Essential libraries for Machine Learning in Python by Shubhi Asthana

Mar 5, 2019

Python is often the language of choice for developers who need to apply statistical techniques or data analysis in their work. It is also used by data scientists whose tasks need to be integrated with web apps or production environments.

Python really shines in the field of machine learning. Its combination of consistent syntax, shorter development time and flexibility makes it well-suited to developing sophisticated models and prediction engines that can plug directly into production systems.

One of Python’s greatest assets is its extensive set of libraries.

Libraries are sets of routines and functions that are written in a given language. A robust set of libraries can make it easier for developers to perform complex tasks without rewriting many lines of code.

Machine learning is largely based upon mathematics. Specifically, mathematical optimization, statistics and probability. Python libraries help researchers/mathematicians who are less equipped with developer knowledge to easily “do machine learning”.

Below are some of the most commonly used libraries in machine learning:

Scikit-learn for working with classical ML algorithms

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