Über diesen Kurs
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Empfohlen: 6-10 hours/week...

Englisch

Untertitel: Englisch, Koreanisch

Kompetenzen, die Sie erwerben

Data AnalysisFeature ExtractionFeature EngineeringXgboost

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Fortgeschritten“

Ca. 48 Stunden zum Abschließen

Empfohlen: 6-10 hours/week...

Englisch

Untertitel: Englisch, Koreanisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
6 Stunden zum Abschließen

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.

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8 Videos (Gesamt 46 min), 7 Lektüren, 6 Quiz
8 Videos
Competition Mechanics6m
Kaggle Overview [screencast]7m
Real World Application vs Competitions5m
Recap of main ML algorithms9m
Software/Hardware Requirements5m
7 Lektüren
Welcome!10m
Week 1 overview10m
Disclaimer10m
Explanation for quiz questions10m
Additional Materials and Links10m
Explanation for quiz questions10m
Additional Material and Links10m
5 praktische Übungen
Practice Quiz8m
Recap8m
Recap12m
Software/Hardware6m
Graded Soft/Hard Quiz8m
2 Stunden zum Abschließen

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.

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7 Videos (Gesamt 73 min), 4 Lektüren, 4 Quiz
7 Videos
Datetime and coordinates8m
Handling missing values10m
Bag of words10m
Word2vec, CNN13m
4 Lektüren
Explanation for quiz questions10m
Additional Material and Links10m
Explanation for quiz questions10m
Additional Material and Links10m
4 praktische Übungen
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
1 Stunde zum Abschließen

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.

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1 Video (Gesamt 4 min), 2 Lektüren
2 Lektüren
Final project10m
Final project advice #110m
Woche
2
2 Stunden zum Abschließen

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.

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8 Videos (Gesamt 80 min), 2 Lektüren, 1 Quiz
8 Videos
Visualizations11m
Dataset cleaning and other things to check7m
Springleaf competition EDA I8m
Springleaf competition EDA II16m
Numerai competition EDA6m
2 Lektüren
Week 2 overview10m
Additional material and links10m
1 praktische Übung
Exploratory data analysis12m
2 Stunden zum Abschließen

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.

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4 Videos (Gesamt 51 min), 3 Lektüren, 2 Quiz
4 Videos
Problems occurring during validation20m
3 Lektüren
Validation strategies10m
Comments on quiz10m
Additional material and links10m
2 praktische Übungen
Validation8m
Validation8m
5 Stunden zum Abschließen

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.

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3 Videos (Gesamt 26 min), 3 Lektüren, 3 Quiz
3 Lektüren
Comments on quiz10m
Additional material and links10m
Final project advice #210m
1 praktische Übung
Data leakages8m
Woche
3
3 Stunden zum Abschließen

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.

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8 Videos (Gesamt 83 min), 3 Lektüren, 2 Quiz
8 Videos
Classification metrics review20m
General approaches for metrics optimization6m
Regression metrics optimization10m
Classification metrics optimization I7m
Classification metrics optimization II6m
3 Lektüren
Week 3 overview10m
Comments on quiz10m
Additional material and links10m
2 praktische Übungen
Metrics12m
Metrics12m
4 Stunden zum Abschließen

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.

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3 Videos (Gesamt 27 min), 2 Lektüren, 2 Quiz
2 Lektüren
Comments on quiz10m
Final project advice #310m
1 praktische Übung
Mean encodings8m
Woche
4
3 Stunden zum Abschließen

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.

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6 Videos (Gesamt 86 min), 4 Lektüren, 2 Quiz
6 Videos
Practical guide16m
KazAnova's competition pipeline, part 118m
KazAnova's competition pipeline, part 217m
4 Lektüren
Week 4 overview10m
Comments on quiz10m
Additional material and links10m
Additional materials and links10m
2 praktische Übungen
Practice quiz6m
Graded quiz8m
4 Stunden zum Abschließen

Advanced feature engineering II

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

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4 Videos (Gesamt 22 min), 2 Lektüren, 2 Quiz
2 Lektüren
Comments on quiz10m
Additional Materials and Links10m
1 praktische Übung
Graded Advanced Features II Quiz12m
10 Stunden zum Abschließen

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.

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8 Videos (Gesamt 92 min), 4 Lektüren, 4 Quiz
8 Videos
Stacking16m
StackNet14m
Ensembling Tips and Tricks14m
CatBoost 17m
CatBoost 27m
4 Lektüren
Validation schemes for 2-nd level models10m
Comments on quiz10m
Additional materials and links10m
Final project advice #410m
2 praktische Übungen
Ensembling8m
Ensembling12m
4.7
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Top reviews from How to Win a Data Science Competition: Learn from Top Kagglers

von 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!

von 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.

Dozenten

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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

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