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.
Predict — predict values from data
Classify — classify data into categories
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
LLMFunction — represents a template for an large language model prompt
LLMSynthesize — generate text following instructions
ImageSynthesize — generate an image from a textual prompt
NetModel — collection of trained and untrained models
NetTrain — train any neural network on CPUs, GPUs, etc.
ImageIdentify — recognize objects in images
TextSummarize — automatically produce different types of summarization
SpeechRecognize — speech to text