competitions.md

Competitions: AI, ML, DL, DS

General

• Kaggle competitions
  • Closed DS Competition: CareerVillage
  • Closed DS Competition: City of Los Angeles
  • Closed ML Competition: Titanic: Machine Learning from Disaster
  • Closed ML Competition: Dogs vs. Cats
  • Active DS Competition: 2019 Data Science Bowl
  • Active ML Competition: RSNA Intracranial Hemorrhage Detection
  • Active ML Competition: ASHRAE - Great Energy Predictor III
  • Active DS Competition: NFL Big Data Bowl
  • Closed NLP Competition: Tweet Sentiment Extraction
    • Useful NLP and competition resources
    • My popular discussions
• Bitgrit competition platform
• CrowdAI
• Crowd Analytix
• CodaLab
• DataKind DataDives
• DataScience.net
• Data Science Challenge
• Driven Data
• Pivigo Challenges
• Imperial College Data Science Society Challenges
• Analytics, Data Science, Data Mining Competitions
• DATA SCIENCE COMPETITIONS by Analytics Vidhya
• Signate Competitions (Japanese)
• Best kept secret about data science competitions by Vincent Granville
• MPP Capstone Challenge
• Topcoder Data Science & Analytics competitions
• data-science-competitions on github
• Data Science Bowl
• Data science competition platforms you need to know about
• KDD Data mining and Knowledge Discovery cup
• VizDoom AI competition [deadlink]
• Numerai - data science tournaments | Keynote
• 10 Data Science Competitions for you to hone your skills for 2020
• QuantQuest

Coding challenges

• Hacker Rank
• Codeacademy
• LeetCode

Resources

• Kaggle Kernels Guide for Beginners — Step by Step Tutorial | Best Data Scientists on Kaggle from 2011-2020 | What Kaggle has learned from almost a million data scientists - Anthony Goldbloom (Kaggle)

• RAPIDS

  • RAPIDS in Kaggle competition LinkedIn
  • Here is the first ever successful implementation of NVIDIA #rapids library in a Kaggle kernel. It achieves 600X speedup of the kNN as compared to #sklearn LinkedIn

• Kaggle, chat with RCTatman

• Episode 1.1: Intro and building a machine learning framework MLFramework

• Episode 1.2: Building an inference for the machine learning framework

• Tips N Tricks #3: Creating a clean inference kernel/notebook on Kaggle

• Interview with Abhishek Thakur | World's First Triple Grandmaster | Kaggle

• My journey to 4x GM on Kaggle

• Also see NVIDIA's RAPIDS

Contributing

Contributions are very welcome, please share back with the wider community (and get credited for it)!

Please have a look at the CONTRIBUTING guidelines, also have a read about our licensing policy.

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