Voltar para Machine Learning: Clustering & Retrieval

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2,264 classificações

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386 avaliações

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 de Jan de 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 de Ago de 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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por Kayvan S

•15 de Fev de 2018

Great course but I think the workload could be spread across the weeks more. Also, I had a lot of trouble with the sklearn toolkit (probably due to installation issues.).

por Piotr Ś

•15 de Fev de 2017

Dependence on GraphLab technology is a big minus. The lectures are poorly balanced in terms of difficulty. Apart from that - interesting course, I'm glad I took it.

por Aayush G

•10 de Nov de 2016

This specific course traded off depth and detail for breadth of topics. Too many ideas were quickly described and not really built up to my liking.

por pavan b

•29 de Jul de 2019

Few concepts were covered in hurry with lot of concepts described abruptly. It took a while for me to do research about those topics to catchup.

por Alexander S

•7 de Ago de 2016

great course, but module 4 lacks a bit in structure. hard to follow. without the forum, it would not be possible to make it in time.

por J N B P

•16 de Out de 2020

If you are familiar with the fundamental concepts of Clustering, unsupervised learning this course will help you move forward.

por Baubak G

•11 de Jul de 2018

Need more details in the coarse. I think many of the topics need more working on, and are not sufficiently described.

por Valentina S

•16 de Ago de 2016

Interesting content but explanations are less clear with respect to the other courses of the ML Specialization

por Michael L

•18 de Mar de 2017

slightly repetitive of classification course with no real use-case value except lots of math..

por Rishabh s

•13 de Ago de 2020

explained with pretty much good effort but can be improved if they focus on coding as well

por Volker H

•18 de Jul de 2016

please rework in particular week 5, part 2

por Nicolas I

•31 de Ago de 2016

A little too superficial and hand waving.

por Harsh A

•18 de Jul de 2018

Too little "case-study" approach

por Stuart L

•30 de Ago de 2016

the homework is getting easy

por Rohan L

•29 de Ago de 2020

I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.

You will learn about Data Science and Machine Learning, but not much about Python.

The course is pretty much abandoned and outdated. Sframes and Turicreate packages (instructor's creations) are used instead of more universal packages. Installation in the beginning took some time and research. Many of the assignments have errors and bugs in the code that have not been updated. Forum assistance is abysmal for clarification or deeper questions. Many links are dead.

There are many times in the lectures where the instructors are writing several sentences in their handwriting on their notes instead of having the text ready to appear.

I would suggest using this course and series as a supplement to other information one as learned, not as an introduction for initial understanding. I found myself frustrated too many times.

por Ryan M

•16 de Set de 2020

While the topics covered in this course are arguably more complex than those in other courses in the Machine Learning specialization, I felt that the instructor did not do a good job covering the complicated material. There is a lot of statistics in this course, and the instructor seemed to assume that students would know many of the statistical terms and concepts without explaining them. I had to use a ton of outside resources to augment the videos presented as part of this course.

Furthermore, many of the assignments seemed to have errors in them. For the last programming assignment, there is no correct answer for at least one of the questions. Since there is no support from instructional staff or Coursera, this is a bit frustrating. Luckily you could pass the quiz without even answering that specific question.

por Pan W

•3 de Jan de 2017

I give 5 star for the teacher, really approach having such a well-organized teaching material.

I also give -1 star for the homework assignment and its (almost) GraphLab only approach. Yes, it mentioned "alternative" approach (which is much more popular than GraphLab), but there are many bugs & trivial difficulties to get it through. With scikit-learn as a great open source package, the only reason (I suspect) to choose GraphLab is commercial purpose. For me, if the homework assignment is only instructed properly for loading data into Pandas, I can finish each programming assignment within 1 hour for sure using scikit learn; but now, it takes 30 minutes and I still cannot load the data correctly. I like to get a certificate, but it is not necessary and spending too much time is a waste on my time.

por ryan

•23 de Set de 2017

requires use of a programming library from a company that was sold and is unmaintained. Challenging to build the environment to run the homework code on my mac pro. An AMI is provided so you can try to do the assignments on a prebuilt machine. Anyway I've found the class quite a hassle.

por SHAHAPURKAR S M

•19 de Jun de 2020

Course content is good but assignments are too lengthy and directions are not clear. Also, no support has been provided for non TuriCreate users. Students face a hard time in figuring out the Scikit-Learn implementations of the functions provided in the notebooks.

por Karl S

•11 de Out de 2016

For me, this course was disappointing. Here is why: First, the level, at which the course material is presented, is very low. It might be freshman level, but certainly not more. There are many buzzwords but no real explanations. The programming assignments are only doable because most of the work has been done by the people designing the assignments. There is very little left for the students. Furthermore, the procedures, that are already given, are not very well documented. Hence, a lot of guess work is required to figure out how things should work. Furthermore, little effort has been spent to structure the procedures that are already given. Altogether, this makes doing the programming assignments very unsatisfying.

Finally, the professor presenting the materials does not take part in the discussion forums. Contrary to other courses that I have attended at Coursera, this time the discussion forum was no help at all.

por Ricardo Y N

•17 de Ago de 2020

some exercises only works if you have a Linux or MacOS, you could not resolve them if you have windows, the explanations are ok, I've never had an anwswer for my questions or issues on hte forum

por Kripakaran R

•12 de Nov de 2018

I wish week4 and week5 were better. It felt so rushed, where most of the important things were covered.

por Andreas

•4 de Jan de 2017

This specialization is delayed for months now - very annoying! Don't give them money!

por Adrien L

•2 de Fev de 2017

No good without the missing course and capstone projects

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