Classification with Support Vector Machines

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Eric Greene


Support Vector Machine (SVM) is a supervised learning technique for building regression and classification models, and it is one of the most popular learning algorithms used in machine learning. Common uses for it include face detection, image classification, handwriting recognition, and text categorization, and it tends to perform better on smaller datasets than other algorithms. Learn how to use scikit-learn to build sophisticated SVM models, and learn why SVM can often find relationships in data when other algorithms can't.

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