About this Course
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다음의 2/7개 강좌

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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 49시간 필요

권장: 6-10 hours/week...

영어

자막: 영어, 한국어

귀하가 습득할 기술

Data AnalysisFeature ExtractionFeature EngineeringXgboost

다음의 2/7개 강좌

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

고급 단계

완료하는 데 약 49시간 필요

권장: 6-10 hours/week...

영어

자막: 영어, 한국어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 6시간 필요

Introduction & Recap

This week we will introduce you to competitive data science. You will learn about competitions' mechanics, the difference between competitions and a real life data science, hardware and software that people usually use in competitions. We will also briefly recap major ML models frequently used in competitions....
8 videos (Total 46 min), 7 readings, 6 quizzes
8개의 동영상
Meet your lecturers2m
Course overview7m
Competition Mechanics6m
Kaggle Overview [screencast]7m
Real World Application vs Competitions5m
Recap of main ML algorithms9m
Software/Hardware Requirements5m
7개의 읽기 자료
Welcome!10m
Week 1 overview10m
Disclaimer10m
Explanation for quiz questions10m
Additional Materials and Links10m
Explanation for quiz questions10m
Additional Material and Links10m
5개 연습문제
Practice Quiz8m
Recap8m
Recap12m
Software/Hardware6m
Graded Soft/Hard Quiz8m
완료하는 데 2시간 필요

Feature Preprocessing and Generation with Respect to Models

In this module we will summarize approaches to work with features: preprocessing, generation and extraction. We will see, that the choice of the machine learning model impacts both preprocessing we apply to the features and our approach to generation of new ones. We will also discuss feature extraction from text with Bag Of Words and Word2vec, and feature extraction from images with Convolution Neural Networks....
7 videos (Total 73 min), 4 readings, 4 quizzes
7개의 동영상
Numeric features13m
Categorical and ordinal features10m
Datetime and coordinates8m
Handling missing values10m
Bag of words10m
Word2vec, CNN13m
4개의 읽기 자료
Explanation for quiz questions10m
Additional Material and Links10m
Explanation for quiz questions10m
Additional Material and Links10m
4개 연습문제
Feature preprocessing and generation with respect to models8m
Feature preprocessing and generation with respect to models8m
Feature extraction from text and images8m
Feature extraction from text and images8m
완료하는 데 29분 필요

Final Project Description

This is just a reminder, that the final project in this course is better to start soon! The final project is in fact a competition, in this module you can find an information about it....
1 video (Total 4 min), 2 readings
1개의 동영상
2개의 읽기 자료
Final project10m
Final project advice #110m
2
완료하는 데 2시간 필요

Exploratory Data Analysis

We will start this week with Exploratory Data Analysis (EDA). It is a very broad and exciting topic and an essential component of solving process. Besides regular videos you will find a walk through EDA process for Springleaf competition data and an example of prolific EDA for NumerAI competition with extraordinary findings....
8 videos (Total 80 min), 2 readings, 1 quiz
8개의 동영상
Building intuition about the data6m
Exploring anonymized data15m
Visualizations11m
Dataset cleaning and other things to check7m
Springleaf competition EDA I8m
Springleaf competition EDA II16m
Numerai competition EDA6m
2개의 읽기 자료
Week 2 overview10m
Additional material and links10m
1개 연습문제
Exploratory data analysis12m
완료하는 데 2시간 필요

Validation

In this module we will discuss various validation strategies. We will see that the strategy we choose depends on the competition setup and that correct validation scheme is one of the bricks for any winning solution. ...
4 videos (Total 51 min), 3 readings, 2 quizzes
4개의 동영상
Validation strategies7m
Data splitting strategies14m
Problems occurring during validation20m
3개의 읽기 자료
Validation strategies10m
Comments on quiz10m
Additional material and links10m
2개 연습문제
Validation8m
Validation8m
완료하는 데 5시간 필요

Data Leakages

Finally, in this module we will cover something very unique to data science competitions. That is, we will see examples how it is sometimes possible to get a top position in a competition with a very little machine learning, just by exploiting a data leakage. ...
3 videos (Total 26 min), 3 readings, 3 quizzes
3개의 동영상
Leaderboard probing and examples of rare data leaks9m
Expedia challenge9m
3개의 읽기 자료
Comments on quiz10m
Additional material and links10m
Final project advice #210m
1개 연습문제
Data leakages8m
3
완료하는 데 3시간 필요

