Top 10 Libraries In Python For Machine Learning

Python shines in the field of machine learning, especially due to its greatest asset which is the extensive set of libraries. Python is the usual language choice for developers who need to apply data analysis or statistical techniques to their work.


What is Python?


It is an object-oriented, high-level programming language with built-in data structures combined with dynamic typing and dynamic binding. Simply put, it is used to save data and process text, images, and numbers, and solve scientific equations. It is thus attractive for rapid application development as it helps in connecting the existing components together. Its syntax is easy to learn and this emphasizes readability and reduces the cost of program maintenance. It can act as a stepping stone to learn other programming languages and frameworks.


Python provides a productive coding environment unlike C# and Java and it helps coders stay organized and productive. It is the best platform to rely on for general purpose tasks such as data mining, big data, and automation. It helps develop prototypes in an efficient manner.


A library is a collection of functions or methods which allows performing actions without writing codes. Python’s standard library is extensive and contains built-in modules written in C language. It provides access to system functionality such as file input and output which would otherwise be inaccessible to Python programmers and modules are written in Python that provides standardized solutions for day-to-day programming problems. Some of these modules are designed in such a way as to enhance and encourage the portability of Python programs. Python, when installed on the Windows platform, includes the entire standard library and additional components.


The top 10 libraries of Python are:





It is one of the most popular machine learning libraries. It supports supervised and unsupervised learning algorithms. It is associated with NumPy and SciPy. NumPy is a python extension module which allows Python to serve as a high-level language for manipulating numerical data. SciPy is a set of open-source scientific and numerical tools for Python. It is the best library for working with complex data. Scikit-Learn adds a set of algorithms for common data mining and machine learning tasks which include regression, classification, and clustering. It is useful for extracting features from images and text.



It is an open-source library which was developed by Google in collaboration with Brain Team. It is used in every Google application for machine learning. It is a computational library for writing new algorithms. As neural networks can be easily expressed as computational graphs, they can be implemented using TensorFlow. It has a responsive construct thus allowing one to visualize each and every part of the graph. It offers flexibility in operations. It is easily trainable on CPUs and GPUs. It allows training of multiple neural networks. It is used in applications such as Google Voice Search or Google Photos. The libraries are created in C and C++ language.



It is used internally for performing multiple operations on Tensors. The most important feature of Numpy is its array interface. It is interactive, simplifies complex mathematical implementations, makes coding easy, and is an open-source contribution. It is used for expressing sound waves, images, and other binary raw streams. Having knowledge of NumPy is essential for full stack developers to implement this library for machine learning.



Keras is considered to be the coolest machine learning libraries in Python. It provides easy mechanisms to express neural networks. It provides the best utilities for combining models, visualization of graphs, and processing data sets. It is a little slow when compared to the other machine learning libraries. It uses a back-end infrastructure to create a computational graph. All the models offered by Keras are portable. It is incredibly flexible and expressive. We constantly interact with features built using Keras through NetFlix, Uber, Yelp, etc.



It is the largest library. It allows developers to perform Tensor computations, automatic calculation of gradients, and create dynamic computational graphs. It offers rich APIs to solve application issues related to neural networks. It is based on Torch, an open-source machine library implemented in C. Introduced in 2017, it is gaining popularity and attracting a large number of machine learning developers. It’s hybrid front-end provides ease of use and flexibility.



It is a combination of visualization and debugging of all machine learning models and tracking the working steps of an algorithm. It provides accurate predictions for machine learning models. It supports other libraries such as lightning, XGBoost, Scikit-Learn, etc. It is used in mathematical applications which require a lot of computation with limited time.



It is a machine learning library for engineers and application developers. It contains modules for optimization, linear algebra, and statistics. It is developed using NumPy. It provides all efficient numerical routines such as numerical integration and optimization. It is used for solving mathematical functions. It uses NumPy arrays as the basic data structure and includes modules for commonly used tasks in scientific programming.



Gradient boosting is a popular machine learning library which helps build new algorithms using decision trees and redefined elementary models. There are specific libraries designed for fast and efficient implementation. The feature of fast computation ensures high production efficiency. It does not produce errors while considering NaN and canonical values. It offers highly scalable, fast, and optimized implementations of gradient boosting.



It is a library which provides high-level data structures and a wide variety of analysis tools. It has the ability to translate complex operations with data through one or two commands. It has many in-built methods for filtering, time-series functionality, grouping, and combining data. It simplifies the process of manipulating data. It provides support for performing custom type operations. Data analysis is the highlight of Pandas. When combined with other libraries and tools, it ensures high functionality and flexibility.



It is a computational framework for computing multi-dimensional arrays. It is similar to TensorFlow but is not as efficient as TensorFlow due to its inability to fit into production environments. It can be used on distributed or parallel environments. It can perform data-intensive computations faster than a CPU. Expressions are evaluated faster thereby making it more efficient. It is useful in detecting and diagnosing multiple types of ambiguities and errors in the model. It is used in multiple neural network projects and is considered an industry standard for Deep Learning research and development.




Python is a great platform to network with other developers. As Python is an open-source and community developed language, it is used by millions of like-minded developers on a daily basis to improve core functionality. Python and the necessary tools are available on all major platforms and hence there is no exclusivity.


The standard library and the interpreter offered free of charge in both binary and source form are the most promising benefits of Python. This is indeed an enticing option for developers as they need not worry about paying high development costs. Python can be accessed by almost anyone. So, if you have the passion and time to learn something useful, wait no more! Learn the Python language and start creating amazing things.