11 Best Python Libraries for Machine Learning

best python libraries for machine learning

This post was last Updated on August 23, 2021 by Himanshu Tyagi to reflect the accuracy and up-to-date information on the page.

Are you looking for a suitable Python library for your next machine learning project? If yes, we have compiled a list of the 11 best Python libraries for machine learning. Based on your project requirements, this guide will help you pick an efficient machine learning Python library.

Python is a computer language that is widely used in the IT programming sector. Its appeal can be attributed to its simplicity. Python is used quite frequently in developing Machine Learning and Deep Learning models.

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11 Best Python Libraries for Machine Learning

Software developers determine their machine learning needs and then figure out the finest Python machine learning library to import the necessary modules. This article will learn about the top 11 Python libraries for Machine Learning and their benefits and drawbacks.

1. Matplotlib

Matplotlib is an open-source data visualization library. It’s a component of SciPy. It covers NumPy structures as well as high-level models like Pandas. It is regarded as one of the most important libraries for data visualization in Python for machine learning.

It provides a plotting environment similar to MATLAB for creating high-quality plots and charts for visualizations. It also has several tools for creating valuable visualizations.


  • It aids in the creation of flexible, powerful, and accurate graphs.
  • Jupyter Notebook makes it simple to use.
  • It works with GUI toolkits such as wxPython, Qt, and Tkinter.
  • Matplotlib makes use of a structure that supports both Python and IPython shells.


For the SciPy stack, Matplotlib is heavily reliant on NumPy and other similar libraries.
It has a steep learning curve because using Matplotlib necessitates a significant amount of knowledge and application on the part of the learners.

Matplotlib supports two unique frameworks: Object-oriented and MATLAB, which can cause difficulty for developers.

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2. Scikit-Lear

Scikit-Learn is a Python package that extends SciPy. It’s frequently used to implement Machine Learning algorithms. It provides a reliable and straightforward structure that allows ML models to learn, transform, and predict using data.

The Scikit-Learn library includes tools for building classification, regression, and clustering models. It also has many applications for preprocessing, statistical analysis, model evaluation, and so on.


  • The Scikit-Learn library offers a go-to package that contains all methods for implementing Machine Learning’s standard algorithms.
  • It offers a straightforward and consistent user interface that aids in fitting and transforming the model across any dataset.
  • It is the best library for designing pipelines that aid in the creation of a quick prototype.
  • It’s also the most reliable way to deploy Machine Learning models.


Scikit-Learn is unable to use categorical data in its algorithms. It is highly reliant on the SciPy stack.

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3. Pandas

Pandas is one of the most commonly used Python libraries for Machine Learning. It is the most widely-used Python package for data manipulation.

It creates programs for implementing functions using convenient and descriptive data structures like DataFrames. It’s a rapid and easy-to-use library built on top of NumPy.

Pandas can read and write data from various sources, including Excel, HDFS, and many others. If you’re creating a use case for a real-world Machine Learning model, you’ll undoubtedly utilize Pandas to construct it.

The benefits and drawbacks of using Pandas are listed below.


  • It has data structures that are descriptive, fast, and compliant. Grouping, integrating, iterating, re-indexing, and representing data are all supported. The Pandas library is quite adaptable when it comes to combining it with other libraries.
  • It has built-in data manipulation capabilities that you can use with only a few commands.
  • Due to its optimum performance may be used in a wide range of domains, particularly in business and education.


It is built on Matplotlib, which means that a novice programmer will need to be familiar with both libraries to determine which one is best for a given business situation.
Quantitative modeling and n-dimensional arrays are not well suited for Pandas.

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4. Natural Language Toolkit (NLTK)

NLTK is a Python framework and library set for creating symbolic and statistical Natural Language Processing (NLP). It is Python’s standard tool for natural language processing.


  • There are graphical demonstrations and sample data in the Python library.
    Beginners will find it easy to pick up.
  • Supports several Machine Learning tasks such as classification, parsing, and tokenization, among others.
  • Serves as a foundation for prototyping and constructing research systems. Compatible with various languages.


To use the NLTK framework, you must first understand the principles of string processing.
By breaking the text into sentences, NLTK performs sentence tokenization. This has a detrimental effect on the results.

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5. TensorFlow

TensorFlow is one of the most important Python libraries for Machine Learning. It’s one of the most popular open-source libraries for developing Machine Learning and Deep Learning models.

For the community, TensorFlow provides a robust ecosystem of tools and resources. Engineers can use such toolsets to conduct Machine Learning and Deep Learning research and develop practical applications.


