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Machine Learning: Clustering & Retrieval(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Clustering & Retrieval 학습자 리뷰 및 피드백

4.6
별점
2,299개의 평가

강좌 소개

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....

최상위 리뷰

JM

2017년 1월 16일

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.

BK

2016년 8월 24일

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.

필터링 기준:

Machine Learning: Clustering & Retrieval의 381개 리뷰 중 101~125

교육 기관: Cristian A G F

2020년 12월 30일

In general, all of the courses were awesome because of the methodology used by the professors. Thank you!

교육 기관: Prasant K S

2016년 12월 20일

It is explained in simple and lucid language by expert Emily and codes illustrated by Carlos. Go for it.

교육 기관: João S

2016년 8월 7일

Great course. Well packed, well explained, nice practical examples, good all around MOOC with of info.

교육 기관: Geoff B

2016년 7월 14일

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

교육 기관: Susree S M

2018년 11월 14일

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

교육 기관: Viktor K

2021년 5월 14일

The explanation was really good, and now, I find it so simple to use Machine Learning. Thanks a lot!

교육 기관: Gaston F

2016년 10월 10일

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

교육 기관: Sayan B

2019년 12월 5일

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

교육 기관: Suresh K P

2017년 12월 21일

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

교육 기관: Gillian P

2017년 7월 23일

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

교육 기관: Neemesh J

2019년 10월 28일

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

교육 기관: Etienne V

2017년 2월 19일

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

교육 기관: Aakash S

2019년 6월 18일

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

교육 기관: Renato R S

2016년 8월 27일

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

교육 기관: Noor A K

2020년 7월 4일

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

Please unenroll me from this course

교육 기관: Yugandhar D

2018년 10월 29일

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

교육 기관: Sathiraju E

2019년 3월 3일

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

교육 기관: Moises V

2016년 10월 30일

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

교육 기관: Yi W

2016년 9월 27일

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

교육 기관: Priyanshu R S

2020년 11월 27일

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

교육 기관: austin

2017년 8월 9일

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

교육 기관: Val V

2021년 4월 8일

Very well presented. I've throughly enjoyed the course and feel like I've learned a ton.

교육 기관: B P S

2020년 5월 27일

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

교육 기관: Venkateshwaralu S

2016년 8월 7일

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

교육 기관: Jifu Z

2016년 7월 22일

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