Chevron Left
Zurück zu Machine Learning: Clustering & Retrieval

Learner Reviews & Feedback for Machine Learning: Clustering & Retrieval by University of Washington

4.6
1,683 Bewertungen
290 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 of 280 Reviews for 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 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 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 Yufeng X

Jul 09, 2019

It opened the door to more advanced techniques.

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 Jafed E

Jul 06, 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 Christopher M

Jul 01, 2019

Doesn't go quite as deep into the details as some of the other Machine Learning courses from the University of Washington do. Overall though, the course covers a LOT of ground. and provides exposure to many different topics.

I would have liked to have seen an Optional section on the derivation of some of the math that we were given functions for on the Expectation Maximization section. The models in the hierarchical clustering section take longer to fit than is necessary for a course like this (more than 40 times as long as the instructions say it should take), maybe a larger tolerance for convergence should be specified?

von Mohamed A H

Jun 20, 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 Aakash S

Jun 19, 2019

Such a clear explanation of topics of clustering. Without doubt one of the best in business.

von Dimitrios Z

Jun 08, 2019

It has intresting theory but I believe the exercises need to be improvised. Maybe using Jupyter online and guiding the student to write code to solve the problems. In conclusion, I understood the basic theory but mostly that.

von Dohyoung C

Jun 04, 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 YASHKUMAR R T

May 31, 2019

Awesome course to understand the concept behind Gaussian Mixture model.

von Dennis S

May 19, 2019

Amazing course. The Instructors did an awesome job of preparing and presenting the material.

I think there is no better and more approachable in-depth course out there. Thank you so much!

von kripa s

Apr 30, 2019

One of the best training experience...

von Martin B

Apr 11, 2019

Greatly enjoyed it. As with the other courses in this specialization the discussion of the subjects is impeccable, especially if you've taken some preparatory mathematics courses. The reliance on Graphlab Create is a drag though.

von Akash G

Mar 11, 2019

Machine Learning: Clustering & Retrieval good and learn easily

von Sathiraju E

Mar 03, 2019

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

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 Edwin P

Feb 15, 2019

Excellent, good contribution to the technical and practical knowledge ML

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 Srinivas C

Jan 07, 2019

This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.

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 KAI N

Jan 03, 2019

Excellent course with great and reachable explanation

von PRAVEEN R U

Dec 27, 2018

Nice content and well made presentations.