Chevron Left
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
306件の評価
58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
2016年12月22日

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
2016年2月7日

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 / 56 レビュー

by Jonas C

2017年4月18日

The lessons are sometimes completely disconected from the graded assignments. There were some graded assignements that dealt with things I have never heard about and I completed it without even looking the lessons videos. Some of the lessons are disapointing of the lack of assistance to the required software/code to be used. In such a way that the concept worked is very simple, but if you have no experience on the software or code you can have a hard time to complete the assignements with irritating details which are not explained at all in the lessons. The lessons serves more as a guide to what you should search in google and learn through other source of information. I did not expected such poor course from a paid one; I have doen free courses way better than this course. Don´t pay or this course, find some other course free or other paid course with better reviews.

by Qianfan W

2016年5月9日

Do not like the slides and the way it is explained. Compared with other ML courses on cousera, this one makes me feel that it is more like a handbook/dictionary instead of a tutorial to teach students. If you already know it, it would help you refresh the mind. Otherwise, you might find it is just to show off how how complex and mysterious is the data science.

by Yifei G

2019年6月26日

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 Seema P

2016年12月23日

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 Kenneth P

2016年2月8日

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 prasad v

2015年11月12日

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

by Chen Y

2016年7月20日

Nive that the course covered a broad range of topics.

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

by Weng L

2016年6月6日

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

by Giby J

2021年7月17日

This course helpemd me understand more about machine learning and a set of tools to help with the same.

by Bingcheng L

2019年8月7日

Too little people participated and long peer review time.

But the course content is good.

by Kevin R

2015年11月11日

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

by Shota M

2016年2月24日

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

by Dr. B A S

2020年7月3日

Hands on practices are very good. learning predictive model was a challenge.

by francisco y

2016年1月18日

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

by Tamal R

2016年2月17日

Its a great review course. Prior knowledge is necessary

by Artur S

2015年11月24日

Excellent course with amazing practical exercises!

by Shivanand R K

2016年6月18日

Excellent thoughts and concepts presented.

by Menghe L

2017年6月12日

great for learner

by Pankaj A

2021年7月14日

Excellent Course

by Daniel A

2015年11月23日

Great course!

by Yogesh B N

2019年2月20日

Nice course

by Sergio G

2017年10月29日

Excellent!!

by Anand P

2019年2月11日

V

e

r

y

g

o

o

d

by Balaji N

2015年11月16日

i love it

by Mladen M

2015年11月23日

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).