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Practical Predictive Analytics: Models and Methods に戻る

ワシントン大学(University of Washington) による Practical Predictive Analytics: Models and Methods の受講者のレビューおよびフィードバック

4.1
290件の評価
55件のレビュー

コースについて

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection...

人気のレビュー

SP

Dec 23, 2016

Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.

KP

Feb 08, 2016

I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .

フィルター:

Practical Predictive Analytics: Models and Methods: 1 - 25 / 53 レビュー

by Yogesh B N

Feb 20, 2019

Nice course

by Anand P

Feb 11, 2019

V

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by Shota M

Feb 24, 2016

Professor Bill Howe gives great reactions to when there are typos on the slides!

by Kevin R

Nov 11, 2015

Very nice assignments and content. You learn a lot when you complete all assignments.

by Daniel A

Nov 23, 2015

Great course!

by Kenneth P

Feb 08, 2016

I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .

by SIEW W L

Jun 06, 2016

A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.

by Chen

Jul 20, 2016

Nive that the course covered a broad range of topics.

And good to get pushed to do some kaggle competition and peer review.

by Tamal R

Feb 17, 2016

Its a great review course. Prior knowledge is necessary

by Artur S

Nov 24, 2015

Excellent course with amazing practical exercises!

by Balaji N

Nov 16, 2015

i love it

by francisco y

Jan 19, 2016

Its Hard! but AWESOME, some much info packed in a few lectures!

by Menghe L

Jun 12, 2017

great for learner

by Sergio G

Oct 30, 2017

Excellent!!

by Seema P

Dec 23, 2016

Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.

by prasad v

Nov 12, 2015

The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course

by Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

by Yifei G

Jun 26, 2019

I can feel Prof. Howe tried to cover as much as possible and to build a foundation for both practicing as well as further study on the topics. However, I do feel it is not patient enough to give a detailed yet easy-to-follow explanation for some of the topics, and I had to do quite some self-readings to close the gap. I think it will be helpful if the course can provide some reading materials on how some of the formulas are derived (e.g. gradient descent, logistic regression etc.) as a supplement.

by Bingcheng L

Aug 07, 2019

Too little people participated and long peer review time.

But the course content is good.

by Jiancheng

Feb 25, 2016

good course material!

by Mladen M

Nov 23, 2015

A nice and informative course. The only negative side were the problems with the automatic evaluation of the R assignment. In my opinion, the question should have been automatically removed and/or all submittions reevaluated, or all students should have been notified about the need for manual resubmission. As it was, some (like myself) were left with fewer points that they should have received just because they did not check the discussion forums every day (mainly because of other obligations).

by Jason M

Dec 19, 2015

Excellent crash course in machine learning and introduction to the kaggle data science competitions. However, the grading system had bugs and was unable to accept two answers as correct making it very frustrating. The grader was finally fixed so next round of this course should be a better experience.

by Kairsten F

Oct 26, 2016

This course covers a lot of material, but unfortunately lacks depth and thorough examples in many areas. It could also use more hands-on activities. Overall, I learned quite a bit and found it was worth the time and effort.

by Zoltan P

Dec 23, 2015

More dynamic visualisation please, and it will be 5*.

by Harini D

Aug 31, 2016

The entire course is an overview! This course will be a revision if you already know the concepts.