Acerca de este Curso

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Resultados profesionales del estudiante

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
Certificado para compartir
Obtén un certificado al finalizar
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. 53 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano

Habilidades que obtendrás

Data AnalysisFeature ExtractionFeature EngineeringXgboost

Resultados profesionales del estudiante

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
Certificado para compartir
Obtén un certificado al finalizar
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. 53 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano

ofrecido por

Logotipo de National Research University Higher School of Economics

National Research University Higher School of Economics

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up94%(11,793 calificaciones)Info
Semana
1

Semana 1

8 horas para completar

Introduction & Recap

8 horas para completar
9 videos (Total 48 minutos), 8 lecturas, 6 cuestionarios
9 videos
Introduction1m
Meet your lecturers2m
Course overview7m
Competition Mechanics6m
Kaggle Overview [screencast]7m
Real World Application vs Competitions5m
Recap of main ML algorithms9m
Software/Hardware Requirements5m
8 lecturas
About the University10m
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 Quiz30m
Recap30m
Recap30m
Software/Hardware30m
Graded Soft/Hard Quiz30m
4 horas para completar

Feature Preprocessing and Generation with Respect to Models

4 horas para completar
7 videos (Total 73 minutos), 4 lecturas, 4 cuestionarios
7 videos
Numeric features13m
Categorical and ordinal features10m
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 models30m
Feature preprocessing and generation with respect to models30m
Feature extraction from text and images30m
Feature extraction from text and images30m
1 hora para completar

Final Project Description

1 hora para completar
1 video (Total 4 minutos), 2 lecturas
2 lecturas
Final project10m
Final project advice #110m
Semana
2

Semana 2

2 horas para completar

Exploratory Data Analysis

2 horas para completar
8 videos (Total 80 minutos), 2 lecturas, 1 cuestionario
8 videos
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
2 lecturas
Week 2 overview10m
Additional material and links10m
1 ejercicio de práctica
Exploratory data analysis12m
2 horas para completar

Validation

2 horas para completar
4 videos (Total 51 minutos), 3 lecturas, 2 cuestionarios
4 videos
Validation strategies7m
Data splitting strategies14m
Problems occurring during validation20m
3 lecturas
Validation strategies10m
Comments on quiz10m
Additional material and links10m
2 ejercicios de práctica
Validation30m
Validation30m
5 horas para completar

Data Leakages

5 horas para completar
3 videos (Total 26 minutos), 3 lecturas, 3 cuestionarios
3 videos
Leaderboard probing and examples of rare data leaks9m
Expedia challenge9m
3 lecturas
Comments on quiz10m
Additional material and links10m
Final project advice #210m
1 ejercicio de práctica
Data leakages30m
Semana
3

Semana 3

3 horas para completar

Metrics Optimization

3 horas para completar
8 videos (Total 83 minutos), 3 lecturas, 2 cuestionarios
8 videos
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
3 lecturas
Week 3 overview10m
Comments on quiz10m
Additional material and links10m
2 ejercicios de práctica
Metrics30m
Metrics30m
4 horas para completar

Advanced Feature Engineering I

4 horas para completar
3 videos (Total 27 minutos), 2 lecturas, 2 cuestionarios
3 videos
Regularization7m
Extensions and generalizations10m
2 lecturas
Comments on quiz10m
Final project advice #310m
1 ejercicio de práctica
Mean encodings30m
Semana
4

Semana 4

3 horas para completar

Hyperparameter Optimization

3 horas para completar
6 videos (Total 86 minutos), 4 lecturas, 2 cuestionarios
6 videos
Hyperparameter tuning II12m
Hyperparameter tuning III13m
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 quiz30m
Graded quiz30m
4 horas para completar

Advanced feature engineering II

4 horas para completar
4 videos (Total 22 minutos), 2 lecturas, 2 cuestionarios
4 videos
Matrix factorizations6m
Feature Interactions5m
t-SNE5m
2 lecturas
Comments on quiz10m
Additional Materials and Links10m
1 ejercicio de práctica
Graded Advanced Features II Quiz30m
10 horas para completar

Ensembling

10 horas para completar
8 videos (Total 92 minutos), 4 lecturas, 4 cuestionarios
8 videos
Bagging9m
Boosting16m
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
Ensembling30m
Ensembling30m

Revisiones

Principales revisiones sobre HOW TO WIN A DATA SCIENCE COMPETITION: LEARN FROM TOP KAGGLERS

Ver todos los comentarios

Acerca de 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

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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