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4.6
1,762 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....

Top-Bewertungen

JM

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.

BK

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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26 - 50 von 289 Bewertungen für Machine Learning: Clustering & Retrieval

von Renato R R

Jan 05, 2018

This course is amazing. I could really work on real world problems. It is a pity that we are not going to have the following courses:

Recommender Systems & Dimensionality Reduction

Machine Learning Capstone: An Intelligent Application with Deep Learning

Thank you Emily and Carlos.

von Swati D

May 02, 2018

This course is a very structured and progressive learning. It is an advantage , if we know python . However, one can still manage and explore Machine learning and Deep learning concept of AI. The case study and real life approach keeps your quest on. This is a great initiative and gives us an opportunity to be future ready while at job. Many notion went wrong about AI and the chapters are well designed to keep us engaged while we

von Shuang D

Jun 29, 2018

advanced knowledge on ML, great course

von Brian N

May 20, 2018

This course is exciting

von Suresh K P

Dec 21, 2017

Interesting, lot of Algorithms and methods to use iin upcoming projects and real time applications

von Alessandro B

Dec 15, 2017

very useful and structured

von Ruchi S

Jan 24, 2018

E

von Dongliang Z

Mar 22, 2018

I enjoyed this course. This specialization is very good for machine learning beginner. Look forward to the next course anyway.

von Phil B

Feb 13, 2018

Again the lecturing style and course content were excellent, allowing us to write fairly complex functions to implement our own algorithms from scratch but also using pre-built functions when necessary to allow us to explore the effects of different variables. The benefits and costs of the different types of clustering were clearly stated. It's a shame that the specialization stops here, as a capstone project with the same quality of these 4 courses would really provide the students with something they can show off to potential employers. The problem most students will have when coming off this specialization is how to implement and deploy your own model into a service like a website.

von Alvaro M M

Jan 07, 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 Suneel M

May 09, 2018

E

von Manuel T F

Sep 24, 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 Fernando B

Feb 21, 2017

Best Course on ML yet on the Web

von Tripat S

Aug 07, 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 Christopher A

Oct 01, 2016

The best course in the specialization thus far. Very rich and wide ranging, perfect for the motivated part-time learner who wants to be challenged and have ample reason to revisit the material. I only wish this course had been longer, perhaps shortening the classification course to make room.

von Alexandre

Oct 23, 2016

ok

von gaston F

Oct 11, 2016

This course was awesome as all the previous courses, I'm waiting to the next course and the capstone

von Manuel S

Oct 01, 2016

Amazing course, really helpful, as a ML researcher you need this kind of foundation

von Liling T

Aug 15, 2016

Emily Fox did a great job in explaining tough concepts with simple explanation of the components in the formulas!!

It's a little tough to get through the materials though, it's the 4th course in University of Washington's machine learning specialization afterall =)

von Shaowei P

Aug 08, 2016

very good course but the last few topics could be improved with better assignments that could be broken down into smaller sub assignments

von Vaidas A

May 29, 2017

This course was great! With good code examples and algorithm applications and also intuition!

It's a shame that we couldn't finish planned courses due to busy schedules of instructors as I was really looking forward to the capstone project!

von Igor D

Aug 21, 2016

This was AWESOME!

von Mostafa A M

Aug 28, 2016

Fantastic course as usual

von Usman

Nov 28, 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 Antonio P L

Oct 03, 2016

Excellent course.