Life is a Journey
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.
Continue readingPart 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!
Continue readingLast 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.
Continue readingLast 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.
Continue reading“A Whirlwind Tour of Python” by Jake VanderPlas is a handy little reference for those with some prior programming experience but who are new to Python. It is compact (only 98 pages) yet feature rich, and is a good starting point for picking up Python.
Continue readingOne 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.
Continue readingAfter 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.
Continue readingThe 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.
Continue readingI 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.
Continue readingOne of the first tasks is to set up a programming environment on my personal laptop so that I can start coding as quickly as possible. For data science purposes, you can’t go wrong choosing the Anaconda Individual Edition.
It’s open source, free for individuals, has more than 20 million users worldwide and comes with all the key components and packages needed to get started in less than 30 minutes.
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