Machine learning has emerged as an immensely popular discipline in computer science, revolutionizing how data is used across all industries. Python has become the lingua franca of machine learning thanks to its simple syntax and excellent data-handling capabilities. But with over 1/3 of the repositories mentioning machine learning on GitHub written in Python, getting started can seem daunting. Try the following libraries if you are interested in exploring machine learning more: 

Scikit-learn (https://scikit-learn.org/stable/) 

Built on the popular NumPy, SciPy, and matplotlib numeric libraries, scikit-learn is open source under a BSD license. It contains built-in functions for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing algorithms. Most of these models are accessible with only a few lines of code. Excellent data visualization capabilities make it popular for model building and testing. Scikit-learn's ease of use makes it the most popular library on this list, with greater than 1,300 contributors and almost 60,000 dependent codebases. 

import sklearn

Clone the Scikit-learn Test Algorithm. 

PyTorch (https://pytorch.org/) import torch PyTorch is a machine learning ecosystem developed and maintained by Facebook. With steadily increasing numbers of contributors since 2016, it boasts a wide range of tools and libraries to explore machine learning development. Some popular use cases are NLP and neural networks, and it can use skorch to be compatible with scikit-learn. It was designed to integrate with other Pythonic libraries deeply and is freely usable under custom copyright akin to the MIT license. 

Clone the PyTorch Test Algorithm. 

TensorFlow (https://tensorflow.org) Originally developed by the Google Brain team, TensorFlow is an Apache 2.0-licensed platform for machine learning. Although Python is the only language covered by a Language Stability Promise, its API covers a wide range of languages. It provides multiple levels of abstraction, from prototyping models by combining building blocks to fully productionized neural networks. TensorBoard was developed alongside it for visualization tasks. 

import tensorflow

Clone the TensorFlow Test Algorithm. 

Keras (https://keras.io/) Keras is an API that can run on top of multiple machine learning platforms designed to enable rapid prototyping. It is usable under the highly permissive MIT license. Written entirely in Python, it has a user-friendly design that supports convolutional networks, recurrent networks, or combinations of both. By default, Keras uses TensorFlow as its tensor manipulation library but is also compatible with Theano and CNTK. 

import keras

Clone the Keras Test Algorithm.