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How to Win a Data Science Competition: Learn from Top Kagglers(으)로 돌아가기

HSE 대학의 How to Win a Data Science Competition: Learn from Top Kagglers 학습자 리뷰 및 피드백

1,113개의 평가
274개의 리뷰

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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 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:

최상위 리뷰

2018년 3월 28일

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!

2017년 11월 9일

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.

필터링 기준:

How to Win a Data Science Competition: Learn from Top Kagglers의 273개 리뷰 중 51~75

교육 기관: Refik E

2018년 1월 25일

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

교육 기관: Temiloluwa A

2019년 10월 4일

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

교육 기관: Manuel M B

2020년 3월 22일

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.

교육 기관: Maciej

2019년 1월 10일

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

교육 기관: Molin D

2021년 1월 14일

The course itself is very good. However I have some opinion on program assignment scoring system:

1. Machine learning hosting enviroment by Coursera for jupyter notebook is SLOW.

For some task, I could download database from public source, run locally with my GPU.

For some task, I could not move whole resource because of private database, etc. Either I don't want to use Google colab (may forget to cancel GCP service charing...)

2. Classmate review suspended,

I join the this cours late, so for a long time after I submited the assignment no one to review.

better change such task to auto scoring system.

교육 기관: stephane d

2021년 9월 3일

A great course that covers different interesting aspects of "Feature Engineering" such as "Target encoding", "Lag features",... On the modeling side, it covers some very good algorithms such as XGBoost, LightGBM, CatBoost but I recommend to the people who follow the course not to stop on black boxes but to explore more in details by programming directly in Python some algorithms such as Decision Trees,... in order to better understand the hyperparameters to use and how to use them. I recommend this course because competition is a subject that is not often treated.

교육 기관: Andrii Y

2021년 8월 23일

Great course! Not only about competitions but on advanced machine learning in general: feature engineering, preprocessing, validation, metric optimization, hyperparameter tuning. The final project is really good, it allows to apply all the course knowledge and receive some practical data science experience. One of the best courses on Coursera I took so far!

교육 기관: Kaushik P

2018년 12월 22일

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.

교육 기관: Sixing H

2019년 12월 10일

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.

교육 기관: Kirill L

2017년 11월 20일

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.

교육 기관: Vratislav H

2019년 3월 29일

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..

교육 기관: YUYU L

2019년 9월 2일

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.

교육 기관: Mark P

2018년 10월 9일

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!

교육 기관: Lukas K

2019년 8월 3일

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.

교육 기관: Francois G

2020년 10월 11일

It seems to me a very good and actually quite demanding crash course in applied machine learning. It is full of very pragmatic techniques to approach data modeling with the clear goal to improve scores (by any legit means) in competitions. Well put together.

교육 기관: Igor B

2019년 1월 27일

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.

교육 기관: Arnaud R

2018년 3월 17일

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.

교육 기관: Diego T B

2019년 11월 7일

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.

교육 기관: Yotam S

2019년 10월 26일

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.

교육 기관: Holger P

2017년 11월 19일

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!

교육 기관: Sergio A G P

2020년 10월 14일

It was a great, demanding, and very detailed course about machine learning and implementations in the context of competitions. Thus, the focus is very competitive and programmatic, but without forgetting the understanding of the problem.

교육 기관: Steven A

2021년 5월 30일

Excellent course, challenging and interesting. A good mix of theory and practice, taught by a dynamic and passionated team capable of some tongue in cheek humor.

Note: don't expect interactive support from the forums, you're on your own.

교육 기관: Amit K S

2019년 1월 20일

This is so good. Three reasons (1) Helps me revisit the concepts that I learnt in the machine learning course. (2) Helps me to deal with my FOMO (3) I would feel most confident to go for my Data Science or Data Engineering interviews.

교육 기관: ashesh g m

2019년 6월 9일

It was one of the best courses which I've done on coursera. Here, it was all practical and essential knowledge which was taught. Mentors were amazing and inspiring. A must do for any data scientist or aspiring data scientist.

교육 기관: CARLOS A U F

2020년 11월 2일

Excellent course, at the beginning was very frustrating, because of my poor knowledge, but then, with some dedication and new learnings, was great to know and practice new technique to solve real and competition ML problems.