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上級レベル

約48時間で修了

推奨:6-10 hours/week...

英語

字幕:英語, 韓国語

習得するスキル

Data AnalysisFeature ExtractionFeature EngineeringXgboost

次における1の1コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

上級レベル

約48時間で修了

推奨:6-10 hours/week...

英語

字幕:英語, 韓国語

シラバス - 本コースの学習内容

1
6時間で修了

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件のビデオ (合計46分), 7 readings, 6 quizzes
8件のビデオ
Competition Mechanics6 分
Kaggle Overview [screencast]7 分
Real World Application vs Competitions5 分
Recap of main ML algorithms9 分
Software/Hardware Requirements5 分
7件の学習用教材
Welcome!10 分
Week 1 overview10 分
Disclaimer10 分
Explanation for quiz questions10 分
Additional Materials and Links10 分
Explanation for quiz questions10 分
Additional Material and Links10 分
5の練習問題
Practice Quiz8 分
Recap8 分
Recap12 分
Software/Hardware6 分
Graded Soft/Hard Quiz8 分
2時間で修了

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件のビデオ (合計73分), 4 readings, 4 quizzes
7件のビデオ
Datetime and coordinates8 分
Handling missing values10 分
Bag of words10 分
Word2vec, CNN13 分
4件の学習用教材
Explanation for quiz questions10 分
Additional Material and Links10 分
Explanation for quiz questions10 分
Additional Material and Links10 分
4の練習問題
Feature preprocessing and generation with respect to models8 分
Feature preprocessing and generation with respect to models8 分
Feature extraction from text and images8 分
Feature extraction from text and images8 分
1時間で修了

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件のビデオ (合計4分), 2 readings
1件のビデオ
2件の学習用教材
Final project10 分
Final project advice #110 分
2
2時間で修了

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件のビデオ (合計80分), 2 readings, 1 quiz
8件のビデオ
Visualizations11 分
Dataset cleaning and other things to check7 分
Springleaf competition EDA I8 分
Springleaf competition EDA II16 分
Numerai competition EDA6 分
2件の学習用教材
Week 2 overview10 分
Additional material and links10 分
1の練習問題
Exploratory data analysis12 分
2時間で修了

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件のビデオ (合計51分), 3 readings, 2 quizzes
4件のビデオ
Problems occurring during validation20 分
3件の学習用教材
Validation strategies10 分
Comments on quiz10 分
Additional material and links10 分
2の練習問題
Validation8 分
Validation8 分
5時間で修了

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件のビデオ (合計26分), 3 readings, 3 quizzes
3件の学習用教材
Comments on quiz10 分
Additional material and links10 分
Final project advice #210 分
1の練習問題
Data leakages8 分
3
3時間で修了

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件のビデオ (合計83分), 3 readings, 2 quizzes
8件のビデオ
Classification metrics review20 分
General approaches for metrics optimization6 分
Regression metrics optimization10 分
Classification metrics optimization I7 分
Classification metrics optimization II6 分
3件の学習用教材
Week 3 overview10 分
Comments on quiz10 分
Additional material and links10 分
2の練習問題
Metrics12 分
Metrics12 分
4時間で修了

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件のビデオ (合計27分), 2 readings, 2 quizzes
2件の学習用教材
Comments on quiz10 分
Final project advice #310 分
1の練習問題
Mean encodings8 分
4
3時間で修了

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件のビデオ (合計86分), 4 readings, 2 quizzes
6件のビデオ
Practical guide16 分
KazAnova's competition pipeline, part 118 分
KazAnova's competition pipeline, part 217 分
4件の学習用教材
Week 4 overview10 分
Comments on quiz10 分
Additional material and links10 分
Additional materials and links10 分
2の練習問題
Practice quiz6 分
Graded quiz8 分
4時間で修了

Advanced feature engineering II

In this module we will learn about a few more advanced feature engineering techniques.

...
4件のビデオ (合計22分), 2 readings, 2 quizzes
2件の学習用教材
Comments on quiz10 分
Additional Materials and Links10 分
1の練習問題
Graded Advanced Features II Quiz12 分
10時間で修了

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件のビデオ (合計92分), 4 readings, 4 quizzes
8件のビデオ
Bagging9 分
Boosting16 分
Stacking16 分
StackNet14 分
Ensembling Tips and Tricks14 分
CatBoost 17 分
CatBoost 27 分
4件の学習用教材
Validation schemes for 2-nd level models10 分
Comments on quiz10 分
Additional materials and links10 分
Final project advice #410 分
2の練習問題
Ensembling8 分
Ensembling12 分
4.7
135件のレビューChevron Right

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How to Win a Data Science Competition: Learn from Top Kagglers からの人気レビュー

by 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!

by MMNov 10th 2017

This course is fantastic. It's chock full of practical information that is presented clearly and concisely. I would like to thank the team for sharing their knowledge so generously.

講師

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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

ロシア国立研究大学経済高等学院(National Research University Higher School of Economics)について

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...

Advanced Machine Learningの専門講座について

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....
Advanced Machine Learning

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