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Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Clustering & Retrieval von University of Washington

1,766 Bewertungen
301 Bewertungen

Über den Kurs

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....



Jan 17, 2017

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.


Aug 25, 2016

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.

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176 - 200 von 290 Bewertungen für Machine Learning: Clustering & Retrieval

von Samuel d Z

Jul 18, 2017

Brilliant, anyone interested to get proficient in Data Science and Machine Learning need to take this course. It is well structured and although very challenging at times, it is always possible to get the right result.

von Mark W

Aug 12, 2017

Excellent course. Emily and Carlos are fantastic teachers and have clearly put in a huge amount of effort in makign a great course. Thanks guys!

von Pankaj K J

Oct 28, 2017

A great course to understand clustering as well as text mining. Lectures on KDD and LSH are equally important to understand and implement these algo . Many thanks

von Jonathan H

Jul 01, 2017

Emily is great! Excellent course that covers a ton of material!!!

von Sally M

Jan 02, 2017

Great course but hard going at times for those of us without a strong maths background. The assignments took me a long time to complete and I think I'll have to revisit some areas as I become more familiar with them to really get the full benefit.

von Cuiqing L

Nov 05, 2016


von Daniel W

Dec 23, 2016

Excellent course

von Kate S

Jun 30, 2017

I really enjoyed and learned a lot from this class. It made me interested to go out and learn other machine learning methods which are derived from what was taught.

von Nguyen D P

Feb 08, 2018

This is very useful course that can help me more to understand and resolve the complicated issue in the real world. I want to thank Coursera e-learning and the Washinton University for created this course that help people in the developing country like me can access the new technical.

von Mark h

Aug 08, 2017

Very helpful

von Feiwen C ( C I

Jun 02, 2017

Good course. Learned a lot from it. Thanks!

von Prasant K S

Dec 21, 2016

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

von 陈佳艺

May 17, 2017

sometimes difficult,but import so many useful knowledge

von Matheus F

Aug 11, 2018

Excelent course! Very helpful!

von Arun K P

Oct 27, 2018

Very useful and informative .It help and provide confidence to the job more effectively. Thanks for the help and good cour

von Yugandhar D

Oct 29, 2018

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

von Fahad S

Nov 03, 2018

Emily ross is an amazing instructor. The course introduces many complex topics and presents them intuitively.

von Nagendra K M R

Nov 11, 2018



Nov 11, 2018


von Susree S M

Nov 14, 2018

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

von Juan F H

Nov 15, 2018

The teacher is awesome

von Somu P

Nov 17, 2018

Excellent course, which gives you all you need to learn about machine learning. Concepts and hands on practical ex

von Feng G

Aug 09, 2018

Emily is an extremely awesome instructor. For those who have some background in statistics, biostats , econometrics and math and want to study machine learning by themselves, these modules can be an outline that introduce basic topics in machine learning.

I'm looking forward to see more advanced courses in these topics from Carlos and Emily.

von Hans H

Jul 27, 2018

Amazing course, I´ve learned so much stuff that I can use in my job.

von yoon s w

Jul 26, 2018

good to learn what is clustering and retrieval