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Вернуться к Практическое компьютерное обучение

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

Оценки: 3,119
Рецензии: 591

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 581 отзывов о курсе Практическое компьютерное обучение

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.

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

автор: João R

20 авг. 2017 г.

Got confused how to perform cross validation and when. Other than that, very practical. Great job.

автор: Daniel R

14 мая 2016 г.

The course is really great, however it should last a little longer, 4 weeks is hard to accomplish

автор: César A C

26 июля 2018 г.

Very interesting course. May be a little bit harder than the previous ones but it could be done.

автор: Greig R

13 нояб. 2017 г.

Good course, I learnt a lot. It does need to be updated with more modern versions of software.

автор: Pieter v d V

28 июня 2018 г.

Very quick overview. If you really want to know something about it read the reference books.

автор: Guilherme C C

18 мая 2016 г.

Title says everything. Practically and basically no theory explained. Good course though.

автор: Carlos C

12 авг. 2017 г.

Excellent content so I give 4 starts. I stat less because the trainer speaks too fast.

автор: carlos j m r

5 окт. 2017 г.

I thought there were Swirl practice as other courses, however this course is very good.

автор: alon c

10 мар. 2016 г.

Great Course, will be nice to have more projects to see how it goes with different data

автор: Anant S

30 июня 2017 г.

good course for initial understanding of machine learning. SVM can also be included.

автор: Caio H

23 авг. 2019 г.

I learned a lot in this course, but I would recommend taking the courses in order.

автор: danxu

13 мар. 2017 г.

very good, but if it has swirl practice like th other courses it would be perfect.