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

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

1,112 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:

Meilleurs avis

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!

20 déc. 2021

Very interesting course, touching on a lot of topics. It is on the hard side, especially the final project, but it is worth it. I would definitely recommend it to anyone interested in the topic.

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251 - 273 sur 273 Avis pour How to Win a Data Science Competition: Learn from Top Kagglers

par 藤田典明

1 mai 2020

Very Useful Cource. I want to be Kaggle master. and I want to do something useful in the world.

par Gustavo A G G

25 oct. 2020

Materials are really updated and you find a real value with the trainers. thanks

par Vytenis P

28 janv. 2019

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

par Benjamin F

2 févr. 2018

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

par Daya_Jin

13 juin 2018


par Mahboob A

16 avr. 2019

my first was week awesome!

par MHD K M

31 mars 2020

amazing lecturers

par Anders P

27 sept. 2018

Learned a lot

par Santiago P G

14 sept. 2020

Muy exigente


6 juin 2020

Good course

par Fabián S Á M

30 sept. 2020


par Marthala N R

9 sept. 2020

If you have just come out of some bootcamp on machine learning, this course is not for you. You have to have done some ML projects or so i felt listening to the lectures. Topic selection was good. But, I hated the teaching format(reading through the slides) and often times important topics were barely talked about, while obvious things were repeated again and again.

Overall a good starting point for a person trying to make it big in competitions.

par Hidemi A

2 janv. 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.

par Dimitry I

6 oct. 2020

I wish there was a bit more in-depth teaching/learning in this course. A lot of data is thrown at you with one- or few-sentence statements, and no walk-throughs or practicals to make them stick.

par Hiromichi I

7 févr. 2019


par Wenlong W

20 déc. 2018

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

par Anderson S Z

14 sept. 2021

T​he english speaking of the lecturers is difficult to understand.

par Quan C A

20 avr. 2019

Terrible accent

par Enrique C M

23 févr. 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.

par HenryYao

17 mai 2018

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

par Ivan K

7 août 2021

Videos are reproducing highlights that are available in the Readme of tools, provide little or no explanations on how things actually work, and quizes test material that was not explained in the videos. This is a very BAD course.


20 juin 2020

Unenroll me in this course

par Juan D G B

17 juin 2020