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Avis et commentaires pour d'étudiants pour How to Win a Data Science Competition: Learn from Top Kagglers par Université HSE

4.7
étoiles
1,113 évaluations
274 avis

À propos du 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 online 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...

Meilleurs avis

MS
28 mars 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
9 nov. 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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226 - 250 sur 273 Avis pour How to Win a Data Science Competition: Learn from Top Kagglers

par Yan L

1 sept. 2019

nice

par wzm

15 avr. 2018

nice

par Siwei Y

3 avr. 2018

膜拜一下

par Dezhen G

3 févr. 2021

ok

par Kamal S

1 oct. 2020

.

par Anish G

3 mars 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.

par Ho Y C

2 juin 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)

par Cristhian J P S

6 mai 2020

This course has had useful information about the Kaggle Competitions and all weeks you can look at the workflow related to archive a "good" LB score for the Predict Future Sales - Kaggle competition.

The team has a lot of experience and tips for the competition that permits get a view of Kaggle.

At this moment, the most problem is the team didn't any advice if you have any questions. Of course, I'm taking its long time when opening the course.

par Md A R

24 févr. 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!!!

par Jhon F M C

2 juil. 2020

This course was really challenging. It let me get a wide vision about the way to face related projects. The theory section gave me good support to keep on with my education, and the project let me to gain experience using tools like Python, Jupyter notebooks, and some others libraries and modules. Free software like those have a hugh field of application. I really appreciate the time and content shared with the community.

par JUAN P G B

6 août 2020

Was a really challenging course, and that is great, but at least in my case, the final project was really hard and I feelt like I did not get enough information to build my set the skill at the requiered high level, I used a lot of extra information and did a lot of extra research in order to complete the last project, and there are a couple of instructurs that was really hard to understand due to their accent.

par Andreas B

19 févr. 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

par Roland B

24 févr. 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).

par Øystein S

7 janv. 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.

par Sebastián C L

17 nov. 2020

It's a very challenging course. You need strong basics of Machine Learning and programming in Python. The topics are very useful to learn how to improve your models. Nevertheless, there is no support in the forums from tutors.

par Matt V

30 juil. 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.

par Ronak K

14 janv. 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.

par Γεώργιος Κ

7 juil. 2020

The course has many useful topics but needs updating. There are things not well explained while the final assignment is more of a riddle to find how to pass the exam than making an advanced model.

par Divyang S

22 oct. 2020

Great course, but assignments need a bit more clarity in instructions. I had a really hard time trying to figure out the last programming assignment in this course.

par alessandro s

4 févr. 2021

Very good course, this is a advance course.

You must integrate with external documentation but this course give a lot of a good points.

par JULIO C G G

27 mai 2020

This is a really hard course, but the instructors do a big effort to give you many tips to participate in this kind of competitions

par Carlos M

16 oct. 2020

The content was very useful but sometimes difficult to understand. It's required a lot of previous experience and a high level

par Param K

23 juin 2020

This course helped me to understand the right way of applying ML algorithms and to build better pipelines in general.

par 林佳佑

26 janv. 2019

this course is helpful and important for one who become a data science expert, a lot key skill import in dealing data

par Rony A

12 juin 2020

Very good course if you want to learn the way to broach a data challenge when you're a beginner.