Machine Learning

Data-driven applications are ubiquitous (market analysis, agriculture, healthcare, transport networks, ...) and machine learning algorithms have been developed with the specific purpose of analyzing patterns and leveraging correlation within real-world measurements in order to turn data into applications. The Wolfram Language offers fully automated and highly customizable machine learning functions to perform classification, regression, clustering and many other operations. Classical methods are complemented by powerful, symbolic deep-learning frameworks and specialized pipelines for diverse data types such as image, video, text and audio.

Supervised Machine Learning »

Predict predict values from data

Classify classify data into categories

ActivePrediction  ▪  SequencePredict  ▪  Nearest  ▪  FindFit  ▪  ...

Unsupervised Machine Learning »

FindClusters partition data into clusters

FeatureExtraction find how to extract features from data

FeatureSpacePlot visualize data in a dimension-reduced feature space

FeatureImpactPlot visualize the impact of the input features on a model result

AnomalyDetection  ▪  DimensionReduction  ▪  MissingValueSynthesis  ▪  ...

Neural Networks »

NetModel collection of trained and untrained models

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

NetGraph  ▪  LinearLayer  ▪  ConvolutionLayer  ▪  AttentionLayer  ▪  ...

Computer Vision »

ImageIdentify recognize objects in images

ImageCases  ▪  FindFaces  ▪  TextRecognize  ▪  ImageGraphics  ▪  ...

Natural Language Processing »

FindTextualAnswer find answers to questions from text

LanguageIdentify  ▪  TextStructure  ▪  TextCases  ▪  ...

Speech Computation »

SpeechRecognize speech to text

AudioIdentify  ▪  SpeechCases  ▪  SpeakerMatchQ  ▪  PitchRecognize