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381 Bewertungen

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....

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.

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.

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von Ferenc F P

•25. Jan. 2018

Very good course. Even though I had some machine learning background, this course provided new insights and new algorithms, like KDTree, Locally Sensitive Hashing, Latent Dirichlet Allocation, and mixture of Gaussians. the only drawback is that with scikitlearn, not always you get the same results as with GraphLab. Thanks for the instructors for this great specialization.

von Stephen G

•13. Aug. 2016

Yet another really excellent course in this series - the best online course I have ever taken. I really appreciate the fairly high level on which it is taught, and the speed with which they go through the material - it is not here to entertain or waste time, but to get straight to the point - what can I do, and how do I use open source tools to do it?

von Feng G

•8. Aug. 2018

Emily is an extremely awesome instructor. For those who have some background in statistics, biostats , econometrics and math and want to study machine learning by themselves, these modules can be an outline that introduce basic topics in machine learning.

I'm looking forward to see more advanced courses in these topics from Carlos and Emily.

von Miguel P

•13. Juli 2016

I loved the previous 3 courses and what I saw in this course so far seems pretty interesting. I'm really sad that Coursera decided to block access to assignments for not paying users. I really wanted to continue with the specialization but I already purchased another specialization, so I'm going to have to put this course on hold for a now.

von Alessio D M

•1. Aug. 2016

Very nice course, and a great grasp on clustering techniques. If I could just suggest something to improve, it would be the section on LDA and Gibbs: it's very high level and it would be really nice to have some more technical insights on those techniques (perhaps with optional sections, as for other topics).

von Muhammad W K

•22. Okt. 2019

A great course to get the grass-root level understanding of Clustering and Retrieval tasks and going beyond to Unsupervised learning and the core concepts related to it. And starting from the basics all the way to some of the advanced algorithms and models used in the world today. It is simply awesome!

von Christopher A

•1. Okt. 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 krishna k s

•20. Apr. 2018

This is very nice and interesting course. It gives practical application of machine learning application. I would consider this course as applied machine learning course as it lacks mathematical intuition. Nevertheless, course it great and cover major points in the machine learning field.

von Muhammad H A

•13. Aug. 2016

I used to run into a wall at work trying to train models with recursive partitioning or neural nets because of the long time they took to train for high dimensional data. These clustering techniques are an immense help.

Awesome course, with a brilliant instructor and brilliant assignments.

von Phuong N

•7. Feb. 2018

This is very useful course that can help me more to understand and resolve the complicated issue in the real world. I want to thank Coursera e-learning and the Washinton University for created this course that help people in the developing country like me can access the new technical.

von Renato R R

•4. Jan. 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 Liling T

•15. Aug. 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 Martin R

•12. Dez. 2018

I'd bring the last summary video at the beginning (the great summary of all weeks of the course). This would outline the course evolution in advance and give guidance what's ahead. IMHO this would help to not get lost when drill down in a single section.

von Kumiko K

•14. Aug. 2016

This course started off easy, and became challenging in the last 3 weeks. But a lot of details were covered in the slides and also the forum helped deepen my understanding of the material, and I was able to get through the course. I enjoyed the course!

von Sally M

•2. Jan. 2017

Great course but hard going at times for those of us without a strong maths background. The assignments took me a long time to complete and I think I'll have to revisit some areas as I become more familiar with them to really get the full benefit.

von Michael B

•12. Juli 2016

Not for the faint of heart but this course does a really good job of explaining clustering (and retrieval) of images and text. It includes several programming assignments which can be tackled with minimal programming experience if one perseveres.

von Vaidas A

•29. Mai 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 Bhavesh G

•12. Mai 2020

During this course, I learned many new things like 1-NN, clustering using K-means, Gaussian mixture models etc. I would like to suggest this course to all those who want to learn about machine learning and make a career in data science.

von Aditi R

•25. Dez. 2016

This course contain many advance topic which was covered in fast pace by the professor special end lectures. This course contain very important topics of Machine learning could have given more time in explaining things. Thanks professor

von Marcio R

•2. Sep. 2016

Following the overall quality of this Specialization, this course was excellent. From the content, to the assesments, material and teachers. This course is a really good starting point to become an expert in Machine Learning techniques.

von Jafed E G

•6. Juli 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

von Mohamed A H

•19. Juni 2019

A very rich of useful materials course. The instructor has a fantastic explanation ability. The course is pretty organized and the assignments solidifies the understanding of the concepts well.

It was an amazing experience!

von Alfred D

•24. März 2018

KD trees, LSH along with LDH were some real deep techniques I've learnt and benefitted.

Thanks a ton to Emily and Carlos , you guys are amazing teachers for such a complex subject as ML and the algorithms it consists of .

von Atul A

•25. Aug. 2017

Great course. Different from earlier courses in the Specialization, this course is quite challenging in both theory and practice. However, it is super important, as clustering is all around us in real-world data.

Worth it!

von Samuel d Z

•18. Juli 2017

Brilliant, anyone interested to get proficient in Data Science and Machine Learning need to take this course. It is well structured and although very challenging at times, it is always possible to get the right result.

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