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返回到 实用预测分析:模型与方法

学生对 华盛顿大学 提供的 实用预测分析:模型与方法 的评价和反馈

4.1
306 个评分
58 条评论

课程概述

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...

热门审阅

SP
Dec 22, 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 7, 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 .

筛选依据:

1 - 实用预测分析:模型与方法 的 25 个评论(共 56 个)

创建者 Jonas C

Apr 18, 2017

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.

创建者 Qianfan W

May 9, 2016

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.

创建者 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.

创建者 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.

创建者 Kenneth P

Feb 8, 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 .

创建者 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

创建者 Chen Y

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.

创建者 Weng L

Jun 6, 2016

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

创建者 Giby J

Jul 17, 2021

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

创建者 Bingcheng L

Aug 7, 2019

Too little people participated and long peer review time.

But the course content is good.

创建者 Kevin R

Nov 11, 2015

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

创建者 Shota M

Feb 24, 2016

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

创建者 Dr. B A S

Jul 3, 2020

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

创建者 francisco y

Jan 18, 2016

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

创建者 Tamal R

Feb 17, 2016

Its a great review course. Prior knowledge is necessary

创建者 Artur S

Nov 24, 2015

Excellent course with amazing practical exercises!

创建者 Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

创建者 Menghe L

Jun 12, 2017

great for learner

创建者 Pankaj A

Jul 14, 2021

Excellent Course

创建者 Daniel A

Nov 23, 2015

Great course!

创建者 Yogesh B N

Feb 20, 2019

Nice course

创建者 Sergio G

Oct 29, 2017

Excellent!!

创建者 Anand P

Feb 11, 2019

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创建者 Balaji N

Nov 16, 2015

i love it

创建者 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).