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How to Win a Data Science Competition: Learn from Top Kagglers, National Research University Higher School of Economics

4.7
492 ratings
115 reviews

About this Course

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. In this course, you will learn to analyse and solve competitively such predictive modelling tasks. When you finish this class, you will: - Understand how to solve predictive modelling competitions efficiently and learn which of the skills obtained can be applicable to real-world tasks. - Learn how to preprocess the data and generate new features from various sources such as text and images. - Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions. - Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data. - Gain experience of analysing and interpreting the data. You will become aware of inconsistencies, high noise levels, errors and other data-related issues such as leakages and you will learn how to overcome them. - Acquire knowledge of different algorithms and learn how to efficiently tune their hyperparameters and achieve top performance. - Master the art of combining different machine learning models and learn how to ensemble. - Get exposed to past (winning) solutions and codes and learn how to read them. Disclaimer : This is not a machine learning course in the general sense. This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them. Prerequisites: - Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from scikit-learn, XGBoost, LightGBM. - Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting and neural networks....

Top reviews

By MS

Mar 29, 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 GW

Feb 19, 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.

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113 Reviews

By Wesley André Bortolozo Júnior

Mar 16, 2019

Loving the course se far, ending 3rd week now. Very well explained conpets.

By Bapi Reddy

Mar 14, 2019

One of the best course for practical ml

By MASSON

Mar 11, 2019

Great course.

Even if some lessons may seem too theorical, it all comes together during the final project which pushes you to look back and apply what you learned.

By Milos Vlainic

Mar 08, 2019

Very interesting course, and the most practical and useful one. However, lecture are usually too theoretical and super-simple, while assignments are tough and very code oriented. So often there is no real connection between the two (except for Dmitry Altukhov). And final project is too difficult in sense that my Alienware 16 RAM was not enough, so I had to go to Google Cloud Platform. Also, I am not sure is anybody who is learning Machine Learning possible to do the final task in "6 hours" as solely runs could last for a day...

By Xiukun Hu

Feb 25, 2019

Great course to learn practical skills. I love the painful final project.

By Steven Apsel

Feb 25, 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.

By Greg Whittier

Feb 19, 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 Andreas Born

Feb 19, 2019

Really great course learned a lot. The only reason that I did not give 5 stars is that the task in some assignments could be explained somewhat clearer (would have saved me a lot of time) and especially also the scope of the final project. In hintsight after reviewing others, i spend way too much time :P

By Louis Hulot

Feb 17, 2019

A must for every data scientist, the courses are amazing and you learn a lot a tips.

If you have just started data science, you’ll be able to follow the course but you may not understand all the underlying ideas

By

Feb 16, 2019

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