Chevron Left
Zurück zu Machine Learning: Clustering & Retrieval

Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Clustering & Retrieval von University of Washington

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

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.

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.

Filtern nach:

1 - 25 von 289 Bewertungen für Machine Learning: Clustering & Retrieval

von Ernie M

Sep 25, 2017

I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the general public (not even by paying) and then all the knowledge devalued. I wish I had known this and I would have enrolled on a different specialization. The creators still give you the possibility of using numpy, scikit learn and pandas but I had already done a lot with GraphLab create. The time I invested on my nights after work became a waste. I was trying to convince the company I worked for to buy licenses for GraphLab create.

Coursera should not allow folks to create courses that promote a private license course because it would make people waste their time and money if they decide to privatize the software.

Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.

Teaching style: Carlos was good, Emily is not very clear and loses focus of the topics and often rambles. She seems very knowledgeable but she lacks clarity of exposition when compared to Carlos or Andrew Ng.

von Eugene K

Feb 10, 2017

If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework. Despite the good lectures, the assignments don't help you develop the knowledge required for ML developer role.

Taking in consideration the permanent postponing the courses delivery, from summer 2016 to summer 2017, finally the most interesting part of the specialization was cancelled. I'm completely disappointed with the specialization learning expirience.

von akashkr1498

Jul 08, 2019

I like the course very much. I learnt so many advance concept and real life implementation.. but slightly disappointed by the quiz question please be specific what you wanted us to answer. looking forward for SVM and deep learning material.

von Tsz W K

May 15, 2017

The materials presented are excellent with well prepared skeleton codes for all ML models. Comparing this course to its three preceding ones, this course is more challenging both conceptually and computationally. The slight drawback is that, because of the highly technical nature of the last three weeks' materials, there isn't enough guidance about how one may construct the ML algorithms from scratch, that is, learners with less experience in computing will, more or less, have to accept the sample codes with little confidence about how to (re)write such codes in the first place.

As a result, I believe that learners with more experience in algorithms and data structure (or learners who proceed to learn more about this area) are likely to gain more from this course for at least two reasons: i) they are more comfortable with the complicated ML algorithms; ii) they can improve the algorithms to speed up the estimation time (some advanced techniques are quite computationally expensive, say over 20 minutes).

In general, I have learnt very much from this course and love it.

von Hamel H

Aug 07, 2016

This course rushed through the material at the end.

von André F d A F C

Jul 25, 2016

I found this Course less well prepared than the previous 3 modules. Misleading hints in the assignments, code errors, etc... Also, I found the amount of work required higher, which is not in itself a bad thing, just a bit unexpected.

von James F

Aug 10, 2016

The course, and indeed the whole specialization, was advertised as not requiring the Graphlab Create toolkit. This is untrue, as the final programming assignment does require it. The general dependence on SFrame is understandable since it is open source, but requiring any interaction with a licensed product (even if temporary and research licenses are available) greatly negatively impacted my experience in this course.

von Martin R

Dec 12, 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 Manoj K

Nov 26, 2018

session was very helpful & full with relevant contents

von Xue

Dec 19, 2018

Great but hard~!

von PRAVEEN R U

Dec 27, 2018

Nice content and well made presentations.

von KAI N

Jan 03, 2019

Excellent course with great and reachable explanation

von Jay K S

Jan 05, 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 Edwin P

Feb 15, 2019

Excellent, good contribution to the technical and practical knowledge ML

von Jialie ( Y

Feb 21, 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 Sathiraju E

Mar 03, 2019

Very nice course. Things are well explained, however some concepts could be expanded more.

von Zhongkai M

Feb 12, 2019

Great assignments : )

von Vikash S N

Feb 03, 2019

It was great but I was also interested to implement the solutions with pyspark...though I did it eventually. Thank you!

von Akash G

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

von Luiz C

Jul 10, 2018

An excellent Course. I was first doubtful about my interest for this Course, having already read mover Clustering. But this Course surprised me: it more than delivered, presented advanced concepts used in real world, always in a clear and engaging approach. The Tutor of this Course is a key component of my appreciation of this Course. To sum up, great content, great materials (Excellement videos, excellent slides, great assignemtns and quizzes - not a single bug!!!) in a very pleasant and engaging presentation. One work... THANK YOU

von Yang X

Nov 15, 2017

Thank you Emily and Carlos! You guys are amazing!!!

von IDOWU H A

May 20, 2018

Excellent - Goo

von Foo C S G

Mar 04, 2018

Tough slog, but well designed

von Sean S

Apr 03, 2018

Another great course and sadly the last of this specialization. I found the material for this course to be the most challenging yet, specifically the LDA module. The programming assignments were all very manageable thanks to graphlab and the very explicit hints provided but I do not feel like I reached the same level of understanding as I did for the previous courses in the specialization. I have grown to enjoy using graphlab and would likely use it going forward if not for the licensing. I am very disappointed that the remaining courses will not be offered and am now in search for another great machine learning resource.

von JOSE R

Nov 18, 2017

Very well explained. The LDA was difficult to learn. Thanks.