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Kursteilnehmer-Bewertung und -Feedback für How to Win a Data Science Competition: Learn from Top Kagglers von HSE University

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1,081 Bewertungen
262 Bewertungen

Über den Kurs

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...

Top-Bewertungen

MS
28. März 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!

GW
18. Feb. 2019

Really excellent. Very practical advice from top competitors. This specialization is much more information-dense than most machine learning MOOCs. You really get your money's worth.

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201 - 225 von 260 Bewertungen für How to Win a Data Science Competition: Learn from Top Kagglers

von carlos a g b

12. Apr. 2020

good content

von Amandeep S

13. Jan. 2019

Great Course

von PC P K

17. Mai 2018

great course

von Jorge F R

19. Okt. 2020

Great!!!!!

von Harsh N

21. Jan. 2018

Hammer lol

von SOURAV S

28. Aug. 2020

Thank You

von Ricardo M B

28. Juni 2020

Excelente

von Марчевский В Д

12. Sep. 2018

Good one!

von Alexey B

19. März 2018

Good job!

von Kirill K

4. Feb. 2021

A

w

e

s

o

m

e

von Krishna H

7. Aug. 2020

Great!

von Cindy N P P

19. Juli 2020

Great!

von Nicolas M

20. März 2020

good

von Yan L

1. Sep. 2019

nice

von wzm

15. Apr. 2018

nice

von Siwei Y

3. Apr. 2018

膜拜一下

von Dezhen G

3. Feb. 2021

ok

von Kamal S

1. Okt. 2020

.

von Anish G

3. März 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.

von Ho Y C

2. Juni 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)

von 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.

von Md A R

24. Feb. 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!!!

von Jhon F M C

2. Juli 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.

von JUAN P G B

6. Aug. 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.