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

Higher School of Economics via Coursera

2 Reviews 206 students interested
  • Provider Coursera
  • Subject Data Science
  • Cost Free Online Course (Audit)
  • Session Upcoming
  • Language English
  • Certificate Paid Certificate Available
  • Effort 6-10 hours a week
  • Start Date
  • Duration 5 weeks long
  • Learn more about MOOCs

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Overview

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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.

Taught by

Alexander Guschin, Dmitry Ulyanov, Marios Michailidis, Dmitry Altukhov and Mikhail Trofimov

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Reviews for Coursera's How to Win a Data Science Competition: Learn from Top Kagglers
5.0 Based on 2 reviews

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  • 1
Anonymous
5.0 2 months ago
Anonymous is taking this course right now.
One of the best courses on Coursera ever! Highly recommended! A lot of really useful info without tons of blah-blah-blah, as in most courses from the real professional.

The only downside is a terrible accent of a professor. And subtitles (i guess due to this accent) are owful, sometimes changing the meaning dramatically. So, not recommended for those, who can understand oral speech only when reading subtitles.

Was this review helpful to you? Yes
Anonymous
5.0 2 months ago
Anonymous is taking this course right now.
It is really a very useful course where we get to learn from experienced people who achieved top ranks on Kaggle. It is a practical course with many handy tips. However, the accent of some instructors is not very understandable, and the subtitles are auto-generated which makes it hard sometimes to understand what's being said.
Was this review helpful to you? Yes
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