9 Best Python Libraries for Machine Learning

Python was released in 1991, and it currently ranks #1 on the TIOBE index as of June 2022, a worldwide indicator of popularity with which engineers, courses, and third parties from around the globe rate programming languages [1]. Python is efficient and easy to learn, and one of its greatest features is its open-source libraries available for users. The libraries allow users to choose from frameworks that they can build off of to produce new machine learning (ML) models.

Whether you are already familiar with machine learning in your career or want to get started, this field holds plenty of growth opportunities. Machine learning experts had the fourth highest-ranked average salary in India as ranked by Indeed in 2024 [2].

What is a Python library? 

Python libraries are collections of modules that contain useful codes and functions, eliminating the need to write them from scratch. Tens of thousands of Python libraries help machine learning developers, as well as professionals working in data science, data visualisation, and more. 

Python is the preferred language for machine learning because its syntax and commands are closely related to English, making it efficient and easy to learn. Compared with C++, R, Ruby, and Java, Python remains one of the simplest languages, enabling accessibility, versatility, and portability. It can operate on nearly any operating system or platform.

For an introduction to Python libraries NumPy, SciPy, Matplotlib, and Seaborn, check out this video from the University of Michigan’s Statistics with Python Specialisation:

Nine best Python libraries for machine learning

If you’re working with machine learning and deep learning projects, there are thousands of Python libraries to choose from, and they can vary in size, quality, and diversity. Here is a list of some of the best Python libraries to help you start your machine-learning journey. This list is based on popularity derived from their reputation among Python library users.  

1. NumPy

NumPy is a popular Python library for multi-dimensional array and matrix processing because it can be used to perform a great variety of mathematical operations. Its capability to handle linear algebra, Fourier transform, and more makes NumPy ideal for machine learning and artificial intelligence (AI) projects, allowing users to quickly manipulate the matrix to improve machine learning performance. NumPy is faster and easier to use than most other Python libraries.

2. Scikit-learn

Scikit-learn is a popular machine-learning library built on NumPy and SciPy. It supports most of the classic supervised and unsupervised learning algorithms and can be used for data mining, modelling, and analysis. Scikit-learn’s simple design offers a user-friendly library for those new to machine learning.

3. Pandas

Pandas is another Python library built on top of NumPy and is responsible for preparing high-level data sets for machine learning and training. It relies on two data structures, one-dimensional (series) and two-dimensional (DataFrame). This allows Pandas to be applicable in various industries, including finance, engineering, and statistics. Unlike the slow-moving animals, the Pandas library is quick, compliant, and flexible.

4. TensorFlow

TensorFlow’s open-source Python library specialises in differentiable programming, meaning it can automatically compute a function’s derivatives within a high-level language. Both machine learning and deep learning models are easily developed and evaluated with TensorFlow’s flexible architecture and framework. TensorFlow can visualise machine learning models on both desktop and mobile.

This project-based TensorFlow Specialisation from DeepLearning.AI is ideal for anyone ready to deep-dive into applied machine learning—customise ML models with four hands-on courses.

5. Seaborn

Seaborn is another open-source Python library based on Matplotlib (which focuses on plotting and data visualisation) but featuring Pandas’ data structures. It is often used in ML projects because it can generate plots of learning data. It produces the most aesthetically pleasing graphs and plots of all the Python libraries, making it a practical choice if you’ll also use it for marketing and data analysis.

6. Theano

Theano is a Python library focusing on numerical computation and is specifically made for machine learning. It can optimise and evaluate mathematical models and matrix calculations that use multi-dimensional arrays to create ML models. Theano is almost exclusively used by machine learning and deep learning developers or programmers.

7. Keras

Keras is a Python library designed to develop neural networks for ML models. It can run on top of Theano and TensorFlow to train neural networks. Keras is flexible, portable, user-friendly, and easily integrated with multiple functions. 

8. PyTorch

PyTorch is an open-source machine-learning Python library based on the C programming language framework Torch. It is mainly used in ML applications that involve natural language processing or computer vision. PyTorch is known for being exceptionally fast at executing large, dense data sets and graphs. 

9. Matplotlib

Matplotlib is a Python library focused on data visualisation and primarily used for creating beautiful graphs, plots, histograms, and bar charts. It is compatible with plotting data from SciPy, NumPy, and Pandas. If you have experience using other graphing tools, Matplotlib might be your most intuitive choice.

Learn Python with Coursera

Anybody can indeed learn Python. Start your Python journey today and use Coursera’s free trial to start Python for Everybody from the University of Michigan. You’ll learn to develop programs to clean, analyse, and visualise data.