data-scientistJason Brownlee has just published a blog post Machine Learning for Programmers: Leap from developer to machine learning practitioner. Some of the important but not-generally-discussed concepts explained very well are:

  • Differences between developer, machine-learning (ML) engineer, and data scientist
  • Different models of understanding and employing ML for real-world problems
  • Differences between (traditional and “wrong”) bottom-up approach and (better) top-down approach
  • Common psychological  barriers for people in learning and applying ML

He has also mentioned a few useful sites I wasn’t aware of before including the UCI Machine Learning Repository, and KDD cup competitions.

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