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Back to Big Data Applications: Machine Learning at Scale

Big Data Applications: Machine Learning at Scale, Yandex

(62 ratings)

About this Course

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....
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13 Reviews

By Ángel Montaña Redondo

Jan 20, 2019

Unexistant support, failing notebooks

By Kirk Brunson

Jan 16, 2019

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

By Marco Gorelli

Dec 05, 2018

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 Papadopoulos Konstantinos

Nov 06, 2018

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 Martin Tomis

Sep 26, 2018

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

By Alexander Chistyakov

Sep 20, 2018

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 Alberto Bonsanto

Sep 17, 2018

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

By Evgeniy Chipizubov

Sep 02, 2018

need more simple examples and literature links

By Rami

Aug 01, 2018

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

By Сергей Борисов

Jul 08, 2018

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