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返回到 实用预测分析:模型与方法

學生對 华盛顿大学 提供的 实用预测分析:模型与方法 的評價和反饋

4.1
306 個評分
58 條評論

課程概述

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...

熱門審閱

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 .

篩選依據:

1 - 实用预测分析:模型与方法 的 25 個評論(共 56 個)

創建者 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.

創建者 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.

創建者 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.

創建者 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.

創建者 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 .

創建者 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

創建者 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.

創建者 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.

創建者 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.

創建者 Bingcheng L

2019年8月7日

Too little people participated and long peer review time.

But the course content is good.

創建者 Kevin R

2015年11月11日

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

創建者 Shota M

2016年2月24日

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

創建者 Dr. B A S

2020年7月3日

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

創建者 francisco y

2016年1月18日

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

創建者 Tamal R

2016年2月17日

Its a great review course. Prior knowledge is necessary

創建者 Artur S

2015年11月24日

Excellent course with amazing practical exercises!

創建者 Shivanand R K

2016年6月18日

Excellent thoughts and concepts presented.

創建者 Menghe L

2017年6月12日

great for learner

創建者 Pankaj A

2021年7月14日

Excellent Course

創建者 Daniel A

2015年11月23日

Great course!

創建者 Yogesh B N

2019年2月20日

Nice course

創建者 Sergio G

2017年10月29日

Excellent!!

創建者 Anand P

2019年2月11日

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創建者 Balaji N

2015年11月16日

i love it

創建者 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).