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Learner Reviews & Feedback for Practical Predictive Analytics: Models and Methods by University of Washington

4.1
stars
306 ratings
58 reviews

About the Course

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...

Top reviews

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 .

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1 - 25 of 56 Reviews for Practical Predictive Analytics: Models and Methods

By 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.

By 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.

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 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 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 .

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 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.

By Weng L

Jun 6, 2016

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

By Giby J

Jul 17, 2021

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

By Bingcheng L

Aug 7, 2019

Too little people participated and long peer review time.

But the course content is good.

By Kevin R

Nov 11, 2015

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

By Shota M

Feb 24, 2016

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

By Dr. B A S

Jul 3, 2020

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

By francisco y

Jan 18, 2016

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

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 Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

By Menghe L

Jun 12, 2017

great for learner

By Pankaj A

Jul 14, 2021

Excellent Course

By Daniel A

Nov 23, 2015

Great course!

By Yogesh B N

Feb 20, 2019

Nice course

By Sergio G

Oct 29, 2017

Excellent!!

By Anand P

Feb 11, 2019

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By Balaji N

Nov 16, 2015

i love it

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).