Acerca de este Curso
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Fechas límite flexibles

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Nivel avanzado

Aprox. 48 horas para completar

Sugerido: 6-10 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano

Habilidades que obtendrás

Data AnalysisFeature ExtractionFeature EngineeringXgboost

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 48 horas para completar

Sugerido: 6-10 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano

Programa - Qué aprenderás en este curso

Semana
1
6 horas para completar

Introduction & Recap

8 videos (Total 46 minutos), 7 readings, 6 quizzes
8 videos
Competition Mechanics6m
Kaggle Overview [screencast]7m
Real World Application vs Competitions5m
Recap of main ML algorithms9m
Software/Hardware Requirements5m
7 lecturas
Welcome!10m
Week 1 overview10m
Disclaimer10m
Explanation for quiz questions10m
Additional Materials and Links10m
Explanation for quiz questions10m
Additional Material and Links10m
5 ejercicios de práctica
Practice Quiz8m
Recap8m
Recap12m
Software/Hardware6m
Graded Soft/Hard Quiz8m
2 horas para completar

Feature Preprocessing and Generation with Respect to Models

7 videos (Total 73 minutos), 4 readings, 4 quizzes
7 videos
Datetime and coordinates8m
Handling missing values10m
Bag of words10m
Word2vec, CNN13m
4 lecturas
Explanation for quiz questions10m
Additional Material and Links10m
Explanation for quiz questions10m
Additional Material and Links10m
4 ejercicios de práctica
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 horas para completar

Final Project Description

1 videos (Total 4 minutos), 2 readings
1 videos
2 lecturas
Final project10m
Final project advice #110m
Semana
2
2 horas para completar

Exploratory Data Analysis

8 videos (Total 80 minutos), 2 readings, 1 quiz
8 videos
Visualizations11m
Dataset cleaning and other things to check7m
Springleaf competition EDA I8m
Springleaf competition EDA II16m
Numerai competition EDA6m
2 lecturas
Week 2 overview10m
Additional material and links10m
1 ejercicios de práctica
Exploratory data analysis12m
2 horas para completar

Validation

4 videos (Total 51 minutos), 3 readings, 2 quizzes
4 videos
Problems occurring during validation20m
3 lecturas
Validation strategies10m
Comments on quiz10m
Additional material and links10m
2 ejercicios de práctica
Validation8m
Validation8m
5 horas para completar

Data Leakages

3 videos (Total 26 minutos), 3 readings, 3 quizzes
3 lecturas
Comments on quiz10m
Additional material and links10m
Final project advice #210m
1 ejercicios de práctica
Data leakages8m
Semana
3
3 horas para completar

Metrics Optimization

8 videos (Total 83 minutos), 3 readings, 2 quizzes
8 videos
Classification metrics review20m
General approaches for metrics optimization6m
Regression metrics optimization10m
Classification metrics optimization I7m
Classification metrics optimization II6m
3 lecturas
Week 3 overview10m
Comments on quiz10m
Additional material and links10m
2 ejercicios de práctica
Metrics12m
Metrics12m
4 horas para completar

Advanced Feature Engineering I

3 videos (Total 27 minutos), 2 readings, 2 quizzes
2 lecturas
Comments on quiz10m
Final project advice #310m
1 ejercicios de práctica
Mean encodings8m
Semana
4
3 horas para completar

Hyperparameter Optimization

6 videos (Total 86 minutos), 4 readings, 2 quizzes
6 videos
Practical guide16m
KazAnova's competition pipeline, part 118m
KazAnova's competition pipeline, part 217m
4 lecturas
Week 4 overview10m
Comments on quiz10m
Additional material and links10m
Additional materials and links10m
2 ejercicios de práctica
Practice quiz6m
Graded quiz8m
4 horas para completar

Advanced feature engineering II

4 videos (Total 22 minutos), 2 readings, 2 quizzes
2 lecturas
Comments on quiz10m
Additional Materials and Links10m
1 ejercicios de práctica
Graded Advanced Features II Quiz12m
10 horas para completar

Ensembling

8 videos (Total 92 minutos), 4 readings, 4 quizzes
8 videos
Stacking16m
StackNet14m
Ensembling Tips and Tricks14m
CatBoost 17m
CatBoost 27m
4 lecturas
Validation schemes for 2-nd level models10m
Comments on quiz10m
Additional materials and links10m
Final project advice #410m
2 ejercicios de práctica
Ensembling8m
Ensembling12m
4.7
141 revisionesChevron Right

14%

comenzó una nueva carrera después de completar estos cursos

22%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

20%

consiguió un aumento de sueldo o ascenso

Principales revisiones sobre How to Win a Data Science Competition: Learn from Top Kagglers

por 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!

por 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.

Instructores

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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

Acerca de National Research University Higher School of Economics

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...

Acerca del programa especializado Aprendizaje automático avanzado

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....
Aprendizaje automático avanzado

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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