Kaggle Archives | Fire Forty Six


It took me a while to finally complete all 17 modules, but knowing that there was a clear outcome helped motivate me to keep taking the next step, and the step after that.

So, if you’re interested in getting a taste of what the world of machine learning has to offer, beginning with the (free!) online courses on Kaggle Learn is a great starting point. I know that it definitely was for me.

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Part 2 of my Kaggle learning journey focused on the data-centric modules. The tutorials and hands-on coding exercises were rich in content and spanned across Pandas, Intro to SQL, Advanced SQL, Data Cleaning and Feature Engineering. 13 down, 4 more to go!

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Last August, I completed Kaggle’s “30 Days of Machine Learning” challenge. In the spirit of continuous learning, I decided to take the plunge and finish all the remaining 14 modules in Kaggle Learn. It took me another 30 days, over a span of several months, to finally read through all the tutorials and complete all the coding exercises. Here is Part 1 of my learning journey.

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Immediately after completing Kaggle’s “30 Days of ML” challenge, I started on their “Intro to Deep Learning” online course which was estimated to take four hours to complete. It was definitely time well spent.

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Last month, I received an email from Kaggle inviting me to participate in a beginner-friendly “30 Days of Machine Learning” challenge. It was a timely reminder to continue on my data science learning journey, especially since I haven’t made any progress for quite some time.

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One unexpected benefit of joining Kaggle was the discovery of an introductory Python course on Kaggle Learn. It is free to use and consists of short tutorials and hands-on notebook exercises that highlight the key aspects of the language.

The course has a focus on data science applications and is targeted at those with some prior coding experience.

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After completing Alexis Cook’s very useful Titanic Tutorial, I couldn’t help myself and spent a couple of days hacking around to try and improve my score without going through the usual data science workflow of EDA, feature engineering, model selection, hyperparameter tuning, train/test iterations.

I know it’s not the proper way of doing data science, but like I said, I just couldn’t help myself.

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The first port of call for all Kagglers is the “Titanic: Machine Learning from Disaster” practice competition, where you get to use machine learning to create a model that predicts which passengers survived the Titanic shipwreck.

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I was planning to sign-up for Kaggle further down in my data science learning journey, only after I had built up sufficient foundational knowledge and skills. However, I decided to take a quick look under the hood, and found that it was surprisingly easy to get started.

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