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다음 전문 분야의 5개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 76시간 필요

권장: 5 weeks of study, 6-8 hours/week...

영어

자막: 영어

다음 전문 분야의 5개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 76시간 필요

권장: 5 weeks of study, 6-8 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 7분 필요

Welcome

5개 동영상 (총 7분)
5개의 동영상
Course Structure1m
Meet Alexey2m
Meet Pavel37
Meet Ilya1m
완료하는 데 1시간 필요

(Optional) Machine Learning: Introduction

6개 동영상 (총 43분), 1 reading
6개의 동영상
(Optional) Basic concepts11m
(Optional) Types of problems and tasks5m
(Optional) Supervised learning7m
(Optional) Unsupervised learning6m
(Optional) Business applications of the machine learning4m
1개의 읽기 자료
Slack Channel is the quickest way to get answer to your question10m
완료하는 데 5시간 필요

Spark MLLib and Linear Models

11개 동영상 (총 94분), 3 readings, 5 quizzes
11개의 동영상
First example. Linear regression10m
How MLlib library is arranged10m
How to train algorithms. Gradient descent method9m
How to train algorithms. Second order methods8m
Large scale classification. Logistic regression12m
Regularization8m
PCA decomposition9m
K-means clustering7m
How to submit your first assignment3m
How to Install Docker on Windows 7, 8, 104m
3개의 읽기 자료
Grading System: Instructions and Common Problems10m
Docker Installation Guide10m
Assignments. General requirements10m
4개 연습문제
Large scale machine learning. The beginning14m
Large scale regression and classification. Detailed analysis10m
Regularization and Unsupervised Techniques10m
Spark MLLib and Linear Models18m
2
완료하는 데 2시간 필요

Machine Learning with Texts & Feature Engineering

12개 동영상 (총 70분), 5 quizzes
12개의 동영상
Feature Engineering for Texts, part 17m
Feature Engineering for Texts, part 25m
N-grams4m
Hashing trick6m
Categorical Features6m
Feature Interactions2m
Spark ML. Feature Engineering for Texts, part 17m
Spark ML. Feature Engineering for Texts, part 25m
Spark ML. Categorical Features3m
Topic Modeling. LDA.7m
Word2Vec11m
5개 연습문제
Feature Enginering for Texts16m
Categorical Features & Feature Interactions6m
Spark ML Tutorial: Text Processing6m
Advanced Machine Learning with Texts8m
Machine Learning with Texts & Feature Engineering20m
3
완료하는 데 6시간 필요

Decision Trees & Ensemble Learning

13개 동영상 (총 64분), 6 quizzes
13개의 동영상
Decision Trees Basics4m
Decision Trees for Regression6m
Decision Trees for Classification3m
Decision Trees: Summary1m
Bootstrap & Bagging8m
Random Forest6m
Gradient Boosted Decision Trees: Intro & Regression7m
Gradient Boosted Decision Trees: Classification6m
Stochastic Boosting1m
Gradient Boosted Decision Trees: Usage Tips & Summary3m
Spark ML. Decision Trees & Ensembles6m
Spark ML. Cross-validation3m
5개 연습문제
Decision Trees16m
Bootstrap, Bagging and Random Forest6m
Gradient Boosted Decision Trees10m
Spark ML Programming Tutorial: Decision Trees & CV6m
Decision Trees & Ensemble Learning16m
4
완료하는 데 3시간 필요

Recommender Systems

15개 동영상 (총 118분), 1 reading, 4 quizzes
15개의 동영상
Recommender Systems, Introduction. Part II4m
Non-Personalized Recommender Systems9m
Content-Based Recommender Systems8m
Recommender System Evaluation10m
Collaborative Filtering RecSys: User-User and Item-Item10m
RecSys: SVD I7m
RecSys: SVD II8m
RecSys: SVD III5m
RecSys: MF I7m
RecSys: MF II6m
RecSys: iALS I6m
RecSys: iALS II11m
RecSys: Hybrid I7m
RecSys: Hybrid II7m
1개의 읽기 자료
Recommender Systems. Spark Assignment10m
4개 연습문제
Basic RecSys for Data Engineers14m
Moderate RecSys for Data Engineers10m
Advanced RecSys for Data Engineers4m
Recommender Systems16m

강사

Avatar

Alexey A. Dral

Founder and Chief Executive Officer
BigData Team
Avatar

Evgeny Frolov

Data Scientist, PhD Student @Skoltech
Computational and Data Intensive Science and Engineering
Avatar

Ilya Trofimov

Principal Data Scientist
Yandex

Yandex 정보

Yandex is a technology company that builds intelligent products and services powered by machine learning. Our goal is to help consumers and businesses better navigate the online and offline world....

Big Data for Data Engineers 전문 분야 정보

This specialization is made for people working with data (either small or big). If you are a Data Analyst, Data Scientist, Data Engineer or Data Architect (or you want to become one) — don’t miss the opportunity to expand your knowledge and skills in the field of data engineering and data analysis on the large scale. In four concise courses you will learn the basics of Hadoop, MapReduce, Spark, methods of offline data processing for warehousing, real-time data processing and large-scale machine learning. And Capstone project for you to build and deploy your own Big Data Service (make your portfolio even more competitive). Over the course of the specialization, you will complete progressively harder programming assignments (mostly in Python). Make sure, you have some experience in it. This course will master your skills in designing solutions for common Big Data tasks: - creating batch and real-time data processing pipelines, - doing machine learning at scale, - deploying machine learning models into a production environment — and much more! Join some of best hands-on big data professionals, who know, their job inside-out, to learn the basics, as well as some tricks of the trade, from them. Special thanks to Prof. Mikhail Roytberg (APT dept., MIPT), Oleg Sukhoroslov (PhD, Senior Researcher, IITP RAS), Oleg Ivchenko (APT dept., MIPT), Pavel Akhtyamov (APT dept., MIPT), Vladimir Kuznetsov, Asya Roitberg, Eugene Baulin, Marina Sudarikova....
Big Data for Data Engineers

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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