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Вернуться к Практическое компьютерное обучение

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

4.5
Оценки: 2,675
Рецензии: 501

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

JC

Jan 17, 2017

excellent course. Be prepared to learn a lot if you work hard and don't give up if you think it is hard, just continue thinking, and interact with other students and tutors + Google and Stackoverflow!

AD

Mar 01, 2017

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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1–25 из 492 отзывов о курсе Практическое компьютерное обучение

автор: Bernie P

Aug 07, 2018

It needs to be updated. Its probably one of the most in demand skills in the field and this has a weeks worth of content 1 section 25 minutes of video 5 questions. Its just not as good as any of the other courses. 100% needs to be revamped.

автор: Thomas G

Jun 07, 2017

By far the laziest course set up in the track. It's an interesting topic, but without independent study I would have learned almost nothing due to the lack of any "practicals" in this "Practical Machine Learning". A really disappointing course that fails to be worth more than just a couple hours of youtube.

автор: Jean P L

Apr 25, 2018

More practice Items are needed

автор: Thomas B

Nov 08, 2018

Lectures and course material is insufficient to get the right amount of knowledge to be able to do the tests and the course project

автор: Grégoire M

Sep 27, 2017

The worst course of the specialisation so far. The quizzes are full of typos, not clear at all, and the videos teach nothing, always refering to elements of statistical learning book. Now that I have completed the course, I do know a bunch of algorithm names involved in machine learning, but I certainly do not understand what they do and when using them.

автор: Hamid M

Feb 21, 2018

Unsatisfactory and poor course in this specialisation. There are many important parts which are explained inaccurately. In many cases, the lecturer jumps from important points, or assumes students have detailed knowledge about the topic. You can find ambiguity in weekly questions. Very unsatisfied!

автор: Thej K R

Jun 04, 2019

Worst lectures! Worst teaching! I leanrt most of the topics on statquest. Very very very highlevel teaching, very little effort put in by Bcaffo and Rdpeng on this! So many issues in the quizzes. Wasted hours on puzzling out what is to be done! Have a look at the complaints in the course era discussion board. Issues since 3 years are not corrected. The course needs an update. But no m*****F**** is listening. Solutions to quizze are wrong! I have had it with coursera and their useles peer correction. You don't even know if what you are doing is right! Worst FEEDBACK ever!

автор: Erick G A

Aug 18, 2017

That's a pretty rushed course. I think you really should reformulate it and discuss its content with a deeper way.

автор: Andrew C

May 14, 2019

The lectures and quizzes are based on old versions of R and R packages. This course needs a serious update, as some packages work differently, test answers have changed (but not been updated) and coding along with the videos results in different results. Going to the forum you can see that this has been an issue for a few years now.

автор: David S

Dec 18, 2018

lecture material could be cleaner with fewer errors

автор: Humberto R

Feb 13, 2018

I was rather disappointed with this course. I guess it fills the objective of getting you using the caret package and getting you started with some examples. However to understand what you are doing you should defintively go somewhere else. I definitively missed some swirl exercises and more flow diagrams in the slides. It felt for me as I was just copypasting some code from the slides. The course does clearly give some good literature and places to go for details.

автор: Mariana d S e S

Mar 01, 2018

Not enough context for the price payed

автор: Wayne H

Jun 27, 2017

I'm a big fan of the John's Hopkins Data Science series on Coursera; however, they definitely "phoned it in" on this particular course. No practical assignments except for the quizzes and final course project. Too much deference to outside materials, i.e. if you really want to learn these concepts take Andrew Ng's class or read The Elements of Statistical Learning. The video lectures just breeze over the concepts and leave too much for the learner to just go and figure out. The quizzes, instead of testing your knowledge are literal the only practical learning in whole class. The course project is what you make of it.

автор: Lingjian K

Jun 14, 2017

Extremely confusing. Should look at Prof. Andrew Ng's machine learning course for how to clearly convey an idea.

автор: Bob W

Apr 09, 2017

This course was a big let-down compared to other courses in the specialization. It doesn't seem like a lot of effort went into course planning and creation. Much of the content is unclear and there is little depth. course textbook, and some swirl exercises would have helped.

автор: André C L

Dec 13, 2018

very good practical experience using machine learning models, especially regarding PCA usage

автор: Sanjeev K

Dec 16, 2018

Introductory course but it explains the basics easily

автор: Julien N

Dec 18, 2018

nice pace, good overview to start with modelling in R

автор: Javier E S

Dec 02, 2018

Excellent.

автор: Carlo G I

Dec 04, 2018

thank you

автор: David R

Jan 14, 2019

Great introduction to Machine Learning in R. Concepts explained very clearly and project gave opportunity to test out the concepts introduced to real data.

автор: Mohammad A

Jan 17, 2019

Wonderful course and instructor, it was the best in the specialization courses so far.

One note is that for most of the methods the explanation was too much precise and short and needed to reinforce it by extra material

автор: Anuj P

Feb 21, 2019

This is the most interesting of all the courses in this specialization. Sometimes the content covered can be overwhelming. But the end result in the form of project assignment is worth all the efforts.

автор: Dave H

Feb 23, 2019

This was one of my favorite courses in the specialization as it was so easy to understand and follow. I think the basis I was given has really made me want to delve deeper into the topic and apply it to my career. Thank you!!!

автор: Dewald O

Feb 24, 2019

great course in R, really covers the fundamentals.