Making Kaggle the Home of Open Data

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Kaggle is best known for running machine learning competitions. These competitions have helped classify whales in the oceans and galaxies in the sky; they've helped diagnose diabetic retinopathy and predict ad clicks. You can now instantly share and publish data through Kaggle. This creates a home for your dataset and a place for our community to explore it. Your data immediately becomes available in Kaggle Kernels, meaning that all analysis and insights are shared alongside the dataset.


The tips and tricks I used to succeed on Kaggle

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I learned machine learning through competing in Kaggle competitions. I entered my first competitions in 2011, with almost no data science knowledge. I soon ended up in fifth place out of a hundred or so in a stock trading competition. Over the next year, I won several competitions on automated essay scoring and bond price prediction, and placed well in others. Kaggle competitions require a unique blend of skill, luck, and teamwork to win.


How to start on machine learning

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First--try some of the introductory tutorial/competitions. Those get your feet wet. Then just jump head first into a competition. Try and be active on the forums. I have found that the best way to learn is just struggle with it (in most anything--I faked my way into a DB engineer once, 2 years later I was teaching the course on SQL at a Fortune 100 company--I had my share of run-ins with the Admin though--we were on a first name basis)).


Use Kaggle to start (and guide) your ML/ Data Science journey -- Why and How

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This is such an incomplete description of what Kaggle is! I believe that competitions (and their highly lucrative cash prizes) are not even the true gems of Kaggle. Take a look at their website's header-- All of these together have made Kaggle much more than simply a website that hosts competitions. It has, now, also become a complete project-based learning environment for data science. I will talk about that aspect of Kaggle in details after this section.


Winning Tips on Machine Learning Competitions by Kazanova, Current Kaggle #3

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No matter how many books you read, tutorials you finish or problems you solve, there will always be a data set you might come across where you get clueless. Specially, when you are in your early days of Machine Learning. In this blog post, you'll learn some essential tips on building machine learning models which most people learn with experience. These tips were shared by Marios Michailidis (a.k.a Kazanova), Kaggle Grandmaster, Current Rank #3 in a webinar happened on 5th March 2016. The key to succeeding in competitions is perseverance. Marios said, 'I won my first competition (Acquired valued shoppers challenge) and entered kaggle's top 20 after a year of continued participation on 4 GB RAM laptop (i3)'. Were you planning to give up?