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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

2,299 évaluations
393 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


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.


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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176 - 200 sur 381 Avis pour Machine Learning: Clustering & Retrieval

par Arash A

5 janv. 2017

Enjoyed the course and learned a lot. Amazing!

par David F

21 oct. 2016

Excellent course - and of great practical use.

par Nitish V

29 oct. 2017

The Course is good . Covered lots of topics .

par Rahul G

13 juin 2017

Good course but Week 5 LDA needs improvement.

par Stanislav B

15 avr. 2020

one of the best courses Ive seen on coursera

par Jason G

9 août 2017

Harder than the previous ones, but enjoyable

par Krisda L

19 juil. 2017

Good overview of a lot of useful techniques.

par felix a f a

8 août 2016

less complex exercises to check and validate

par Feiwen C ( C I

1 juin 2017

Good course. Learned a lot from it. Thanks!

par Kan C Y

19 mars 2017

Really a good course, succinct and concise.

par parag_verma

7 janv. 2020

Thanks to the entire team of this course.


27 déc. 2018

Nice content and well made presentations.

par Miao J

1 juil. 2016

Another great course. Strongly recommend!

par Veer A S

23 mars 2018

Very informative and interesting course.

par Ted T

29 juil. 2017

Best ML course ever. Easy to understand!

par Dmitri T

4 déc. 2016

Great course! Very simple and practical.

par Veera K R

6 avr. 2020

Very informative and Clearly explained.

par Snehotosh B

3 déc. 2016

Best course available till date as MooC

par kripa s

30 avr. 2019

One of the best training experience...

par Shuang D

29 juin 2018

advanced knowledge on ML, great course

par Garvish

14 juin 2017

Great Information and organised course


21 sept. 2020

Everything was very clearly explained

par Ce J

26 juin 2017

well organized and easy to understand

par 李紹弘

22 août 2017

This course provides concise course.

par Nada M

11 juin 2017

Thank you! I loved all your classes.