Machine Learning For Dummies
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When working with R and Python for machine learning, you gain the benefit of not having to reinvent the wheel when it comes to algorithms. There is a library available to meet your specific needs — you just need to know which one to use. This table provides you with a listing of the libraries used for machine learning for both R and Python. When you want to perform any algorithm-related task, simply load the library needed for that task into your programming environment.
Algorithm Python implementation R implementation
Adaboost sklearn.ensemble.AdaBoostClassifier

sklearn.ensemble.AdaBoostRegressor
library(ada) : ada
Gradient Boosting sklearn.ensemble.GradientBoostingClassifier

sklearn.ensemble.GradientBoostingRegressor
library(gbm) : gbm
K-means sklearn.cluster.KMeans

sklearn.cluster.MiniBatchKMeans
library(stats) : kmeans
K-nearest Neighbors sklearn.neighbors.KNeighborsClassifier

sklearn.neighbors.KNeighborsRegressor
library(class): knn
Linear regression sklearn.linear_model.LinearRegression

sklearn.linear_model.Ridge

sklearn.linear_model.Lasso

sklearn.linear_model.ElasticNet

sklearn.linear_model.SGDRegressor
library(stats) : lm

library(stats) : glm

library(MASS) : lm.ridge

library(lars) : lars

library(glmnet) : glmnet
Logistic regression sklearn.linear_model.LogisticRegression

sklearn.linear_model.SGDClassifier
library(stats) : glm

library(glmnet) : glmnet
Naive Bayes sklearn.naive_bayes.GaussianNB

sklearn.naive_bayes.MultinomialNB

sklearn.naive_bayes.BernoulliNB
library(klaR) : NaiveBayes

library(e1071) : naiveBayes
Neural Networks sklearn.neural_network.BernoulliRBM

(in version 0.18 of Scikit-learn, a new implementation of supervised neural network will be introducted)
library(neuralnet) : neuralnet

library(AMORE) : train

library(nnet) : nnet
PCA sklearn.decomposition.PCA library(stats): princomp

library(stats) : stats
Random Forest sklearn.ensemble.RandomForestClassifier

sklearn.ensemble.RandomForestRegressor

sklearn.ensemble.ExtraTreesClassifier

sklearn.ensemble.ExtraTreesRegressor
library(randomForest) : randomForest
Support Vector Machines sklearn.svm.SVC

sklearn.svm.LinearSVC

sklearn.svm.NuSVC

sklearn.svm.SVR

sklearn.svm.LinearSVR

sklearn.svm.NuSVR

sklearn.svm.OneClassSVM
library(e1071) : svm
SVD sklearn.decomposition.TruncatedSVD

sklearn.decomposition.NMF
library(irlba) : irlba

library(svd) : svd

About This Article

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About the book authors:

John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.

John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.

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