5 Mistakes Programmers Make when Starting in Machine Learning

Last Updated on June 18, 2016

There is no right way to get into machine learning. We all learn slightly different ways and have different objectives of what we want to do with or for machine learning.

A common goal is to get productive with machine learning quickly. If that is your goal then this post highlights five common mistakes programmers make on the path to quickly being productive machine learning practitioners.

1. Put Machine Learning on a pedestal

Machine learning is just another bag of techniques that you can use to create solutions to complex problems.

Because it is a burgeoning field, machine learning is typically communicated in academic publications and textbooks for postgraduate students. This gives it the appearance that it is elite and impenetrable.

A mindset shift is required to be effective at machine learning, from technology to process, from precision to “good
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