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Bewertung und Feedback des Lernenden für Practical Predictive Analytics: Models and Methods von University of Washington

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306 Bewertungen
58 Bewertungen

Über den Kurs

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-Bewertungen

SP
22. Dez. 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
7. Feb. 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 von 56 Bewertungen für Practical Predictive Analytics: Models and Methods

von Jonas C

18. Apr. 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.

von Qianfan W

9. Mai 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.

von Yifei G

26. Juni 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.

von Seema P

23. Dez. 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.

von Kenneth P

8. Feb. 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 .

von prasad v

12. Nov. 2015

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

von Chen Y

20. Juli 2016

Nive that the course covered a broad range of topics.

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

von Weng L

6. Juni 2016

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

von Giby J

17. Juli 2021

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

von Bingcheng L

7. Aug. 2019

Too little people participated and long peer review time.

But the course content is good.

von Kevin R

11. Nov. 2015

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

von Shota M

24. Feb. 2016

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

von Dr. B A S

3. Juli 2020

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

von francisco y

18. Jan. 2016

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

von Tamal R

17. Feb. 2016

Its a great review course. Prior knowledge is necessary

von Artur S

24. Nov. 2015

Excellent course with amazing practical exercises!

von Shivanand R K

18. Juni 2016

Excellent thoughts and concepts presented.

von Menghe L

12. Juni 2017

great for learner

von Pankaj A

14. Juli 2021

Excellent Course

von Daniel A

23. Nov. 2015

Great course!

von Yogesh B N

20. Feb. 2019

Nice course

von Sergio G

29. Okt. 2017

Excellent!!

von Anand P

11. Feb. 2019

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von Balaji N

16. Nov. 2015

i love it

von Mladen M

23. Nov. 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).