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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
Оценки: 289
Рецензии: 55

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

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.

KP

Feb 08, 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 .

Фильтр по:

1–25 из 53 отзывов о курсе Practical Predictive Analytics: Models and Methods

автор: Anand P

Feb 11, 2019

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автор: Yogesh B N

Feb 20, 2019

Nice course

автор: Shota M

Feb 24, 2016

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

автор: 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.

автор: 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

автор: Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.

автор: Tamal R

Feb 17, 2016

Its a great review course. Prior knowledge is necessary

автор: Chen

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.

автор: Artur S

Nov 24, 2015

Excellent course with amazing practical exercises!

автор: Balaji N

Nov 16, 2015

i love it

автор: Kenneth P

Feb 08, 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 .

автор: francisco y

Jan 19, 2016

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

автор: SIEW W L

Jun 06, 2016

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

автор: Kevin R

Nov 11, 2015

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

автор: Daniel A

Nov 23, 2015

Great course!

автор: Sergio G

Oct 30, 2017

Excellent!!

автор: Menghe L

Jun 12, 2017

great for learner

автор: 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.

автор: Bingcheng L

Aug 07, 2019

Too little people participated and long peer review time.

But the course content is good.

автор: Antonio P L

Jan 08, 2016

Great Course but the assigment don't show the understanding of the course

автор: Roberto S

Jun 13, 2017

Very good approach to each method; the assignments are a good test for the topics.

автор: William L K

Jun 06, 2017

Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.

автор: Jason M

Dec 19, 2015

Excellent crash course in machine learning and introduction to the kaggle data science competitions. However, the grading system had bugs and was unable to accept two answers as correct making it very frustrating. The grader was finally fixed so next round of this course should be a better experience.

автор: Zoltan P

Dec 23, 2015

More dynamic visualisation please, and it will be 5*.

автор: Harini D

Aug 31, 2016

The entire course is an overview! This course will be a revision if you already know the concepts.