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學生對 华盛顿大学 提供的 Machine Learning: Clustering & Retrieval 的評價和反饋

4.6
1,766 個評分
301 個審閱

課程概述

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....

熱門審閱

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.

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1 - Machine Learning: Clustering & Retrieval 的 25 個評論(共 290 個)

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 Hamel H

Aug 07, 2016

This course rushed through the material at the end.

創建者 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.

創建者 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.

創建者 Somu P

Nov 17, 2018

Excellent course, which gives you all you need to learn about machine learning. Concepts and hands on practical ex

創建者 Xue

Dec 19, 2018

Great but hard~!

創建者 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.

創建者 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.

創建者 KAI N

Jan 03, 2019

Excellent course with great and reachable explanation

創建者 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!

創建者 Manoj K

Nov 26, 2018

session was very helpful & full with relevant contents

創建者 Nagendra K M R

Nov 11, 2018

G

創建者 VITTE

Nov 11, 2018

Excellent.

創建者 Susree S M

Nov 14, 2018

This course is very useful to know about the concepts of machine learning and do hands-on activities.

創建者 Zhongkai M

Feb 12, 2019

Great assignments : )

創建者 Edwin P

Feb 15, 2019

Excellent, good contribution to the technical and practical knowledge ML

創建者 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

創建者 Sathiraju E

Mar 03, 2019

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

創建者 Akash G

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

創建者 PRAVEEN R U

Dec 27, 2018

Nice content and well made presentations.

創建者 Juan F H

Nov 15, 2018

The teacher is awesome

創建者 Feng G

Aug 09, 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.