.org/wiki/Convolutional_neural_network"
},
{
"key" : "Stacked Auto-Encoders",
"link" : "http://ufldl.stanford.edu/wiki/index.php/Stacked_Autoencoders"
}
],
"Ensemble Techniques" : [
{
"key" : "Boosting",
"link" : "https://en.wikipedia.org/wiki/Boosting_(machine_learning)"
},
{
"key" : "Bootstrapped Aggregation (Bagging)",
"link" : "https://en.wikipedia.org/wiki/Bootstrap_aggregating"
},
{
"key" : "AdaBoost",
"link" : "https://en.wikipedia.org/wiki/AdaBoost"
},
{
"key" : "Extreme Gradient Boosting",
"link" : "http://www.analyticsvidhya.com/blog/2016/01/xgboost-algorithm-easy-steps/"
},
{
"key" : "Gradient Boosting Machines (GBM)",
"link" : "https://en.wikipedia.org/wiki/Gradient_boosting"
},
{
"key" : "Random Forest",
"link" : "https://en.wikipedia.org/wiki/Random_forest"
}
],
"Probabilistic Graphical Models" : [
{
"key" : "Maximum Entropy Markov Model",
"link" : "https://en.wikipedia.org/wiki/Maximum-entropy_Markov_model"
},
{
"key" : "Conditional Random Field",
"link" : "https://en.wikipedia.org/wiki/Conditional_random_field"
},
{
"key" : "Hidden Markov Model",
"link" : "https://en.wikipedia.org/wiki/Hidden_Markov_model"
}
]
}
}