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Отзывы учащихся о курсе Big Data Applications: Machine Learning at Scale от партнера Яндекс

3.8
Оценки: 71
Рецензии: 18

О курсе

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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1–17 из 17 отзывов о курсе Big Data Applications: Machine Learning at Scale

автор: Pramod W

May 24, 2018

many things was impressive

автор: sekhar

Mar 27, 2018

Excellent course

автор: Сергей Б

Jul 08, 2018

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

автор: Egor M

Nov 03, 2017

Oh, that lovely accent

автор: Alberto B

Sep 17, 2018

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

автор: Evgeniy C

Sep 02, 2018

need more simple examples and literature links

автор: Rami

Aug 01, 2018

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

автор: Kiselyov A

May 23, 2019

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

автор: Mykola K

Aug 23, 2019

I wish there were more practice

автор: Paulo H C

Jul 13, 2019

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

автор: Marco G

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 :)

автор: Martin T

Sep 26, 2018

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

автор: Papadopoulos K

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.

автор: Delgrange C

Oct 20, 2019

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.

автор: Ángel M R

Jan 20, 2019

Unexistant support, failing notebooks

автор: Alexander C

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)

автор: Kirk B

Jan 16, 2019

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