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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

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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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151 - 170 de 170 revisiones para How to Win a Data Science Competition: Learn from Top Kagglers

por Roland B

Feb 24, 2020

Bon cours qui permet d'aller plus loin dans son apprentissage du machine learning.

Je regrette qu'il n'y ait pas plus de travaux pratiques sur différents datasets qui nécessiteraient différentes approches (on travaille essentiellement sur le même dataset avec des techniques de plus en plus évoluées).

por Øystein S

Jan 07, 2018

Some of the stuff is really great! I learned a lot. Thanks. On the other hand, there are some bugs in the codes provided, specially in the additional assignment in week 3. Bugs in the online assignment grader and so on... without the bugs I would have rated this 5 stars.

por Matt V

Jul 30, 2018

Great course. Very challenging. My only real complaint is about the limitations on the frequency of final project submission (even if the submission is ungraded for any reason) which are a little unreasonable.

por Ronak K

Jan 14, 2020

Very good course for intermediate to the advanced level group. It covers various number of models and practical approach which can be used in Competitions in the Kaggle and also in a real-world problem.

por 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

por Vytenis P

Jan 28, 2019

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

por Benjamin F

Feb 02, 2018

The final project is tough, but it's worth it !

por Daya_Jin

Jun 14, 2018

内容非常不错,就是作业比较难,光看视频很难把作业完成。要是视频中能讲的再详细点就好了

por Mahboob A

Apr 16, 2019

my first was week awesome!

por MHD K M

Mar 31, 2020

amazing lecturers

por Anders P

Sep 27, 2018

Learned a lot

por HA

Jan 03, 2020

Lots of useful information, but far from what you need to win a data science competition. So i suggest the title be changed to "Basics to start learning how to compete in a Kaggle competition.- Learn by PAST top Competitors" . As they will not help you at all in current competitions, only what they have done in the past, which is of public knowledge already.

por Hiromichi I

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。

por Waylon W

Dec 20, 2018

This course is Okay but not perfect. I learned something from this course.

por Cao A Q

Apr 20, 2019

Terrible accent

por Enrique C M

Feb 23, 2018

Very interesting, original and revealing materials and tricks to tackle competitive machine learning more efficiently.

Sadly, teaching is quite poor and shallow, focusing on personal examples and "I did that and it just worked"-type of experiences that introduce more noise rather than clearing the way to a full understanding of issues during ML competitions (this also includes practical examples).

I guess in a second version and after a review on the teaching methods, this course could be easily a must for ML engineers on the making.

por Refik E

Jan 25, 2018

Content is interesting but language is very difficult to understand and due to that fact the course was not engaging for me.

por HenryYao

May 18, 2018

a little too difficult for new pandas learner, some quizzes are confusing, the reply from the teachers is slow.

por Manuel M B

Mar 23, 2020

Si bien los instructores tienen mucha experiencia en Kaggle, no me resultó muy util a la hora de aprender Data science para un entorno empresarial y por proyectos. Si tu objetivo es dedicarte a estas competencias te lo recomiendo, pero como parte de una especialización en ciencia de datos, No.

por Maciej

Jan 10, 2019

Very dry presentation. Video is not a good medium for this material.