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机器学习, 斯坦福大学

4.9
99,742 個評分
24,929 個審閱

課程信息

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....

熱門審閱

創建者 MN

Jun 15, 2016

Excellent starting course on machine learning. Beats any of the so called programming books on ML. Highly recommend this as a starting point for anyone wishing to be a ML programmer or data scientist.

創建者 OK

Apr 18, 2018

You need to know, what do you want to get out of this course. It gives you a lot of information, but be prepared to work hard with linear algeabra and make efforts to compute things in Mathlab/Octave.

篩選依據:

24,051 個審閱

創建者 Ayush Dhaka

Apr 20, 2019

The best and the easiest way to understand the basics of machine learning.

創建者 sathiyajith babu sutti manoharan

Apr 20, 2019

This is the best Learning material for Beginners into the field of ML/DL.

創建者 Zhen Dong

Apr 20, 2019

Thank you Andrew Ng for your amazing tutorial course! I benefit a lot from your great explanations of the complex technique with a simple language. I especially like the assignments part which make me understand the concept deeply and practice to verify my own understanding.

THANK YOU!!!

創建者 Anand Mathew M S

Apr 20, 2019

Andrew Ng's course on Machine Learning is a delight. The topics are handled beautifully giving us a problem definition or scope for the algorithm at the beginning and masterfully giving us the necessary intuition through to the end. The course gives anyone interested in ML confidence to try their own projects and effective understanding of topics covered.

創建者 Shireesh Potnuru

Apr 20, 2019

Good course for Beginners

創建者 Tarek Naous

Apr 20, 2019

Excellent

創建者 Badrinath Nagarajan

Apr 20, 2019

Excellent coverage of key concepts of machine learning and algorithms that were well supplemented by exercises. Truly enjoyed the course!

創建者 Mauricio Acuna Valdes

Apr 20, 2019

Very good introduction and a little bit more to machine learning. A lot of concepts, very well explained. Very good teacher

創建者 wansijie

Apr 20, 2019

excellent course, hope i can apply this meachine learning on my job

創建者 Anas BARIK

Apr 19, 2019

best ML existing course