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返回到 How to Win a Data Science Competition: Learn from Top Kagglers

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

4.7
408 个评分
88 个审阅

课程信息

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....

热门审阅

创建者 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!

创建者 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.

筛选依据:

87 个审阅

创建者 Oleg Ovcharenko

Dec 09, 2018

Very handy course, except I wasn't motivated enough to do home assignments. However, I gained a lot of new concepts

创建者 Yu Qinyuan

Dec 05, 2018

I competed this course within almost 3 months, far more time than I planed. The most time I spent on was to create new features via feature engineering and verify the cross-validation method. This course was difficult, but very helpful and inspirational. Thanks to each teacher and tutor!

创建者 superfantastic

Nov 06, 2018

One of the Most Great course I have participate in . Thank you for all the instructors.

创建者 yanqiang

Oct 29, 2018

从理论到实践,不错

创建者 Prashanth T

Oct 26, 2018

By far the most useful course i have ever taken! :)

创建者 Evgeny Kriukov

Oct 18, 2018

Learned many interesting moments in competitive ML! The course is systematic and structured very good, recommend!

创建者 李继杨霖

Oct 17, 2018

This is the first course I've finished on coursera. At the beginning, the motivation of taking this course is only to get an better score in Kaggle competition because I major in statistics and am interested in data science. But during the processing of learning, I found many important ideas and experience to deal with the real problem and enjoyed the communication with other people from forum and Kaggle, I also aquired some special experience such as peer review, which is not only very fun but also can provide me different aspects to see the problem I'm dealing with again. Thanks Dmitry Ulyanov, Alexander Guschin, Mikhail Trofimov, Dmitry Altukhov, and Marios Michailidis for sharing your important knowledge and experience with us.

创建者 Steffen Röhrsheim

Oct 16, 2018

extremely bad supported.

创建者 Shanaya Mehta

Oct 12, 2018

Very informative course. Aspiring data scientists could benefit greatly through this course.

创建者 Mark Pinches

Oct 09, 2018

This is a fantastic course for anyone looking to extend their skills in data science. Its packed full of tips and tricks and techniques that are well explained and very useful for data science. I would go so far as saying that it has been my favorite data science course OF ALL TIME!