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Top 9 Python Libraries for Machine Learning

Nivedha Varun
Top 9 Python Libraries for Machine Learning

Python is the most widely used programming language which involves artificial intelligence and machine learning. Python has gained popularity over time. Another fundamental feature of Python that attracts many of its users is its extensive collection of open source libraries. These libraries can be used by programmers of all experience levels for tasks related to ML and AI, data science, image and data manipulation, and much more.

Why Python for Machine Learning?

Python's open source libraries are not the only feature that makes it favorable for machine learning and AI tasks. Python is also very versatile and flexible, meaning it can also be used alongside other programming languages ​​when needed. 

Implementing deep neural networks and machine learning algorithms can be time-consuming, but Python offers many packages that reduce this. It is also an object-oriented programming (OOP) language, making it extremely useful for efficient use and categorization of data. 

Python has become a favorable programming language mostly for beginners because it is a growing community of users. Python developers and software development services  have skyrocketed as python became the fastest growing programming languages ​​in the world. The Python community is growing along with the language, with active members always looking to use it to address new problems in business.

9 best Python libraries for machine learning 

1. Science

NumPy is the foundation for SciPy, a free and open source library. It is especially useful for large data sets, being able to perform scientific and technical computing. 

The programming language includes all the features of NumPy, but turns them into easy-to-use scientific tools. It offers fundamental processing capabilities for complex, non-scientific mathematical operations and is frequently used for image manipulation.

SciPy is one of the fundamental Python libraries thanks to its role in scientific analysis and engineering. 


  • Easy to use.
  • Data visualization and manipulation.
  • Scientific and technical analysis. 
  • Calculate large data sets. 

2. Theano

Theano is a numerical computing Python library specifically developed for machine learning. It allows the optimization, definition and evaluation of mathematical expressions and matrix calculations. This allows the use of dimensional matrices to build deep learning models. 

Theano is a very specific library, and is mainly used by machine learning and deep learning developers and programmers. It supports integration with NumPy and can be used with a graphics processing unit (GPU) instead of a central processing unit (CPU), resulting in 140x faster data-intensive calculations. 

Features of Theano:

  • Integrated validation and unit testing tools.
  • Fast and stable evaluations.
  • Data-intensive calculations.
  • High-performance mathematical calculations.

3. Pandas

Another of the best Python libraries on the market is Pandas, which is often used for machine learning. It acts as a data analysis library that analyzes and manipulates data, and allows enterprise software development company to easily work with structured multidimensional data and time series concepts. 

The Pandas library provides Series and DataFrames, which efficiently represent data while manipulating it in various ways, to provide a quick and effective way to manage and explore data.

Features of Pandas:

  • Data indexing.
  • Data alignment
  • Merging/joining data sets.
  • Data manipulation and analysis. 

4. TensorFlow

TensorFlow, another free and open source Python library, specializes in differentiable programming. The library consists of a collection of tools and resources that allows beginners and professionals to build DL and ML models, as well as neural networks.

TensorFlow consists of an architecture and framework that is flexible, allowing it to run on various computing platforms such as CPUs and GPUs. That said, it works best when operating on a Tensor Processing Unit (TPU). The Python library can directly visualize machine learning models and is often used to implement reinforcement learning in ML and DL.

Features of TensorFlow: 

  • Flexible architecture and framework.
  • It requires a variety of computing platforms. 
  • Abstraction Capabilities
  • Manages deep neural networks. 

5. Keras

An open-source Python library called Keras is used to create and assess neural networks in deep learning and machine learning models. You can train neural networks with minimal code because it can operate on top of Tensorflow and Theano.

The Keras library is often preferred because it is modular, extensible, and flexible. This makes it an easy-to-use option for beginners. It can also be integrated with targets, layers, optimizers, and activation functions.Keras can run on a CPU or a GPU and can function in a variety of environments. It also provides one of the most extensive selections of data types.

Features of Keras: 

  • Data grouping.
  • Development of neuronal layers.
  • Build deep learning and machine learning models.
  • Activation and cost functions. 

6. PyTorch

PyTorch, an open source Python machine learning library built on top of the C programming language framework Torch, is an additional choice. NumPy and other Python libraries can be integrated with PyTorch, a data science library. The library can create computational graphics that can be changed while the program is running. It is especially useful for ML and DL applications such as natural language processing (NLP) and computer vision .

Some of PyTorch's main selling points include its high execution speed, which it can achieve even when handling heavy graphics. Additionally, it is a versatile library that can run on CPUs and GPUs or on processors that have been streamlined. PyTorch has powerful APIs that allow you to extend the library, as well as a set of natural language tools. 

Features of PyTorch:

  • Statistical distribution and operations.
  • Control over data sets.
  • Development of DL models.
  • Highly flexible. 

7. Scikit-Learn

Scikit-learn, which was once a third-party addon to the SciPy library, is now available on Github as a stand-alone Python library. It is used by large companies like Spotify, and there are many benefits to using it. On the one hand, it is very useful for classic machine learning algorithms, such as spam detection, image recognition, prediction and customer segmentation. 

The ease of interoperability of Scikit-learn with other SciPy stack tools is another important selling point. The consistent and user-friendly interface of Scikit-learn facilitates the sharing and utilization of data.

Features of Scikit-learn:

  • Data classification and modeling.
  • End-to-end machine learning algorithms.
  • Data preprocessing.
  • Model selection. 

8. Matplotlib

Matplotlib is a unit of NumPy and SciPy, and was designed to replace the need to use the proprietary MATLAB statistical language. The complete, free and open source library is used to create static, animated and interactive visualizations in Python. 

The Python library helps you understand data before passing it on to data processing and training for machine learning tasks. It relies on Python GUI toolkits to produce diagrams and graphs with object-oriented APIs. It also provides a MATLAB-like interface so that a user can perform MATLAB-like tasks. 

Features of Matplotlib:

  • Create publication quality plots.
  • Customize the visual style and layout.
  • Export to various file formats.
  • Interactive figures that can zoom, pan and update. 

9.Plotly _

Closing out our list of the 10 best Python libraries for machine learning and AI is Plotly, which is another free and open source visualization library. It is very popular among software development company thanks to its high-quality, immersive and publish-ready graphics. It works across different data analysis and visualization tools and allows you to easily import data into a chart. 

Features of Plotly: 

  • Charts and dashboards.
  • Snapshot engine.
  • Big data for Python.
  • Easily import data into charts. 

Nivedha Varun
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