此课程适用人群: This class is for students interested in Computational Linguistics and Natural Language Processing. Some previous experience with probabilities will be helpful. Prior or concurrent experience with programming, preferably in Python, is expected.


制作方:   密歇根大学

  • Dragomir R. Radev, Ph.D.

    教学方:    Dragomir R. Radev, Ph.D., Professor of Information, School of Information, Professor of Electrical Engineering and Computer Science, College of Engineering, and Professor of Linguistics, College of Literature, Science, and the Arts

    College of Engineering, School of Information, School of Literature, Science and the Arts
级别Intermediate
语言
English
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用户评分
4.1 stars
Average User Rating 4.1查看学生的留言
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课程作业

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制作方
密歇根大学
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
评分和审阅
已评分 4.1,总共 5 个 119 评分

Great course in NLP which provides a broad overview of the topic. The programming assignments can make you confident in solving NLP-based problems in business applications.

Its a tough course but worth it!

More in depth details will be much better.

Coding Assignments can be explained much better. In this course there is problem in understanding coding assignment details as in some cases they are not explained correctly.

I got very good introduction not only for NLP but also for other adjacent topics like ML, probability theory, linear algebra. Highly recommend if you want to get involved in these topics.

I found the topics well chosen, providing an good overview of techniques used for NLP. The introduction to NLP was a bit long, assuming that people who take this course know what NLP is about and why it is difficult for computers. However my main critics is the lengthy programming exercises that were badly described. I spend a significant amount of time just to try to figure out what to do. It required reading of papers and chapters of books to understand the algorithms. I missed the formulas and methods in the lecture notes. A little bit more technical details in selected topics (that are used today) and less of a broad overview of historical approaches would earn 5 stars from me.