Top 6 Python Libraries for Machine Learning (Infographic)

Moiz Khan Apr 29, 2020

Recently, with the push in the AI sector, machine learning and deep learning have been on the rise and the early adopters of these technologies are starting to see their results. More and more businesses have hopped onto the bandwagon and started investing their time and effort in fully understanding this untapped domain’s potential.

Here are our top picks:

Numpy

Numpy is a library that handles data, particularly one that helps us to manage large multidimensional arrays along with a huge collection of mathematical operations. It is recognized for its execution speed and ability to vectorize. It is ‘core dependence’ for other commonly used libraries.

TensorFlow

TensorFlow is an end-to-end python machine learning library to run numerical high-end computations. TensorFlow Python offers excellent architecture support to allow fast computation deployments over a wide range of platforms, such as desktops, servers and mobile devices.

Theano

Theano is a popular python library widely used to identify and detect different types of errors for unit-testing and self-verification. It evaluates and optimizes mathematical expressions concerning multi-dimensional arrays. It is done by optimizing CPU and GPU utilization.

Keras

Keras is a leading open-source Python library written to build neural networks and projects of machine learning. It provides nearly all standalone modules including optimizers, neural layers, functions for activation, schemes for initialization, cost functions, and regularization schemes.

Pandas

Pandas is an open-source Python package offering high-performance, easy-to-use data models and data analysis tools for the Python programming for the labeled data. It is a handy tool for munging or wrangling data.

SciPy

Scipy works alongside NumPy arrays to offer a framework that delivers numerous mathematical approaches. It presents a complete stack of Linear Algebra functions used for more complex computations. Moreover, it supports signal processing, data structures and numerical algorithms, creating sparse matrices, and more.