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
Learner Career Outcomes
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
This course is quit hard, specially during the Programming Assignments. Some quizzes were hard, but for the most part I will recommend this course to everyone that will like to learn Machine Learning.
this course is very basic. does not explain the concepts in details. Course instructor is very nice. Looking forward for a course in depth of machine learning and related algorithms from Andrew ng.
A brilliant sequence of topics and fundamentals to get a stronghold on ML . The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.
This course is extremely helpful and understandable for engineers and researchers in the CS field. Many thanks to the prof. Ng Yew Kwang for his great course as well as supporters in the course forum.
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