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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

4.6
étoiles
2,209 évaluations
380 avis

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

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.

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.

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301 - 325 sur 368 Avis pour Machine Learning: Clustering & Retrieval

par Patrick A

30 sept. 2020

Very interesting but the LDA and Gibbs sampling for LDA concepts were not easy to understand. May be we could find a simpler way to explain them. Nonetheless, with all these concepts and practical case studies learned in this specialization, we can start solving real world problems. Thanks once more!

par Maxence L

15 déc. 2016

Comme les précédents dans de cette spécialisation, ce cours est très riche et donne les clés pour utiliser des outils complexes et puissants. Toutefois, un peu plus de détails sur certains aspects, notamment théoriques, pourraient améliorer la compréhension de certains chapitres plus techniques.

par Alexandru I

25 sept. 2020

I think all the advanced concepts presented in this course were a little bit rushed. Maybe I would have been better had we received more information, meaning more lecture materials. But over all, I feel really good about choosing this Specialization.

par Steve S

26 août 2016

Like all the courses in this specialization so far, the material has been good. The reason for only 4 stars rather than 5 is the difficulty in getting questions answered in a timely manner. There don't seem to be any active mentors for this class.

par Martin B

11 avr. 2019

Greatly enjoyed it. As with the other courses in this specialization the discussion of the subjects is impeccable, especially if you've taken some preparatory mathematics courses. The reliance on Graphlab Create is a drag though.

par Raj

27 mai 2017

Clustering & Retrieval was a lot tougher compared to courses on regression & classification because the match concepts behind this course were too complex. Nevertheless Emily tried to make this course as intuitive as possible

par Abhishek S

10 févr. 2018

Till Expectation Maximization, the learning is tremendous. However, once past that, everything would feel incomplete since most assignments are spoon fed after that. Rating it four stars because of initial lectures.

par Siva J

26 févr. 2017

Good and deep dive into ML!

Absolutely disappointed that the course was delayed and the promise to take it through Course 5 and Capstone Project didn't come through.

Not at all happy with that!!

par Srinivas C

7 janv. 2019

This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.

par Ahmad A

31 mars 2017

This course was my first encounter with Machine Learning! The course gave me a good understanding of the different ML algorithms used in clustering and retrieval of data!

par Andrey

9 avr. 2017

Overall is great. The LDA and Dendrograms lack quality/specificity and depth of the previous topics. So sad the Specialization collapsed at 4 courses instead of 6.

par Marco A d S M

20 oct. 2017

As explicações poderiam ser um pouco mais detalhadas neste curto. Tive certa dificuldade em alguns conceitos apresentados, mais do que nos outros cursos.

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 N A

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 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 Stéphane 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.