If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science.
This course is part of the Advanced Machine Learning Specialization
Offered By
Advanced Machine Learning SpecializationNational Research University Higher School of EconomicsAbout this Course
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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.
Course 2 of 7 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 53 hours to complete
English
Subtitles: French, Portuguese (Brazilian), Korean, Russian, English, Spanish
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.
Course 2 of 7 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 53 hours to complete
English
Subtitles: French, Portuguese (Brazilian), Korean, Russian, English, Spanish
Offered by
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.
Syllabus - What you will learn from this course
8 hours to complete
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 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
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.
4 hours to complete
7 videos (Total 73 min), 4 readings, 4 quizzes
7 videos
Overview6m
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
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 hour to complete
1 video (Total 4 min), 2 readings
1 video
2 readings
Final project10m
Final project advice #110m
2 hours to complete
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.
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
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.
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
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.
5 hours to complete
3 videos (Total 26 min), 3 readings, 3 quizzes
3 readings
Comments on quiz10m
Additional material and links10m
Final project advice #210m
1 practice exercise
Data leakages30m
3 hours to complete
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.
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
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.
4 hours to complete
3 videos (Total 27 min), 2 readings, 2 quizzes
2 readings
Comments on quiz10m
Final project advice #310m
1 practice exercise
Mean encodings30m
3 hours to complete
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.
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
In this module we will learn about a few more advanced feature engineering techniques.
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
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.
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
by MSMar 28, 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 9, 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.
by GWFeb 18, 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.
by SAFeb 24, 2019
I really enjoyed this course but it was probably 2-3 times more work than I anticipated. Most of that extra time comes from working on the final project, testing things out, etc.
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.
Frequently Asked Questions
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What will I get if I subscribe to this Specialization?
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Is financial aid available?
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Will I earn university credit for completing the Course?
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