About this course: 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.
Created by: Stanford University
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Taught by: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain