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Machine Learning, Stanford University

4.9
87,963 calificaciones
22,554 revisiones

Acerca de este Curso

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. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas....

Principales revisiones

por SK

Oct 26, 2017

Amazing course for people looking to understand few important aspects of machine learning in terms of linear algebra and how the algorithms work! Definitely will help me in my future modelling efforts

por EJ

Mar 27, 2018

Very well structured and delivered course. Progressive introduction of concepts and intuitive description by Andrew really give a sense of understanding even for the more complex area of the training.

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21,695 revisiones

por Chatherine lynn

Dec 17, 2018

an excellent calss!

por Chimezie Iwuanyanwu

Dec 17, 2018

This course is very good and informational! I would definitely recommend taking it to get a good grasp on machine learning.

por Ho Phu Hien

Dec 17, 2018

Great course

por Kumari Madhu

Dec 17, 2018

nice interface and interactive sessions. Andrew Ng puts forward the concepts in a way that demands very minimal effort to understand.

por Anthony Chiu

Dec 17, 2018

Excellent course for people who has limited math background and wish to jump start on pursuing a career in Machine Learning

por Tarsoly Gergely

Dec 17, 2018

It was a great introduction to the topic and gives a great insight on the maths behind it. I wish it had some convolutional networks in it! Everywhere else I looked, no one could explain with such depth and clarity as professor Ng explains this course.

por chen zhiyuan

Dec 17, 2018

Best online courses I've ever taken

por Kinyua Wachira

Dec 17, 2018

I really loved this course. The materials are well thought out and expressed. The tests and assignments are designed to focus student attention on the problem rather than the details of implementation. All in all, an excellent course.

por Chinmay Krishnan

Dec 17, 2018

Glad I took it. Wouldn't be able to keep up with the ML course in college.

por Yue

Dec 16, 2018

Las primeras semanas tienen resumen después de cada vídeo. Es de gran utilidad y lo he echado en falta en la últimas semanas.