Author's): Rohan Mistry
Originally published in Towards Artificial Intelligence.
You know how every algorithm works. But you have no idea which one to use properly.
You have successfully passed the ML course. You know Random Forest, XGBoost, SVM, neural networks.
This article discusses the confusion that many people face when choosing the right machine learning algorithm for various projects. He highlights that while many machine learning courses teach how algorithms work, they often neglect to instruct students in the practical considerations needed to choose the right algorithm for specific problems. By providing a systematic method for selecting an algorithm based on problem type, data size, and interpretability requirements, the author aims to enable data scientists to make confident, logic-based decisions rather than relying on guesswork.
Read the entire blog for free on Medium.
Published via Towards AI
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Note: The content contains the views of the authors and not Towards AI.
