About this Course

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Learner Career Outcomes

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got a tangible career benefit from this course

20%

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Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 53 hours to complete
English
Subtitles: English, Korean

Skills you will gain

Data AnalysisFeature ExtractionFeature EngineeringXgboost

Learner Career Outcomes

14%

started a new career after completing these courses

22%

got a tangible career benefit from this course

20%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 53 hours to complete
English
Subtitles: English, Korean

Offered by

National Research University Higher School of Economics logo

National Research University Higher School of Economics

Syllabus - What you will learn from this course

Content RatingThumbs Up94%(11,920 ratings)Info
Week
1

Week 1

8 hours to complete

Introduction & Recap

8 hours to complete
9 videos (Total 48 min), 8 readings, 6 quizzes
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 readings
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 practice exercises
Practice Quiz30m
Recap30m
Recap30m
Software/Hardware30m
Graded Soft/Hard Quiz30m
4 hours to complete

Feature Preprocessing and Generation with Respect to Models

4 hours to complete
7 videos (Total 73 min), 4 readings, 4 quizzes
7 videos
Numeric features13m
Categorical and ordinal features10m
Datetime and coordinates8m
Handling missing values10m
Bag of words10m
Word2vec, CNN13m
4 readings
Explanation for quiz questions10m
Additional Material and Links10m
Explanation for quiz questions10m
Additional Material and Links10m
4 practice exercises
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 hour to complete

Final Project Description

1 hour to complete
1 video (Total 4 min), 2 readings
2 readings
Final project10m
Final project advice #110m
Week
2

Week 2

2 hours to complete

Exploratory Data Analysis

2 hours to complete
8 videos (Total 80 min), 2 readings, 1 quiz
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 readings
Week 2 overview10m
Additional material and links10m
1 practice exercise
Exploratory data analysis12m
2 hours to complete

Validation

2 hours to complete
4 videos (Total 51 min), 3 readings, 2 quizzes
4 videos
Validation strategies7m
Data splitting strategies14m
Problems occurring during validation20m
3 readings
Validation strategies10m
Comments on quiz10m
Additional material and links10m
2 practice exercises
Validation30m
Validation30m
5 hours to complete

Data Leakages

5 hours to complete
3 videos (Total 26 min), 3 readings, 3 quizzes
3 videos
Leaderboard probing and examples of rare data leaks9m
Expedia challenge9m
3 readings
Comments on quiz10m
Additional material and links10m
Final project advice #210m
1 practice exercise
Data leakages30m
Week
3

Week 3

3 hours to complete

Metrics Optimization

3 hours to complete
8 videos (Total 83 min), 3 readings, 2 quizzes
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 readings
Week 3 overview10m
Comments on quiz10m
Additional material and links10m
2 practice exercises
Metrics30m
Metrics30m
4 hours to complete

Advanced Feature Engineering I

4 hours to complete
3 videos (Total 27 min), 2 readings, 2 quizzes
3 videos
Regularization7m
Extensions and generalizations10m
2 readings
Comments on quiz10m
Final project advice #310m
1 practice exercise
Mean encodings30m
Week
4

Week 4

3 hours to complete

Hyperparameter Optimization

3 hours to complete
6 videos (Total 86 min), 4 readings, 2 quizzes
6 videos
Hyperparameter tuning II12m
Hyperparameter tuning III13m
Practical guide16m
KazAnova's competition pipeline, part 118m
KazAnova's competition pipeline, part 217m
4 readings
Week 4 overview10m
Comments on quiz10m
Additional material and links10m
Additional materials and links10m
2 practice exercises
Practice quiz30m
Graded quiz30m
4 hours to complete

Advanced feature engineering II

4 hours to complete
4 videos (Total 22 min), 2 readings, 2 quizzes
4 videos
Matrix factorizations6m
Feature Interactions5m
t-SNE5m
2 readings
Comments on quiz10m
Additional Materials and Links10m
1 practice exercise
Graded Advanced Features II Quiz30m
10 hours to complete

Ensembling

10 hours to complete
8 videos (Total 92 min), 4 readings, 4 quizzes
8 videos
Bagging9m
Boosting16m
Stacking16m
StackNet14m
Ensembling Tips and Tricks14m
CatBoost 17m
CatBoost 27m
4 readings
Validation schemes for 2-nd level models10m
Comments on quiz10m
Additional materials and links10m
Final project advice #410m
2 practice exercises
Ensembling30m
Ensembling30m

Reviews

TOP REVIEWS FROM HOW TO WIN A DATA SCIENCE COMPETITION: LEARN FROM TOP KAGGLERS

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About the Advanced Machine Learning Specialization

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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  • 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.

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