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Вернуться к Практическое компьютерное обучение

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

4.5
звезд
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

MR
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

AD
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:

526–550 из 583 отзывов о курсе Практическое компьютерное обучение

автор: Rafael S

24 июля 2018 г.

this course seemed too rushed for me, too little content for such a extense subject

автор: Raj V J

24 янв. 2016 г.

more needs to be taught in class. what is taught is not sufficient for quizzes.

автор: Surjya N P

2 июля 2017 г.

Overally course is good. But weekly programming assignments will be great.

автор: 王也

17 дек. 2016 г.

Too different for beginners but not deep enough for ones already know R.

автор: james

10 сент. 2016 г.

Quizzes are useful exercises but need to do a lot of self studying.

автор: Philip A

26 февр. 2017 г.

mentorship was great, but the video lectures were almost useless.

автор: Christoph G

4 дек. 2016 г.

The topic is too big, for one course from my point of view.

автор: Foo C B

28 мар. 2021 г.

Much of the material and instructions need to be updated.

автор: Ariel S G

27 июня 2017 г.

In my opinion, this course needs a few extra exercises.

автор: Jorge L

13 окт. 2016 г.

Fair but assignments are not very well explained

автор: Bahaa A

20 окт. 2016 г.

Good enough to open up mind of researcher

автор: Johnnery A

20 мар. 2020 г.

I need study more this course

автор: Sergio R

20 сент. 2017 г.

I miss Swirl

автор: Serene S

29 апр. 2016 г.

too easy

автор: Estrella P

7 июля 2020 г.

.

автор: Miguel C

10 мая 2020 г.

I really enjoyed the content of the course. I already knew a fair amount about machine learning but I learned a lot more than I thought I would. Most contents of weeks 3 and 4 - decision trees and random forests, bagging and boosting, linear discriminant analysis and naive Bayes, forecasting and unsupervised predictions - were my favourite topics in this course.

The biggest disappointment in this course for me were the outdated quizzes. I worked really hard through this course and most of the Data Science specialisation. But the quizzes are set up for older versions of R and some of its packages, so the results are completely different from what I got most of the time. I found this extremely frustrating and disheartening and had to repeat the quizzes several times. I do realise that most quizzes enumerate at the beginning the versions they are using, but there is no mention of how one goes about to set that up in R. On top of that, given that I rarely passed the quiz on the first try my Skill Tracking score dropped considerably, undermining weeks and weeks of hard work.

Unfortunately, this tainted my view of this course and I would advise the course organisers to update it as soon as possible.

автор: Michael S

6 февр. 2016 г.

Had big expectations for this one... really one of the ones to look forward to after working through the beginning of the specialization, but for some reason, it seemed any prof or even TA interaction was absent this time around like in none of the other specialization coursed to date. Bugs in the new interface and quizzes weren't really addressed. Couldn't even get an official response about the apparent removal of Distinction-level now (which I'd been working to get in all specialization courses and now seems no longer an option). Still interesting content. As a "free" course, it's still really valuable. As one of the people that paid for this and all others in this specialization, this is the one I felt didn't return as much value to justify the payment with no "official" course staff seeming to be involved this round.

автор: Agatha L

22 янв. 2018 г.

I was disappointed with this course. For better or worse ML is a part of data science and, in this course, the instructional depth was lacking. The lectures provided examples of how to implement a few ML algorithms in R, with very little actual instruction on the intricacies of these algorithms, theoretical foundations etc. Taking the course I felt somewhat cheated (a google search would have done the job of the class), and frustrated with various little bugs in Quiz/Assignment content.

автор: Fulvio B

24 мая 2020 г.

This course is not at the same level of the other courses I followed in the data science specialization. The lessons seem easy but when confronted with practicalities you realise you are missing practical tools. Moreover, sometimes the code is not up to date with a package and some datasets not available anymore. This creates problems with the quizzes since sometimes is not possible to reproduce one of the given options. I do not think this is acceptable for these kind of courses.

автор: Damon G

1 мар. 2016 г.

The mathematics in this course are at a high level (similar to Statistical Inference) - and are presented at a pace that is challenging without significant background in the field. There is little guidance presented on the methods required. It is recommended that students source out plenty of support material (intro to statistical inference and similar).

автор: Marshall M

23 сент. 2017 г.

A lot of the concepts in the course are grazed over very briefly and don't go into that much depth. In addition, some of the concepts are taught as concepts, they are taught through examples which tends to contextualize the material. Good content but could be put together in a more in depth manner.

автор: Mehrshad E

28 мар. 2018 г.

This course really lack something like SWIRL. The lectures only provide a summary, which is not helpful for someone new to the machine learning. Also, the instructure tries to cover pretty much everything but not in depth; instead, I think fewer topics should be covered in depth.

автор: Arcenis R

25 февр. 2016 г.

The instructions for the final project were very unclear and even though I submitted all assignments well before their respective deadlines and reviewed the required number of projects my work was not processed for a grade thereby delaying my specialization completion.

автор: Felipe M S J

2 дек. 2016 г.

No es un curso en el que se aprenda demasiado.

Parece demasiado avanzado en el uso de "caret" y en vez de enseñar, parece ser que todo debe ser aprendido con anterioridad.

Todo el material adicional que se necesita en el curso, es en general contenido externo.

автор: Jonathan O

18 апр. 2016 г.

I saw two main issues with this course: 1) dated lecture videos, oftentimes with R code that can't be replicated using up-to-date packages, and 2) lack of thoughtful design: example after example after example after example doesn't really teach you anything.