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373 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....

Top-Bewertungen

BK
24. Aug. 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. Jan. 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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51 - 75 von 361 Bewertungen für Machine Learning: Clustering & Retrieval

von ANGELICA D C

30. Sep. 2020

Al inicio del curso, toda la programación es fácil, pero a medida que avanza se va complicando. Sugeriría que pusieran más notas en el código para entender las operaciones más complejas.

von Pankaj K

8. Sep. 2017

Great course, all the explanations are so good and well explained in the slides. Programming assignments are pretty challenging, but give really good insight into the algorithms!.

Thanks!

von Li Y S

30. Okt. 2016

I really learn a lot in this course, although the materials are very difficult at first read, but Emily's explanation were clear and I would be able to get the idea after a few review.

von Usman

28. Nov. 2016

This was another great course. I hope that the instructors indulge in a little bit more theory. Anyway it was a magnificent course. Hope the coming courses are as good as this one.

von Diogo J A P

25. Jan. 2017

The material is complex and challenging, but the teaching procedure is carefully thought out in a way that you quickly get it, giving you a great sense of accomplishment.

von Subba R V O

30. Jan. 2020

A great course, well organized and delivered with detailed info and examples. The quiz and the programming assignments are good and help in applying the course attended.

von Nelson P

16. Dez. 2019

Excellent course. I liked the material and the assignments are great to consolidate the learning. I really liked the recap videos to solidify even more what I learned.

von Olga K

23. Sep. 2016

Excellent course! Subjects are explained very well! Excellent quizzes that allow understanding of lectures better and excellent (challenging ) programming assignments.

von Illia K

1. Sep. 2020

Everything is clear in the course. A suggestion: for the programming assignments it would be better to write more than just 1 line of code in the proposed functions.

von Kate S

29. Juni 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 Pankaj K J

28. Okt. 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 Alvaro M M

6. Jan. 2018

I liked it a lot. My only problem was to get the GraphLab to work here. Loved the option to download the videos and material before and the content is awesome.

von Ch S

12. Feb. 2020

Excellent Course. This course provides in depth understanding of what's going in the background when an algorithm runs and how we can tune it for our purpose.

von Jay K S

4. Jan. 2019

Excellent course material and fantastic delivery. You guys made this complex learning so simple and interesting . Thanks for all this, keep the good works.

von Dohyoung C

3. Juni 2019

Fascinating course…

LDA is little bit difficult to understand, but K-mean and Mixture models are easy to understand and quite important for clustering..

von Rama K R N R G

9. Sep. 2017

Good presentation of topics. Detailed walk through of few advanced topics covered at the end would have been great. Felt the presentation went too fast.

von David P

4. Aug. 2020

A challenging course!!! It's necessary to fix some compatibility problems with Tury and Windows, because Python 2.7 it's obsolete. I really enjoy it!!!

von Chandrashekar T

11. Okt. 2016

The material covered in this course is immense and gives a deep understanding of several algorithms required to perform unsupervised learning tasks.

von Mohd A

14. Aug. 2016

This is the toughest courses in the specialization so far. But if you manage to complete it, you'll have some really advance skills under your belt.

von Jialie ( Y

21. Feb. 2019

The course is really helpful, though it would be better for teacher to illustrate the concepts by using examples, instead of abstract terminologies

von Mark W

12. Aug. 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 Manuel T F

24. Sep. 2017

Since I took the courses 1, 2 and 3 of this series, I really enjoyed this fourth part a lot!

Now I'm really looking forward to do some clustering!

von Brandon H

14. Dez. 2016

This was probably the most challenging course of them all, I thoroughly enjoyed it! Looking forward to dimensionality reduction and the capstone.

von Tripat S

7. Aug. 2016

This is the best course in ML - would recommend it ...the sequence of the courses is the best...the specialization in this ML is a career boost

von sandeep d

20. Aug. 2020

excellent course by Emily and Carlos

I am glad to have this course

it contains clear view regarding clustering and its applications from roots