Support vector machines (SVMs) are supervised learning models that analyze data for classification and regression analysis. They build a model based on training examples to assign new examples to categories, using a non-probabilistic binary linear classifier. SVMs can also perform non-linear classification using the kernel trick, and the support vector clustering algorithm applies the statistics of support vectors to categorize unlabeled data using unsupervised learning approaches.

Stanford University

Winter 2023

This comprehensive course covers various machine learning principles from supervised, unsupervised to reinforcement learning. Topics also touch on neural networks, support vector machines, bias-variance tradeoffs, and many real-world applications. It requires a background in computer science, probability, multivariable calculus, and linear algebra.

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+ 32 more conceptsCarnegie Mellon University

Spring 2018

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.

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+ 55 more conceptsStanford University

Spring 2022

This is a deep-dive into the details of deep learning architectures for visual recognition tasks. The course provides students with the ability to implement, train their own neural networks and understand state-of-the-art computer vision research. It requires Python proficiency and familiarity with calculus, linear algebra, probability, and statistics.

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+ 55 more concepts