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學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

4.6
2,193 個評分
376 條評論

課程概述

Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python....

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BK
2016年8月24日

excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.

JM
2017年1月16日

Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.

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101 - Machine Learning: Clustering & Retrieval 的 125 個評論(共 364 個)

創建者 Geoff B

2016年7月14日

Another great introduction. The assignments are notably a little bit harder than the previous courses.

創建者 Susree S M

2018年11月14日

This course is very useful to know about the concepts of machine learning and do hands-on activities.

創建者 Gaston F

2016年10月10日

This course was awesome as all the previous courses, I'm waiting to the next course and the capstone

創建者 Sayan B

2019年12月5日

This is actually a tremendous course. Assignments are not so good, but the materials are wonderful.

創建者 Suresh K P

2017年12月21日

Interesting, lot of Algorithms and methods to use iin upcoming projects and real time applications

創建者 Gillian P

2017年7月23日

A very good course with two engaging and sympathetic teachers. Would love to see the next courses

創建者 Neemesh J

2019年10月28日

Coursera is the best learning app. I am really thankful for getting very good training lectures.

創建者 Etienne V

2017年2月19日

Excellent course! Thanks a lot for the effort in compiling this course... I really enjoyed it!

創建者 Aakash S

2019年6月18日

Such a clear explanation of topics of clustering. Without doubt one of the best in business.

創建者 Renato R S

2016年8月27日

A perfect and balanced introduction to the subjects, adding theory and practice beautifully.

創建者 Noor A K

2020年7月4日

I don't know that there was some prerequisite of python.

Please unenroll me from this course

創建者 Yugandhar D

2018年10月29日

Excellent course on clustering and retreival. The assignments were thorough and productive.

創建者 Sathiraju E

2019年3月3日

Very nice course. Things are well explained, however some concepts could be expanded more.

創建者 Moises V

2016年10月30日

I loved this course. then content is designed to acquire strong foundations in clustering.

創建者 Yi W

2016年9月27日

As someone very keen on math, more math background as optimal video would be more helpful.

創建者 Priyanshu R S

2020年11月27日

These are amazing courses. A big big thanks to the team for making me more knowledgeable.

創建者 austin

2017年8月9日

Awesome course. Very detailed and thorough, and the bonus sections are really useful too.

創建者 B P S

2020年5月27日

It helped me to give concepts of machine learning and clustering techniques and modules.

創建者 Venkateshwaralu

2016年8月7日

Sets a new benchmark for the specialization !!! A great offering on Machine Learning :)

創建者 Jifu Z

2016年7月22日

Good class, But it would be much better if the quiz is open to those who doesn't pay.

創建者 Robi s

2017年9月17日

Great instruction, great course, and provide information I used directly in my work.

創建者 Russell H

2016年10月9日

Detailed coverage of several approaches to clustering. Not easy but learned a lot.

創建者 Manuel S

2016年10月1日

Amazing course, really helpful, as a ML researcher you need this kind of foundation

創建者 Shuyi C

2019年8月19日

I think it is easy to understand and good to practice. Nice entry level course!

創建者 Anshumaan K P

2020年11月11日

Good Specialization. But some assignments make it more cool i.e, not here :)