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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

933 calificaciones
211 revisiones

Acerca del Curso

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:

Principales revisiones


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!


Feb 19, 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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26 - 50 de 212 revisiones para How to Win a Data Science Competition: Learn from Top Kagglers

por Aman S

Jun 03, 2019

Teaching style is not engaging at all. I am very confused

por Milos V

Mar 08, 2019

Very interesting course, and the most practical and useful one. However, lecture are usually too theoretical and super-simple, while assignments are tough and very code oriented. So often there is no real connection between the two (except for Dmitry Altukhov). And final project is too difficult in sense that my Alienware 16 RAM was not enough, so I had to go to Google Cloud Platform. Also, I am not sure is anybody who is learning Machine Learning possible to do the final task in "6 hours" as solely runs could last for a day...

por Sailesh G

Apr 07, 2020

It was just not working for me. Perhaps the fault is mine, because I found it hard to grasp and was always trying to get on the same page. The course presentation only added to the challenge. One thing for sure, beginner (or intermediates) should stay away from this one until you're ready. I may perhaps revisit this course if I feel right, but that's a long way off.

Thank you.

por Lun Y

May 07, 2019

There are too many things need the learner to investigate by themselves. We are here to learn but not guess. And the condition to close the course is very hard to achieve. I'd say it is not a well designed course including contents and how they are organized.

por Mithun G

Jan 14, 2018

Content is really good. But delivery is at times incomprehensible. Assignments questions are also not very clear

por Temiloluwa A

Oct 04, 2019

Couldn't follow because the English Accent was difficult to understand.

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 Tirth P

Apr 25, 2019

Five stars for the amount of hard work the authors have actually put in to make this course the best of all courses in the specialization. It is one of the best courses to succeed in the field of competitive data science. Has a lot of assignments and quizzes to go through in each week. I would highly recommend the course if you want to learn advanced feature engineering and EDA.

Thank You!

por Toghrul J

Apr 21, 2018

Very useful course with full of ideas to apply not only on Kaggle competition but also on the daily projects. If you are a Data Scientist and want to get another level, this course is for You. What makes this course so special? whatever they explain it comes from experience and it is very practical. So during classes do not forget take notes, otherwise you can forget :)

por Aymen S

Feb 26, 2020

This course is the biggest achievement in my Coursera until now, I wanna thank every one of the organizers of the course. For me it is the most advanced course related to data science that one can take because it's a combination between theory and practice, and I have learned a lot of staffs like how to deal with overfitting, mean encoding, lag features and stacking .


Jul 17, 2020

muy buen curso en el que se enseñan técnicas avanzadas de Machine Learning.

es un poco complejo para personas que no tengan unas buenas bases de conocimiento en el tema de Machine learning.

(very good course in which advanced Machine Learning techniques are taught.

It is a bit complex for people who do not have a good knowledge base on the subject of Machine learning.)

por Alouini M Y

Oct 18, 2019

A very challenging ML course but worth all the efforts. This course contains various interesting analyses of techniques to get better at ML modelling: from mean encoding (and how to do properly) to nearest neighbors features and model stacking. This is very useful for Kaggle challenges specifically but is valuable for day-to-day data science tasks more generally.

por Kaushik P

Dec 23, 2018

This course is just what I was looking for as I am really interested in competitive Machine Learning and data science. Hopefully , I will be able to perform better in competitions from now on.

But the only down side I can think of is that the programming assignments are pretty difficult at times, but none the less it was a great experience.

por Sixing H

Dec 10, 2019

A very needed course in not just Kaggle competition but also machine learning. Even not for the Kaggle tips, the machine learning alone should be reason enough for taking this course. The code exercise provides excellent framework for further application in my own projects. I hope there are more such courses in coursera.

por Kirill L

Nov 20, 2017

Even though, it revolves around Kaggle competitions which are usually simpler than real-life, this course is full of down-to-earth practical techniques and examples which is really valuable for me.

Idea to organize Kaggle competition as a course project is very good.

Lectors are easy to follow and nice to listen to.

por Vratislav H

Mar 29, 2019

It is diffifult but when you reach the end, you are glad that you were able to finish it. Because I gained a lot of knowledge and best practices. There is a lot of work but it helps you sharpen your brain. I recommend to work simultaneously on project because otherwise it will be difficult to finish it..

por YUYU L

Sep 02, 2019

Teaching the clear work flow with data science project and learned some trick method at feature engineering. In the final, playing kaggle competition was funny. But, you should take the competition as early as possible, if waited week 5 to join the competition it would be very hard.

por Mark P

Oct 09, 2018

This is a fantastic course for anyone looking to extend their skills in data science. Its packed full of tips and tricks and techniques that are well explained and very useful for data science. I would go so far as saying that it has been my favorite data science course OF ALL TIME!

por Lukas K

Aug 03, 2019

One of the best course I had on Coursera so far. Really good explanation of problems you can face in DataScience competition and ability to see many useful approaches for solving the problems. The final assignment is a little bit longer, but totally worth the time.

por Margarita C

Nov 07, 2019

This course is awesome. The more effort I invested in it - the more results I got, and I feel like I haven't reached the limit. The course is very thought-out, the tests and programming assignments are great, teachers are inspiring. I enjoyed every part of it!

por Igor B

Jan 27, 2019

This course requires much time, but gives hardcore experience in practical data science and machine learning. The final project, which is a proving ground for the acquired skills, is both an interesting competition to participate in and a real-world-task.

por Arnaud R

Mar 17, 2018

This course is a gold mine of knowledge and tricks for anyone working with the data science toolkit. It requires good prior knowledge of the different algorithm used and Python fluency. The course is demanding but you will get out of it so much stronger.

por Diego T B

Nov 07, 2019

This is an awesome course! I really learned a lot from this top kagglers. I just have one recommendation. I think some sessions were very though and difficult to catch: the data leakage part and the Kappa metric. Try to make this even much easier.

por Yotam S

Oct 26, 2019

Amazing course. Teaches the theoretical aspects of ML in within a practical point of view. Enables use to improve your models by understanding the framework much better. Not recommended as first ML course, but definitely as an advanced one.

por Holger P

Nov 19, 2017

This course is amazing. Taught by experts in the field with a proven track record of outstanding performance in Kaggle competitions. They teach how to fine tune ML models to achieve better performance. My choice for best course on Coursera!