Among the most popular competitive platforms out there for AI, Kaggle comes to mind as one of the top choices. With competitions cutting across almost all domains in AI, Kaggle offers a level playground to experts and aspiring data scientists alike. But it's not easy to compete in a Kaggle competition. I'm David Shaw, and in this episode of AI News, we look at how you can speed through the learning curve and come out on top.
According to Kaggle competitor and Intel Greenbelt software developer, Srivata S., some of the most common Kaggle problems are associated with having huge data sets that make little sense at first glance, lack of computation power, and no knowledge about the subject and the problems presented. Most of these issues, however, can be solved easily.
Unlike other competitive platforms, where any public discussion is strictly forbidden, Kaggle adopts an open discussion strategy where anyone is free to discuss their approach. In addition, participants can form teams with other competitors and share relevant research work, all of which can be helpful in your process. Srivata recounts the first experience submitting and discusses the approach taken.
One piece of advice, quote, "Not to use any approach without sufficient evidence that it will work. Certain results can be deceptive and make you believe that it's the correct approach, but I suggest you validate it over numerous iterations before going ahead with a submission." Check out the article and read up on the case study to get prepared for your first Kaggle competition. Follow the links to find trending Kaggle competitions and I'll see you next week.