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

Opiniones y comentarios de aprendices correspondientes a How to Win a Data Science Competition: Learn from Top Kagglers por parte de National Research University Higher School of Economics

4.7
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810 calificaciones
171 revisiones

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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. Do you have technical problems? Write to us: coursera@hse.ru...

Principales revisiones

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!

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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126 - 150 de 170 revisiones para How to Win a Data Science Competition: Learn from Top Kagglers

por Ujjwal U

Jan 27, 2018

Exceptional course!

por Moti T

Jan 04, 2018

Interesting and fun

por Chiang y

Jul 26, 2018

Excellent class!!!

por GUO S

Jul 23, 2018

Like it very much!

por Mauricio D A

Nov 19, 2017

Very nice tricks!

por Diego A G S

Feb 04, 2019

Very good course

por himanshu m t

Jan 23, 2018

really great..!!

por PRASHANT K R

Jul 21, 2018

awesome course

por Aditya S

May 01, 2018

Amazing Course

por Mike K

Jan 17, 2019

Отличный курс

por Ivan S

Jan 12, 2019

Great course!

por Amandeep S

Jan 14, 2019

Great Course

por PC P K

May 17, 2018

great course

por Harsh N

Jan 21, 2018

Hammer lol

por Марчевский В Д

Sep 12, 2018

Good one!

por Alexey B

Mar 19, 2018

Good job!

por Nicolás M C

Mar 21, 2020

good

por Yan L

Sep 01, 2019

nice

por wzm

Apr 15, 2018

nice

por Siwei Y

Apr 03, 2018

膜拜一下

por Anish G

Mar 03, 2018

Great course. Teaches you a lot of techniques and hands-on assignments. The course covers extensively on how to achieve a better score in Kaggle with tips and techniques. The real-world data science would be slightly different to this. But nevertheless, the content is refreshing along with the links, supplement materials associated.

I would have given a 5-star rating if not the russian accent which is not clear at times (the subtitles don't help much either) and the badly worded assignments that can leave you pondering over a simple question for hours.

por Chan H Y

Jun 03, 2018

I think some of the materials in this course are useful for competition only. People in academia may think some of these techniques are non-standard (or lack of solid theoretical ground), while commercial world may think some of the techniques non practical (e.g. ensemble several ten or even hundred systems, or the methods are not generalize for different environments). Yet, this course still provide pretty much useful information (and you can always learn something from different people)

por Md A R

Feb 24, 2020

As this an advance course it is assumed that you have prior knowledge of lots of topics. Moreover, you may have a hard time comprehending lots of topics. So, you have to invest a good amount of time here. Furthermore, the assignments are really challenging so don't take this granted. If you have just started learning Machine Learning you will get to know some amazing topics and approaches that will improve your result. Happy Learning!!!

por Andreas B

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