Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
Machine LearningStanford University
About this Course
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Learner Career Outcomes
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
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TOP REVIEWS FROM MACHINE LEARNING
Um curso incrível com uma ótima didática e exercícios que realmente estimulam o que foi aprendido em aula. Sem dúvida é a melhor fonte de conhecimento para adentrar no mundo de Máquina de Aprendizado.
Great course. A progressive discovery of the maths inner to the learning algorithms. This course gives that insight many ML practitioners don't have and is so important for making real use cases work.
Enjoyed following the course (videos) and reading notes, resources, discussions as well as doing assignments using GNU Octave (visualizing the results). Well organized. A big thanks to the whole team.
Very nice course,. Give a fundamental knowledge of machine learning in a clear, logic and easy-to-understand way. Suitable for those who has relatively weak background of math and statistics to learn.
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