A machine learning practitioner’s tour of 10 under-appreciated PyPi packages

Introduction

“The power of Open Source is the power of the people. The people rule”: Philippe Kahn
Ever since my doctoral studies that mostly entailed performing statistical analysis in R ( and admittedly Octave/MATLAB), I have strongly embraced the emergence of Python as the lingua franca amongst machine learners / data scientists / *insert latest profession-buzzword here*.

My daily workflow involves quickly reacting to the vagaries of messy real-world data, in all it’s naive-assumption-shattering glory. One major difference between graduate school and industry to me is the conquest of the inner-ego that goads you to implement algorithms from scratch.

Once past the white-boarding/hypothesis building phase I quickly parse through the PyPi repository to check if any of the constituent modules have already been authored. This is typically followed by a
>> pip install *PACKAGE_NAME*ritual and voila, I find myself standing on the shoulders of the open-source giants whose careful work I am now harnessing to scale the DIKW pyramid.

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Lingaraj Senapati
Hey There! I am Lingaraj Senapati, the Co-founder of lingarajtechhub.com My skills are Freelance, Web Developer & Designer, Corporate Trainer, Digital Marketer & Youtuber.
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