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

How to Win a Data Science Competition: Learn from Top Kagglers, 국립 연구 고등 경제 대학

4.7
561개의 평가
126개의 리뷰

About this Course

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....

최상위 리뷰

대학: MS

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!

대학: MM

Nov 10, 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.

필터링 기준:

125개의 리뷰

대학: Murat Öztürkmen

May 21, 2019

It is a well prepared course which includes lots of tips and trick and theoretical background to be successful.

대학: abensaid

May 19, 2019

very good courses makes me learn a lot practical examples

대학: Lun Yu

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.

대학: Tirth Patel

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!

대학: Cao Anh Quan

Apr 20, 2019

Terrible accent

대학: Joseph Burdis

Apr 18, 2019

Very nice course and final project is actually challenging and a great learning experience when learner attempts to do it completely on their own without reading forums or looking at examples on Kaggle.

대학: Mahboob Alam

Apr 16, 2019

my first was week awesome!

대학: Fabrice Lacout

Apr 11, 2019

A looooot of content!!!

I like the fact that it talk about broad data science topics, and doesn't specialize into one specific domain. You gain some good tricks about pandas, EDA, modeling, feature engineering... etc The skill coverage is very wide.

This is definitely advance, and challenging soemtimes, but you'll learn a lot.

대학: Stephane Hemery

Apr 10, 2019

Great course, truly invaluable information in there, also the hardest i've ever done, took me months and a couple hundred hours. The knowledge and experience you gain is incredible, not for the faint of heart though.

대학: Vratislav Havlík

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..