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Introduction
With the rise of no-code / low-code machine learning platforms and libraries, there are fewer barriers to use and apply machine learning models on your applications.
Citizen data scientists now have the ability to perform tasks that, in the past, would have needed a significant amount of coding experience thanks to low-code platforms and frameworks.
Although using a drag-and-drop interface to train a machine learning model on a no-code platform is the quickest and easiest method to do so, these platforms lack flexibility. On the other side, low-code ML is the optimal solution that strikes a balance between extremes.
In this article, I will discuss some of the most useful low-code machine learning libraries in Python. The list is subjective and is not organized in any specific fashion.
PyCaret
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and can help to make you more productive.
In comparison with the other open-source machine learning libraries, PyCaret is an alternative low-code library that can be…
