課程信息
3.9
43 個評分
11 個審閱
專項課程

第 3 門課程(共 5 門),位於

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立即開始,按照自己的計劃學習。
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高級

高級

完成時間(小時)

完成時間大約為24 小時

建議:5 weeks of study, 6-8 hours/week...
可選語言

英語(English)

字幕:英語(English)...
專項課程

第 3 門課程(共 5 門),位於

100% online

100% online

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
高級

高級

完成時間(小時)

完成時間大約為24 小時

建議:5 weeks of study, 6-8 hours/week...
可選語言

英語(English)

字幕:英語(English)...

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
完成時間為 7 分鐘

Welcome

...
Reading
5 個視頻(共 7 分鐘)
Video5 個視頻
Course Structure1分鐘
Meet Alexey2分鐘
Meet Pavel分鐘
Meet Ilya1分鐘
完成時間(小時)
完成時間為 1 小時

(Optional) Machine Learning: Introduction

...
Reading
6 個視頻(共 43 分鐘), 1 個閱讀材料
Video6 個視頻
(Optional) Basic concepts11分鐘
(Optional) Types of problems and tasks5分鐘
(Optional) Supervised learning7分鐘
(Optional) Unsupervised learning6分鐘
(Optional) Business applications of the machine learning4分鐘
Reading1 個閱讀材料
Slack Channel is the quickest way to get answer to your question10分鐘
完成時間(小時)
完成時間為 5 小時

Spark MLLib and Linear Models

...
Reading
11 個視頻(共 94 分鐘), 3 個閱讀材料, 5 個測驗
Video11 個視頻
First example. Linear regression10分鐘
How MLlib library is arranged10分鐘
How to train algorithms. Gradient descent method9分鐘
How to train algorithms. Second order methods8分鐘
Large scale classification. Logistic regression12分鐘
Regularization8分鐘
PCA decomposition9分鐘
K-means clustering7分鐘
How to submit your first assignment3分鐘
How to Install Docker on Windows 7, 8, 104分鐘
Reading3 個閱讀材料
Grading System: Instructions and Common Problems10分鐘
Docker Installation Guide10分鐘
Assignments. General requirements10分鐘
Quiz4 個練習
Large scale machine learning. The beginning14分鐘
Large scale regression and classification. Detailed analysis10分鐘
Regularization and Unsupervised Techniques10分鐘
Spark MLLib and Linear Models18分鐘
2
完成時間(小時)
完成時間為 2 小時

Machine Learning with Texts & Feature Engineering

...
Reading
12 個視頻(共 70 分鐘), 5 個測驗
Video12 個視頻
Welcome1分鐘
Feature Engineering for Texts, part 17分鐘
Feature Engineering for Texts, part 25分鐘
N-grams4分鐘
Hashing trick6分鐘
Categorical Features6分鐘
Feature Interactions2分鐘
Spark ML. Feature Engineering for Texts, part 17分鐘
Spark ML. Feature Engineering for Texts, part 25分鐘
Spark ML. Categorical Features3分鐘
Topic Modeling. LDA.7分鐘
Word2Vec11分鐘
Quiz5 個練習
Feature Enginering for Texts16分鐘
Categorical Features & Feature Interactions6分鐘
Spark ML Tutorial: Text Processing6分鐘
Advanced Machine Learning with Texts8分鐘
Machine Learning with Texts & Feature Engineering20分鐘
3
完成時間(小時)
完成時間為 6 小時

Decision Trees & Ensemble Learning

...
Reading
13 個視頻(共 64 分鐘), 6 個測驗
Video13 個視頻
Welcome1分鐘
Decision Trees Basics4分鐘
Decision Trees for Regression6分鐘
Decision Trees for Classification3分鐘
Decision Trees: Summary1分鐘
Bootstrap & Bagging8分鐘
Random Forest6分鐘
Gradient Boosted Decision Trees: Intro & Regression7分鐘
Gradient Boosted Decision Trees: Classification6分鐘
Stochastic Boosting1分鐘
Gradient Boosted Decision Trees: Usage Tips & Summary3分鐘
Spark ML. Decision Trees & Ensembles6分鐘
Spark ML. Cross-validation3分鐘
Quiz5 個練習
Decision Trees16分鐘
Bootstrap, Bagging and Random Forest6分鐘
Gradient Boosted Decision Trees10分鐘
Spark ML Programming Tutorial: Decision Trees & CV6分鐘
Decision Trees & Ensemble Learning16分鐘
4
完成時間(小時)
完成時間為 3 小時

Recommender Systems

...
Reading
15 個視頻(共 118 分鐘), 1 個閱讀材料, 4 個測驗
Video15 個視頻
Recommender Systems, Introduction. Part II4分鐘
Non-Personalized Recommender Systems9分鐘
Content-Based Recommender Systems8分鐘
Recommender System Evaluation10分鐘
Collaborative Filtering RecSys: User-User and Item-Item10分鐘
RecSys: SVD I7分鐘
RecSys: SVD II8分鐘
RecSys: SVD III5分鐘
RecSys: MF I7分鐘
RecSys: MF II6分鐘
RecSys: iALS I6分鐘
RecSys: iALS II11分鐘
RecSys: Hybrid I7分鐘
RecSys: Hybrid II7分鐘
Reading1 個閱讀材料
Recommender Systems. Spark Assignment10分鐘
Quiz4 個練習
Basic RecSys for Data Engineers14分鐘
Moderate RecSys for Data Engineers10分鐘
Advanced RecSys for Data Engineers4分鐘
Recommender Systems16分鐘

講師

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Pavel Mezentsev

Senior Data Scientist
PulsePoint inc
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Alexey A. Dral

Founder and Chief Executive Officer
BigData Team
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Ilya Trofimov

Principal Data Scientist
Yandex
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Evgeny Frolov

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

關於 Yandex

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....

關於 Big Data for Data Engineers 專項課程

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

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