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2020년 12월 26일 (토) 05:18 판

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  • So, faced with a Kaggle competition, how should you spend your time?[1]
  • Companies come to Kaggle with a load of data and a question.[1]
  • As competitors upload their algorithms, Kaggle shows them in real time how they are doing in relation to the other competitors.[1]
  • Kaggle is a machine learning and data science community site created in 2010 by founder and CEO Anthony Goldbloom.[2]
  • Goldbloom said the goal for Kaggle was to create a robust set of tools for data scientists.[2]
  • There are Kaggle competitions that function as interviews, and the prize is a job interview with the sponsoring company.[2]
  • For first timers feeling overwhelmed, Kaggle provides a library full of resources and forums to make it easier.[2]
  • While some buyouts result in startups and small teams being disbanded, in Kaggle's case, the team will remain together.[3]
  • Kaggle can be a great way for newcomers to build data science skills.[4]
  • But should an aspiring data scientist rely solely on Kaggle to get a foot in the industry?[4]
  • Personally, I believe that data scientists shouldn’t use Kaggle as a yardstick.[4]
  • In fact, aside from educational purposes and its usefulness in discovering data sets, I prefer to stay away from Kaggle contests completely.[4]
  • These tricks are obtained from solutions of some of Kaggle’s top tabular data competitions.[5]
  • Google bought Kaggle in 2017 to provide a data science community for its big data processing tools on Google Cloud.[6]
  • , Google acquires Kaggle and Udacity launches a Robotics Nanodegree.[6]
  • Kaggle Sources tell us that Google is acquiring Kaggle, a platform that hosts data science and machine learning competitions.[6]
  • Kaggle’s community comes to the platform to learn and apply their skills in machine learning competitions.[7]
  • Finally, you can browse any of the over 30,000 public GPU notebooks shared on Kaggle.[7]
  • Kaggle is the world's largest data science community with powerful tools and resources to help companies achieve their data science goals.[8]
  • The SOA encourages actuaries to enter as individuals or to form groups to participate in Kaggle competitions.[8]
  • What he didn’t anticipate was his team of Kaggle newcomers placing in the top ten percent of their challenge.[8]
  • In a Kaggle competition, you can compete for jobs or money (or glory).[9]
  • In 2017, Kaggle was acquired by Google.[9]
  • To make the most of Kaggle, having some ability to work with code is helpful.[9]
  • Kaggle has 3.5 million members contributing code and data.[9]
  • The online data science and machine learning community Kaggle is just one of the many two-sided platforms that have emerged in recent years.[10]
  • Various organizations use Kaggle to sponsor contests to develop machine learning algorithms for a slew of purposes.[10]
  • By allowing organizations to crowd-source answers to extremely complex questions, Kaggle serves as a marketplace for these solutions.[10]
  • Finally, Kaggle creates value for society by organizing competitions on a pro bono basis for non-profit and research organizations.[10]
  • This interactive tutorial by Kaggle and DataCamp on Machine Learning data sets offers the solution.[11]
  • Throughout this course, you'll learn several tips and tricks for competing in Kaggle competitions that will help you place highly.[12]
  • It's no surprise that some beginners hesitate to get started on Kaggle.[13]
  • Before jumping into Kaggle, we recommend training a model on an easier, more manageable dataset.[13]
  • Now we're ready to try Kaggle competitions, which fall into several categories.[13]
  • Most Kaggle participants will never win a single competition, and that's completely fine.[13]
  • This is the first page that will appear on opening Kaggle.[14]
  • Each user in Kaggle has their own profile page section containing basic information about the user so that others get to know.[14]
  • This is the most important section according to me in Kaggle.[14]
  • Kaggle conducts data science competitions which are considered as benchmarks in the data science world.[14]
  • This post was written by Vladimir Iglovikov, and is filled with advice that he wishes someone had shared when he was active on Kaggle.[15]
  • Kaggle is the most well known competition platform for predictive modeling and analytics.[16]
  • Like many people, I had some preconceived notions about Kaggle competitions.[16]
  • I had heard the legend that retired PhD’s with decades of experience were the ones winning the Kaggle comps.[16]
  • What I discovered is that Kaggle competitions are a lot like the NYC marathon.[16]
  • Kaggle has a ranking system that helps data scientists track their progress and performance.[17]
  • A word to the wise, learn from everyone on Kaggle, especially the higher ranking individuals![17]
  • One of the reasons behind Kaggle’s great success is its learning-friendly environment and the ease of learning new skills.[17]
  • Kaggle provides a vast amount of available datasets in its “Datasets” tab.[17]
  • In this article, I am going to explain to you about getting started with kaggle and making use of it to master your data science skills.[18]
  • Doing data science in Kaggle is quite different.[19]
  • Kaggle competitions have improved the state of the machine learning art in several areas.[19]
  • Most competitions on Kaggle follow this format, but there are alternatives.[19]
  • You can create public and private datasets on Kaggle from your local machine, URLs, GitHub repositories, and Kaggle Notebook outputs.[19]
  • Kaggle competitions regularly attract over a thousand teams and individuals.[20]
  • Kaggle's community has thousands of public datasets and code snippets (called "kernels" on Kaggle).[20]
  • Kaggle Kernels: a cloud-based workbench for data science and machine learning.[20]
  • Work is shared publicly through Kaggle Kernels to achieve a better benchmark and to inspire new ideas.[20]
  • If you run into a kaggle: command not found error, ensure that your python binaries are on your path.[21]
  • You can see where kaggle is installed by doing pip uninstall kaggle and seeing where the binary is.[21]
  • To use the Kaggle API, sign up for a Kaggle account at https://www.kaggle.com.[21]

소스

  1. ↑ 1.0 1.1 1.2 Anthony Goldbloom gives you the secret to winning Kaggle competitions
  2. ↑ 2.0 2.1 2.2 2.3 Getting Started With Kaggle Competitions
  3. ↑ Google confirms acquisition of data science community Kaggle
  4. ↑ 4.0 4.1 4.2 4.3 Is Kaggle Worth It for Data Scientists?
  5. ↑ Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions
  6. ↑ 6.0 6.1 6.2 kaggle – TechCrunch
  7. ↑ 7.0 7.1 How Kaggle Makes GPUs Accessible to 5 Million Data Scientists – NVIDIA Developer News Center
  8. ↑ 8.0 8.1 8.2 Kaggle Involvement Program
  9. ↑ 9.0 9.1 9.2 9.3 Kaggle: A Marketer’s Guide for Analytics and Data Science
  10. ↑ 10.0 10.1 10.2 10.3 Kaggle: Building a Market for Data Science (and Scientists)
  11. ↑ Kaggle R Tutorial on Machine Learning
  12. ↑ Kaggle Fundamentals for Data Science – Dataquest
  13. ↑ 13.0 13.1 13.2 13.3 The Beginner’s Guide to Kaggle
  14. ↑ 14.0 14.1 14.2 14.3 How To Get Started With Kaggle: A Quick Starter Guide
  15. ↑ Kaggle Blog – Medium
  16. ↑ 16.0 16.1 16.2 16.3 To Kaggle Or Not
  17. ↑ 17.0 17.1 17.2 17.3 Getting Started with Kaggle
  18. ↑ Kaggle – Towards Data Science
  19. ↑ 19.0 19.1 19.2 19.3 Kaggle: Where data scientists learn and compete
  20. ↑ 20.0 20.1 20.2 20.3 Wikipedia
  21. ↑ 21.0 21.1 21.2 Kaggle/kaggle-api: Official Kaggle API

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