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The importance of Machine Learning (ML) and Artificial Intelligence (AI) is expanding over various industries, and many enterprises have begun actively funding these technologies. 

This is because, with the increase of volume along with the complexity of data, ML and AI have proven to be highly beneficial for structuring, analysing and processing this data.

With the use of AI, companies can avail specific insights, and forecasts to improve business efficiency, enhance productivity and reduce production cost. In fact, by 2025, the global AI market is predicted to be at almost $60 billion and with the assistance of AI, the global GDP will grow by $15.7 trillion in 2030. 

But, how can you get started with AI and machine learning effectively?

There are several programming languages that can help you with AI, ML and DL. Each language offers expertise on a specific concept. Among these languages, some of the most common are Jula, R, and JAVA. However, arguably, the most prominent programming language is Python. In this blog, we will cover the best Python libraries you need for machine learning and AI. However, let’s first start with the basics. 


What Is a Python Library?


AI and ML projects vary from traditional software projects. It modifies depending on the technology stack, the abilities for ML-based projects and the need for in-depth research. 

For building an ML and AI outline, you need to pick a programming language, which should be compliant, stable and should involve predefined libraries & frameworks. 

Python is a language wherein you can view many Python machine learning and Artificial Intelligence projects emerging today. So, what are libraries in Python?

A Python library is a piece of code that is reusable which you might want to incorporate in your programs/ projects. Related to languages like C++ or C, Python libraries do not concern any particular context in Python. 

Here, a ‘library’ loosely represents an accumulation of core modules. 


Why Is Python Preferred For Machine Learning And AI?


Python helps developers throughout the entire software development lifecycle to be fruitful as well as positive about the product they are creating. Python provides plenty of advantages for building AI and ML projects. Here are a few benefits:


  • Inbuilt Libraries

  • Moderate Learning Curve

  • Easy to Integrate

  • Easy to create Prototypes

  • Free and Open Source

  • Object-Oriented Paradigm

  • Portability

  • High-Productivity

  • Platform Agnostic


These characteristics add significance to the overall prevalence of the programming language. The broad collections of Python libraries for machine learning clarify the development expenses and decrease the development time. 

It's easy syntax as well as readability helps rapid testing of the complicated process and makes the language easy to understand for non-programmers. 

There have been quite a few debates related to PHP versus Python and which is more effective for web development and AI. Python usually takes the victory crown due its vast features, yet ease of use. 


Executing ML and AI algorithms need a well-structured & well-tested setting to allow developers to come up with the most reliable quality coding solutions. 

To decrease development time, there are many Python libraries for machine learning. So, if you’re wondering “what are the different libraries in Python,” we’ve covered the most beneficial  Python machine learning libraries. 


1. NumPy


NumPy or Numerical Python is linear algebra produced in Python. 

Nearly all Python machine-learning packages such as Mat-plot lib, SciPy, Scikit-learn, etc. depend on this library to a moderate extent. 

It arises with capacities for dealing with complicated mathematical methods such as linear algebra, Fourier transformation, random number and characteristics that operate with matrices and n-arrays in Python. 

The NumPy Python package further performs scientific calculations. It is extensively used in handling sound waves, images, and additional binary functions.




  • Provides support for logical and mathematical operations 

  • Ability for shape manipulation 

  • Capable of sorting and selecting 

  • Discrete Fourier transformations

  • Statistical operations and basic linear algebra

  • Random simulations

  • Support for n-dimensional arrays


2. SciPy


SciPy is an open-source and free Python library utilized for scientific computing and technical computing.

SciPy includes modules for optimization, linear algebra, integration, interpolation, unique functions, FFT, signal and image processing, ODE solvers and additional tasks popular in science and engineering.

SciPy forms on the NumPy array object and also is part of the NumPy stack which involves tools such as Matplotlib, pandas and SymPy, including an expanding collection of scientific computing libraries. 

The NumPy stack is additionally sometimes mentioned as the SciPy stack.




  • Syntax highlighting

  • The ability to execute code

  • Debugging tools

  • Autocompletion

  • Project management.


3. Scikit-learn


Scikit-learn is another notable open-source Python machine learning library with a wide series, which includes clustering, regression and classification algorithms. Some prominent examples are DBSCAN, gradient boosting, random forests, vector machines, and k-means

It could interoperate with numeric and scientific libraries of Python such as NumPy and SciPy.

It is a commercially valuable artificial intelligence library. This Python library helps both supervised as well as unsupervised ML. 




  • Classification

  • Regression

  • Clustering

  • Dimensionality Reduction

  • Model Selection

  • Preprocessing


4. Theano


Since its appearance in 2007, Theano has caught the Python developers and researchers of ML and AI. 

