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Вернуться к Практическое компьютерное обучение

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

Оценки: 3,161
Рецензии: 604

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.

Фильтр по:

401–425 из 595 отзывов о курсе Практическое компьютерное обучение

автор: Matthew C

11 дек. 2017 г.

Lots of good material, but some things (like PCA) didn't receive enough coverage in the lectures. The quizzes also weren't great at testing the material in the lectures.

автор: Utkarsh Y

17 нояб. 2016 г.

Great course. Only missing piece is the working information / maths behind the models. But as the name suggests it teaches practical approach towards machine learning.

автор: Craig S

12 февр. 2018 г.

Not as detailed as some others in the specialization which is a shame but good none the less. The videos go through the info quickly so be prepared to go back over.

автор: Roberto G

20 мая 2017 г.

Great as an introduction for someone with no practical experience. Lectures are too theoretical and lack some examples to translates the theory into practice

автор: Nicholas T

3 июля 2020 г.

Very good course. Fast paced and a lot of self study required to fully understand some of the nuances of the R (if you're not familiar with the language).

автор: Eric L

2 июня 2016 г.

Great course, very high paced with a lot of information. would have been great to add two more weeks and another project to use more machine learning

автор: Igor H

10 сент. 2016 г.

Rather basic, nevertheless a good introduction to the topic of machine learning with R. Mostly concentrated on applications of the R caret package.

автор: Lee G

22 сент. 2017 г.

A very good starter course on Machine Learning in R with great links to various resources that students and delve deeper into the various topics.

автор: Yashaswi P

24 мая 2020 г.

Good Course the covers a lot of practical aspects and relevant to the real world solution.

Good References and Learning Materails are available

автор: Ann B

6 сент. 2017 г.

Good class to get the basics of Practical Machine Learning. This course is best taken as a part of the data science series from John Hopkins.

автор: Gabriela C V

14 дек. 2020 г.

It's harder than the previous one. it would be nice to update some the quizzes as they are based on older versions of R Studio libraies.

автор: Hernan S

13 дек. 2016 г.

The quiz should be constructed in a way that depends less on the version of the libraries used. The rest of course was excellent.

автор: Jakub W

24 сент. 2018 г.

Vary practical approach, almost no theory or in-depth explanation of the subject, but a lot of focus on applying ML in practice

автор: Md F A

14 авг. 2017 г.

To me with this course, the best learning aspect is the final project; how to use Machine Learning Algorithms on data analysis.

автор: Rhys T

10 окт. 2017 г.

Good course, some aspects of the assignment were a bit beyond the scope of what the course teaches but overall I learnt a lot.

автор: Níck F

27 сент. 2016 г.

Was pretty good, but quite short and some assignments did not align as well with the lecture material as they could have.

автор: Michael O D

10 янв. 2020 г.

This is a great course, but it would be good to see it updated to use the newer evolution of the caret package, parsnip.

автор: Tongesai K

8 февр. 2016 г.

Very good course. I am very knew to this topic but am sure will find a lot of application in my speciality - geophysics

автор: Kevin S

2 мар. 2016 г.

Good introduction to machine learning, might suffer a bit from trying to cover too much ground in such a short time.


17 авг. 2021 г.

Maybe final review must be verified by an expert, also the kind of data to analyse must be change over the time.

автор: Sulan L

19 нояб. 2018 г.

I hope we can have more détails in this cours and to see how to use the algorithms for the big data. Thank you.

автор: A. R C

20 окт. 2017 г.

I enjoyed it but it needs indeed to deep into many concepts, which are just briefly named during the course.

автор: marcelo G

14 авг. 2016 г.

Great course, very demanding, but it could use more reading material, ebooks instead of links on video.

автор: Jeffrey E T

28 мар. 2016 г.

Good overview of available techniques and the Caret package. Will get you started in machine learning.

автор: BIBHUTI B P

24 июля 2017 г.

This was a superb module which created a deep learning insight within me focusing on future technology