The Wolfram Language has functions that work directly on many types of data and automatically extract some sort of structure from it. FindClusters, ClusteringTree and ClusteringComponents are examples of functions that perform the unsupervised learning task of clustering. ClusterClassify classifies new samples based on information gathered from unlabeled input data via clustering. Other functions like FeatureExtract, FeatureNearest, FeatureSpacePlot and DimensionReduce provide tools for automatic exploration of the data in the feature space. This video introduces these functions to get you started on unsupervised machine learning tasks. It is suitable for beginners without previous knowledge of machine learning.
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You'll Learn To
- Use functions for unsupervised learning
- Identify and visualize clusters in data
- Classify data based on existing clusters
- Explore data in the feature space