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How to Win a Data Science Competition: Learn from Top Kagglers, Université nationale de recherche, École des hautes études en sciences économiques

4.7
527 notes
121 avis

À propos de ce cours

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....

Meilleurs avis

par 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!

par 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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120 avis

par Joseph Burdis

Apr 18, 2019

Very nice course and final project is actually challenging and a great learning experience when learner attempts to do it completely on their own without reading forums or looking at examples on Kaggle.

par Mahboob Alam

Apr 16, 2019

my first was week awesome!

par Fabrice Lacout

Apr 11, 2019

A looooot of content!!!

I like the fact that it talk about broad data science topics, and doesn't specialize into one specific domain. You gain some good tricks about pandas, EDA, modeling, feature engineering... etc The skill coverage is very wide.

This is definitely advance, and challenging soemtimes, but you'll learn a lot.

par Stephane Hemery

Apr 10, 2019

Great course, truly invaluable information in there, also the hardest i've ever done, took me months and a couple hundred hours. The knowledge and experience you gain is incredible, not for the faint of heart though.

par Vratislav Havlík

Mar 29, 2019

It is diffifult but when you reach the end, you are glad that you were able to finish it. Because I gained a lot of knowledge and best practices. There is a lot of work but it helps you sharpen your brain. I recommend to work simultaneously on project because otherwise it will be difficult to finish it..

par Nikolay Chervyakov

Mar 25, 2019

Excellent course! Previously I had a small experience in Kaggle competitions, but this course really charged me with new superskills! :)

par Ramil Gizatullin

Mar 25, 2019

This course provides some unique knowledge you can't obtain anywhere else

par Wesley André Bortolozo Júnior

Mar 16, 2019

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

par Bapi Reddy

Mar 14, 2019

One of the best course for practical ml

par 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.