At the core, it is a famous scientific computing library that enables you to determine, optimize as well as assess mathematical definitions, which dispenses with multidimensional arrays. 

The fundamental of various ML and AI applications is the repeated computation of a complex mathematical expression. Theano enables you to create data-intensive calculation up to a hundred times quicker than when performing on your CPU alone. 

Additionally, it is fully optimized for GPUs, which gives useful symbolic differentiation and involves thorough code-testing capabilities.




  • Support for GPUs to perform better in heavy-duty computations compared to CPUs

  • Strong integration support with NumPy

  • Faster and stable evaluations of even the trickiest of variables

  • Ability to create custom C code for your mathematical operations


5. TensorFlow


TensorFlow is a python machine learning library for delivering high-end numerical calculations. 

TensorFlow can manage deep neural networks for image identification, handwritten digit distribution, recurrent neural networks, NLP (Natural Language Processing), word embedding and PDE (Partial Differential Equation). 

TensorFlow Python assures great architecture assistance to provide secure computation deployments over a wide range of platforms, including desktops, servers, and mobile phones. 

Abstraction is the main benefit of TensorFlow Python towards machine learning and AI projects. 

This characteristic enables the developers to concentrate on the comprehensive logic of the app instead of administering with the ordinary details of implementing algorithms. 

By using this library, python developers can currently effortlessly leverage AI and ML to build unique responsive applications, which answers to user information like facial or voice expression.




  • Handling deep neural networks

  • Natural Language Processing

  • Partial Differential Equation

  • Abstraction capabilities

  • Image, Text, and Speech recognition

  • Effortless collaboration of ideas and code


6. Keras


Keras is an advanced open-source Python library formulated for building neural networks and machine learning projects. 

It can work on Deeplearning4j, MXNet, Microsoft Cognitive Toolkit (CNTK), Theano or TensorFlow. 

It provides nearly all standalone modules, including optimizers, neural layers, activation purposes, initialization systems, cost functions, and regularization systems. 

It makes it simple to include new modules, just like adding new functions and classes. 

As the design is already fixed in the code, you don’t need to have a different model config files. Keras makes it manageable for machine learning newcomers to create and develop a neural network. 

Keras Python further works with convolution neural networks. It involves algorithms for normalization, optimizer, and activation layers. 

Rather than being an end-to-end Python machine learning library, Keras works as a user-friendly, extensible interface that improves modularity & entire expressiveness.




  • Neural layers

  • Activation and cost functions

  • Objectives

  • Batch normalization

  • Dropout

  • Pooling


7. PyTorch


A production-ready Python machine-learning library, PyTorch comes with great examples, applications and application cases backed by a strong community. 

This library consumes strong GPU acceleration and allows you to use it from applications like NLP. 

As it backs GPU and CPU calculations, it gives you performance optimization and scalable classified training in research as well as production. 


It involves a machine learning compiler named Glow that increases the production of deep learning frameworks.




  • Tensor computing with the ability for accelerated processing via Graphics Processing Units

  • Easy to learn, use and integrate with the rest of the Python ecosystem

  • Support for neural networks built on a tape-based auto diff system


8. Pandas


In machine learning projects, a significant amount of time is used on preparing the data as well as examining fundamental trends & patterns. 

This is where the Python Pandas acquires machine learning experts’ attention. 

Python Pandas is an open-source library that gives a broad range of tools for data administration & analysis. With this library, you can see data from a wide range of sources like CSV, SQL databases, JSON files, and Excel.

It allows you to handle complicated data operations with just one or two commands. Python Pandas arises with various inbuilt methods for consolidating data, and grouping & refining time-series functionality. 

Overall, Pandas is not just restricted to manage data-related tasks; it works as the best opening point to generate more focused and compelling data tools.




  • Dataset reshaping and pivoting

  • Merging and joining of datasets

  • Handling of missing data and data alignment

  • Various indexing options such as Hierarchical axis indexing, Fancy indexing

  • Data filtration options




These libraries are remarkably valuable when you’re operating on machine learning projects as it preserves time and notably provides specific functions that one can build on. 

Amongst the excellent collection of Python libraries for machine learning, these are arguably the most beneficial libraries. 

With the aid of these Python machine learning libraries, you can include high-level analytical functions, even with the least knowledge of the underlying algorithms you are operating with. 

If you’re looking for the perfect Python library for you, our team of experts at Citrusbug. Contact us today and grow your business with our python development services.

Ishan Vyas

Ishan Vyas



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