EEWORLD University ---- Machine Learning Andrew Ng

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EEWORLD University ---- Machine Learning Andrew Ng [Copy link]

Machine Learning by Andrew Ng : https://training.eeworld.com.cn/course/4486

Machine learning is the science of getting computers to act without being explicitly programmed. Over the past decade, machine learning has enabled self-driving cars, useful speech recognition, effective web search, and improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without even realizing it. Many researchers also believe it is the best way to achieve true "artificial intelligence." In this course, you'll learn the most effective machine learning techniques, gain hands-on experience applying them to your own benefit. Finally, you'll learn some of the best practices of innovation in Silicon Valley as it pertains to machine learning and artificial intelligence.
Machine learning is the science of getting computers to act without being explicitly programmed. Over the past decade, machine learning has enabled the development of self-driving cars, effective speech recognition, accurate web search, and improved understanding of the human genome. Machine learning is so pervasive that you've probably used it countless times without even realizing it. Many researchers believe it is the best way to achieve human-level AI. In this course, you'll learn effective machine learning techniques and how to use them to your benefit. The point is that you'll not only learn the theoretical foundations, but also how to quickly and effectively apply these techniques to new problems. Finally, you will be exposed to several outstanding examples of machine learning and AI applications in Silicon Valley innovation.

This course will provide a broad introduction to machine learning, data mining, 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, recommendation systems, deep learning). (iii) Excellent examples of machine learning (bias/variance theory; the innovation process of machine learning and artificial intelligence) The course will combine case studies and applications to learn how to apply learning algorithms to intelligent robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, data mining, and other fields.