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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
460 notes
105 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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104 avis

par

Feb 16, 2019

Practice

par Hiromichi Izuoka

Feb 08, 2019

実際にタスクを与えられたときに、EDAからモデリング、チューニングまで網羅的に触れている点については非常に良かったです。しかし、クイズの解答に疑問が残る点が散見し、かつ、アンサンブル学習のプログラミング課題においてはLightGBMのバージョンが違うと正解にならない点については良くないと感じました。

par Diego Alexis Galván Sandoval

Feb 04, 2019

Very good course

par Vytenis Pranculis

Jan 28, 2019

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

par 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.

par 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

par Vishal Bajaj

Jan 25, 2019

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

par Amit Kumar Singh

Jan 20, 2019

This is so good. Three reasons (1) Helps me revisit the concepts that I learnt in the machine learning course. (2) Helps me to deal with my FOMO (3) I would feel most confident to go for my Data Science or Data Engineering interviews.

par Mike Korotkov

Jan 17, 2019

Отличный курс

par Amandeep Singh

Jan 14, 2019

Great Course