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Retour à Machine Learning: Clustering & Retrieval

Avis et commentaires pour d'étudiants pour Machine Learning: Clustering & Retrieval par Université de Washington

4.7
étoiles
2,310 évaluations

À propos du cours

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....

Meilleurs avis

JM

16 janv. 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.

BK

24 août 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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326 - 350 sur 383 Avis pour Machine Learning: Clustering & Retrieval

par Keith D

19 juin 2017

I'm disappointed that courses 5 and 6 of the specialization were cancelled. The cancelled capstone was why I purchased this specialization package.

par Manish G

15 janv. 2020

This topic was very deep and I learnt many complex algos. Would suggest to have some more examples for the algorithms presented in this modules.

par Marcin W

9 août 2016

Very good course. Too long interval between modules make hard for non-Python developers. Easy to forget some of the Python structures.

par Farrukh

17 mars 2017

Great course on machine learning, however, left us in middle of learning, Recommender System + Deep Learning Capstone is missing

par Iurii S

26 nov. 2017

Good course overall.

Starting to get more on the side of being mostly implemented and only needing to insert a line or two.

par Ayush K G

24 févr. 2018

At some topics more explaination (eg. Map reduce and LDA) needed although as a whole it is good course.

par Big O

21 déc. 2018

More detail on theory behind LDA and HMMs would have been useful. Otherwise, another brilliant course!

par Evan

10 oct. 2021

Although the concept is good, datasets and code in assignments are modified and give strange result

par Michael B

4 sept. 2016

Good survey of the material, but assignments are superficial and don't test thorough understanding.

par Peter

26 juil. 2016

Great course. Some week were tough others too easy, but general a very interesting course.

par Hristo V

31 août 2016

The last weeks, we went through the material a little bit too fast.

par Iñaki D R

14 sept. 2020

Excellent course with very detailed explanations and assignments

par stephane d

20 avr. 2021

Great Course!

Too bad we don't have the last 2 courses....

par Andrey T

11 août 2016

I did not understand LDA from the course materials.

par Charan S

30 juil. 2017

Nice intuitive course with lots of understanding.

par Jack B

3 mars 2017

Should use pandas instead of Graph Lab Create

par Mehul P

10 sept. 2017

Nice explanation on clustering methods.

par Adwait B

26 janv. 2018

Great Course! Tough topics well taught

par Jayesh N J

25 janv. 2022

the course was just awesome

par Pascal U E

20 août 2016

Great course like the others

par Dony A

5 janv. 2017

awesome clustering course

par Galen S

8 mai 2017

I liked the slides.

par Koen O

27 août 2017

I liked it a lot

par VYSHNAVI P

13 déc. 2021

good

par Dhanasekar S

24 déc. 2016

I have enrolled myself in the other Machine Learning courses offered by Uwash , but have to say this was not properly organized. I had got my certificates for the other courses easily , not because the contents was easy , but was easily understandable and well organized and there was a great sense of satisfaction after getting the certificate because of the knowledge gained.But unfortunately for this course , especially the week 4 and week 5 was lengthy and not up to the point and the quizzes were hence not seem to be related. So got my certificate after a bit of struggle.

I'm planning to see other online materials related to week 4 and week 5 , as couldn't completely understand from this one. If you can modify those two weeks, it would be great. I hope you continue the great work of illuminating millions of young people's interests through your great courses and organization. Thank you from the bottom of my heart.