Learning from machine learning: deliberate practice

In my downtime, I’ve been using Kaggle to get better at applying machine learning to solve problems. The process is not only teaching me new technical skills, but also reminding me of some useful principles that can be applied elsewhere. To keep things digestible, this is the first post of two.

Deliberate practice, with Kaggle

Deliberate practice–practice that is repeatable, hard, and has fast feedback (e.g. with a coach)–is needed to master any skill. Kaggle provides a great medium for machine learning deliberate practice: you can still solve the problems that were for old competitions, read about what the top performers did, and get instant feedback on how well your machine learning model performed vs. other peoples’.

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Aside from accessible deliberate practice, self-learning this way has another big benefit over some of the in-person data science/machine learning classes I’ve observed: the student has control. I can learn as fast or as slow as I need to. I can learn about what I want: not only about what I find most interesting, but about what the top performers on Kaggle and other experts are doing to be successful.

I attempt to solve a machine learning problem on Kaggle, see how I performed, read about and take notes on what the top performers did, and fill in my knowledge gaps with lots of research on Google, continuously cycling between writing down questions about new terms or concepts that come up and answering them. The self-paced, deliberate nature of this learning avoids what Sal Khan calls “Swiss cheese gaps” in education–though of course, it is up to the learner him/herself to stay disciplined and engaged.

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The “cycle” of deliberate practice described. Important things to note: it is closed, which allows for the learning from feedback, and it is fastwhich allows for that learning to happen quickly, and to be timely.

Something like Khan Academy provides a great structure for self-paced, deliberate-practice-oriented learning for more “traditional” academic topics. I see opportunity for more things like it, in other educational areas. Also, if anyone has found any helpful tools for self-learning, would love to hear about them. I personally use a lot of Google Docs for note-taking, mind42 for topic hierarchies, pinboard to keep track of my online research, sometimes Quizlet to help me memorize things.

Next: 80/20-ing machine learning

In the next post, I will get slightly more technical and into some of the “highest leverage” machine learning concepts and skills, as well as share some resources (including advice from one of the most helpful machine learning educators and practitioners I’ve had the pleasure to interact with). There should also be at least one principle/mental model for those less interested in the technicals of machine learning. As always, please be critical and feel free to discuss anything and everything, I love learning from other perspectives.


Also published on Medium.

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