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How to Win a Data Science Competition: Learn from Top Kagglers, Национальный исследовательский университет "Высшая школа экономики"

Оценки: 465
Рецензии: 107

Об этом курсе

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....

Лучшие рецензии

автор: 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!

автор: MM

Nov 10, 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.

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Рецензии: 107

автор: 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.

автор: 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

автор: 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


Feb 16, 2019


автор: Hiromichi Izuoka

Feb 08, 2019


автор: Diego Alexis Galván Sandoval

Feb 04, 2019

Very good course

автор: Vytenis Pranculis

Jan 28, 2019

Course has good tips, but should not be in this specialization

автор: Igor Buzhinskii

Jan 27, 2019

This course requires much time, but gives hardcore experience in practical data science and machine learning. The final project, which is a proving ground for the acquired skills, is both an interesting competition to participate in and a real-world-task.

автор: 林佳佑

Jan 26, 2019

this course is helpful and important for one who become a data science expert, a lot key skill import in dealing data

автор: Vishal Bajaj

Jan 25, 2019

Really great course, with so great insights! I really enjoyed the talks on feature engineering and ensemble methods!