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Вернуться к Практическое компьютерное обучение

Отзывы учащихся о курсе Практическое компьютерное обучение от партнера Университет Джонса Хопкинса

Оценки: 3,121
Рецензии: 593

О курсе

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Лучшие рецензии

13 авг. 2020 г.

recommended for all the 21st centuary students who might be intrested to play with data in future or some kind of work related to make predictions systemically must have good knowledge of this course

28 февр. 2017 г.

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.

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501–525 из 583 отзывов о курсе Практическое компьютерное обучение

автор: Vinay K S

19 февр. 2017 г.

I like initial courses like Exploratory Data Analysis but later on it got harder to follow the lectures. A lot of topics were just rushed through and little effort was made to make them engaging or interesting.

автор: Andrew W

13 мар. 2018 г.

Very interesting subject area, I think there is simply too much to cram into one course. Should consider spliting the subject into 2 courese or simply concentrate on only 1 or 2 main areas (e.g. cla

автор: Andrew W

9 февр. 2017 г.

The videos are really tutorials on R functions for machine learning and data wrangling. A good substitute for "Machine Learning" by Andrew Ng in terms of managing data sets and exploratory analysis.

автор: M. D

11 июля 2020 г.

Content somewhat outdated. Referenced packages don't always work in current version of R. Material can be better explained with more detailed discussion of examples rather than theory.

автор: Robert C

1 авг. 2017 г.

This course needs swirl assignments. I did fine on the quizzes and assignments, but I only feel like I learned a minimal amount of machine learning, even practical machine learning.

автор: Raul M

12 февр. 2019 г.

The class is good but it is too simple. I expected the professor will provide more detail about the models. This is just an introduction and weak for a specialization.

автор: Brian F

15 авг. 2017 г.

There was some good material in here, but it was rushed and is deserving of a much longer course - especially compared to some of the other modules in this course.

автор: Chuxing C

5 февр. 2016 г.

the lack of assisted practices made it harder to digest the contents and methodologies.

strongly suggest to develop some practice problems with explanations.

автор: Michalis F

26 мая 2017 г.

Good in introducing caret package and getting some experience in running algorithms. Was expecting more in-depth discussion about the methods though.

автор: Davin G

26 авг. 2019 г.

It's an excellent crash course to machine learning but the stats part was rushed. Had to look up external resources to understand what was going on.

автор: Léa F

9 янв. 2018 г.

Rather good overview. The contents could dig deeper into each subject, and it would improve the course a lot if some exercises in Swirl were added.

автор: Miguel J d S P

19 мая 2017 г.

I didn't enjoy the supporting materials and the quizzes weren't very interesting. The final project was fine.

The subject is super interesting.

автор: Max M

12 дек. 2017 г.

Should have gone into more depth and included swirl lessons, like previous courses. The quizzes were very challenging though, so that helped.

автор: Kyle H

9 мая 2018 г.

A brisk introduction to some of the basics of Machine Learning. Will leave with an understanding of a few ways to use the caret package.

автор: Manuel E

8 авг. 2019 г.

Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.

автор: Noelia O F

19 июля 2016 г.

Good course for learning the basics of the caret package. However, it is not a good course for learning machine learning.

автор: Joseph I

1 февр. 2020 г.

Material was very interesting but was covered at a very high level and a lot of additional learning was required.

автор: José A G R

5 февр. 2017 г.

Superfluous but the existence of the package "caret" covers the gap of other libraries like "skilearn" of python


1 мар. 2017 г.

Instructor rushes the course and does not explain much in the same level of details as respective quiz requires

автор: Hongzhi Z

2 янв. 2018 г.

All the formulas and code in slides are too abstract. If can be more charts to interpret that will be better.

автор: Henrique C A

13 окт. 2016 г.

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

автор: Alex F

29 дек. 2018 г.

A fine introduction, but there are much more engaging and better quality courses out there...

автор: Yingnan X

11 февр. 2016 г.

If you have taken Andrew Ng's machine learning class, it's not necessary to take this one.

автор: Yohan A H

6 сент. 2019 г.

I think it was a very fast course and I feel more real examples would have been useful,

автор: fabio a a l l

14 нояб. 2017 г.

Poor supporting material in a course that tries to cover a lot in a very limited time.