Free Course
16 weeks duration

In this course, you'll learn how to apply Supervised, Unsupervised and Reinforcement Learning techniques for solving a range of data science problems.

Machine Learning is a comprehensive course on Udacity that covers a wide range of topics. Starting with supervised learning, students will learn about decision trees, regression, classification, neural networks, and instance-based learning. The course also delves into ensemble methods, kernel methods, and support vector machines. Students will explore computational learning theory, VC dimensions, Bayesian learning, and Bayesian inference. Moving on to unsupervised learning, the course covers randomized optimization, clustering, feature selection, and feature transformation. Additionally, students will gain knowledge in information theory, reinforcement learning, Markov decision processes, and game theory. With a focus on practical applications and hands-on projects, this course provides a comprehensive understanding of machine learning concepts and techniques.

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Udacity is an online learning platform that offers a wide range of courses and programs in various fields such as technology, business, data science, and artificial intelligence. It was founded in 2012 by Sebastian Thrun, David Stavens, and Mike Sokolsky with the aim of providing accessible and affordable education to individuals worldwide. Udacity's courses are designed in collaboration with industry experts and leading companies, ensuring that the content is relevant and up-to-date. The platform offers both self-paced courses and guided programs, allowing learners to choose the learning style that suits them best. Udacity also provides career services and support, including resume reviews, interview preparation, and job placement assistance, to help learners transition into their desired careers.
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