10 Best Python Libraries for Machine Learning and AI

Python has grown in popularity over the years to become one of the most popular programming languages ​​for machine learning (ML) and artificial intelligence (AI) tasks. It has replaced many existing languages ​​in the industry, and it is more efficient compared to those traditional programming languages. On top of all that, its English-like controls make it accessible to beginners and experts alike.

Another fundamental feature of Python that attracts many users is its vast collection of open source libraries. These libraries can be used by programmers of all experience levels for tasks involving ML and AI, data science, image and data manipulation, and much more.

Why Python for Machine Learning and AI?

Python’s open-source libraries aren’t the only feature that makes it favorable for machine learning and AI tasks. Python is also very versatile and flexible, which means it can also be used with other programming languages ​​when needed. Moreover, it can work on almost all operating systems and platforms in the market.

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

Another factor that makes Python favorable, especially for beginners, is its growing community of users. Since it is one of the fastest growing programming languages ​​in the world, the number of Python developers and development departments has exploded. The Python community is growing alongside the language, with active members always looking to use it to solve new business problems.

Now that you know why Python is one of the best programming languages, here are the top 10 Python libraries for machine learning and AI:

1. NumPy

NumPy is widely considered the best Python library for machine learning and AI. It is an open source numerical library which can be used to perform various mathematical operations on different matrices. NumPy is considered one of the most widely used scientific libraries, which is why many data scientists rely on it to analyze data.

NumPy arrays require much less storage space than other Python lists, and they are faster and more convenient to use. You can manipulate the data in the matrix, transpose and reshape it with NumPy. Overall, NumPy is a great option for increasing the performance of machine learning models without too much complex work.

Here are some of the main features of NumPy:

  • High-performance N-dimensional array object.
  • Shape manipulation.
  • Data cleaning/manipulation.
  • Statistical operations and linear algebra.

2. SciPy

SciPy is a free open source library based on NumPy. It is particularly useful for large datasets, being able to perform scientific and engineering calculations. SciPy also comes with built-in modules for array optimization and linear algebra, just like NumPy.

The programming language includes all the functions of NumPy, but it turns them into user-friendly scientific tools. It is often used for image manipulation and provides basic processing functionality for non-scientific high-level mathematical functions.

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

Here are some of the main features of SciPy:

  • User-friendly.
  • Data visualization and manipulation.
  • Scientific and technical analysis.
  • Computes large data sets.

3. Theano

A Python numerical computation library, Theano was developed specifically for machine learning. It allows the optimization, definition and evaluation of mathematical expressions and matrix calculations. This allows using dimensional arrays to build deep learning models.

Theano is a very specific library, and it 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.

Here are some of the key features of Theano:

  • Built-in validation and unit testing tools.
  • Fast and stable evaluations.
  • Data-intensive calculations.
  • Powerful mathematical calculations.

4. Pandas

Another top Python library in the market is Pandas, which is often used for machine learning. It acts as a data analysis library that analyzes and manipulates data, and it allows developers to easily work with structured multidimensional data and time series concepts.

The Pandas library offers a fast and efficient way to manage and explore data by providing Series and DataFrames, which efficiently represent data while manipulating it in different ways.

Here are some of the main features of Pandas:

  • Data indexing.
  • Data Alignment
  • Merging/grouping of datasets.
  • Data manipulation and analysis.

5. TensorFlow

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

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

Here are some of the main features of TensorFlow:

  • Flexible architecture and framework.
  • Works on a variety of computing platforms.
  • Abstraction skills
  • Manages deep neural networks.

6. Keras

Keras is an open-source Python library for developing and evaluating neural networks within machine learning and deep learning models. It is able to run on Theano and Tensorflow, which means it can train neural networks with little code.

The Keras bookcase is often preferred because it is modular, expandable and flexible. This makes it a beginner-friendly option. It can also integrate with lenses, layers, optimizers and activation functions. Keras works in various environments and can run on CPUs and GPUs. It also offers one of the widest ranges for data types.

Here are some of the main features of Keras:

  • Pooling of data.
  • Develop neural layers.
  • Creates deep learning and machine learning models.
  • Activation and cost functions.


Another option for an open source machine learning Python library is PyTorch, which is based on Torch, a C programming language framework. PyTorch is a data science library that can be integrated with other Python libraries, such than NumPy. The library can create calculation graphs that can be modified while the program is running. It is especially useful for ML and DL applications like 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. It is also a flexible library, capable of running on processors or simplified CPUs and GPUs. PyTorch has powerful APIs that allow you to extend the library, as well as a natural language toolkit.

Here are some of the main features of PyTorch:

  • Statistical distribution and operations.
  • Control of datasets.
  • Development of DL models.
  • Very flexible.

8. Scikit-Learn

Originally a third-party extension to the SciPy library, Scikit-learn is now a standalone Python library on Github. It is used by big companies like Spotify, and there are many advantages to using it. On the one hand, it is very useful for classic machine learning algorithms, such as those for spam detection, image recognition, prediction and customer segmentation.

Another of Scikit-learn’s main selling points is that it is easily interoperable with other SciPy stack tools. Scikit-learn has a friendly and consistent interaction that allows you to easily share and use data.

Here are some of the main features of Scikit-learn:

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

9. Matplotlib

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

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

Here are some of the main features of Matplotlib:

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

10. Plot

Closing our list of top 10 Python libraries for machine learning and AI is Plotly, which is another free and open source visualization library. It is very popular among developers due to its high-quality, immersive, and release-ready graphics. Some of the graphs accessible through Plotly include box plots, heatmaps, and bubble graphs.

Plotly is one of the best data visualization tools on the market, and it’s built on D3.js, HTML, and CSS visualization toolkit. Written in Python, it uses the Django framework and can help create interactive graphics. It works on different data analysis and visualization tools and allows you to easily import data into a chart. You can also use Plotly to create slideshows and dashboards.

Here are some of the main features of Plotly:

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

Sherry J. Basler