Machine Learning

The Wolfram Language includes a wide range of integrated machine learning capabilities, from highly automated functions like Predict and Classify to functions based on specific methods and diagnostics. The functions work on many types of data, including numerical, categorical, time series, textual, and image.

Learning from Input and Output

Classify classify data into categories using a built-in classifier or learning from examples

Predict predict values from data using a built-in predictor or learning from examples

ClassifierFunction symbolic representation of a classifier to be applied to data

PredictorFunction symbolic representation of a predictor to be applied to data

ClassifierMeasurements, PredictorMeasurements performance on test data

Learning from Sequences

SequencePredict predict subsequent elements from sequence examples

SequencePredictorFunction symbolic representation of a sequence predictor

Specific Methods

Nearest  ▪  FindFit  ▪  NonlinearModelFit  ▪  FindHiddenMarkovStates  ▪  ...

Automated Structure Discovery

DimensionReduction find how to project data onto lower-dimensional space

DimensionReduce  ▪  DimensionReducerFunction

FeatureExtraction find how to extract features from data

FeatureExtract  ▪  FeatureExtractorFunction  ▪  FeatureNearest

ClusterClassify classify data into clusters

FindClusters  ▪  ClusteringTree  ▪  ClusteringComponents

FindDistribution find a simple symbolic distribution from data

Visualization

FeatureSpacePlot visualization of dimension-reduced feature space

Dendrogram visualization of hierarchical clusters

Specific Methods

SingularValueDecomposition  ▪  FindGraphCommunities  ▪  SmoothKernelDistribution  ▪  ...

Learning from Actions

BayesianMinimization model-based minimization of arbitrary objective functions

ActiveClassification learn a classifier by actively probing a system

ActivePrediction learn a predictor by actively probing a system

ActiveClassificationObject  ▪  ActivePredictionObject

Neural Networks »

NetGraph represent an arbitrary neural network structure

NetChain  ▪  LinearLayer  ▪  ConvolutionLayer  ▪  GatedRecurrentLayer  ▪  ...

NetTrain train any neural network on CPUs, GPUs, etc.

NetModel collection of trained and untrained models

Machine Learning Options

FeatureExtractor how to extract features to learn from

FeatureTypes feature types to assume for input data

PerformanceGoal whether to optimize for memory, quality, or speed

Preparing Data »

DeleteMissing delete missing elements in data

Standardize transform data to have zero mean and unit variance

Clip  ▪  Rescale  ▪  Threshold  ▪  LogisticSigmoid  ▪  ImageAdjust

CountsBy  ▪  GroupBy  ▪  SortBy  ▪  DeleteDuplicates

Filtering Data »

MovingAverage compute moving averages of lists, time series, etc.

GaussianFilter  ▪  MeanFilter  ▪  MeanShiftFilter  ▪  LowpassFilter  ▪  ...

Machine Vision Applications

ImageIdentify recognize objects in images

FindFaces  ▪  TextRecognize  ▪  ImageGraphics

Natural Language Processing Applications

LanguageIdentify  ▪  TextStructure  ▪  TextCases  ▪  ...