Metrics Optimization

This week we will first study another component of the competitions: the evaluation metrics. We will recap the most prominent ones and then see, how we can efficiently optimize a metric given in a competition....
8 videos (Total 83 min), 3 readings, 2 quizzes
8개의 동영상
Regression metrics review I14m
Regression metrics review II8m
Classification metrics review20m
General approaches for metrics optimization6m
Regression metrics optimization10m
Classification metrics optimization I7m
Classification metrics optimization II6m
3개의 읽기 자료
Week 3 overview10m
Comments on quiz10m
Additional material and links10m
2개 연습문제
Metrics12m
Metrics12m
완료하는 데 4시간 필요

Advanced Feature Engineering I

In this module we will study a very powerful technique for feature generation. It has a lot of names, but here we call it "mean encodings". We will see the intuition behind them, how to construct them, regularize and extend them. ...
3 videos (Total 27 min), 2 readings, 2 quizzes
3개의 동영상
Regularization7m
Extensions and generalizations10m
2개의 읽기 자료
Comments on quiz10m
Final project advice #310m
1개 연습문제
Mean encodings8m
4
완료하는 데 3시간 필요

Hyperparameter Optimization

In this module we will talk about hyperparameter optimization process. We will also have a special video with practical tips and tricks, recorded by four instructors....
6 videos (Total 86 min), 4 readings, 2 quizzes
6개의 동영상
Hyperparameter tuning II12m
Hyperparameter tuning III13m
Practical guide16m
KazAnova's competition pipeline, part 118m
KazAnova's competition pipeline, part 217m
4개의 읽기 자료
Week 4 overview10m
Comments on quiz10m
Additional material and links10m
Additional materials and links10m
2개 연습문제
Practice quiz6m
Graded quiz8m
완료하는 데 4시간 필요

Advanced feature engineering II

In this module we will learn about a few more advanced feature engineering techniques....
4 videos (Total 22 min), 2 readings, 2 quizzes
4개의 동영상
Matrix factorizations6m
Feature Interactions5m
t-SNE5m
2개의 읽기 자료
Comments on quiz10m
Additional Materials and Links10m
1개 연습문제
Graded Advanced Features II Quiz12m
완료하는 데 10시간 필요

Ensembling

Nowadays it is hard to find a competition won by a single model! Every winning solution incorporates ensembles of models. In this module we will talk about the main ensembling techniques in general, and, of course, how it is better to ensemble the models in practice. ...
8 videos (Total 92 min), 4 readings, 4 quizzes
8개의 동영상
Bagging9m
Boosting16m
Stacking16m
StackNet14m
Ensembling Tips and Tricks14m
CatBoost 17m
CatBoost 27m
4개의 읽기 자료
Validation schemes for 2-nd level models10m
Comments on quiz10m
Additional materials and links10m
Final project advice #410m
2개 연습문제
Ensembling8m
Ensembling12m
5
완료하는 데 2시간 필요

Competitions go through

For the 5th week we've prepared for you several "walk-through" videos. In these videos we discuss solutions to competitions we took prizes at. The video content is quite short this week to let you spend more time on the final project. Good luck!...
6 videos (Total 81 min), 2 readings
6개의 동영상
Springleaf Marketing Response6m
Microsoft Malware Classification Challenge19m
Walmart: Trip Type Classification7m
Acquire Valued Shoppers Challenge, part 119m
Acquire Valued Shoppers Challenge, part 217m
2개의 읽기 자료
Week 5 overview10m
Additional material and links10m
완료하는 데 5시간 필요

Final Project

Final project for the course....
2 quizzes
4.7
127개의 리뷰Chevron Right

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25%

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20%

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최상위 리뷰

대학: MSMar 29th 2018

Top Kagglers gently introduce one to Data Science Competitions. One will have a great chance to learn various tips and tricks and apply them in practice throughout the course. Highly recommended!

대학: GWFeb 19th 2019

Really excellent. Very practical advice from top competitors. This specialization is much more information-dense than most machine learning MOOCs. You really get your money's worth.

강사

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Dmitry Ulyanov

Visiting lecturer
HSE Faculty of Computer Science
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Alexander Guschin

Visiting lecturer at HSE, Lecturer at MIPT
HSE Faculty of Computer Science
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Mikhail Trofimov

Visiting lecturer
HSE Faculty of Computer Science
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Dmitry Altukhov

Visiting lecturer
HSE Faculty of Computer Science
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Marios Michailidis

Research Data Scientist
H2O.ai

국립 연구 고등 경제 대학 정보

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more. Learn more on www.hse.ru...

고급 기계 학습 전문 분야 정보

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings....
고급 기계 학습

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