  • TensorFlow is a library that aids in the implementation of reinforcement learning.
  • TensorBoard, a tool in the TensorFlow framework, allows us to visualize Machine Learning models right away.
  • We can use TensorFlow to build models that run on both CPUs and GPUs.


  • In comparison to CPUs/GPUs that use other frameworks, it is significantly slower.
  • TensorFlow’s computational graphs are sluggish to execute.

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6. PyTorch

PyTorch is a framework that allows tensor computations to be performed. It aids in creating efficient computational graphs and provides a comprehensive API for dealing with neural network faults.

The Pytorch library is entirely based on Torch, an open-source framework written in the C programming language.


  • The PyTorch framework is well-known for its lightning-fast execution.
  • It can handle complex graphs.
  • It also aids in the integration of Python objects and libraries.


The PyTorch community is small, and it takes a long time to respond to queries.
PyTorch provides fewer features in terms of delivering visuals and application debugging than other Python frameworks.

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7. SciPy

SciPy is regarded as one of the best Python libraries for Machine Learning and for doing scientific computations. The SciPy library is a subset of the SciPy stack and is based on NumPy.

It comes with several modules that you can use to create a variety of Machine Learning methods. Its importance for Machine Learning stems from the fact that it ensures a speedy and high-quality execution. It’s also a user-friendly and quick computing library.


  • It is ideal for picture manipulation.
  • It comes with basic mathematical processing capabilities.
  • It allows for excellent numeric integration and optimization.
  • It also makes signal processing simpler.


Using SciPy has no significant drawbacks. However, because the SciPy library is included in the stack, there may be some confusion between the stack and SciPy.

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8. Seaborn

Seaborn is a Python library that lets us make analytical graphs. Seaborn is a Matplotlib-based library that includes Pandas data structures.


  • The graphs made with Seaborn are more appealing than those created with Matplotlib.
  • It comes with built-in packages that Matplotlib lacks.
  • The Seaborn library utilizes less code to visualize graphs.
  • For data visualization and analysis, it’s integrated with Pandas.


  • To work with Seaborn, we should be familiar with Matplotlib.
  • Customization is not available in Seaborn.

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9. Theano

Let’s start by claiming that Theano is what Numpy is to Machine Learning in deep learning.

Theano was one of the first libraries to provide multi-dimensional array manipulation features. Because it predates Tensorflow, it is less performant and expressive. Theano can use GPUs transparently.


  • Because of its strong relationship with Numpy, it’s simple to learn.
  • It can transparently use GPUs.
  • It has a large community for help and support because it was one of the earliest deep learning libraries.


  • It was once the workhorse for deep learning use-cases, but it’s now a deprecated project.
  • Its low-level APIs were notorious for having a high learning curve.

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10. Keras

Next in our list of Python libraries for Machine Learning is Keras. It is synonymous with simplicity.

It is a high-level Deep Learning framework that has simplified the development and implementation of deep neural networks.

It is built on the shoulders of giants like Theano, Tensorflow, and MXNet, and is mainly written in Python. Keras uses these backends to handle the heavy lifting while still allowing us to think in layers. A layer is the most fundamental building unit in Keras.


A straightforward and easy-to-understand interface aids rapid prototyping.

A large number of pre-trained and ready-to-use models.

Ability to deal with a variety of low-level libraries, including TensorFlow, Theano, and mxnet.


It’s challenging to create new components/loss functions because it’s a high-end library.
The underlying backend determines the performance of the application.

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11. NumPy

NumPy is commonly considered to be one of the top Python libraries for Machine Learning. Other libraries use it to implement tensor operations, such as TensorFlow and Keras.


  • We can deal with multi-dimensional data with NumPy.
  • The library aids in data matrix manipulation and operations like transpose, reshape, and many others.
  • NumPy provides a dynamic data structure, which allows for improved performance and garbage collection management.
  • It enables us to improve the Machine Learning model’s performance.


  • NumPy is heavily reliant on non-Pythonic objects. It makes use of Cython’s features as well as those of other C/C++ libraries.
  • Its tremendous efficiency comes at a cost.
  • Because the data types are hardware-native rather than Python-native, it is expensive to convert NumPy entities to Python-equivalent entities and vice versa.

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With this, we sum up our list of the 11 best Python libraries for machine learning. If you are looking for a sound machine learning library in Python, this article will help you pick the right one. Based on your project requirements, you can choose any machine learning library for your next Python project.