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Big Data Applications: Machine Learning at Scale に戻る

Yandex による Big Data Applications: Machine Learning at Scale の受講者のレビューおよびフィードバック



Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system. As a practical assignment, you will - build and apply linear models for classification and regression tasks; - learn how to work with texts; - automatically construct decision trees and improve their performance with ensemble learning; - finally, you will build your own recommender system! With these skills, you will be able to tackle many practical machine learning tasks. We provide the tools, you choose the place of application to make this world of machines more intelligent. Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting....

Big Data Applications: Machine Learning at Scale: 1 - 21 / 21 レビュー

by Egor M


Oh, that lovely accent

by Marco G


The videos are good, it's just the assignments that are frustrating.

I spent at least 10 times as long trying to get them to pass the autograder as I did solving them.

You need to improve this aspect of the course if you're expecting 4 or 5-star reviews :)

by Martin T


The assignments are not clear and the teacher support is poor (despit the slack channel being a welcome improvement!).

by Nitinkumar


Course content is good but Quiz questions is way to different than content

by Кряжевских С В


Cons outweigh the pros in this course: poor course' structure, terrible pronunciation, persistent headache in passing assignments with grader system, lack any feedback from teachers. I think this course is a loss of Yandex reputation.

by Pramod W


many things was impressive

by Egorov A


Perfect, thanks!

by sekhar


Excellent course

by Сергей Б


Course contained lots of new information for me, but exercises were too simple in comparison with other courses of specialization.

by Alexander K


Nice course, but the impression about practical tasks is really awful. The tasks are ok, but grading system is too buggy

by Paulo H C


Lack of clarity on how to answer questions both in quiz and programming

by Alberto B


Needs more examples and to reduce the speed in many subjects.

by Evgeniy C


need more simple examples and literature links

by Rami M


It was good. People made lot of work on it...

by Mykola K


I wish there were more practice

by Papadopoulos K


The course is interesting and challenging. Some work is needed on how to best transfer the right message to the students. Some videos are going way to deep into technical details and not focusing enough on the end goal. Overall interesting and mind-engaging course.

by Delgrange C


An introduction to machine learning. The transition from courses, whose concepts are easily understood with a good mathematical foundation, to practical exercises is sometimes quite difficult.

by Samir V


A lot of complex math which is neither derived in lecture nor about which intuitions are provided. Equations are written on slides as if we are to just read them and remember. I am sorry for this negative review but the concepts could have been taught with more details.

by Alexander C


Just a highlevel introduction to machine learning with comments like "you can also do it on spark". No details about the parallel learning process (parameter server, etc)

by Ángel M R


Unexistant support, failing notebooks

by Kirk B


The autograders are broken and team support is lacking. Decent lecture content however.