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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.

Фильтр по:

251–275 из 492 отзывов о курсе Практическое компьютерное обучение

автор: Laro N P

Jul 22, 2018

Good course, I miss more practice exercise because theory is always welcome but when we are capable to understand is doing real practice.

автор: Harland H

Oct 01, 2018

Very informative and the project was fun to accomplish.

автор: Bojan B

Oct 14, 2018

Great course with great materials. Easy to understand and to learn.

автор: asma m

Oct 02, 2018

The professor has a very clear lecture, brief and persistent comparing to others. I just love this course .

автор: Shashwat K

Oct 15, 2018

insightfull

автор: Peter B

Sep 06, 2018

Excellent lectures.

автор: Athanasios S

Aug 09, 2018

Great class! I wish you would do a little more explanation about what methods are best for which scenarios. If you did in fact explain that and it went over my head or I missed it, I apologize. Great class that I learned a lot from.

автор: Matthew S

May 08, 2019

Good introduction to machine learning. Provides pretty comprehensive coverage of major algorithms and approaches.

автор: Jerome S P

Jun 18, 2019

Very good explanation! Trying to do the examples help me understand more plus the explanation which is not on the slide helps a lot. Thank you

автор: Jeffrey M H

Jun 10, 2019

So far, one of the most fulfilling courses in the Data Science specialization!

автор: Andrew

Jul 24, 2019

Great intro to machine learning. Covers the basics to allow you to being using ML concepts on your own.

автор: Klever M

Jul 29, 2019

It was a great overview of the fascinating word of ML.

автор: Gustavo C G

Aug 07, 2019

Excellent introduction to machine learning. Great examples and detailed explanations, as usual

автор: Nino P

May 24, 2019

It's good that they teach you basics of machine learing in R (caret package), but it's very introductory course. I definetly recommend this course to beginner, but I also recommend taking more courses on this topic (Andrew Ng's for example).

автор: Martin G

Aug 13, 2019

Excellent course

автор: Deogratias K

Aug 16, 2019

I liked everything abt it

автор: Mary

Aug 19, 2019

Very informational with good variety of code to take back and apply to projects.

автор: Khalid S A

Aug 18, 2019

excellent course and very beneficial

автор: Andreas P

Aug 27, 2019

Thank you for helpful learning.

автор: John D M

Jul 15, 2019

A fast-paced course that got me going in building models and understanding the pitfalls. I felt the directions for the final project were somewhat poorly worded and vague (and calling one of the files test when it was not to be used for testing the model was initially confusing), but overall it was good. I would have liked to have seen the final project uploaded as a secure file as has been done in other courses, and Github was a poor platform for viewing html files. Additionally, the question about out of sample error caused many people problems in the projects as they confused it with with Accuracy, yet it was weighted heavily in the rubric: I'd like the instructors to review the materials how that material is presented in terms of models. I got 100%, but as always you have to pay very close attention to the rubric.

As always with this specialization, you are really just given a taste and there is no way you can fully explore all the material and references presented., but it is enough to get you going and wanting to come back and explore the material more.

автор: Umair R

Aug 29, 2019

Brian Caffo's courses are, as always brilliant.

автор: Charbel L

Sep 07, 2019

Excellent course. Shows how simple it is to start running models with machine learning...! Well done

автор: Muhammad Z H

Sep 15, 2019

learning alot

автор: YANAN D

May 27, 2019

elementary course and not too much work

автор: Weiqun T

Sep 23, 2019

This is a very good basic course for machine learning. I got the basic ideas and skills for it.