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Вернуться к Practical Predictive Analytics: Models and Methods

Отзывы учащихся о курсе Practical Predictive Analytics: Models and Methods от партнера Вашингтонский университет

4.1
звезд
Оценки: 306
Рецензии: 58

О курсе

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...

Лучшие рецензии

SP
22 дек. 2016 г.

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 февр. 2016 г.

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 .

Фильтр по:

1–25 из 56 отзывов о курсе Practical Predictive Analytics: Models and Methods

автор: Jonas C

18 апр. 2017 г.

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.

автор: Qianfan W

9 мая 2016 г.

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.

автор: Yifei G

26 июня 2019 г.

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.

автор: Seema P

23 дек. 2016 г.

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.

автор: Kenneth P

8 февр. 2016 г.

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 .

автор: prasad v

12 нояб. 2015 г.

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

автор: Chen Y

20 июля 2016 г.

Nive that the course covered a broad range of topics.

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

автор: Weng L

6 июня 2016 г.

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

автор: Giby J

17 июля 2021 г.

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

автор: Bingcheng L

7 авг. 2019 г.

Too little people participated and long peer review time.

But the course content is good.

автор: Kevin R

11 нояб. 2015 г.

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

автор: Shota M

24 февр. 2016 г.

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

автор: Dr. B A S

3 июля 2020 г.

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

автор: francisco y

18 янв. 2016 г.

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

автор: Tamal R

17 февр. 2016 г.

Its a great review course. Prior knowledge is necessary

автор: Artur S

24 нояб. 2015 г.

Excellent course with amazing practical exercises!

автор: Shivanand R K

18 июня 2016 г.

Excellent thoughts and concepts presented.

автор: Menghe L

12 июня 2017 г.

great for learner

автор: Pankaj A

14 июля 2021 г.

Excellent Course

автор: Daniel A

23 нояб. 2015 г.

Great course!

автор: Yogesh B N

20 февр. 2019 г.

Nice course

автор: Sergio G

29 окт. 2017 г.

Excellent!!

автор: Anand P

11 февр. 2019 г.

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автор: Balaji N

16 нояб. 2015 г.

i love it

автор: Mladen M

23 нояб. 2015 г.

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).